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Attack and Defense Game with Intuitionistic Fuzzy Payoffs in Infrastructure Networks

Tsinghua science and technology 2025年第1期30卷 384-401页

作者： Yibo Dong Jin Liu Jiaqi Ren Zhe Li Weili Li Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China

Due to our increasing dependence on infrastructure networks,the attack and defense game in these networks has draw great concerns from security ***,when it comes to evaluating the payoffs in practical attack and defense games in infrastructure networks,the lack of consideration for the fuzziness and uncertainty of subjective human judgment brings forth significant challenges to the analysis of strategic interactions among decision *** paper employs intuitionistic fuzzy sets(IFSs)to depict such uncertain payoffs,and introduce a theoretical framework for analyzing the attack and defense game in infrastructure networks based on intuitionistic fuzzy *** take the changes in three complex network metrics as the universe of discourse,and intuitionistic fuzzy sets are employed based on this universe of discourse to reflect the satisfaction of decision *** employ an algorithm based on intuitionistic fuzzy theory to find the Nash equilibrium,and conduct experiments on both local and global *** show that:(1)the utilization of intuitionistic fuzzy sets to depict the payoffs of attack and defense games in infrastructure networks can reflect the unique characteristics of decision makers’subjective preferences.(2)the use of differently weighted proportions of the three complex network metrics has little impact on decision makers’choices of different strategies.

关键词： infrastructure networks attack and defense game intuitionistic fuzzy set Nash equilibrium

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Domain generalization with semi-supervised learning for people-centric activity recognition

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science China(information sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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Multimodal and Temporal Graph Fusion Framework for Advanced Phishing Website Detection

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IEEE Access 2025年 13卷 74128-74146页

作者： Kavya, S. Sumathi, D. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore632014 India

Phishing attacks are among the persistent threats that are dynamically evolving and demand advanced detection mechanisms to counter more sophisticated techniques. Traditional detection approaches are usually based on single-modal features or static analysis, failing to capture the complex, multi-faceted nature of phishing websites and their dynamic behaviors. Thus, we present a robust Multi-Modal and Temporal Graph Fusion Framework integrating advanced learning paradigms that enhance accuracy and adaptability in phishing detection. Our work proposes four brand-new methods: Multi-Modal Hypergraph Fusion Network (MM-HFN), Temporal Graph Neural Network with Attention (TGNN-Att), Federated Graph Contrastive Learning Network (FGCL-Net), and Multi-Modal Temporal Hypergraph Fusion Network (MMTHF-Net). MM-HFN leverages hypergraphs to capture complex, high-order relationships at textual levels (BERT) and graph-based features versus visual ones (CNNs) for an accuracy in the 95-97% range. TGNN-Att addresses temporal variations in phishing behavior by using attention-enhanced temporal graph networks and LSTMs, providing dynamic detection with 94-96% accuracy. FGCL-Net ensures privacy-preserving learning across decentralized datasets through federated contrastive learning, achieving 93-95% accuracy while safeguarding data privacy. Finally, MMTHF-Net fuses multi-modal and temporal features into a dynamic hypergraph framework, achieving state-of-the-art accuracy of 96-98% with an F1-score of 0.97. These approaches together allow for exact, real-time phishing detection by capturing static and temporal behaviors, high-order relationships, and cross-modal features. The framework proposed demonstrates significant improvements compared to the state of the art, eliminating the shortcomings of single-modality and static analysis while offering scalability, privacy, and adaptability levels. © 2013 IEEE.

关键词： Federated learning

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Data-driven output regulation control for constrained linear systems

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science China(information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace Prior and graph neural networks

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Neural Computing and Applications 2025年第12期37卷 7877-7895页

作者： ArulDass, Stephen Dass Jayagopal, Prabhu School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac

关键词： Electroencephalography

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Multi-parameter ultrasound imaging for musculoskeletal tissues based on a physics informed generative adversarial network

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Chinese Physics B 2025年第4期34卷 442-455页

作者： Pengxin Wang Heyu Ma Tianyu Liu Chengcheng Liu Dan Li Dean Ta Institute of Biomedical Engineering&Technology Academy for Engineering and TechnologyFudan UniversityShanghai 200433China State Key Laboratory of Integrated Chips and Systems Fudan UniversityShanghai 201203China Department of Electronic Engineering School of Information Science and TechnologyFudan UniversityShanghai 200438China Department of Biomedical Engineering School of Information Science and TechnologyFudan UniversityShanghai 200438China

Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal ***,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging *** inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping *** learning methods have been applied in musculoskeletal imaging,but need a large amount of data for *** by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and *** the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue *** results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best *** specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.

关键词： ultrasound image physics informed generative adversarial network musculoskeletal imaging

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DSCCNet for high-quality 4K computer-generated holograms

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Optics Express 2025年第6期33卷 13733-13747页

作者： Xu, Zhenqi Leng, Junmin Dai, Ping Wang, Chao Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China School of Information and Communications Engineering Beijing Information Science and Technology University Beijing100101 China

With the increasing demand for high-quality 3D holographic reconstruction, visual clarity and accuracy remain significant challenges in various imaging applications. Current methods struggle for higher image resolution and to resolve such issues as detail loss and checkerboard artifacts. To address these challenges, we propose the model Depthwise Separable Complex-valued Convolutional Network (DSCCNet) for phase-only computer-generated holography (CGH). This deep learning framework integrates complex-valued convolutions with depthwise separable convolutions to enhance reconstruction precision and improve model training efficiency. Additionally, the diffuser is employed to reduce checkerboard artifacts in defocused parts of 3D CGH. Experimental results demonstrate that DSCCNet can obtain 4K images reconstructed with more intricate details. The reconstruction quality of both 2D and 3D layered objects is enhanced. Validation on 100 images from the DIV2K dataset shows an average PSNR above 37 dB and an average SSIM above 0.95. The proposed model provides an effective solution for high-quality CGH applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computer generated holography

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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Prognostic Survival Analysis for AD Diagnosis and Progression Using MRI Data: An AI-Based Approach

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IEEE Access 2025年 13卷 89059-89078页

作者： Deenadayalan, T. Shantharajah, S.P. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

Alzheimer’s Disease is a progressive neuro-degenerative disorder and a leading cause of dementia, marked by cognitive decline, memory loss, and behavioral changes. Despite advancements in medical imaging and Artificial Intelligence (AI), early detection and accurate prognostic modeling remain challenging, particularly in distinguishing overlapping disease stages. This study proposes a novel AI framework combining EfficientNetB0, a state-of-the-art deep learning model, with dual attention mechanisms for robust feature extraction and survival analysis using the Alzheimer’s MRI dataset hosted on the Kaggle platform. The model was trained and evaluated on a balanced dataset of 6,400 MRI images, classified into 4 AD stages: Non-Demented (NoD), very Mild-Demented (vMiD), Mild-Demented (MiD), and Moderate-Demented (MoD). The pre-processing steps, including resizing, normalization, and data augmentation, ensured robustness and generalizability. The framework achieved classification accuracies of 99. 93% (training), 93. 60% (validation) and 93. 59% (testing), with a perfect Concordance Index of 1.0 for survival predictions, demonstrating its ability to accurately rank survival times. By integrating survival analysis with deep learning, the framework addresses limitations in previous work, such as reliance on linear assumptions and limited classification capabilities, and provides actionable insights for clinical workflows, supporting personalized treatment and early intervention strategies. Future work will focus on integrating longitudinal data, exploring time-dependent co-variates, and enhancing explainability to promote adoption in diverse clinical settings. This approach represents a significant advancement in AI-driven diagnosis and progression modeling of AD, with the potential to transform patient care and outcomes. © 2013 IEEE.

关键词： Deep learning

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Inexact proximal gradient algorithm with random reshuffling for nonsmooth optimization

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science China(information sciences) 2025年第1期68卷 219-237页

作者： Xia JIANG Yanyan FANG Xianlin ZENG Jian SUN Jie CHEN National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center School of Electronic and Information Engineering Tongji University

Proximal gradient algorithms are popularly implemented to achieve convex optimization with nonsmooth regularization. Obtaining the exact solution of the proximal operator for nonsmooth regularization is challenging because errors exist in the computation of the gradient; consequently, the design and application of inexact proximal gradient algorithms have attracted considerable attention from researchers. This paper proposes computationally efficient basic and inexact proximal gradient descent algorithms with random reshuffling. The proposed stochastic algorithms take randomly reshuffled data to perform successive gradient descents and implement only one proximal operator after all data pass through. We prove the convergence results of the proposed proximal gradient algorithms under the sampling-without-replacement reshuffling *** computational errors exist in gradients and proximal operations, the proposed inexact proximal gradient algorithms can converge to an optimal solution neighborhood. Finally, we apply the proposed algorithms to compressed sensing and compare their efficiency with some popular algorithms.

关键词： proximal operator random reshuffling inexact computation compressed sensing nonsmooth optimization

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