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arXiv 2025年

作者： Duan, Wenying Guo, Shujun Zhou, Zimu Huang, Wei Rao, Hong He, Xiaoxi Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau China

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

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Confidence-oriented Contrastive Graph Clustering

Confidence-oriented Contrastive Graph Clustering

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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Virtual Reality for Health and Wellbeing 22

Virtual Reality for Health and Wellbeing

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21st International Conference on Mobile and Ubiquitous Multimedia, MUM 2022

作者： Ferreira-Brito, Filipa Gjoreski, Hristijan Mayora, Oscar Luštrek, Mitja Kizhevska, Emilija Guerreiro, João Gerling, Kathrin Bermúdez I Badia, Sergi Guerreiro, Tiago LASIGE Faculdade de Ciências da Universidade de Lisboa Portugal Ss. Cyril and Methodius University in Skopje Skopje Macedonia Center for Health and Wellbeing Fondazione Bruno Kessler Trento Italy Department of Intelligent Systems Jožef Stefan Institute Slovenia Slovenia Ss. Cyril and Methodius University in Skopje Macedonia KU Leuven Department of Computer Science Human-Computer Interaction Belgium Departamento de Engenharia Informatica and NOVA Laboratory for Computer Science and Informatics Universidade da Madeira Portugal and ARDITI Portugal

ISBN: (纸本)9781450398213

Virtual Reality (VR) has become a mainstream opportunity to develop immersive environments where people are exposed to relevant stimuli in a systematic but safe way. Besides the controlled exposure to stimuli, VR systems enable a more efficient and unbiased gathering of information with practical applications in diagnosis, intervention, and health monitoring processes. Nonetheless, scientific literature has demonstrated that interacting with VR technology and its peripherals can be challenging, especially in populations less familiar with technology (e.g., older adults) or with clinical conditions that are not adequately accommodated by the technology, such as cognitive (e.g., dementia) or physical impairments (e.g., blindness). This workshop will focus on systems, technological approaches, and theoretical frameworks to develop user/patient-centred VR tools for diagnosis, intervention, and monitoring of health and wellbeing outcomes. Moreover, we welcome work focused on accessibility issues, aiming to promote equal access and opportunity to use VR tools by people with diverse abilities. © 2022 ACM.

关键词： Virtual reality

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Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models

arXiv

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arXiv 2024年

作者： Zhang, Shenglin Zhu, Pengtian Ma, Minghua Wang, Jiagang Sun, Yongqian Li, Dongwen Wang, Jingyu Guo, Qianying Hua, Xiaolei Zhu, Lin Pei, Dan Nankai University China Microsoft United States Tsinghua University China China Mobile Research Institute China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Beijing National Research Center for Information Science and Technology China

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile's daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

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Temporal aware Mixed Attention-based Convolution and Transformer Network for cross-subject EEG emotion recognition

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computers in Biology and Medicine 2024年 181卷 108973-108973页

作者： Si, Xiaopeng Huang, Dong Liang, Zhen Sun, Yulin Huang, He Liu, Qile Yang, Zhuobin Ming, Dong Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Institute of Applied Psychology Tianjin University Tianjin300072 China School of Biomedical Engineering Medical School Shenzhen University Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China

Emotion recognition is crucial for human–computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://***/ThreePoundUniverse/MACTN. © 2024 Elsevier Ltd

关键词： Emotion Recognition

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EmotionMap:Visual Analysis of Video Emotional Content on a Map

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Journal of computer science & Technology 2020年第3期35卷 576-591页

作者： Cui-Xia Ma Jian-Cheng Song Qian Zhu Kevin Maher Ze-Yuan Huang Hong-An Wang State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China Academy of Arts and Design Tsinghua UniversityBeijing 100084China

Emotion plays a crucial role in gratifying users’needs during their experience of movies and TV series,and may be underutilized as a framework for exploring video content and *** this paper,we present EmotionMap,a novel way of presenting emotion for daily users in 2D geography,fusing spatio-temporal information with emotional *** interface is composed of novel visualization elements interconnected to facilitate video content exploration,understanding,and *** allows understanding of the overall emotion at a glance while also giving a rapid understanding of the ***,we develop EmotionDisc which is an effective tool for collecting audiences’emotion based on emotion representation *** collect audience and character emotional data,and then integrate the metaphor of a map to visualize video content and emotion in a hierarchical *** combines sketch interaction,providing a natural approach for users’active *** novelty and the effectiveness of EmotionMap have been demonstrated by the user study and experts’feedback.

关键词： video visualization emotion analysis visual analysis sketch interaction

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Dynamic Localisation of Spatial-Temporal Graph Neural Network 25

Dynamic Localisation of Spatial-Temporal Graph Neural Networ...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Wenying Duan Shujun Guo Zimu Zhou Wei Huang Hong Rao Xiaoxi He Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau Macao Macao Macao

ISBN: (纸本)9798400712456

关键词： graph sparsification

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Design lessons from AI’s two grand goals: human emulation and useful applications

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2020年第2期1卷 73-82页

作者： Shneiderman, Ben Department of Computer Science University of Maryland at College Park College ParkMD20742 United States Human–Computer Interaction Laboratory University of Maryland at College Park College ParkMD20742 United States

Researchers’ goals shape the questions they raise, collaborators they choose, methods they use, and outcomes of their work. This article offers a fresh vision of artificial intelligence (AI) research by suggesting a simplification to two goals: 1) emulation to understand human abilities to build systems that perform tasks as well as or better than humans and 2) application of AI methods to build widely used products and services. Researchers and developers for each goal can fruitfully work along their desired paths, but this article is intended to limit the problems that arise when assumptions from one goal are used to drive work on the other goal. For example, autonomous humanoid robots are prominent with emulation researchers, but application developers avoid them, in favor of tool-like appliances or teleoperated devices for widely used commercial products and services. This article covers four such mismatches in goals that affect AI-guided application development: 1) intelligent agent or powerful tool;2) simulated teammate or teleoperated device;3) autonomous system or supervisory control;and 4) humanoid robot or mechanoid appliance. This article clarifies these mismatches to facilitate the discovery of workable compromise designs that will accelerate human-centered AI applications research. A greater emphasis on human-centered AI could reduce AI’s existential threats and increase benefits for users and society, such as in business, education, healthcare, environmental preservation, and community safety. © 2020 IEEE. All right reserved.

关键词： Anthropomorphic robots

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Hydrocephalus: An update on latest progress in pathophysiological and therapeutic research

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Biomedicine and Pharmacotherapy 2024年 181卷 117702-117702页

作者： Anwar, Faheem Zhang, Kuo Sun, Changcheng Pang, Meijun Zhou, Wanqi Li, Haodong He, Runnan Liu, Xiuyun Ming, Dong Medical School Tianjin University Tianjin 300072 China School of Pharmaceutical Science and Technology Tianjin University 300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300380 China State Key Laboratory of Advanced Medical Materials and Devices Tianjin 300072 China

Hydrocephalus is a severe and life-threatening disease associated with the imbalance of CSF dynamics and affects millions globally at any age, including infants. One cause of pathology that is wide-ranging is genetic mutations to post-traumatic injury. The most effective current pharmacological treatments provide only symptomatic relief and do not address the underlying pathology. At the same time, surgical procedures such as VP shunts performed in lower-income countries are often poorly tolerated due to insufficient diagnostic resources and suboptimal outcomes partially attributable to inferior materials. These problems are compounded by an overall lack of funding that keeps high-quality medical devices out of reach for all but the most developed countries and even among those states. There is a massive variance in treatment effectiveness. This review indicates the necessity for innovative and low-cost, accessible treatment strategies to close these gaps, focusing on current advances in novel therapies, including Pharmacological, gene therapy, and nano-based technologies, which are currently at different stages of clinical trial phases. This review provides an overview of pathophysiology, current treatments, and promising new therapeutic strategies for hydrocephalus. © 2024 The Authors

关键词： Clinical trials Hydrocephalus Non-surgical treatment Pathophysiology Pharmacological treatment Surgical interventions

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Natural human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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