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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Wang, Haolin Xu, Wei Li, Zezhao Yang, Yanni Huang, Zhijian Shandong University School of Computer Science and Technology Qingdao China National Key Laboratory of Science and Technology on Information System Security China

ISBN: (纸本)9798331515966

Two-dimensional vibration measurement is essential for monitoring structure and machine health, especially providing more information than those 1D solutions. However, most existing 2D vibration monitoring methods require multi-dimension sensors, which can be costly for large-scale deployment. In this paper, we propose m^2 Vib, the first work that can realize accurate 2D vibration measurement using a single mmWave radar. Our system is achieved by creating a virtual radar with non-line-of-sight (NLoS) signals from a nearby reflector (e.g., wall) to offer the 2nd-dimension vibration observations. We further establish a geometric model to unveil the relationship between 2D vibration displacement and mmWave signal. Such an idea still encounters challenges from significantly attenuated NLoS signals and low-resolution geometric parameters from the commercial mmWave radar. Thus, we design a new retroreflector to enhance NLoS signals and advanced signal processing methods to obtain fine-grained geometric parameters. Extensive experimental results show that we can achieve sub-mm level 2D vibration measurement with an average vibration displacement error of 62.2 um and frequency error of 0.01 Hz in the X-axis and Y-axis. © 2024 IEEE.

关键词： Vibration measurement

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Leaving None Behind: Data-Free Domain Incremental Learning for Major Depressive Disorder Detection

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IEEE Transactions on Affective Computing 2024年第2期16卷 758-770页

作者： Chen, Tao Guo, Yanrong Hao, Shijie Hong, Richang Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Hefei University of Technology Ministry of Education and School of Computer Science and Information Engineering Hefei230009 China

While deep learning techniques have shown promising performance in the Major Depressive Disorder (MDD) detection task, they still face limitations in real-world scenarios. Specifically, given the data scarcity, some efforts have resorted to aggregating data from different domains to expand the data volume. However, their effectiveness is currently limited by the domain gap and data privacy. Additionally, the class imbalance issue is particularly severe in our application, leading to biased classifying performance accordingly. To address these challenges, we propose Data-Free Domain Incremental Learning for the MDD detection (DIL-MDD) task, accommodating multiple feature distributions by only accessing well-trained models from previous domains and the data in the current domain. Specifically, DIL-MDD consists of two key modules: Adaptive Class-tailored Threshold Learning (ACTL) and Data-Free Domain Alignment (DFDA). The first module measures the discrepancy between the outputs of two sequential domains, based on which we learn a class-tailored threshold adaptively. Building on this, we differentiate between samples that either exhibit similarities or dissimilarities with the previous domain, where this similar sample set is identified to investigate the feature distribution of the historical data. The second module imposes an alignment constraint to narrow the gap between these two sample sets, thereby exploring the expertise of the previous domain. To validate the effectiveness of the proposed method, we conduct extensive experiments on the public MDD datasets, i.e., DAIC-WOZ, MODMA, and CMDC. We also apply our method to another mental health condition, Autism Spectrum Disorder (ASD), to further demonstrate its applicability. Finally, the ablation studies validate the superiority of the proposed modules. © 2010-2012 IEEE.

关键词： Adversarial machine learning

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

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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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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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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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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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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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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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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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