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SSRN 2023年

作者： Deng, Jinna Liu, Qiumei Yang, Aimin Chen, Yiming College of Science North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Tangshan063210 China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Tangshan063210 China School of Science Yanshan University Qinhuangdao066004 China LE STUDIUM Loire Valley Institute for Advanced Studies Orléans & Tours Orléans45000 France

According to fractional order model, the governing equation of viscoelastic plate is established. Using the properties of the shifted Bernstein polynomials, the fractional order equation of viscoelastic plate is resolved, the conversion of fractional differential equations to algebraic equations, and operator matrices represented by Kronecker products are solved in the domain. The convergence study validates the algorithm's efficacy and precision in solving differential equations. The displacement of viscoelastic metal thin plate under different external loads (uniform distribution and linear distribution), the effect of white Gaussian noise on the thin metal plate displacement, the effect of different boundary conditions on thin metal plate displacement under harmonic load are studied, the displacements of metal and polymer plates under different loads are contrasted and stress and strain relationship of two kinds of plates are discussed. Based on the outcomes, it can be concluded that the shift Bernstein polynomial approach is useful for numerically simulating viscoelastic plates. © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation

Cross-domain Object Detection through Coarse-to-Fine Feature...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yangtao Zheng Di Huang Songtao Liu Yunhong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach.

关键词： Feature extraction Subspace constraints Object detection Detectors Task analysis Semantics Prototypes

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Nonlinear variational inequality problems involving uncertain variable

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2019年第4期81卷 65-74页

作者： Li, Cunlin Jia, Zhifu Ningxia Key Laboratory of intelligent information and Big Data Processing Governance and social management research center of Northwest Ethnic regions North Minzu University Yinchuan China State Key Laboratory of Mechanics Control of Mechanical Structures Institute of Nano Science and Department of Mathematics Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Mathematics and Information Science North Minzu University Yinchuan China

Variational inequality problems have been extensively studied and applied in the area of optimization theory, but much of it has been developed in a deterministic domain. In this paper, the uncertain variational inequality is studied. Under the compact uncertain event space, we establish an uncertain nonlinear expected residual minimization (UNERM) model. Considering that uncertain variable usually has no density function, we propose integration by parts method to solve UNERM model. Under reasonable assumptions and Slater’s constraint qualification, two kinds of convergence, such as convergence of global optimal solutions and convergence of stationary points, are investigated. © 2019, Politechnica University of Bucharest. All rights reserved.

关键词： Uncertainty analysis

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Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs

arXiv

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arXiv 2020年

作者： Li, Xing Wei, Wei Feng, Xiangnan Liu, Xue Zheng, Zhiming School of Mathematical Science Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China Peng Cheng Laboratory Shenzhen Guangdong China and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Mathematical Science Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction, etc. However, most of the existing approaches start from the binary relationship (i.e., edges) in the graph and have not leveraged the higher order local structure (i.e., motifs) of the graph. Here, we propose mGCMN – a novel framework which utilizes node feature information and the higher order local structure of the graph to effectively generate node embeddings for previously unseen data. Through research we have found that different types of networks have different key motifs. And the advantages of our method over the baseline methods have been demonstrated in a large number of experiments on citation network and social network datasets. At the same time, a positive correlation between increase of the classification accuracy and the clustering coefficient is revealed. It is believed that using high order structural information can truly manifest the potential of the network, which will greatly improve the learning efficiency of the graph neural network and promote a brand-new learning mode establishment. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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LCL-AKA: Lightweight Authentication and key Agreement Protocol for Power IoT

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IEEE Transactions on Smart Grid 2025年

作者： Liu, Zewei Hu, Chunqiang Ruan, Conghao Pu, Yuwen Hu, Pengfei Yu, Jiguo Cyber Physical Society Ministry of Education Key Laboratory of Dependable Service Computing Chongqing University China Chongqing University School of Big Data and Software Engineering Chongqing China China Southern Power Grid Joint Laboratory on Cyberspace Security China Shandong University School of Computer Science and Technology Shandong China Qilu University of Technology Shandong Academy of Sciences School of Computer Science and Technology Jinan250353 China Shandong Laboratory of Computer Networks Jinan250014 China

The rapid adoption of power Internet of Things (PIoT) systems has made security a critical concern, particularly as existing certificateless authentication and key agreement (CL-AKA) protocols face three fundamental limitations that hinder their practical deployment: excessive computational overhead from multi-round interactions, inadequate protection against advanced cryptanalytic attacks, and inflexible architectures unsuitable for dynamic grid environments. To tackle the previously mentioned challenges, this paper proposes lightweight authentication and key agreement protocol (LCL-AKA) for PIoT. Firstly, this protocol efficiently completes the process of identity authentication and session key agreement within a single round of communication interaction, significantly reducing computational costs and improving communication efficiency. Security is rigorously validated through formal proofs under the eCK model and comprehensive analysis showing resistance to key compromise impersonation, unknown key-share attack. Ultimately, comparative experiments are executed to delve into the security and performance characteristics of the proposed protocol. Compared to benchmark protocols deployed in PIoT, the authentication delay of LCL-AKA is decreased by at least 14.88%;in addition, the energy consumption of LCL-AKA is decreased by at least 11.28%. © 2010-2012 IEEE.

关键词： certificateless authentication and key agreement communication efficiency PIoT security proofs

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A Privacy-Preserving and Verifiable Federated Learning Scheme

A Privacy-Preserving and Verifiable Federated Learning Schem...

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IEEE International Conference on Communications (ICC)

作者： Xianglong Zhang Anmin Fu Huaqun Wang Chunyi Zhou Zhenzhu Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing PR China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Due to the complexity of the data environment, many organizations prefer to train deep learning models together by sharing training sets. However, this process is always accompanied by the restriction of distributed storage and privacy. Federated learning addresses this challenge by only sharing gradients with the server without revealing training sets. Unfortunately, existing research has shown that the server could extract information of the training sets from shared gradients. Besides, the server may falsify the calculated result to affect the accuracy of the trained model. To solve the above problems, we propose a privacy-preserving and verifiable federated learning scheme. Our scheme focuses on processing shared gradients by combining the Chinese Remainder Theorem and the Paillier homomorphic encryption, which can realize privacy-preserving federated learning with low computation and communication costs. In addition, we introduce the bilinear aggregate signature technology into federated learning, which effectively verifies the correctness of aggregated gradient. Moreover, the experiment shows that even with the added verification function, our scheme still has high accuracy and efficiency.

关键词： Servers Machine learning Training Encryption Aggregates Computational modeling Privacy

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Urban subsidence zones prone to flooding: mitigated deformation trends post-2024 Guilin megaflood

npj Natural Hazards

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npj Natural Hazards 2025年第1期2卷 1-9页

作者： Pinglang Kou Xu Dong Yuxiang Tao Ruikun Chen Zhengwu Yuan Ying Xia Qiang Xu Ali P. Yunus Rui Liu Qin Ji Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism (TMDPD MCT) Chongqing University of Posts and Telecommunications Chongqing 400065 China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu Sichuan 610059 China Department of Earth and Environmental Sciences Indian Institute of Science Education and Research Mohali Sahibzada Ajit Singh Nagar Punjab 140 306 India Chongqing Key Laboratory of GIS Application School of Geography and Tourism Chongqing Normal University Chongqing 401331 China

Climate change intensifies urban flood hazards, yet existing research often overlooks the complex dynamic relationships between surface deformation, soil properties, and flooding. This study uses the 2024 Guilin flood event as a case study, integrating SBAS-InSAR, DInSAR techniques, and various machine learning methods to explore the complex interactions between surface deformation, soil characteristics, and flooding. The results show that the flood caused significant water expansion, with ground subsidence mainly concentrated in the southern and eastern parts of Guilin, highly coinciding with the severely flooded areas. The flood-inundated areas exhibited opposite deformation trends before and after the flood, shifting from subsidence to uplift, while road subsidence also showed a dynamic process. Different machine learning methods showed varying performance in predicting surface deformation, with the ERT model performing relatively well. Soil thickness was positively correlated with surface subsidence within a certain range, and this relationship exhibited noticeable nonlinear characteristics post-flood. The findings of this study have important practical implications for urban flood mitigation, aiding urban planners in more accurately identifying flood-prone areas, especially those experiencing subsidence.

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On the Detection of Shilling Attacks in Federated Collaborative Filtering

On the Detection of Shilling Attacks in Federated Collaborat...

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Reliable Distributed Systems

作者： Yangfan Jiang Yipeng Zhou Di Wu Chao Li Yan Wang School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University) Ministry of Education China Macquarie University Sydney NSW Australia Tencent Inc Shenzhen China

ISBN: (数字)9781728176260

ISBN: (纸本)9781728176277

Federated collaborative filtering (Fed-CF) is a variant of federated learning (FL) models, which can protect user privacy in recommender systems. In Fed-CF, the recommendation model is collectively trained across multiple decentralized clients by exchanging gradients only. However, the decentralized nature of Fed-CF makes it vulnerable to shilling attacks, which can be realized by inserting fake ratings of target items to distort recommendation results. Unfortunately, previous detection algorithms cannot work well in the FL framework, as all original data samples are not disclosed at all. In this paper, we are the first to systematically study the problem of shilling attacks in the context of federated learning, and propose an effective detection method called Federated Shilling Attack Detector (FSAD) to detect shilling attackers in Fed-CF. We first show the feasibility of shilling attacks in Fed-CF. Next, we dedicatedly design four novel features based on exchanged gradients among clients. By incorporating these gradient-based features, we train a semi-supervised Bayes classifier to identify shilling attackers effectively. Finally, we conduct extensive experiments based on real-world datasets to evaluate the performance of our proposed FSAD method. The experimental results show that FSAD can detect shilling attackers in Fed-CF with high accuracy, with the F 1 value as high as 0.90 on the Netflix dataset, which approaches the performance of the optimal detector that utilizes complete private user information for detection.

关键词： Privacy Collaborative filtering Detectors Collaborative work Information filters Reliability Recommender systems

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Proxy Re-Encryption Scheme For Complicated Access Control Factors Description in Hybrid Cloud

Proxy Re-Encryption Scheme For Complicated Access Control Fa...

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IEEE International Conference on Communications (ICC)

作者： Zheng Lian Mang Su Anmin Fu Huaqun Wang Chunyi Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing PR China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Hybrid cloud has both the strong computing power of public cloud and easy control of private cloud. It provides users with robust services and convenience, meanwhile faces numerous security challenges. How to implement the effective access control is one of them, the purpose of which is deploying policy in private cloud to protect the ciphertext in public cloud. Furthermore, it becomes more and more difficult to describe the access control policy, which is suitable for multi-factor and dynamic updating. Considering the issues above, we propose a proxy re-encryption (PRE) scheme for complicated access control factors description in hybrid cloud. Firstly, we build the system model combining PRE with access control in hybrid cloud. Secondly, we design the algorithm for our scheme including the key construction with multi-factor and its weight, which achieve the target of dynamic updating. Finally, we analyze the security of this scheme by the mathematical method and performance by theory, experiment and comparisons with some other works. Our scheme has made the deployment of access control in hybrid cloud more reliable and scalable.

关键词： Cloud computing Access control Cryptography XML Reliability Privacy

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On the practicality of differential privacy in federated learning by tuning iteration times

arXiv

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arXiv 2021年

作者： Fu, Yao Zhou, Yipeng Wu, Di Yu, Shui Wen, Yonggang Li, Chao Department of Computer Science Sun Yatsen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Department of Computing FSE Macquarie University 2122 Australia School of Computer Science University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore Co. Ltd China

In spite that Federated Learning (FL) is well known for its privacy protection when training machine learning models among distributed clients collaboratively, recent studies have pointed out that the naive FL is susceptible to gradient leakage attacks. In the meanwhile, Differential Privacy (DP) emerges as a promising countermeasure to defend against gradient leakage attacks. However, the adoption of DP by clients in FL may significantly jeopardize the model accuracy. It is still an open problem to understand the practicality of DP from a theoretic perspective. In this paper, we make the first attempt to understand the practicality of DP in FL through tuning the number of conducted iterations. Based on the FedAvg algorithm, we formally derive the convergence rate with DP noises in FL. Then, we theoretically derive: 1) the conditions for the DP based FedAvg to converge as the number of global iterations (GI) approaches infinity;2) the method to set the number of local iterations (LI) to minimize the negative influence of DP noises. By further substituting the Laplace and Gaussian mechanisms into the derived convergence rate respectively, we show that: 3) The DP based FedAvg with the Laplace mechanism cannot converge, but the divergence rate can be effectively prohibited by setting the number of LIs with our method;4) The learning error of the DP based FedAvg with the Gaussian mechanism can converge to a constant number finally if we use a fixed number of LIs per GI. To verify our theoretical findings, we conduct extensive experiments using two real-world datasets. The results not only validate our analysis results, but also provide useful guidelines on how to optimize model accuracy when incorporating DP into FL. Copyright © 2021, The Authors. All rights reserved.

关键词： Laplace transforms

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