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arXiv 2025年

作者： Cui, Shuang Li, Yi Li, Jiangmeng Tang, Xiongxin Su, Bing Xu, Fanjiang Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science & Engineering the Hong Kong University of Science and Technology Hong Kong

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu 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) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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CRRL: Contrastive Region Relevance Learning Framework for Cross-city Traffic Prediction

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Information Fusion 2025年 122卷

作者： Dai, Genan Yi, Wenfeng Cao, Jinzhou Gong, Zhaoya Fu, Xianghua Zhang, Bowen College of Big Data and Internet Shenzhen Technology University Shenzhen China China College of Applied Science Shenzhen University Shenzhen China School of Urban Planning & Design Peking University Shenzhen Graduate School Shenzhen China Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China Peking University Shenzhen Graduate School Shenzhen China

Accurate traffic prediction plays a pivotal role in smart urban systems by enabling effective traffic management and improving the quality of life for residents. While intra-city traffic prediction has been extensively studied, cross-city traffic prediction remains a challenging task due to data scarcity in target cities, domain gaps between cities, and the risk of negative transfer. To address these challenges, we propose a novel Contrastive Region Relevance Learning (CRRL) framework. CRRL leverages contrastive learning to align region-level spatiotemporal patterns and transfer high-quality knowledge between cities. Specifically, CRRL integrates three key modules: (1) a Dual-branch Spatiotemporal Encoder (DSE) to capture region-pair and high-order region group relationships, (2) a Pseudo-Label Generation (PLG) module for aligning cross-city embedding similarities, and (3) a Reliable Region Selection (RRS) module for contrastive learning within consistent regions. Extensive experiments on real-world datasets demonstrate that CRRL achieves state-of-the-art performance in cross-city traffic prediction under data-scarce scenarios, showcasing its practicality and effectiveness in addressing urban traffic challenges. © 2025

关键词： Contrastive Learning

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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Robust Dynamic Broadcasting for Multi-Hop Wireless Networks Under Time-Varying Connectivity and Dynamic SINR

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IEEE Transactions on Mobile Computing 2025年

作者： Tian, Xiang Yu, Jiguo Luo, Chuanwen Yu, Dongxiao Wang, Guijuan Feng, Bin Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Taishan University School of Information Science and Technology 271000 China

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless Networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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A class of time-dependent quasi-variational–hemivariational inequalities with applications

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Nonlinear Analysis: Real World Applications 2025年 86卷

作者： Liu, Yongjian Migórski, Stanislaw Dudek, Sylwia Center for Applied Mathematics of Guangxi and Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science Chair of Optimization and Control ul. Lojasiewicza 6 Krakow30348 Poland Cracow University of Technology Faculty of Computer Science and Telecommunications ul. Warszawska 24 Krakow31155 Poland

In this paper a class of time-dependent multivalued quasi-variational inequalities of elliptic type with a solution dependent constraint set, is studied. Its solvability and the closedness of the solution set are proved. Then, the results are applied to constrained quasi-variational–hemivariational inequalities for which the relaxed monotonicity condition is not required. Finally, the abstract results are illustrated by two applications. The first one is a time-dependent frictional contact problem with locking materials, and the second one is the stationary incompressible Navier–Stokes equation which models a generalized Newtonian fluid of Bingham type. Results on existence and the closedness of the solution sets are established for both applications. © 2025 Elsevier Ltd

关键词： Navier Stokes equations

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing data processing Vectors Space exploration Resource management Speech processing Mutual information

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FaceBench: A Multi-View Multi-Level Facial Attribute VQA dataset for Benchmarking Face Perception MLLMs

arXiv

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arXiv 2025年

作者： Wang, Xiaoqin Ma, Xusen Hou, Xianxu Ding, Meidan Li, Yudong Chen, Junliang Chen, Wenting Peng, Xiaoyang Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China AIAC Xi’an Jiaotong-Liverpool University China Tsinghua University China The Hong Kong Polytechnic University Hong Kong City University of Hong Kong Hong Kong Sun Yat-sen University China

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely unexplored. To address this gap, we introduce FaceBench, a dataset featuring hierarchical multi-view and multi-level attributes specifically designed to assess the comprehensive face perception abilities of MLLMs. Initially, we construct a hierarchical facial attribute structure, which encompasses five views with up to three levels of attributes, totaling over 210 attributes and 700 attribute values. Based on the structure, the proposed FaceBench consists of 49,919 visual question-answering (VQA) pairs for evaluation and 23,841 pairs for fine-tuning. Moreover, we further develop a robust face perception MLLM baseline, Face-LLaVA, by training with our proposed face VQA data. Extensive experiments on various mainstream MLLMs and Face-LLaVA are conducted to test their face perception ability, with results also compared against human performance. The results reveal that, the existing MLLMs are far from satisfactory in understanding the fine-grained facial attributes, while our Face-LLaVA significantly outperforms existing open-source models with a small amount of training data and is comparable to commercial ones like GPT-4o and Gemini. The dataset will be released at https://***/CVI-SZU/FaceBench. Copyright © 2025, The Authors. All rights reserved.

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Effects of Reduced Frequency on Network Configuration and Synchronization Transition

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Chinese Physics Letters 2016年第5期33卷 14-17页

作者：朱留华 Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing College of Physics Science and Engineering TechnologyYulin Normal University

Synchronization of networked phase oscillators depends essentially on the correlation between the topological structure of the graph and the dynamical property of the elements. We propose the concept of ＇reduced frequency＇, a measure which can quantify natural frequencies of each pair of oscillators. Then we introduce an evolving network whose linking rules are controlled by its own dynamical property. The simulation results indicate that when the linking probability positively correlates with the reduced frequency, the network undergoes a first-order phase transition. Meanwhile, we discuss the circumstance under which an explosive synchronization can be ignited. The numerical results show that the peculiar butterfly shape correlation between frequencies and degrees of the nodes contributes to an explosive synchronization transition.

关键词： of work as it Effects of Reduced Frequency on Network Configuration and Synchronization Transition is that

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THE-TAFL: Transforming Healthcare Edge with Transformer-based Adaptive Federated Learning and Learning Rate Optimization

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Internet of Things (The Netherlands) 2025年 32卷

作者： Ullah, Farhan Mohammad, Nazeeruddin Mostarda, Leonardo Cacciagrano, Diletta Ullah, Shamsher Zhao, Yue Cybersecurity Center Prince Mohammad Bin Fahd University Khobar 34754 Saudi Arabia Department of Mathematics and Computer Science University of Perugia Perugia 06123 Italy Division of Computer Science University of Camerino Camerino 62032 Italy National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 China Department of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou 325060 China

The healthcare industry is becoming more vulnerable to privacy violations and cybercrime due to the pervasive dissemination and sensitivity of medical data. Advanced data security systems are needed to protect privacy, data integrity, and dependability as confidentiality breaches increase across industries. Decentralized healthcare networks face challenges in feature extraction during local training, hindering effective federated averaging and learning rate optimization, which affects data processing and model training efficiency. This paper introduces a novel approach of Transforming Healthcare Edge with Transformer-based Adaptive Federated Learning (THE-TAFL) and Learning Rate Optimization. In this paper, we combine Transformer-based Adaptive Federated Learning (TAFL) with learning rate optimization to improve the privacy and security of healthcare information on edge devices. We used data augmentation approaches that generate robust and generalized input datasets for deep learning models. Next, we use the Vision Transformer (ViT) model for local training, generating Local Model Weights (LMUs) that enhance feature extraction and learning. We designed a training optimization method that improves model performance and stability by combining a loss function with weight decay for regularization, learning rate scheduling, and gradient clipping. This ensures effective training across decentralized clients in a Federated Learning (FL) framework. The FL server receives LMUs from many clients and aggregates them. The aggregation procedure utilizes adaptive federated averaging to aggregate the LMUs based on the performance of each client. This adaptive method ensures that high-performing clients contribute more to the Global Model Update (GMU). Following aggregation, clients receive the GMU to continue training with the updated parameters, ensuring collaborative and dynamic learning. The proposed method provides better performance on two standard datasets using various numb

关键词： Adaptive Federated Learning Edge computing Healthcare Privacy & Security Medical imaging Transforming Healthcare Edge Vision transformer

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