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An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

Science China(information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN school of Electrical and information Engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment school of Electrical Engineering and Automation Henan Institute of Technology school of Electronics and information Zhongyuan University of Technology KINDI center for computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

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Path signature-based XAI-enabled network time series classification

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Science China(information Sciences) 2024年第7期67卷 91-106页

作者： Le SUN Yueyuan WANG Yongjun REN Feng XIA Department of Jiangsu Collaborative Innovation center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology school of computing Technologies Royal Melbourne Institute of Technology

Classifying network time series(NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks,and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification *** include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature(Recur Sig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning(DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems(NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that Recur Sig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.

关键词： network time series classification explainable artificial intelligence path signature automated network management recurrent neural network

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An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

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Chinese Physics B 2024年第5期33卷 223-237页

作者：吴凯周日贵罗佳 College of information Engineering Shanghai Maritime UniversityShanghai 201306China Research center of Intelligent information Processing and Quantum Intelligent computing Shanghai 201306China school of Mathematics and Computational Science Shangrao Normal UniversityShangrao 334001China

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum ***-pass filtering can effectively achieve anti-aliasing effects on ***,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum *** paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial *** achieves the effect of anti-aliasing filtering on quantum images during the scaling ***,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical *** aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of ***,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing *** constructed pyramid model is then used to select an appropriate image for upscaling to the original image ***,the complexity of the circuit is *** to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer ***,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

关键词： quantum color image processing anti-aliasing filtering algorithm quantum multiplier pyramid model

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Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1819-1835页

作者： Kangjia Qiao Jing Liang Kunjie Yu Xuanxuan Ban Caitong Yue Boyang Qu Ponnuthurai Nagaratnam Suganthan school of Electrical and information Engineering Zhengzhou UniversityZhengzhou 450001China school of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China school of Electronic and information Zhongyuan University of TechnologyZhengzhou 450007China KINDI center for computing Research College of EngineeringQatar UniversityDoha 999043Qatar

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for *** all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto ***,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single ***,this paper attempts to identify promising single constraints and utilize them to help solve *** be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint ***,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance ***,an improved tentative method is designed to find and transfer useful knowledge among *** results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

关键词： Constrained multi-objective optimization(CMOPs) evolutionary multitasking knowledge transfer single constraint.

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An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

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China Communications 2024年第10期21卷 267-287页

作者： R Arthi S Krishnaveni Sherali Zeadally Computational Intelligence School of ComputingSRMISTKattankulathurChennaiIndia College of Communication and information University of KentuckyUSA

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every ***,it is essential to make medical services connected to internet,available in every remote location during these ***,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical ***,services in the healthcare ecosystem need rapid,uninterrupted,and secure *** solution provided in this research addresses security concerns and services availability for patients with critical health in remote *** research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare *** propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare *** addition,this system can efficiently monitor connected devices and suspicious ***,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application *** experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

关键词： deep neural network healthcare intrusion detection system IoT machine learning software-defined networks

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A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1-40页

作者： Xianwei Jiang Yanqiong Zhang Juan Lei Yudong Zhang school of Mathematics and information Science Nanjing Normal University of Special EducationNanjing210038China school of computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into *** article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 *** Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional *** from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung *** Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence *** addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification *** language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition ***,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among *** to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition ***,suitable datasets and evaluation criteria are also worth pursuing.

关键词： Chinese Sign Language Recognition deep neural networks artificial intelligence transfer learning hybrid network models

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua Science and Technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou school of information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China school of computing and information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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Chaotic Map-Based Authentication and Key Agreement Protocol with Low-Latency for Metasystem

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Computers, Materials & Continua 2024年第3期78卷 4471-4488页

作者： Guojun Wang Qi Liu school of Electronics&information Engineering Nanjing University of Information Science&TechnologyNanjing210044China Yancheng Polytechnic College Yancheng224000China Jiangsu Province Engineering Research center of Advanced computing and Intelligent Services School of SoftwareNanjing University of Information Science and TechnologyNanjingChina

With the rapid advancement in exploring perceptual interactions and digital twins,metaverse technology has emerged to transcend the constraints of space-time and reality,facilitating remote AI-based *** this dynamic metasystem environment,frequent information exchanges necessitate robust security measures,with Authentication and Key Agreement(AKA)serving as the primary line of defense to ensure communication ***,traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the *** address this challenge and enable nearly latency-free interactions,a novel low-latency AKA protocol based on chaotic maps is *** protocol not only ensures mutual authentication of entities within the metasystem but also generates secure session *** security of these session keys is rigorously validated through formal proofs,formal verification,and informal *** confronted with the Dolev-Yao(DY)threat model,the session keys are formally demonstrated to be secure under the Real-or-Random(ROR)*** proposed protocol is further validated through simulations conducted using VMware workstation compiled in HLPSL language and C *** simulation results affirm the protocol’s effectiveness in resisting well-known attacks while achieving the desired low latency for optimal metaverse interactions.

关键词： Metasystem authentication and key agreement chaotic map secure communication

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Joint modulation format identification and OSNR monitoring based on Stokes vector distribution features for digital coherent optical receivers

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Chinese Optics Letters 2024年第5期22卷 22-28页

作者：蒲正宇蒋林闫连山易安林何海军潘炜罗斌 center for information Photonics&Communications School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 611756China

For joint modulation format identification(MFI)and optical signal-to-noise ratio(OSNR)monitoring,a simple and intelligent optical communication performance monitoring method is proposed,and the feasibility is demonstrated by digital coherent optical communication *** experiment results show that for all modulation formats,including 28 GBaud polarization division multiplexing(PDM)QPSK/8-QAM/16-QAM/64-QAM,100%MFI accuracies are achieved even at OSNR values lower than the corresponding theoretical 20%forward error correction limit,as well as the high accuracies for OSNR ***,the proposed scheme has a reasonable monitoring level when chromatic dispersion and fiber nonlinear effects are varied.

关键词： coherent communication optical performance monitoring deep neural network

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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge computing for Makespan Optimization

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Tsinghua Science and Technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu school of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China school of Software Nanjing University of Information Science and TechnologyNanjing 210044China school of information Engineering Huzhou UniversityHuzhou 313000China school of computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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