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Learning to Learn Transferable Generative Attack for Person Re-Identification

IEEE Transactions on Image processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Unified Feature Selection Approach for Complex Data Based on Fuzzy β-Covering Reduction via information Granulation

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IEEE Transactions on Neural Networks and Learning Systems 2025年 PP卷 PP页

作者： Zou, Xiongtao Dai, Jianhua Hunan Normal University Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education) Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering School of Mathematics and Statistics Changsha 410081 China

Feature selection, as an important step of data analysis, is widely used in the fields of data mining, machine learning, and artificial intelligence. It can not only effectively alleviate the curse of dimensionality and improve model performance but also enhance model interpretability. In the real world, data is usually complex such as different feature types, the presence of missing values, and so on. However, most existing feature selection approaches are only capable of handling data with a single feature type. To address the issue of feature selection under the environment of complex data, this article proposes a unified feature selection (UFS) approach for complex data based on fuzzy β-covering reduction via information granulation. To begin with, several monotonic uncertainty measures for fuzzy β-covering are constructed from the viewpoints of algebra and information theory. Based on the proposed measures, two forward heuristic algorithms are designed for fuzzy β-covering reduction. Meanwhile, the complex data with multiple features is represented by fuzzy β-covering via information granulation. On this basis, a UFS approach is put forward for complex data. Finally, the effectiveness and superiority of the proposed approach are verified through a series of experiments compared with 12 state-of-the-art feature selection approaches. © 2012 IEEE.

关键词： Complex data feature selection fuzzy conditional entropy (FCE) fuzzy rough set (FRS) fuzzy β-covering granular computing

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Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems

arXiv

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arXiv 2025年

作者： Dong, Li Jiang, Feibo Peng, Yubo School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Intelligent Software and Engineering Nanjing University Suzhou215163 China

Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. To address these issues, we present an Attention-based UAV Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (ATOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UAVs. TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UAV trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Antennas

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A Bi-selection Method Based on Consistent Matrix for Large-Scale Datasets

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IEEE Transactions on Fuzzy Systems 2025年

作者： Quan, Jinsheng Qiao, Fengcai Yang, Tian Shen, Shuo Qian, Yuhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Institute of Interdisciplinary Studies Changsha410081 China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410081 China Shanxi University Institute of Big Data Science and Industry Taiyuan030006 China

Biselection (feature and sample selection) enhances the efficiency and accuracy of machine learning models when handling large-scale data. Fuzzy rough sets, an uncertainty mathematics model known for its excellent interpretability, are widely used in machine learning, particularly for feature selection. While the consistent matrix has significantly improved the computational efficiency and scalability of feature selection, like most fuzzy rough set-based methods, it focuses only on feature selection and seldom incorporates sample selection. This feature-centric approach can limit classification performance, particularly in noisy and large-scale datasets where both features and samples require judicious selection. To overcome these limitations, this article explores the integration of sample selection with feature selection. First, we introduce a β-consistent granulation method to generate more accurate and concise fuzzy information granules, reducing the impact of noisy samples. In addition, a novel membership function is employed to distinguish noise while simultaneously evaluating both features and samples. As a result, a biselection algorithm with linear computation complexity is proposed to select high-quality features and samples. Numerical experiments demonstrate that, compared to eleven representative algorithms, our proposed method achieves an average accuracy improvement of 9.66% and a 933-fold increase in efficiency. © 2025 IEEE.

关键词： Biselection consistent matrix (CM) fuzzy sets granular computing rough sets

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HTL-WASI preconditioner for finite element discretization of complex ADR equation and its application

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Journal of Computational and Applied Mathematics 2025年 470卷

作者： Yang, Ronghua Wang, Junxian Shu, Shi School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan Hunan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan411105 China

The convection diffusion reaction (ADR) equation is widely used in engineering applications. In this paper, we focus on a complex ADR equation arising from Helmholtz equation with impedance boundary conditions, prove the coerceiveness and boundedness of the sesquilinear functional and discuss the fast solver of the discretization. Firstly, we design a Weighted Additive Schwarz with Impedance (WASI) preconditioner, in order to overcome the dependence of the WASI preconditioner on the number of subdomains and the overlapping width, an ideal coarse space is designed. Although it can be proved that the corresponding hybrid two-level preconditioner is the exact inverse of the ADR coefficient matrix, the dimension of this coarse space is too big. In order to overcome this deficiency, a smaller dimensional coarse space was constructed by introducing local generalized eigenvalue problems (GEP) on each subdomain, and the descent rate of the corresponding two-level preconditioned GMRES method was rigorously analyzed which does not depend on mesh size, overlapping width, wave number k, and the absorption parameter. Since the size of the coarse space is sensitive to k and the GEP on each subdomain needs to be solved, the computational cost is too high. Therefore, we design an iterative method based on an economical two-level preconditioner, and establish a heuristic convergence theory that is supported by numerical results. Numerical experiments show the robustness of GMRES with the corresponding economical two-level preconditioner(HTLE-WASI-GMRES). Finally, for the Helmholtz finite element discretization, by using the Shifted Laplace technique and ADR equation, we design a novel fast solver combining with HTLE-WASI-GMRES for ADR equation. Numerical experiments have demonstrated the effectiveness of the algorithm. © 2025

关键词： Convergence of numerical methods

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Conservative mixed Discontinuous Galerkin finite element method with full decoupling strategy for incompressible MHD problems

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Communications in Nonlinear Science and Numerical Simulation 2025年 147卷

作者： Huang, Huayi Huang, Yunqing Tang, Qili Yin, Lina Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China National Center for Applied Mathematics in Hunan Xiangtan University Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China

In this research, we propose a mixed Discontinuous Galerkin (DG) finite element method (FEM) for the numerical solution of incompressible magnetohydrodynamics (MHD) problems, ensuring the divergence-free condition for the velocity field. Our proposed methodology is characterized by its energy stability. Subsequently, we construct a novel finite element discretization scheme, for which we establish the theoretical framework of energy stability. Furthermore, we incorporate a preconditioning strategy that facilitates the development of an alternative positive definite, fully decoupled linearization approach. A significant advantage of this scheme is its ability to circumvent the complexities associated with solving saddle point problems at each time step. We demonstrate the uniqueness and convergence of the solutions within this fully decoupled, block-structured linear system. This theoretical validation is complemented by a series of numerical experiments that substantiate the effectiveness and accuracy of our proposed method. © 2025 Elsevier B.V.

关键词： Finite element method

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An Efficient WRF Framework for Discovering Risk Genes and Abnormal Brain Regions in Parkinson's Disease Based on Imaging Genetics Data

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Journal of Computer Science & Technology 2021年第2期36卷 361-374页

作者： Xia-An Bi Zhao-Xu Xing Rui-Hui Xu Xi Hu College of Information Science and Engineering Hunan Normal UniversityChangsha 410006China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha 410006China

As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data *** data mining method after fusion and the overall analysis framework are *** this study,we propose a weighted random forest(WRF)model as the feature screening *** interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis *** implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of *** experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are *** fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of *** also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.

关键词： multimodal fusion feature Parkinson's disease pathogenic factor detection sample classification weighted random forest model

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Lightweight Vision Model-based Multi-user Semantic Communication Systems

arXiv

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arXiv 2025年

作者： Jiang, Feibo Tu, Siwei Dong, Li Wang, Kezhi Yang, Kun Liu, Ruiqi Pan, Cunhua Wang, Jiangzhou Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Semantic Communication (SemCom) is a promising new paradigm for next-generation communication systems, emphasizing the transmission of core information, particularly in environments characterized by uncertainty, noise, and bandwidth constraints. However, existing image SemCom systems face several challenges, such as inefficient knowledge base construction, insufficient semantic encoding, and lack of multi-user semantic sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-user Semantic Communication System (LVM-MSC). First, we construct a Lightweight Knowledge Base (LKB) based on the fast Segment Anything Model (SAM). LKB incorporates the extensive image knowledge of the SAM model while significantly reducing the number of parameters through its convolutional architecture. Next, we design an Efficient Semantic Codec (ESC) based on the Masked AutoEncoder (MAE) architecture. ESC enhances semantic compression at both the pixel and semantic levels and implements lightweight semantic decoding tailored for user devices. Furthermore, we propose a Multi-user Semantic Sharing (MSS) transmission for the multi-user SemCom. By calculating the similarity of semantic information among different users in the sharing semantic space, we unify the transmissions of similar semantic information through broadcasting, further improving the transmission efficiency. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed LVM-MSC system. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Security-Reliability Analysis and Optimization for Cognitive Two-Way Relay Network with Energy Harvesting

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China Communications 2024年第11期21卷 163-179页

作者： Luo Yi Zhou Lihua Dong Jian Sun Yang Xu Jiahui Xi Kaixin Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha 410081China School of Electronic Information Central South UniversityChangsha 410075China

This paper investigates the security and reliability of information transmission within an underlay wiretap energy harvesting cognitive two-way relay *** the network,energy-constrained secondary network(SN)nodes harvest energy from radio frequency signals of a multi-antenna power *** SN sources exchange their messages via a SN decode-and-forward relay in the presence of a multiantenna eavesdropper by using a four-phase time division broadcast protocol,and the hardware impairments of SN nodes and eavesdropper are *** alleviate eavesdropping attacks,the artificial noise is applied by SN *** physical layer security performance of SN is analyzed and evaluated by the exact closed-form expressions of outage probability(OP),intercept probability(IP),and OP+IP over quasistatic Rayleigh fading ***,due to the complexity of OP+IP expression,a self-adaptive chaotic quantum particle swarm optimization-based resource allocation algorithm is proposed to jointly optimize energy harvesting ratio and power allocation factor,which can achieve security-reliability tradeoff for *** simulations demonstrate the correctness of theoretical analysis and the effectiveness of the proposed optimization algorithm.

关键词： artificial noise energy harvesting cognitive two-way relay network hardware impairments physical layer security security-reliability tradeoff self-adaptive quantum particle swarm optimization

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Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

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Frontiers of Computer Science 2021年第6期15卷 165-173页

作者： Xia-an BI Yiming XIE Hao WU Luyun XU Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha 410081China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China Business School of Hunan Normal University Changsha 410081China

Mild cognitive impairment(MCI)as the potential sign of serious cognitive decline could be divided into two stages,i.e.,late MCI(LMCI)and early MCI(EMCI).Although the different cognitive states in the MCI progression have been clinically defined,effective and accurate identification of differences in neuroimaging data between these stages still needs to be further *** this paper,a new method of clustering-evolutionary weighted support vector machine ensemble(CEWSVME)is presented to investigate the alterations from cognitively normal(CN)to EMCI to *** CEWSVME mainly includes two *** first step is to build multiple SVM classifiers by randomly selecting samples and *** second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs,thereby improving the final classification ***,we extracted the optimal features to detect the differential brain regions in MCI progression,and confirmed that these differential brain regions changed dynamically with the development of *** exactly,this study found that some brain regions only have durative effects on MCI progression,such as parahippocampal gyrus,posterior cingulate gyrus and amygdala,while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the *** work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.

关键词： machine learning MCI progression optimal feature extraction differential brain regions functional magnetic resonance imaging

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