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Zero-Dynamics Attacks for Multi-Agent systems Without Velocity Measurements

Journal of systems science & Complexity 2024年第5期37卷 1809-1831页

作者： GAO Jinming WANG Yijing ZUO Zhiqiang ZHANG Wentao Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China Continental-NTU Corporate Lab School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore 639798Singapore

This paper studies the periodic zero-dynamics attacks(ZDAs)in multi-agent systems without velocity measurements under directed ***,two types of attack modes are addressed,i.e.,infinite number and finite number of zero-dynamics *** the former case,the authors show that the consensus of the considered system cannot be *** the latter case,the dynamic evolution of the agents is investigated and it is found that only attacking the rooted agents can destroy the ***,a sufficient condition which quantifies whether or not the consensus value is destroyed is given,revealing the relationship among parameters of system model,filter and attack ***,simulations are carried out to verify the effectiveness of the theoretical findings.

关键词： Consensus directed graph multi-agent systems zero-dynamics attacks

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IMCSCL: Image-Based Malware Classification using Self-Supervised and Contrastive Learning 23

IMCSCL: Image-Based Malware Classification using Self-Superv...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Jia, Yetao Meng, Yangyang Zhuang, Honglin National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9798350319583

The use of malware for illicit cyber activities, including network attacks and information theft, poses a severe threat to cybersecurity. In comparison to traditional malware detection methods based on signature and heuristics, machine learning and deep learning-based malware detection methods demonstrate superior generalization ability. However, existing research still faces challenges such as reliance on relatively single malware features, inadequate ability to describe malware features, and overdependence on labeled data. In this paper, we propose an image-based malware classification method using self-supervised and contrastive learning, named IMCSCL. We visualize malware using opcode semantic features, and then detect malware using a contrastive learning method with improved feature encoder network. Experimental results demonstrate that IMCSCL achieves higher detection accuracy compared to supervised malware detection methods, achieving 98.85% accuracy on the Microsoft Malware Classification Challenge dataset. Fine-tuning the model using randomly selected 5% labeled samples from the training set still achieved high accuracy of 94.22%. IMCSCL exhibits superior generalization ability, faster convergence speed, and better training stability. Moreover, contrastive learning significantly reduces malware labeling costs while effectively enhancing detection performance. © 2023 IEEE.

关键词： Image classification

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Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

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Computers, Materials & Continua 2024年第7期80卷 1543-1561页

作者： Bing Li Liangyu Wang Xia Liu Hongbin Fan Bo Wang Shoudi Tong School of Automation Harbin University of Science and TechnologyHarbin150080China Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin150080China School of Computer Engineering Technology Guangdong Institute of Science and TechnologyZhuhai519090China

Nuclearmagnetic resonance imaging of breasts often presents complex *** tumors exhibit varying sizes,uneven intensity,and indistinct *** characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor ***,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention ***,the breast region of interest is extracted to isolate the breast area from surrounding tissues and ***,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor *** incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion ***,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel ***,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional *** was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the *** results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

关键词： Dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) breast tumor segmentation multi-scale dilated convolution boundary attention the hybrid loss function with boundary weight

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Adversarial Example Detection for Deep Neural Networks: A Review 8

Adversarial Example Detection for Deep Neural Networks: A Re...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Zhao, Chongyang Li, Hu Wang, Dongxia Liu, Ruiqi National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9798350331035

Deep neural networks have been widely applied in various critical domains. However, they are vulnerable to the threat of adversarial examples. It is challenging to make deep neural networks inherently robust to adversarial examples, while adversarial example detection offers advantages such as not affecting model classification accuracy. This paper introduces common adversarial attack methods and provides an explanation of adversarial example detection. Recent advances in adversarial example detection methods are categorized into two major classes: statistical methods and adversarial detection networks. The evolutionary relationship among different detection methods is discussed. Finally, the current research status in this field is summarized, and potential future directions are highlighted. © 2023 IEEE.

关键词： adversarial detection adversarial example deep neural network

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Neural-based prescribed-time consensus control for multiagent systems via dynamic memory event-triggered mechanism

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science China(Technological sciences) 2025年第3期68卷 217-227页

作者： Xiaohong ZHENG Hui MA Qi ZHOU Hongyi LI School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Controland Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative ControlGuangdong University of TechnologyGuangzhou510006China School of Mathematics and Statistics Guangdong University of TechnologyGuangzhou510006China College of Electronic and Information Engineering and the Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest UniversityChongqing400715China

This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a composite learning technique in NN *** method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity ***,by introducing a time-varying transformation,the study establishes a distributed prescribed-time control *** notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control ***,the DMETM is established to reduce the actuation frequency of the *** the conventional memoryless dynamic event-triggered mechanism,the DMETM incorporates a memory term to further increase triggering *** a distributed estimator for the leader,the DMETM-based NN prescribed-time controller is designed in a fully distributed manner,which guarantees that all signals in the closed-loop system remain bounded within the prescribed ***,simulation results are presented to validate the effectiveness of the proposed algorithm.

关键词： consensus control composite learning control dynamic memory event-triggered control prescribed-time control multiagent systems(MASs)

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Multispectral radiation thermometry approach based on feasible region constraints-divide and conquer optimization

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Optics Express 2025年第10期33卷 20736-20753页

作者： Huang, Hairui Zhang, Wei Yu, Peifeng Xu, Jiawen Liu, Xin Gao, Shan Wang, Chao Zhang, Zezhan Niu, Yi Jiang, Jing Clean Energy Materials and Engineering Center School of Electronic Science and Engineering State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu611731 China The State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Tsinghua University Beijing100084 China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology School of Information and Communication Engineering Harbin Engineering University Harbin150001 China

Multispectral radiation thermometry is a widely used non-contact temperature measurement method, particularly in extreme environments. However, accurately retrieving the true temperature remains challenging due to the unknown emissivity of the object. This paper proposes a collaborative optimization approach (feasible region constraints-divide and conquer optimization), which combines a feasible domain constraints method (PCR-PSO) with a divide and conquer optimization algorithm (multi-BFGS). Simulation results show that the proposed approach reduces the temperature inversion error from 0.59% to 0.19% compared to the traditional BFGS algorithm. Experimental results obtained with stainless steel samples indicate an average error of less than 0.2% and an average processing time of 0.2 seconds, highlighting the potential for real-time temperature measurement in engineering applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Temperature measurement

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Unraveling environmental influences on the spatial and temporal dynamics of cyanobacterial blooms in Lake Erhai during its early stage of eutrophication

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地球空间信息科学学报（英文版） 2024年第6期27卷中插14,1989-2007页

作者： Shangbo Yang Jianzhong Lu Xiaoling Chen Xuejiao Hou Zushuai Wei Jinru Wu State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanChina School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhenChina State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanChina School of Geospatial Engineering and Science Sun Yat-Sen UniversityGuangzhouChina South China Institute of Environmental Sciences Ministry of Ecology and EnvironmentGuangzhouChina

Lake Erhai,a lake in the early stage of eutrophication,has been threatened by algal blooms(particularly the overproliferation of blue-green algae),which can have an impact on drinking water safety and the lake's *** the governing factors of cyanobacterial blooms is critical for taking timely and effective action during this key eutrophication-transition ***,long-term records of cyanobacterial bloom and its key dominating factors remain *** is,therefore,essential to understand the bloom dynamics and the driving forces before any control strategies can be *** investigated the cyanobacterial phycocyanin concentration variability based on satellite observations from 2003 to 2019,by using the empirical orthogonal function *** observed a decrease in the coverage of the dominant mode of variability in phycocyanin magnitudes compared to the period 2003 to 2011,with variations primarily occurring in the northern *** largest variability was identified to be predominant in July,and an apparent timing shift in variability was observed in December 2016 and *** 95％quantile regression model indicated a distinct upper boundary response in cyanobacteria proliferation to the joint Total Nitrogen(TN)and Total Phosphorus(TP)concentrations,which occurred in summer from 2003 to *** apparent response of cyanobacterial bloom to TP was observed during the winters from 2016 to ***,water level and TN:TP ratio played a central role in summer from 2003 to 2011,while from 2016 to 2019,TN:TP ratio was found to dominate in the summer *** winter,air temperature turned out to be a significant modulating factor compared to water *** results suggest that implementing a phosphorus reduction strategy,while controlling TN:TP ratio and suitable water level manipulation,should be considered to ensure the sustainability of Lake Erhai,especially under a consistent global warming scenario.

关键词： Cyanobacterial phycocyanin concentration spatio-temporal variability nutrient reduction remote sensing

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WaveCRNet: Wavelet Transform-Guided Learning for Semantic Segmentation in Adverse Railway Scenes

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IEEE Transactions on Intelligent Transportation systems 2025年第6期26卷 8794-8809页

作者： Wang, Zhangyu Liao, Zhihao Wang, Pengcheng Chen, Peng Luo, Wenwen Beihang University State Key Laboratory of Intelligent Transportation System School of Transportation Science and Engineering Beijing100191 China Beihang University School of Cyber Science and Technology Beijing100191 China

Semantic segmentation is pivotal in autonomous train perception, significantly impacting the system’s intelligence and reliability. However, its performance in railway scenes is hindered by various challenges, including severe weather conditions, low-light situations, tunnel settings, and diverse and dynamic unstructured scenes. To address these challenges, this study proposes WaveCRNet, a novel architecture for real-time semantic segmentation in challenging conditions, simulating wavelet-constrained PID controller in feature and wavelet space. This study first designs an effective wavelet information enhancement algorithm using a differentiable wavelet transform to bridge the gap between the wavelet and feature information domains. Then, the wavelet-guided attention pag module (WAPM) is introduced to guide the learning and fusion of detailed features based on wavelet priors. Moreover, the traditional wavelet transforms are spectral aliasing and shift sensitivity. Inspired by dual-tree complex wavelet transform (DTCWT), the DTCWT-based channel reconstruction module (DCRM) is designed to assist the channel-based learning of boundary information from coarse to fine. Finally, the proposed architecture is evaluated by the public dataset RailSem19. The experimental results validate the consistent performance gains between accuracy and inference speed, improving by 63.7% mIoU and 87 FPS, surpassing those of the advanced methods for real-time segmentation. © 2000-2011 IEEE.

关键词： Semantic Segmentation

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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