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Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System

Chinese Journal of Electronics 2025年第2期34卷 563-575页

作者： Hongyuan Cheng Yining Liu Fei Zhou Zhiyuan Tan Xianchao Zhang School of Computer Science & Engineering Linyi University Linyi China School of Data Science and Artificial Intelligence Wenzhou University of Electronic Technology Wenzhou China Guangxi Key Laboratory of Digital Infrastructure Nanning China School of Computing Engineering & the Built Environment Edinburgh Napier University Edinburgh UK Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province College of Information Science and Engineering Jiaxing University Jiaxing China

Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been proposed to obtain and recommend reliable platoon head vehicles. These trustworthiness evaluation protocols for PSRS fail to achieve both reliability and fairness. We first provide a reliable trustworthiness evaluation method to ensure that the reputation level of platoon head vehicle can be calculated by cloud service provider (CSP) with the help of key agreement mechanism and truth discovery technology. The semi-trusted entity CSP may maliciously tamper with the reputation level of the platoon head vehicle. We also provide a reputation level confirmation method to ensure the fairness of trustworthiness evaluation. Formal security proof and security analysis are provided to show that our trustworthiness evaluation protocol can achieve the goals of privacy protection, reliability, fairness and resistance to several security attacks. Experiments demonstrate that this protocol can save execution time and achieve reliable and fair trustworthiness evaluation for PSRS.

关键词： Resistance Privacy Protocols Uncertainty Reliability engineering Delays Reliability Security Protection Recommender systems

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Phase synchronization on small-world networks with community structure

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Chinese Physics B 2010年第2期19卷 92-100页

作者：王晓华焦李成吴建设 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Aeronautical Computing Technique Research Institute

In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region （r ≤ 0.001）, the network has even worse synchronizability than the case of isolated communities （r = 0）. Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales： the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network.

关键词： complex networks small-world community structure phase synchronization

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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： image registration

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Effective ensemble learning approach for SST field prediction using attention-based PredRNN

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Frontiers of Computer Science 2023年第1期17卷 143-155页

作者： Baiyou QIAO Zhongqiang WU Ling MA Yicheng Zhou Yunjiao SUN School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang 110169China

Accurate prediction of sea surface temperature (SST) is extremely important for forecasting oceanic environmental events and for ocean studies. However, the existing SST prediction methods do not consider the seasonal periodicity and abnormal fluctuation characteristics of SST or the importance of historical SST data from different times;thus, these methods suffer from low prediction accuracy. To solve this problem, we comprehensively consider the effects of seasonal periodicity and abnormal fluctuation characteristics of SST data, as well as the influence of historical data in different periods, on prediction accuracy. We propose a novel ensemble learning approach that combines the Predictive Recurrent Neural Network(PredRNN) network and an attention mechanism for effective SST field prediction. In this approach, the XGBoost model is used to learn the long-period fluctuation law of SST and to extract seasonal periodic features from SST data. The exponential smoothing method is used to mitigate the impact of severely abnormal SST fluctuations and extract the a priori features of SST data. The outputs of the two aforementioned models and the original SST data are stacked and used as inputs for the next model, the PredRNN network. PredRNN is the most recently developed spatiotemporal deep learning network, which simulates both spatial and temporal representations and is capable of transferring memory across layers and time steps. Therefore, we used it to extract the spatiotemporal correlations of SST data and predict future SSTs. Finally, an attention mechanism is added to capture the importance of different historical SST data, weigh the output of each step of the PredRNN network, and improve the prediction accuracy. The experimental results on two ocean datasets confirm that the proposed approach achieves higher training efficiency and prediction accuracy than the existing SST field prediction approaches do.

关键词： SST prediction ensemble learning XGBoost PredRNN attention mechanism

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A novel diagnosis method utilizing MDBO-SVM and imaging genetics for Alzheimer's disease

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Computerized medical Imaging and Graphics 2025年 123卷 102542-102542页

作者： Xin, Yu Sheng, Jinhua Zhang, Qiao Song, Yan Wang, Luyun Yang, Ze School of Computer Science and Technology Hangzhou Dianzi University Zhejiang Hangzhou310018 China Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health Ministry of Industry and Information Technology of China Zhejiang Hangzhou310018 China Beijing Hospital Beijing100730 China National Center of Gerontology Beijing100730 China Institute of Geriatric Medicine Chinese Academy of Medical Sciences Beijing100730 China

Alzheimer's disease (AD) is the most common neurodegenerative disorder, yet its underlying mechanisms remain elusive. Early and accurate diagnosis is crucial for timely intervention and disease management. In this paper, a multi-strategy improved dung beetle optimizer (MDBO) was proposed to establish a new framework for AD diagnosis. The unique aspect of this algorithm lies in its integration of the Osprey Optimization Algorithm, Lévy flight, and adaptive t-distribution. This combination endows MDBO with superior global search capabilities and the ability to avoid local optima. Then, we presented a novel fitness function for integrating imaging genetics data. In experiments, MDBO demonstrated outstanding performance on the CEC2017 benchmark functions, proving its effectiveness in optimization problems. Furthermore, it was used to classify individuals with AD, mild cognitive impairment (MCI), and control normal (CN) using limited features. In the multi-classification of CN, MCI, and AD, the algorithm achieved excellent results, with an average accuracy of 81.7 % and a best accuracy of 92 %. Overall, the proposed MDBO algorithm provides a more comprehensive and efficient diagnostic tool, offering new possibilities for early intervention and disease progression control. © 2025 Elsevier Ltd

关键词： Neurodegenerative diseases

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Memory Layout Optimization for Task-Based Intermittent computing Systems 2

Memory Layout Optimization for Task-Based Intermittent Compu...

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2nd International Conference on intelligent Technology and Embedded Systems, ICITES 2022

作者： Ji, Dong Liu, Songran Wei, Yangjie Yi, Wang Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China

ISBN: (数字)9781665451048

ISBN: (纸本)9781665451048

The embedded system with energy harvest equipment collects the energy required for system operation from its working environment and releases it from the battery. However, the equipment can only provide intermittent power because environmental energy fluctuates. As a result, to assure the program's progress, the program states must be stored timely and frequently. A promising paradigm for providing this capability is the task-based intermittent computing, in which each task is executed atomically and the states stored in shared memory across task boundaries must be preserved. The total number of saved states influences execution energy and time, and memory layout influences the saved memory interval of each task. In this paper, we model and analyze the influence of memory layout on the task-based computing system, and we establish an optimization problem for memory layout aimed at reducing the unnecessary operations on state backup. A heuristic algorithm based on genetic algorithms is proposed to obtain the approximate optimal solution in polynomial time. The benchmark test evaluation results show that, by optimizing the layout of memory variables, the proposed method significantly reduces the cost of state saving and improves execution efficiency. © 2022 IEEE.

关键词： Heuristic algorithms

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Network evolution driven by dynamics applied to graph coloring

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Chinese Physics B 2013年第6期22卷 262-267页

作者：吴建设李力光王晓华于昕焦李成 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China 224# Xidian University Aeronautical Computing Technique Research Institute

An evolutionary network driven by dynamics is studied and applied to the graph coloring problem. From an initial structure, both the topology and the coupling weights evolve according to the dynamics. On the other hand, the dynamics of the network are determined by the topology and the coupling weights, so an interesting structure-dynamics co-evolutionary scheme appears. By providing two evolutionary strategies, a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics. The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors. In this way, a co-evolution phenomenon is applied to the graph coloring problem. The proposed scheme is tested on several benchmark graphs for graph coloring.

关键词： network dynamics evolution of network evolutionary strategies graph coloring problem

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A Progressive Peak-Finding and Weak Peak-Preserving LM Algorithm for Isotope Separation in Miniature Mass Spectrometers

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Rapid Communications in Mass Spectrometry 2025年第17期39卷

作者： Zhiwei Wang Ang Li Wei Xu Dayu Li School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China School of Medical Technology Beijing Institute of Technology Beijing China

Rationale In recent years, performance enhancement strategies for miniature mass spectrometers through signal processing techniques have garnered significant attention, primarily due to their advantage of not changing their mechanical structure. Gaussian decomposition, as a signal processing approach, has shown considerable potential in the field of overlapping peak identification. Method In this study, a novel PPWP-LM (progressive peak-finding and weak peak-preserving Levenberg-Marquardt) algorithm integrating Gaussian decomposition techniques is proposed for use in miniature ion trap mass spectrometers for aliased peak separation. Result The feasibility of the algorithm was verified using simulation data, and the anti-noise performance of the PPWP-LM algorithm was verified under different signal-to-noise ratios. The analytical capability of the algorithm was further evaluated using samples at different concentrations and different scanning speeds, and the results showed that the algorithm maintained stable performance and was adaptable under high-speed scanning conditions. In addition, aliased signal separation was successfully demonstrated by mixing samples, and the results show that it is suitable for rapid analysis in the field and meets the requirements of practical applications. Conclusion Through optimized strategies including a progressive search, pseudo-peak removal, and weak peak protection, the algorithm successfully achieves isotope separation under high-speed scanning conditions in miniature ion trap mass spectrometers, significantly enhancing their analytical efficiency and performance.

关键词： Gaussian decomposition isotope separation miniature ion trap mass spectrometer parameter estimation

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Modeling Fine-Grained Relations in Dynamic Space-Time Graphs for Video-Based Facial Expression Recognition

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IEEE Transactions on Affective computing 2025年

作者： Huang, Changqin Jiang, Fan Han, Zhongmei Huang, Xiaodi Wang, Shijin Zhu, Yanlai Jiang, Yunliang Hu, Bin Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Jinhua321004 China Charles Sturt University School of Computing Mathematics and Engineering AlburyNSW2640 Australia State Key Laboratory of Cognitive Intelligence Anhui Hefei230088 China Hangzhou Hikvision Digital Technology Co. Ltd Education Business Department Hangzhou310051 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Beijing Institute of Technology School of Medical Technology Beijing10081 China

Facial expressions in videos inherently mirror the dynamic nature of real-world facial events. Consequently, facial expression recognition (FER) should employ a dynamic graph-based representation to effectively capture the relational structure of facial expressions rather than relying on conventional grid or sequence methods. However, existing graph-based approaches have their limitations. Frame-level graph methods provide a coarse representation of the facial graph across time and space, while landmark-based graph methods need to introduce additional facial landmarks, resulting in a static graph structure. To address these challenges, we propose spatial-temporal relation-aware dynamic graph convolutional networks (ST-RDGCN). This fine-grained relation modeling approach enables the dynamic modeling of evolving facial expressions in videos through dynamic space-time graphs, eliminating the need for facial landmarks. ST-RDGCN encompasses three graph construction paradigms: dynamic independent space graph, dynamic joint space-time graph, and dynamic cross space-time graph. Furthermore, we propose a relation-aware space-time graph convolution (RSTG-Conv) operator to learn informative spatiotemporal correlations in dynamic space-time graphs. In extensive experimental evaluations, our ST-RDGCN demonstrates state-of-the-art performance on the five popular video-based FER datasets, achieving overall accuracy scores of 99.69%, 91.67%, 56.51%, 69.37%, and 49.03% on the CK+, Oulu-CASIA, AFEW, DFEW, and FERV39 k datasets, respectively. In particular, our ST-RDGCN outperforms the current best method by 3.6% in UAR on the most challenging FERV39 k dataset. Furthermore, our analysis reveals that the dynamic cross space-time graph scheme is the most effective among the three dynamic graph construction schemes. © 2010-2012 IEEE.

关键词： Graphic methods

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Delving into Invisible Semantics for Generalized One-shot Neural Human Rendering

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IEEE Transactions on Visualization and Computer Graphics 2025年

作者： Lin, Yihong Xu, Xuemiao Zhang, Huaidong Xu, Cheng Li, Weijie Xie, Yi Qin, Jing He, Shengfeng South China University of Technology Guangdong China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China South China University of Technology China Hong Kong Polytechnic University Centre for Smart Health Hong Kong CAS-Hong Kong Joint Laboratory for Multimodal Medical Molecular Imaging Hong Kong Singapore Management University School of Computing and Information Systems Singapore

Traditional human neural radiance fields often overlook crucial body semantics, resulting in ambiguous reconstructions, particularly in occluded regions. To address this problem, we propose the Super-Semantic Disentangled Neural Renderer (SSD-NeRF), which employs rich regional semantic priors to enhance human rendering accuracy. This approach initiates with a Visible-Invisible Semantic Propagation module, ensuring coherent semantic assignment to occluded parts based on visible body segments. Furthermore, a Region-Wise Texture Propagation module independently extends textures from visible to occluded areas within semantic regions, thereby avoiding irrelevant texture mixtures and preserving semantic consistency. Additionally, a view-aware curricular learning approach is integrated to bolster the model's robustness and output quality across different viewpoints. Extensive evaluations confirm that SSD-NeRF surpasses leading methods, particularly in generating quality and structurally semantic reconstructions of unseen or occluded views and poses. © 2025 IEEE.

关键词： Semantics

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