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A dynamic algorithm for trust inference based on double DQN in the internet of things

Digital Communications and Networks 2024年第4期10卷 1024-1034页

作者： Xiaodong Zhuang Xiangrong Tong School of Computer and Control Engineering Yantai UniversityYantai264005China

The development of the Internet of Things(IoT)has brought great convenience to ***,some information security problems such as privacy leakage are caused by communicating with risky *** is a challenge to choose reliable users with which to interact in the ***,trust plays a crucial role in the IoT because trust may avoid some *** usually choose reliable users with high trust to maximize their own interests based on reinforcement ***,trust propagation is time-consuming,and trust changes with the interaction process in social *** track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each *** proposed algorithm simulates the interactions among users by double ***,CurrentNet and TargetNet networks are used to select users for *** users with high trust are chosen to interact in future ***,the trust value is updated dynamically until a reliable trust path is found according to the result of the ***,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation *** are carried out on the FilmTrust and the Epinions *** with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.

关键词： Internet of things Information security Reinforcement learning Trust propagation Trust inference

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Join multiple Riemannian manifold representation and multi-kernel non-redundancy for image clustering

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1305-1319页

作者： Mengyuan Zhang Jinglei Liu School of Computer and Control Engineering Yantai UniversityYantaiShandongChina

Image clustering has received significant attention due to the growing importance of image *** have explored Riemannian manifold clustering,which is capable of capturing the non-linear shapes found in real-world ***,the complexity of image data poses substantial challenges for modelling and feature *** methods such as covariance matrices and linear subspace have shown promise in image modelling,and they are still in their early stages and suffer from certain ***,these include the uncertainty of representing data using only one Riemannian manifold,limited feature extraction capacity of single kernel functions,and resulting incomplete data representation and *** overcome these limitations,the authors propose a novel approach called join multiple Riemannian manifold representation and multi-kernel non-redundancy for image clustering(MRMNR-MKC).It combines covariance matrices with linear subspace to represent data and applies multiple kernel functions to map the non-linear structural data into a reproducing kernel Hilbert space,enabling linear model analysis for image ***,the authors use matrix-induced regularisation to improve the clustering kernel selection process by reducing redundancy and assigning lower weights to identical ***,the authors also conducted numerous experiments to evaluate the performance of our approach,confirming its superiority to state-of-the-art methods on three benchmark datasets.

关键词： clustering data mining image representation machine learning manifolds

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Joint Feature Encoding and Task Alignment Mechanism for Emotion-Cause Pair Extraction

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computers, Materials & Continua 2025年第1期82卷 1069-1086页

作者： Shi Li Didi Sun College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant ***-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying *** comprehension is crucial for making informed strategic decisions in various business and societal ***,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause *** address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment *** model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause ***,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among *** proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 *** results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its *** research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-c

关键词： Emotion-cause pair extraction interactive information enhancement joint feature encoding label consistency task alignment mechanisms

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Physical Layer Security Scheme With AoI-Awareness for Industrial IoT Based on Covert Communications

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 276-278页

作者： Yaping Li Zhi-Xin Liu Jia-Wei Su Ya-Zhou Yuan the Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University

Dear Editor,Industrial Internet of things(IIoT) is a typical application of cyberphysical system(CPS). In the IIoT, wireless communication is an inevitable trend to replace the deployment-limited wired transmission for cases with large-scale and mobile devices. However, wireless communication gives rise to critical issues related to physical security, such as malicious detections and attacks [1].

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Lightweight Malicious Code Classification Method Based on Improved Squeeze Net

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computers, Materials & Continua 2024年第1期78卷 551-567页

作者： Li Li Youran Kong Qing Zhang School of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so *** this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious ***,it is important to identify malicious codes on computer systems ***,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited ***,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution *** feature slicing module reduces the number of parameters by grouping *** multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel *** addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model *** malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,*** proves that LCMISNet has a powerful malicious code recognition *** addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.

关键词： Lightweight neural network malicious code classification feature slicing feature splicing multi-size depthwise separable convolution

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A GAN-EfficientNet-Based Traceability Method for Malicious Code Variant Families

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computers, Materials & Continua 2024年第7期80卷 801-818页

作者： Li Li Qing Zhang Youran Kong School of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains *** this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code *** method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code *** method includes a lightweight classifier and a *** classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other *** simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s *** process helps identify model vulnerabilities and security risks,facilitating model enhancement and *** classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,*** simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±*** classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network *** meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.

关键词： Malicious code variant traceability feature reuse lightweight neural networks code visualization attention mechanism

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Crowd-Based Traffic control Model and Simulation

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International Journal of Crowd Science 2024年第1期8卷 1-9页

作者： Wu, Dingding Sun, Hongbo Li, Zhihui School of Computer and Control Engineering Yantai University Yantai264005 China

With the development of modern science and economy, congestions and accidents are brought by increasing traffics. And to improve efficiency, traffic signal based control is usually used as an effective model to alleviate congestions and to reduce accidents. However, the fixed mode of existing phase and cycle time restrains the ability to satisfy ever complex environments, which lead to a low level of efficiency. To further improve traffic efficiency, this paper proposes a crowd-based control model to adapt complex traffic environments. In this model, subjects are deemed as digital selves who can perform actions in complex traffic environments, such as vehicles and traffic lights. These digital selves have their own control processing mechanisms, properties, and behaviors. And each digital self is continuously optimizing its behaviors according to its learning ability, road conditions, and information interactions from connections with the others. Without a fixed structure, the connections are diverse and random to form a more complex traffic environment, which may be connected or disappeared at any time with continues movements. Finally, feasibility and effectiveness of the crowd-based traffic control model is demonstrated by comparison with fixed traffic signal control model, indicating that the model can alleviate traffic congestion effectively. © The author(s) 2024.

关键词： Traffic congestion

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An efficient schedulability analysis based on worst-case interference time for real-time systems

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Science China(Information Sciences) 2024年第9期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of Computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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An Improved YOLOv8-Based Method for Real-Time Detection of Harmful Tea Leaves in Complex Backgrounds

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Phyton-International Journal of Experimental Botany 2024年第11期93卷 2963-2981页

作者： Xin Leng Jiakai Chen Jianping Huang Lei Zhang Zongxuan Li College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China

Tea,a globally cultivated crop renowned for its uniqueﬂavor proﬁle and health-promoting properties,ranks among the most favored functional beverages ***,diseases severely jeopardize the production and quality of tea leaves,leading to signiﬁcant economic *** early and accurate identiﬁcation coupled with the removal of infected leaves can mitigate widespread infection,manual leaves removal remains time-con-suming and *** robots for pruning can signiﬁcantly enhance efﬁciency and reduce ***-ever,the accuracy of object detection directly impacts the overall efﬁciency of pruning *** complex tea plantation environments,complex image backgrounds,the overlapping and occlusion of leaves,as well as small and densely harmful leaves can all introduce interference *** algorithms perform poorly in detecting small and densely packed *** address these challenges,this paper collected a dataset of 1108 images of harmful tea leaves and proposed the YOLO-DBD *** model excels in efﬁciently identifying harmful tea leaves with various poses in complex backgrounds,providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning *** improvements proposed in this study encompass the Cross Stage Partial with Deformable Convolutional Networks v2(C2f-DCN)module,Bi-Level Routing Atten-tion(BRA),Dynamic Head(DyHead),and Focal Complete Intersection over Union(Focal-CIoU)Loss function,enhancing the model’s feature extraction,computation allocation,and perception *** to the baseline model YOLOv8s,mean Average Precision at IoU 0.5(mAP0.5)increased by 6%,and Floating Point Operations Per second(FLOPs)decreased by 3.3 G.

关键词： Harmful tea leaves YOLO-DBD Focal-CIoU Loss dynamic head Bi-Level Routing Attention

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Privacy-preserving filtering, control and optimization for industrial cyber-physical systems

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Science China(Information Sciences) 2025年第4期68卷 267-283页

作者： Derui DING Qing-Long HAN Xiaohua GE Xian-Ming ZHANG Jun WANG Department of Control Science and Engineering University of Shanghai for Science and Technology School of Engineering Swinburne University of Technology Department of Computer Science City University of Hong Kong

Industrial cyber-physical systems closely integrate physical processes with cyberspace, enabling real-time exchange of various information about system dynamics, sensor outputs, and control decisions. The connection between cyberspace and physical processes results in the exposure of industrial production information to unprecedented security risks. It is imperative to develop suitable strategies to ensure cyber security while meeting basic performance *** the perspective of control engineering, this review presents the most up-to-date results for privacy-preserving filtering,control, and optimization in industrial cyber-physical systems. Fashionable privacy-preserving strategies and mainstream evaluation metrics are first presented in a systematic manner for performance evaluation and engineering *** discussion discloses the impact of typical filtering algorithms on filtering performance, specifically for privacy-preserving Kalman filtering. Then, the latest development of industrial control is systematically investigated from consensus control of multi-agent systems, platoon control of autonomous vehicles as well as hierarchical control of power systems. The focus thereafter is on the latest privacy-preserving optimization algorithms in the framework of consensus and their applications in distributed economic dispatch issues and energy management of networked power systems. In the end, several topics for potential future research are highlighted.

关键词： industrial cyber-physical systems privacy preservation distributed control distributed optimization power systems

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