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Robust Deep One-Class Classification Time Series Anomaly Detection

Computers, Materials & Continua 2025年第6期83卷 5181-5197页

作者： Zhengdao Yang Xuewei Wang Yuling Chen Hui Dou Haiwei Sang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550000China College of Computer Science and Technology Weifang University of Science and TechnologyWeifang261000China School of Mathematics and Big Data Guizhou Education UniversityGuiyang550018China

Anomaly detection(AD)in time series data is widely applied across various industries for monitoring and security applications,emerging as a key research focus within the field of deep *** many methods based on different normality assumptions performwell in specific scenarios,they often neglected the overall normality *** feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection,leading to decreased ***,real-world time series samples are rarely free from noise,making them susceptible to outliers,which further impacts detection *** address these challenges,we propose a novel anomaly detection method called Robust One-Class Classification Detection(ROC).This approach utilizes an autoencoder(AE)to learn features while constraining the context vectors fromthe AE within a sufficiently small hypersphere,akin to One-Class Classification(OC)*** simultaneously optimizing two hypothetical objective functions,ROC captures various aspects of *** categorize the input raw time series into clean and outlier sequences,reducing the impact of outliers on compressed feature *** results on public datasets indicate that our approach outperforms existing baselinemethods and substantially improves model robustness.

关键词： Time series anomaly detection self-supervised learning robustness

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Security Performance Analysis for MIMO Systems: From Artificial Noise's Power and Dimension Perspective

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9985-9989页

作者： Xu, Panpan Li, Zhenling Li, Chunguo Tan, Weijie Guizhou University School of Mathematics and Statistics Guiyang550025 China Southeast University School of Information Science and Engineering Nanjing210096 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

This paper analyzes the influence of power and dimension of artificial noise (AN) on security performance of multiple-input multiple-output (MIMO) system with multiple randomly located eavesdroppers. We derive the closed-form expressions of the secrecy outage probability (SOP) and the optimal secrecy capacity by utilizing the irrelevance among transmission channels, and the orthogonality between AN and the basis vectors of the legitimate channel matrix's null space. We use the Poisson point process to determine the number and location of eavesdroppers, then analyze the security performance of the communication system under the 3D channel condition of probability-based line-of-sight (LoS) or non-line-of-sight (NLoS) channel. The simulation results show the following conclusions. When the AN dimension reaches 1/4 highest dimension (i.e., one quarter of the number of transmitting antennae less than one) and the power splitter factor (PSF) falls in the open interval (0.6, 0.9), we can achieve a trade-off between secrecy capacity and design complexity. © 1967-2012 IEEE.

关键词： Vector spaces

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A potential game-based outsourcing computation framework for multiple calculators

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Digital Communications and Networks 2022年第5期8卷 654-663页

作者： Duo Zhang Youliang Tian Linjie Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Mathematics and Statistics Guizhou UniversityGuiyang550025China

Outsourcing computation enables a computationally weak client to outsource the computation of a function f to a more powerful but untrusted *** traditional outsourcing computation model forbids communication between players,but it has little *** on the game theory,this paper establishes an outsourcing computation model which is more in line with the actual ***,we construct a structural mapping relationship between security outsourcing computation and the optimization ***,by designing the individual potential function and the global potential function,the individual goal is consistent with the global goal to ensure the correctness of the calculation ***,in the information exchange environment between calculators,we construct a Zero-determinant strategy to ensure that the calculator chooses the strategy according to the predetermined target.

关键词： Outsourcing computation Game theory Potential function Zero-determinant strategy Information exchange

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A survey of unmanned aerial vehicle flight data anomaly detection:Technologies,applications,and future directions

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Science China(Technological Sciences) 2023年第4期66卷 901-919页

作者： YANG Lei LI ShaoBo LI ChuanJiang ZHANG AnSi ZHANG XuDong School of Mechanical Engineering Guizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China

Flight data anomaly detection plays an imperative role in the safety and maintenance of unmanned aerial vehicles(UAVs).It has attracted extensive attention from ***,the problems related to the difficulty in obtaining abnormal data,low model accuracy,and high calculation cost have led to severe challenges with respect to its practical ***,in this study,firstly,several UAV flight data simulation softwares are presented based on a brief presentation of the basic concepts of anomalies,the contents of UAV flight data,and the public datasets for flight data anomaly ***,anomaly detection technologies for UAV flight data are comprehensively reviewed,including knowledge-based,model-based,and data-driven ***,UAV flight data anomaly detection applications are briefly described and ***,the future trends and directions of UAV flight data anomaly detection are summarized and prospected,which aims to provide references for the following research.

关键词： unmanned aerial vehicle(UAV) flight data anomaly detection data-driven

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Self-supervised probabilistic models for exploring shape memory alloys

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npj Computational Materials 2024年第1期10卷 1338-1347页

作者： Yiding Wang Tianqing Li Hongxiang Zong Xiangdong Ding Songhua Xu Jun Sun Turab Lookman State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an710049China National Engineering Laboratory for Big Data Analytics School of Mathematics and StatisticsXi’an Jiaotong UniversityXi’an710049China AiMaterials Research LLC Santa FeNM87501USA

Recent advancements in machine learning(ML)have revolutionized the field of high-performance materials ***,developing robust ML models to decipher intricate structure-property relationships in materials remains challenging,primarily due to the limited availability of labeled datasets with well-characterized crystal *** is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic *** a selfsupervised probabilistic model(SSPM)that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures,utilizing solely the existing crystal structure data from materials *** significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal *** showcase SSPM’s capability by discovering shapememory alloys(SMAs).Amongst the top 50 predictions,23 have been confirmed as SMAs either experimentally or theoretically,and a previously unknown SMA candidate,MgAu,has been identified.

关键词： alloys probabilistic shape

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Asynchronous Parallel Stochastic Proximal Gradient Methods for Non-Convex Composite Optimization

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 638-662页

作者： He, Wen-Wu Gong, Xuan-Jin Cui, Zhuo-Xu School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China School of Mathematics and Statistics Wuhan University Wuhan430072 China

This paper considers the parallel stochastic composite optimization for general asynchronous cases where both the objective function and the regularizer are non-convex. In particular, based on the randomized stochastic proximal gradient protocol, an asynchronous parallel algorithm termed ARSPG is proposed which incorporates well the non-convex regularization to obtain the desired oracle property. Theoretical analysis shows that ARSPG has the convergence rate in the order of O(1/ϵ2). Further, a gradient-free version termed ARSPGF is developed to handle cases where only noisy objective function values are available. The analysis shows that ARSPGF has the same order of convergence rate as that of the normal version. Experiments over benchmark data sets validate well theoretical analyses and experimental results also show interesting practical aspects of the proposed algorithms. © 2025 ISSN 2414-8105 (Online).

关键词： Convex optimization

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On Boolean elements and derivations in 2-dimension linguistic lattice implication algebras

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Applied mathematics(A Journal of Chinese Universities) 2023年第2期38卷 274-292页

作者： ZHU Hua ZHAO Jian-bin School of Mathematics and Statistics Zhengzhou UniversityZhengzhou 450001China Henan Academy of Big Data Zhengzhou UniversityZhengzhou 450001China Henan Key Laboratory of Financial Engineering Zhengzhou 450001China Key Laboratory of Big Data Analysis and Application Zhengzhou 450001China

A 2-dimension linguistic lattice implication algebra(2DL-LIA)can build a bridge between logical algebra and 2-dimension fuzzy linguistic *** this paper,the notion of a Boolean element is proposed in a 2DL-LIA and some properties of Boolean elements are *** derivations on 2DL-LIAs are introduced and the related properties of derivations are ***,it proves that the derivations on 2DL-LIAs can be constructed by Boolean elements.

关键词： derivation Boolean element Lattice implication algebra(LIA) 2-dimension linguistic lattice implication algebra(2DL-LIA) 2-dimension fuzzy linguistic information

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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

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Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. Security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling data privacy Protocols databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

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data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

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Science China(Technological Sciences) 2023年第5期66卷 1304-1316页

作者： YANG Lei LI ShaoBo LI ChuanJiang ZHU CaiChao ZHANG AnSi LIANG GuoQiang School of Mechanical Engineering Guizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China State Key Laboratory of Mechanical Transmission Chongqing UniversityChongqing400044China

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from ***-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous ***,flight data with random noise pose a significant challenge for anomaly *** work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight ***,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the ***-based feature subset selection is subsequently performed to reduce the reliance on expert ***,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised ***,the method's effectiveness is validated on real UAV flight data.

关键词： unmanned aerial vehicle(UAV) anomaly detection spatiotemporal correlation based on long short-term memory and autoencoder(STC-LSTM-AE) Savitzky-Golay feature selection

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A blockchain-empowered authentication scheme for worm detection in wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 265-272页

作者： Yuling Chen Xiong Yang Tao Li Yi Ren Yangyang Long State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Computing Science University of East AngliaNorwichNR57TJUK

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and *** special structure of WSN brings both convenience and *** example,a malicious participant can launch attacks by capturing a physical ***,node authentication that can resist malicious attacks is very important to network ***,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for *** our scheme,all nodes are managed by utilizing the identity information stored on the ***,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection *** experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

关键词： Wireless Sensor Network(WSN) Node authentication Blockchain Tangle Worm detection

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