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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengji Shen Zhenjiang Liu Kaixuan Chen Jie Lei Mingli Song Zunlei Feng State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Idlefish Network Technology Alibaba Group College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusion reconstruction error has emerged, as images generated by diffusion are more easily reconstructed by any diffusion model. However, we find that existing methods only use reconstruction error from a single time step, failing to fully leverage the entire reconstruction process. To this end, we propose to comprehensively consider every single time step to form the Temporal Reconstruction Error (TRE) that offers a richer feature representation. Furthermore, we design temporal aggregation and spatial focusing modules from two dimensions respectively to more effectively extract discriminative information from the TRE feature. Finally, we validate the proposed method on two popular datasets, and experimental results demonstrate that the proposed approach achieves state-of-the-art performance.

关键词： Deepfakes Image synthesis Focusing Signal processing Feature extraction Diffusion models Forgery Data mining Speech processing Image reconstruction

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FedSimRep: A Federated Learning Method Based on Data Representation Selection 25

FedSimRep: A Federated Learning Method Based on Data Represe...

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Proceedings of the 2025 3rd International Conference on Communication networks and Machine Learning

作者： Falu Zhai Lu Wu Chunxiao Wang Tongyang Hao Shucang Zang 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 Shandong China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400713231

As an emerging distributed technology, Federated learning enables model training while protecting privacy without sharing local data. However, the heterogeneity of geographic locations and devices leads to response delays from stragglers. Client grouping in federated learning can tolerate stragglers from a global perspective, but differences in data distribution within groups may result in local performance bias. To address this issue, we propose a federated learning method called FedSimRep, which selects representative clients based on data distribution similarity. FedSimRep performs group training by collecting readiness signals from topological communications and maintains synchronization tables and graphs to track training progress. Additionally, it selects representative clients by leveraging the relationship between local optimization similarity and data distribution similarity to optimize the global model. Experiments and theoretical analyses on public datasets such as MNIST, CIFAR-10, and UEU-Fed demonstrate that FedSimRep achieves significant advantages in terms of accuracy and efficiency.

关键词： Client Grouping

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Comprehensive Perturbation Consistency for Semi-Supervised Change Detection in Remote Sensing Images

Comprehensive Perturbation Consistency for Semi-Supervised C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zan Mao Xin Li Ze Luo Yingjuan Tang Dongmei Jiang Computer Network Information Center Chinese Academy of Sciences University of the Chinese Academy of Sciences PengCheng Laboratory National Key Laboratory of Advanced Vehicle Integration and Control Beijing Institute of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, many change detection (CD) methods rely on supervised learning, which necessitates extensive manually annotated data, resulting in significant labor and time requirements. Recently, semi-supervised (SS) approaches have emerged in the CD community, which exploit large amounts of unlabeled data by utilizing consistency regularization. However, these methods do not consider the broader perturbation consistency to confer better generalization of the model. In this paper, we propose a novel SS CD framework with a comprehensive perturbation consistency called CPC, which extends perturbation consistency to the entire learning period. Specifically, our CPC combines the input, feature, and network perturbations for comprehensive perturb space. And, we design two distinct structures in practice, decoupled CPC and coupled CPC. Furthermore, we propose a change-aware input perturbation that introduces expensive annotation information to further expand the input perturb space. Extensive experiments conducted on the WHUCD, and GZ-CD datasets demonstrate that the proposal performs favorably against the state-of-the-art methods.

关键词： Codes Annotations Perturbation methods Supervised learning Signal processing Acoustics Proposals Labeling Speech processing Remote sensing

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STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection

STAD: Joint Spatial-Temporal Dimension and Channel Correlati...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingle Zhou Xingli Wang Delong Han Jin Wan Jiachen Li 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 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately identifying real anomalies and pseudo-anomalies in complex multi-dimensional time series data has been a difficult problem in time series anomaly detection. To solve this problem, this paper proposes a new framework, STAD, that joint temporal and spatial dimensions. This framework guides the model to capture the correlation information between channels It also aims to learn the deep feature representation of sequences by mining potential information in the spatialtemporal dimension. It can effectively distinguish between true and false anomalies by comparing information from spatial and temporal dimensions. STAD identifies and integrates correlated channels by using a correlation aggregation mechanism to join multiple channels and detect *** addition, the KAN mixer designed in this paper can effectively extract features from different spatial locations in the spatial-temporal dimension. Through extensive experiments on several public datasets, STAD demonstrates its superiority in terms of accuracy and robustness.

关键词： Correlation Accuracy Time series analysis Signal processing Feature extraction Robustness Acoustics Speech processing Mixers Anomaly detection

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Double-RIS Enabled Physical Layer security for Wireless-Powered Communication Systems Over Rayleigh Fading Channels

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IEEE Transactions on Communications 2025年

作者： Chen, Jingyu Cao, Kunrui Ding, Haiyang Lv, Lu Ye, Yinghui Chi, Haolian Wang, Tao Yang, Liang National University of Defense Technology School of Information and Communications Wuhan430035 China Xidian University School of Telecommunications Engineering Xi'an710071 China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This paper investigates the physical layer security for a double-reconfigurable intelligent surface (DRIS) aided wireless-powered communication (WPC) system in the presence of an eavesdropper, where one RIS (termed as RIS-1) is deployed between power station (PS) and information user (U) while the other RIS (termed as RIS-2) is deployed between U and access point (AP). Moreover, an idle user acts as a cooperative jamming user (J) to emit the artificial noise to improve the transmission security of U. According to different types in energy harvesting (EH) and information transmission (IT)/noise transmission (NT) of U/J, we propose four DRIS enabled wireless-powered cooperative jamming transmission schemes based on jointly enhancing EH and IT of U (namely DRIS-1), jointly enhancing EH of U and NT of J (namely DRIS-2), jointly enhancing EH of J and IT of U (namely DRIS-3), and jointly enhancing EH and NT of J (namely DRIS-4), respectively. We analyze connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) of the proposed four schemes, respectively. The results show that DRIS-1 scheme has the highest diversity gain among the four schemes. Besides, the gain of the number of reflecting elements at RIS-1 has the same order as that at RIS-2 in DRIS-1 scheme and DRIS-4 scheme. In DRIS-2 scheme, the gains of the number of reflecting elements at RIS-1 and RIS-2 are two powers and one power, respectively, while the opposite is true in DRIS-3 scheme. In addition, DRIS-1 scheme achieves the best COP, while DRIS-4 scheme achieves the best SOP. DRIS-1 scheme, DRIS-3 scheme, and DRIS-4 scheme respectively achieve the best EST in the low, middle, and high region of transmission power or number of RIS elements. © 1972-2012 IEEE.

关键词： Inductive power transmission

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On the Performance of Coexisting Passive RIS and Active Relay-Assisted Short-Packet Communications

On the Performance of Coexisting Passive RIS and Active Rela...

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IEEE Conference on Wireless Communications and networking

作者： Chenyao Wei Liqin Shi Xiaoli Chu Xi Song Guangyue Lu Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China University of Sheffield U.K. North China Electric Power University Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Although coexisting passive reconfigurable intelligent surface (RIS) and active relay have been considered for enhancing long-packet communications, their potential in supporting short-packet communications (SPC) where the direct link is blocked has not been sufficiently studied. Motivated by this, this work investigates the performance of the coexisting passive RIS and active relay-assisted SPC network, where a source (S) transmits its short-packet data to the destination (D) with the help of a passive RIS and an active relay (R). More specifically, in the first time slot of the two-hop transmission, S transmits its short-packet signal to both the RIS and R. The RIS then reflects the incident signal towards D. If D is unable to decode the short-packet information while R successfully decodes it, R forwards the decoded signal to D with the assistance of the RIS in the second time slot. Following a decode-and-forward (DF) relaying protocol, we analytically derive the closed-form expression of the block error rate (BLER) for the short-packet transmission. Simulation results verify the correctness of the derived expression and show that the combination of the passive RIS and active relay can enhance the BLER performance as compared with the benchmarks that employ only a passive RIS or an active relay.

关键词： Closed-form solutions Protocols Error analysis Simulation Reconfigurable intelligent surfaces Benchmark testing Relays

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A two Stage Mixed Integer Programming Model for Distributionally Robust State Based Non-Intrusive Load Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Chen Chai, Zimo Yang, Linfeng University of Shanghai for Science and Technology School of Mechanical Engineering Shanghai China Guangxi University School of Computer Electronics and Information the Guangxi Key Laboratory of Multimedia Communication and Network Technology Nanning China

This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this paper model introduces fewer integer variables and richer absolute error function of load decomposition, which makes the power state selection of each device more accurate and the power consumption more accurate fitting. First, to tackle the issues related to noisy load data, an innovative load feature extraction model based on Kullback-Leibler distributionally robust optimization principles is introduced. Then the key features (power boundary/fluctuation features) of each device identified through this robust model are integrated into the constraints of the two-stage NILM model. The two-stage complementary framework includes: the determination of device state interval in the first stage;and the accurate fitting of device power consumption within the device state interval in the second stage. Comparative validation against existing optimization-based models on the AMPds, REFIT, and actual laboratory data sets demonstrate that our proposed model significantly enhances power decomposition accuracy and computational efficiency. In addition, the two-stage complementary framework and load feature extraction model can be applied to other optimization-based models of NILM to improve the computational efficiency of each model and the accuracy of load decomposition. © 2025 IEEE.

关键词： Mixed-integer linear programming

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th International Conference on Educational Technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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A Time-Driven UAV Deployment and Content Caching Approach for Multi-UAV-Enabled Consumer Electronics network

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Jianshan Yang, Xu Ming, Rui Luo, Haibo Wang, Xiaoding Piran, Md. Jalil Minjiang University School of Computer and Big Data Fuzhou350108 China Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Chinese Academy of Tropical Agricultural Sciences National Key Laboratory for Tropical Crop Breeding Institute of Tropical Bioscience and Biotechnology Sanya Research Institute Hainan Sanya572024 China Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

To address the problem of high delay due to the explosive growth of mobile data traffic in consumer electronics network (CEN), in this paper, we investigate the multiple-unmanned aerial vehicle (UAV)-enabled CEN content caching problem, focusing on minimizing content access delay by jointly optimizing UAV deployment, ground user (GU) access, and content caching while satisfying other resource constraints. We propose an algorithm based on alternating optimization for the scenario where multiple UAVs and base station (BS) collaborate to provide caching services. The algorithm attains a sub-optimal solution with a convergence guarantee by iteratively solving the three sub-problems derived from the decomposition of the target problem. First, a convex difference-based algorithm solves the UAV deployment sub-problem. Second, a greedy-based algorithm is used to solve the content caching sub-problem. Then, a continuous convex approximation algorithm based on the penalty function is used to solve the user access sub-problem. Finally, the above procedure is repeated to obtain a sub-optimal solution to the target problem. The proposed joint optimization algorithm is shown to outperform the benchmark algorithms in terms of average content access delay and cache hit ratio, as evidenced by numerical results. © 1975-2011 IEEE.

关键词： Antennas

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Symbiosis Rather Than Aggregation: Towards Generalized Federated Learning via Model Symbiosis

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IEEE Internet of Things Journal 2025年

作者： Liu, Yuange Liu, Yuru Zhang, Weishan Zheng, Chaoqun Jiang, Wei Luo, Daobin Qiao, Qiao Meng, Lingzhao Zhao, Hongwei Yang, Su College of Computer Science and Technology Qingdao266580 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China Information Science Academy of China Electronics Technology Group Corporation Beijing100036 China Fudan University School of Computer Science Shanghai200433 China

Federated Learning (FL) faces significant challenges in scenarios with non-IID (non-independent and identically distributed) data distributions across participating clients. Traditional aggregation-based approaches often struggle with the inherent misalignment between local and global optimization objectives, leadingwhich leads to gradient divergence and suboptimal generalization performance. This paper proposes a novel FL framework that replaces conventional aggregation with a biologically inspired model symbiosis approach called FedSym, which employs a dual-level symbiotic mechanism. Ectosymbiosis performs coarse-grained hierarchical parameter recombinations through random layer-wise model combination, while endosymbiosis enables fine-grained intra-layer parameter fusion through weighted averaging, collectively steering model updates towards flatter loss landscapes. Our theoretical analysis demonstrates that FedSym's convergence rate is O(1T) under non-IID conditions, which matches the convergence properties of FedAvg. Extensive experimentsevaluations across multiple datasets and model architectures show that FedSym achieves substantial improvements over state-of-the-art FL methods, particularly in challenging scenarios with high data heterogeneity, and demonstrates robust performance across varying numbers of participating clients and federation scales. © 2014 IEEE.

关键词： Federated learning

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