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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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A Uniformly Spaced Double-Latent Variable Probabilistic Model for Monitoring of Dynamic Processes with Multiple Sampling Rates

SSRN

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SSRN 2024年

作者： Ying, Ze Chang, Yuqing He, Yuchen Wang, Fuli College of Information Science and Engineering Northeastern University Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

The collections and statistical analysis of production data are essential for process monitoring in modern industries, yet often faces challenges such as multi-sample rates and dynamics. In this paper, we propose a uniformly spaced double-latent variable probabilistic (USDLVP) model that can obtain the measurement correlations within diverse sampling rates (SR). The model utilizes the greatest common divisors of the SR to construct a latent variable structure, effectively addressing the challenges encountered by conventional methods in handling practical multi-sampling rate data. Moreover, to gain a comprehensive understanding of the dynamics in multi-sampling rate processes, a Markov-enhanced double latent variable structure is presented to extract data autocorrelation features from both quality-correlated and uncorrelated perspectives. The expectation maximization (EM) algorithm is employed for model training. Finally, the model is adopted to develop a fault detection method, which is subsequently applied in two industrial plants. The experimental results demonstrate the superiority of the proposed model in handling multi-sampling rate issues. © 2024, The Authors. All rights reserved.

关键词： Process monitoring

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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International Conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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Chirality-2 fermion induced anti-Klein tunneling in a two-dimensional checkerboard lattice

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Physical Review B 2024年第11期109卷 115429-115429页

作者： Jiannan Hua Z. F. Wang W. Zhu Weiwei Chen Department of Physics School of Science Westlake University Hangzhou Zhejiang 310024 China Institute of Natural Sciences Westlake Institute for Advanced Study Hangzhou Zhejiang 310024 China Hefei National Laboratory for Physical Sciences at the Microscale CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics Department of Physics University of Science and Technology of China Hefei Anhui 230026 China Hefei National Laboratory University of Science and Technology of China Hefei Anhui 230088 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou 310018 China

The quantum tunneling effect in a two-dimensional (2D) checkerboard lattice is investigated. By analyzing the pseudospin texture of the states in a 2D checkerboard lattice based on the low-energy effective Hamiltonian, we find that this system has a chiral symmetry with chirality equal to 2, although compared with regular chiral fermions, its pseudospin orientation does not vary uniformly. This suggests that the perfect reflection chiral tunneling, also known as the anti-Klein tunneling, is expected to appear in a checkerboard lattice as well. To verify the conjecture, we calculate the transmission probability and find that normally incident electron states can be perfectly reflected by the barrier with hole states inside and vice versa. Furthermore, we numerically calculate the tunneling conductance of the checkerboard nanotube using the recursive Green's function method. The results show that a perfect on-off ratio can be achieved by confining the energy of the incident states within a certain range. It also suggests that, by tuning the barrier, the checkerboard nanotube can work as a perfect band filter or tunneling field effect transistor, which transmits electrons selectively with respect to the pseudospin of the incident electrons.

关键词： Transport phenomena Nanomechanical devices Transistors Tunnel junctions Landauer formula S-matrix method in transport

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Hierarchical Finite-Horizon Optimal Control for Stackelberg Games via Integral Reinforcement Learning 7th

Hierarchical Finite-Horizon Optimal Control for Stackelberg ...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Chen, Jiayu Cui, Xiaohong Lu, Yao Chen, Wenjie Peng, Binbin College of Mechanical and Electrical Energineering China Jiliang University Zhejiang Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

ISBN: (纸本)9789811672125

This paper presents the hierarchical optimal multi-control algorithm for the partially unknown finite-horizon linear systems. The key to the multi-control problem is to find the solution to the coupled leader-follower Hamilton-Jacobi (HJ) equations. The coupled leader-follower HJ equations are derived and we define the Stackelberg equilibrium of the games. To solve the HJ equations for the follower, an integral reinforcement learning (IRL) approach is given on the basis of the PI algorithm, which uses partial knowledge of the dynamic systems. The Finite-Neural-Network (FNN) structure is used to approximate the value functions in the proposed algorithm. Simulation results are shown to verify the effectiveness of the scheme. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Maximizing Area Coverage in Privacy-Preserving Worker Recruitment: A Prior Knowledge-Enhanced Geo-indistinguishable Approach

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5138-5151页

作者： Zhang, Pengfei Cheng, Xiang Zhang, Zhikun Zhu, Youwen Zhang, Ji Anhui University of Science and Technology State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining the School of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Yunnan 650221 China Key Laboratory of Equipment Data Security and Guarantee Technology Ministry of Education Key Laboratory of Computing Power Network and Information Security Ministry of Education China Beijing University of Posts and Telecommunications China Zhejiang University China Nanjing University of Aeronautics and Astronautics China University of Southern Queensland China

Worker recruitment for area coverage maximization, typically requires participants to upload location information, which can deter potential participation without proper protection. While existing studies resort to geo-indistinguishability to address this concern, they primarily focus on either specific task locations (Target Coverage) or operate under pre-defined recruitment quotas for an interested region (Area Coverage). These focuses not only yield suboptimal area coverage when scaled but also fail to leverage valuable prior knowledge in the form of participants’ noisy historical registered locations, to enhance both location obfuscation and worker identification processes. To address these limitations, we present WILTON, which optimizes area coverage under geo-indistinguishability by recruiting the minimum number of participants through the strategic utilization of noisy prior knowledge. In WILTON, to generate obfuscated locations, we propose a probabilistic and weight-aware input perturbation mechanism, which groups and weights prior locations rather than using only personal prior locations. To privately identify the recruited workers, we design a grid-based worker identification method, which provides a worst-case performance guarantee of ratio (Formula Presented) to the optimum. We provide a theoretical analysis of the privacy, utility, and complexity guarantees of WILTON. Experimental results over two real-world datasets and one synthetic dataset show that WILTON surpasses the state-of-the-arts by at least 8% in area coverage improvement. © 2005-2012 IEEE.

关键词： Knowledge acquisition

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Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set

arXiv

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arXiv 2024年

作者： Luo, Nanfang Zhang, Qinghua Xie, Qin Wang, Yutai Yin, Longjun Wang, Guoyin Chongqing Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life scenarios. Most existing researches lack tools to fuse information quickly and interpret decision results for partially formed decisions. This limitation is particularly noticeable when there is a need to improve the efficiency of GDM. To address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) method is constructed from the perspective of granular computing. This method simultaneously considers the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) environment. First, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the concepts of expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are defined. Second, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function. Then, the granular structure of DHHFLTS based on the sequential three-way decision (S3WD) is defined to improve the decision-making efficiency, and the decision-making strategy and interpretation of each decision-level are proposed. Furthermore, the algorithm of S3W-GDM is given. Finally, an illustrative example of diagnosis is presented, and the comparative and sensitivity analysis with other methods are performed to verify the efficiency and rationality of the proposed method. © 2024, CC BY.

关键词： Efficiency

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms

Last-X-Generation Archiving Strategy for Multi-Objective Evo...

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Congress on Evolutionary Computation

作者： Tianye Shu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time.

关键词： Costs Sociology Evolutionary computation Statistics Optimization Standards

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Padding Adversarial Training: Enhancing Robustness with Adversarial Space Hypothesis

SSRN

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SSRN 2022年

作者： Sun, Jiaze Wen, Sulei School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi’an710127 China

Adversarial training, represented by the projected gradient descent(PGD) adversarial training, effectively improves adversarial robustness within the upper bound of perturbation to the deep neural network(DNN) by applying adversarial examples to model retraining. When the perturbation is beyond the pale, though, the defense it provides to the model is invalid. Furthermore, it is also vulnerable to adaptive attacks and novel attacks uninvolved in adversarial training. To solve these problems, we propose the adversarial space hypothesis based on the manifold theory to explain the existence of adversarial examples from a feature space perspective. We further introduce padding adversarial training, a technique enhancing adversarial training with the adversarial space hypothesis. Padding adversarial training improves the generalization of the standard adversarial training’s robustness by 20% and provides an additional detection defense for adversarial examples with high perturbation. Moreover, we propose trap smoothing, which optimizes the target manifold’s distribution and establishes the inducing relationship between the target manifold and the feature space labelled by the padding classes. On MNIST and CIFAR-10, padding adversarial training is robust to white, black and adaptive attacks in low perturbation conditions and provides the 80%~98% detection rate of adversarial examples in high perturbation conditions. The code is available at:https://***/Ccsk-Xian/Padding-adversarial-training. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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