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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement Image segmentation Computer vision Codes Pipelines Logic gates Transformers data models Monitoring Anomaly detection

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Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns 4th

Interpersonal Relationship Analysis with Dyadic EEG Signals...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Ji, Wenqi Liu, Fang Du, Xinxin Liu, Niqi Zhou, Chao Yu, Minjing Guo, Xinyue Zhao, Guozhen Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University Beijing100084 China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China Institute of Software Chinese Academy of Sciences Beijing100190 China College of Intelligence and Computing Tianjin University Tianjin300072 China CAS Key Laboratory of Behavioral Science Institute of Psychology Beijing100049 China

ISBN: (纸本)9789819640003

Interpersonal relationship quality is pivotal in social and occupational contexts. Existing analysis of interpersonal relationships mostly rely on subjective self-reports, whereas objective quantification remains challenging. In this paper, we propose a novel social relationship analysis framework using spatio-temporal patterns derived from dyadic EEG signals, which can be applied to quantitatively measure team cooperation in corporate team building, and evaluate interpersonal dynamics between therapists and patients in psychiatric therapy. First, we constructed a dyadic-EEG dataset from 72 pairs of participants with two relationships (stranger or friend) when watching emotional videos simultaneously. Then we proposed a deep neural network on dyadic-subject EEG signals, in which we combine the dynamic graph convolutional neural network for characterizing the interpersonal relationships among the EEG channels and 1-dimension convolution for extracting the information from the time sequence. To obtain the feature vectors from two EEG recordings that well represent the relationship of two subjects, we integrate deep canonical correlation analysis and triplet loss for training the network. Experimental results show that the social relationship type (stranger or friend) between two individuals can be effectively identified through their EEG data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Probing globular cluster with MeerKAT and FAST: a pulsar polarization census

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science Bulletin 2025年第10期70卷 1568-1571页

作者： Zhang, Lei Abbate, Federico Li, Di Possenti, Andrea Bailes, Matthew Ridolfi, Alessandro Freire, Paulo C.C. Ransom, Scott M. Zhang, Yong-Kun Guo, Meng Ni, Meng-Meng Hu, Jia-Le Feng, Yi Wang, Pei Zhang, Jie Zhi, Qi-Jun National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology VIC3122 Australia INAF – Osservatorio Astronomico di Cagliari Via della Scienza 5 SelargiusI-09047 Italy Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany New Cornerstone Science Laboratory Department of Astronomy Tsinghua University Beijing100084 China Swinburne University of Technology VIC3122 Australia Faculty of Physics University of Bielefeld Bielefeld33501 Germany National Radio Astronomy Observatory Charlottesville22903 United States National Supercomputing Center in Jinan Qilu University of Technology Jinan250103 China Jinan Institute of Supercomputing Technology Jinan250103 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China College of Physics and Electronic Engineering Qilu Normal University Jinan250200 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China

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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big data Intelligence in Control Systems

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Computers, Materials and Continua 2025年第3期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu Chong Lv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Library of Shanghai Lixin University of Accounting and Finance Shanghai 201209 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250014 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing 100081 China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University) Ministry of Education Kunming 650092 China

As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment. Specifically, the enhanced PBFT consensus mechanism (VRPBFT) significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function (VRF) and a node reputation grading model. Experimental results demonstrate that compared to traditional PBFT, the proposed VRPBFT algorithm reduces consensus latency by approximately 30% and decreases the proportion of Byzantine nodes by 40% after 100 rounds of consensus. Furthermore, the DRL-based SFC deployment algorithm (SDRL) exhibits rapid convergence during training, with improvements in long-term average revenue, request acceptance rate, and revenue/cost ratio of 17%, 14.49%, and 20.35%, respectively, over existing algorithms. Additionally, the CPU resource utilization of the SDRL algorithm reaches up to 42%, which is 27.96% higher than other algorithms. These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency, service quality, and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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Semantic Pre-Extraction for Energy-Efficient AoI Minimization in UAV-Assisted Wireless networks

Semantic Pre-Extraction for Energy-Efficient AoI Minimizatio...

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IEEE Conference on Wireless Communications and networking

作者： Yusi Long Gary C.F. Lee Lanhua Li Shimin Gong Sumei Sun Dusit Niyato School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Institute for Infocomm Research (IR) A*STAR Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines.

关键词： Energy consumption Trajectory planning Processor scheduling Wireless networks Semantic communication Minimization Information age Energy efficiency Trajectory data mining

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Meta-Transfer Learning Based Cross-Domain Gesture Recognition Using WiFi Channel State Information

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IEEE Transactions on Consumer Electronics 2025年

作者： Dai, Penglin Zhou, Junfei Ma, Jialong Zhang, Hao Wu, Xiao Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Ministry of Education Engineering Research Center of Sustainable Urban Intelligent Transportation China Tangshan Institute of Southwest Jiaotong University Tangshan063000 China Chongqing University of Posts and Telecommunications College of Computer Science and Technology Key Laboratory of Data Engineering and Visual Computing Chongqing400065 China

Gesture recognition plays a crucial role in a wide range of consumer electronics applications, including human-computer interaction and virtual reality, by enabling the identification and interpretation of human gestures. In recent times, WiFi-based gesture recognition has garnered significant attention due to its privacy protection and unobtrusive nature. However, this approach heavily depends on neural network-based models and is notably influenced by environmental conditions, such as specific location and orientation. To address these environmental impacts, we propose a network framework that leverages transfer learning and meta-learning. The primary focus is on utilizing transfer learning to train a convolutional neural network (ResNet) for feature extraction, enabling the extraction of domain-independent features specifically related to gestures, rather than being influenced by environmental factors. Additionally, we employ the meta-learning algorithm MAML to train the fully connected network for gesture classification. Following training, a small set of samples can be utilized for rapid adaptation to diverse domains, maintaining high accuracy across different domains to achieve cross-domain gesture recognition. Our performance evaluation is based on the Widar 3.0 dataset, encompassing both in-domain and cross-domain scenarios. The simulation results demonstrate that our proposed algorithm surpasses Widar3.0 and WiGRUNT by approximately 7.9% and 1.12% in gesture recognition accuracy across all scenarios. © 2025 IEEE.

关键词： Wi-Fi

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Rethinking attention mechanism for enhanced pedestrian attribute recognition

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Neurocomputing 2025年 639卷

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou350108 China Engineering Research Center of BigData Intelligence Ministry of Education Fujian Fuzhou350108 China Australian Artificial Intelligence Institute University of Technology Sydney SydneyNSW2007 Australia Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fujian Fuzhou350108 China School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia

Pedestrian Attribute Recognition (PAR) plays a crucial role in various computer vision applications, demanding precise and reliable identification of attributes from pedestrian images. Traditional PAR methods, though effective in leveraging attention mechanisms, often suffer from the lack of direct supervision on attention, leading to potential overfitting and misallocation. This paper introduces a novel and model-agnostic approach, Attention-Aware Regularization (AAR), which rethinks the attention mechanism by integrating causal reasoning to provide direct supervision of attention maps. AAR employs perturbation techniques and a unique optimization objective to assess and refine attention quality, encouraging the model to prioritize attribute-specific regions. Our method demonstrates significant improvement in PAR performance by mitigating the effects of incorrect attention and fostering a more effective attention mechanism. Experiments on standard datasets showcase the superiority of our approach over existing methods, setting a new benchmark for attention-driven PAR models. © 2025 The Authors

关键词： Perturbation techniques

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UAV-Aided Covert Federated Learning networks

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IEEE network 2025年

作者： Wang, Chao Guo, Shaoyong Xiong, Zehui Xing, Chengwen Zhao, Nan Niyato, Dusit Karagiannidis, George Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Beijing Institute of Technology School of Information and Electronics Beijing100081 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

With the increasing emphasis on data privacy, federated learning (FL) networks show great potential through distributed training without directly sharing the raw data. However, the coverage of FL terrestrial servers is usually limited. Therefore, we propose leveraging the unmanned aerial vehicle (UAV) as a mobile flying server to further improve the wireless coverage and training efficiency of FL. Nevertheless, frequent exchanges of model parameters in UAV-assisted FL can result in serious security risks. In order to achieve this, we propose a covert FL scheme assisted by the UAV, which can protect the transmission key features of local models from being detected by wardens. Furthermore, we discuss the FL network and covert communications, as well as the exceptional features of UAV-assisted covert FL. Finally, we present two case studies, one of which focuses on minimising the network latency, while the other concentrates on reducing the energy consumption of covert FL. The simulation results demonstrate the efficacy of the proposed schemes in addressing future challenges. © 1986-2012 IEEE.

关键词： data privacy

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Symbiotic evolution of photonics and artificial intelligence: a comprehensive review

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Advanced Photonics 2025年第02期 8-85页

作者： Fu Feng Dewang Huo Ziyang Zhang Yijie Lou Shengyao Wang Zhijuan Gu Dong-Sheng Liu Xinhui Duan Daqian Wang Xiaowei Liu Ji Qi Shaoliang Yu Qingyang Du Guangyong Chen Cuicui Lu Yu Yu Xifeng Ren Xiaocong Yuan Zhejiang Lab Research Center for Frontier Fundamental Studies Westlake Institute for Optoelectronics Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization Beijing Institute of Technology School of Physics Center for Interdisciplinary Science of Optical Quantum and NEMS Integration Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics University of Science and Technology of China CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics Zhejiang Lab Research Center for Life Sciences Computing

The rapid advancement of artificial intelligence(AI) has significantly impacted photonics, creating a symbiotic relationship that accelerates the development and applications of both fields. From the perspective of AI aiding photonics, deep-learning methods and various intelligent algorithms have been developed for designing complex photonic structures, where traditional design approaches fall short. AI's capability to process and analyze large data sets has enabled the discovery of novel materials, such as for photovoltaics,leading to enhanced light absorption and efficiency. AI is also significantly transforming the field of optical imaging with improved performance. In addition, AI-driven techniques have revolutionized optical communication systems by optimizing signal processing and enhancing the bandwidth and reliability of data transmission. Conversely, the contribution of photonics to AI is equally profound. Photonic technologies offer unparalleled advantages in the development of AI hardware, providing solutions to overcome the bottlenecks of electronic systems. The implementation of photonic neural networks, leveraging the high speed and parallelism of optical computing, demonstrates significant improvements in the processing speed and energy efficiency of AI computations. Furthermore, advancements in optical sensors and imaging technologies not only enrich AI applications with high-quality data but also expand the capabilities of AI in fields such as autonomous vehicles and medical imaging. We provide comprehensive knowledge and a detailed analysis of the current state of the art, addressing both challenges and opportunities at the intersection of AI and photonics. The multifaceted interactions between AI and photonics will be explored, illustrating how AI has become an indispensable tool in the development of photonics and how photonics, in turn,facilitates advancements in AI. Through a collection of case studies and examples, we underscore the potential

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