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Forward Legal Anonymous Group Pairing-Onion Routing for Mobile Opportunistic Networks

IEEE Transactions on Mobile Computing 2025年第07期24卷 6595-6612页

作者： Zhu, Xiuzhen Lin, Limei Huang, Yanze Wang, Xiaoding Hsieh, Sun-Yuan Wu, Jie Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan Temple University Department of Computer and Information Science 1925 N. 12th St PhiladelphiaPA19122 United States

Mobile Opportunistic Networks (MONs) often experience frequent interruptions in end-to-end connections, which increases the likelihood of message loss during delivery and makes users more susceptible to various cyber attacks. However, most currently proposed anonymous routing protocols are primarily designed for networks with stable connections, making it challenging to protect user identities in MONs. To address these challenges, we propose FLAG-POR (Forward Legal Anonymous Group Pairing-Onion Routing), a novel anonymous routing protocol specifically tailored to enhance message delivery anonymity and security in MONs. Specifically, we abstract the mobile opportunistic network as a contact graph. By introducing the concept of "groups» into the pairing-onion routing protocol, which encrypts messages and relay nodes layer by layer, we develop a novel group-based pairing-onion routing protocol. This protocol ensures message confidentiality and relay node anonymity, while also improving message forwarding rates, as any node within a group can potentially act as a relay. To ensure message authenticity, we employ the efficient SM2 signing algorithm to generate signatures for the message source. Furthermore, by incorporating parameters such as the public key validity period and master key validity period into the group pairing-onion routing protocol, we achieve forward security in message delivery. We conduct a thorough theoretical analysis of the protocol's security and performance. The experimental results demonstrate that our FLAG-POR protocol outperforms baseline anonymous protocols in terms of delivery success rate, traceability rate, path anonymity, and node anonymity. Additionally, the FLAG-POR scheme effectively resists three potential threats to the routing system: collusion attack threat, node identification threat, and path identification threat, in any situation. © 2002-2012 IEEE.

关键词： Anonymity

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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Joint scheduling control of workflow in information communication network based on hybrid modelling

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International Journal of Internet Manufacturing and Services 2022年第3期8卷 231-242页

作者： Liu, Yuehong Zhu, Changhong Shen, Xianhao College of Information Science and Engineering Guilin University of Technology Guilin541004 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin541004 China

In order to overcome the problems of high packet loss rate of workflow scheduling data and high scheduling control data delay in traditional methods, a workflow joint scheduling control method based on hybrid modelling is proposed in this paper. Firstly, the network energy consumption model is constructed to obtain the link transmission rate and calculate the workflow energy consumption of information communication network. Secondly, the workflow task scheduling model is designed according to the calculated energy consumption. Then, calculate the data transmission time of workflow task. Finally, the hybrid modelling is realised by integrating the above three models, and the joint scheduling control of workflow is realised by using binary decision variables. The experimental results show that the data packet loss rate of this method can be reduced by 66.98%, and the scheduling delay is only 18 ms, which shows that this method has good control effect. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Scheduling

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A path planning method based on deep reinforcement learning for crowd evacuation

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Journal of Ambient Intelligence and Humanized Computing 2024年第6期15卷 2925-2939页

作者： Meng, Xiangdong Liu, Hong Li, Wenhao School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China

Deep reinforcement learning (DRL) is suitable for solving complex path-planning problems due to its excellent ability to make continuous decisions in a complex environment. However, the increase in the population size in the crowd evacuation path-planning problem causes a substantial computational burden for the algorithm, which leads to an unsatisfactory efficiency of the current DRL algorithm. This paper presents a path planning method based on DRL for crowd evacuation to solve the problem. First, we divide crowds into groups based on their relationship and distance from each other and select leaders from them. Next, we expand the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to propose an Optimized Multi-Agent Deep Deterministic Policy Gradient (OMADDPG) algorithm to obtain the global evacuation path. The OMADDPG algorithm uses the Cross-Entropy Method (CEM) to optimize policy and improve the neural network’s training efficiency by applying the Data Pruning (DP) algorithm. In addition, the social force model is improved, incorporating the relationship between individuals and psychological factors into the model. Finally, this paper combines the improved social force model and the OMADDPG algorithm. The OMADDPG algorithm transmits the path information to the leaders. Pedestrians in the environment are driven by the improved social force model to follow the leaders to complete the evacuation simulation. The method can use a leader to guide pedestrians safely arrive the exit and reduce evacuation time in different environments. The simulation results prove the efficiency of the path planning method. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Motion planning

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A Novel Approach for Photo-Realistic High Quality Sign Language Video Synthesis 3

A Novel Approach for Photo-Realistic High Quality Sign Langu...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pravine Mukesh, R. Natarajan, B. Elakkiya, R. Vaidhya, G.K. Murali, P. Geetha, K. Amrita Vishwa Vidyapeetham Chennai601103 India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Birla Institute of Technology and Science Department of Computer Science Dubai United Arab Emirates Srm Institute of Science and Technology Vadapalani Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Tamilnadu Chennai India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350361186

This research offers a brand-new framework for producing high-quality, photo-realistic sign language videos using sign language corpora. The framework consists of three main components such as Neural Machine Translation (NMT) using transformer, an open pose estimation approach, and a Generative Adversarial Network (GAN) with VGG-19 classifier. The NMT transformer translates the sign language corpus into a sequence of sign glosses, pose estimation model performs mapping of equivalent signer images and further assists to generate sign images. The GAN with VGG-19 classifier then generates high- quality sign language videos from the sign language images. The proposed method performs at the cutting edge in terms of evaluation on three benchmark dataset using metrics such as PSNR, SSIM, FID, and Inception Score. In addition, the framework offers better translation outcomes with less processing overhead and produces high-quality, photo-realistic sign language videos without the need for animation or avatar techniques. The proposed framework has the potential to offer the uninterrupted communication medium between common people with deaf-mute society. The evaluation scores highlights the improved perfor- mance of the proposed model compared with earlier approaches in terms of visual quality. © 2024 IEEE.

关键词： Generative adversarial networks

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Correlation Fusion with Instance-level Auto-weighted for Multi-view Graph Clustering 25

Correlation Fusion with Instance-level Auto-weighted for Mul...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Xuan Chen, Zhikui Yao, Liang Ma, Shubin Li, Peng Yu, Shuo School of Software Dalian University of Technology Da'lian China School of Electrical Engineering Anhui Polytechnic University Wu'hu China School of Computer Science and Technology Dalian University of Technology Da'lian China

ISBN: (纸本)9798350330014

Multi-view graph clustering (MGC) has emerged as a hot research topic due to its effectiveness. Previous works mainly focus on the fusion of similarity graphs to exploit the complementary and consistent. However, these approaches suffer from three shortcomings: 1) The distance similarity estimate by the linear Euclidean metric may be incorrect in revealing the association relationship of heterogeneous multi-view samples. 2) Most of them often weight the whole view to balance the effect of different views, which ignores the importance of local samples. 3) They fail to preserve the inherent manifold structure between views via directly applying similarity matrix fusion to obtain a consistent graph. To fill these gaps, this paper proposed a novel framework called correlation fusion with instance-level auto-weighted for MGC (CFIMGC). In particular, the Spearman distance is leveraged to effectively characterize the versatile correlation matrix. The sample-level auto-weighted fusion mechanism is presented for flexible assigning weight values to different sample-wise relations in intra-view and inter-view, through which the complementary of numerous correlation matrices is appropri-ately explored to produce a consistent correlation matrix. What's more, a probability graph is adaptively learned based on the fusion correlation matrix for constructing a clustering-friendly similarity graph. Meanwhile, an alternating iteration algorithm is devised to address the non-convex joint optimization problem. Comprehensive experiment results on seven widely used but challenging multi-view datasets demonstrate that the proposed method yields superiority in clustering performance against six state-of-the-art counterparts. © 2023 IEEE.

关键词： Iterative methods

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ST-SIGMA:Spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting

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CAAI Transactions on Intelligence technology 2022年第4期7卷 744-757页

作者： Yang Fang Bei Luo Ting Zhao Dong He Bingbing Jiang Qilie Liu School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqingChina School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)DaejeonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)***,most proposed methods aim at addressing one of the two challenges mentioned above with a single *** tackle this dilemma,this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting(STSIGMA),an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified ***-SIGMA adopts a trident encoder-decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view(BEV)maps ***,an iterative aggregation network is first employed as the scene semantic encoder(SSE)to learn diverse scene *** preserve dynamic interactions of traffic agents,ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction ***,a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is *** experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-theart(SOTA)methods in terms of scene perception and trajectory forecasting,***,the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in realworld AD scenarios.

关键词： feature fusion graph interaction hierarchical aggregation scene perception scene semantics trajectory forecasting

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LAME:Layout-Aware Metadata Extraction Approach for Research Articles

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computers, Materials & Continua 2022年第8期72卷 4019-4037页

作者： Jongyun Choi Hyesoo Kong Hwamook Yoon Heungseon Oh Yuchul Jung Department of Computer Engineering Kumoh National Institute of Technology(KIT)GumiKorea Korea Institute of Science and Technology Information(KISTI) DaejeonKorea School of Computer Science and Engineering Korea University of Technology and Education(KOREATECH)CheonanKorea

The volume of academic literature,such as academic conference papers and journals,has increased rapidly worldwide,and research on metadata extraction is ***,high-performing metadata extraction is still challenging due to diverse layout formats according to journal *** accommodate the diversity of the layouts of academic journals,we propose a novel LAyout-aware Metadata Extraction(LAME)framework equipped with the three characteristics(e.g.,design of automatic layout analysis,construction of a large meta-data training set,and implementation of metadata extractor).In the framework,we designed an automatic layout analysis using PDF *** on the layout analysis,a large volume of metadata-separated training data,including the title,abstract,author name,author affiliated organization,and keywords,were automatically ***,we constructed a pre-trainedmodel,Layout-Meta BERT,to extract the metadata from academic journals with varying layout *** experimental results with our metadata extractor exhibited robust performance(Macro-F1,93.27%)in metadata extraction for unseen journals with different layout formats.

关键词： Automatic layout analysis layout-MetaBERT metadata extraction research article

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Fracture Detection in Radiology with Resnet-152 & CNN 9

Fracture Detection in Radiology with Resnet-152 & CNN

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Chauhan, Shivam Chauhan, Rahul Rawat, Ruchira Aluvala, Srinivas Singh, Rahul Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

In the field of medicine, fracture identification in medical imaging is a crucial task that has significant effects on patient diagnosis and therapy. Deep learning methods have demonstrated amazing promise in improving fracture diagnosis efficiency and accuracy in recent years. This survey paper offers a thorough examination of the most recent developments, difficulties, and breakthroughs in deep learning-based fracture identification. We explore the key concepts of deep learning and how it applies to many types of medical imaging, including MRIs, CT scans, and X-rays. This survey covers widely used deep learning concepts including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and more recent developments like DenseNet and *** goal is to give researchers, medical professionals, and policymakers a thorough grasp of the state of deep learning-based fracture diagnosis by analyzing a wide spectrum of recent publications. The authors highlight areas of prospective future research as well as how these developments might affect clinical practice. Will also discuss about the benefits deep learning brings to this field. In conclusion, the authors will discuss the future directions of this technology. © 2024 IEEE.

关键词： computerized tomography

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