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A Population Initialization Method Based on Similarity and Mutual information in Evolutionary Algorithm for Bi-Objective Feature Selection

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-21页

作者： Cai, Xu Xue, Yu Artificial Intelligence Research Institute School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Software Nanjing University of Information Science and Technology Nanjing China

Feature selection (FS) is an important data pre-processing technique in classification. It aims to remove redundant and irrelevant features from the data, which reduces the dimensionality of data and improves the performance of the classifier. Thus, FS is a bi-objective optimization problem, and evolutionary algorithms (EAs) have been proven to be effective in solving bi-objective FS problems. EA is a population-based metaheuristic algorithm, and the quality of the initial population is an important factor affecting the performance of EA. An improper initial population may negatively affect the convergence speed of the EA and even make the algorithm fall into the local optimum. In this article, we propose a similarity and mutual information-based initialization method, named SMII, to improve the quality of the initial population. This method determines the distribution of initial solutions based on similarity and shields features with high correlation to the selected features according to mutual information. In the experiment, we embed SMII, the latest four initialization methods, and a traditional random initialization method into NSGA-II and compared their performance on 15 public datasets. The experimental results show that SMII performs best on most datasets and can effectively improve the performance of the algorithm. Moreover, we compare the performance of two other EAs before and after embedding SMII on 15 datasets, and the results further prove that the proposed method can effectively improve the search capability of the EA for FS. © 2024 Copyright held by the owner/author(s).

关键词： Evolutionary algorithms

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Research on Faster R-CNN material micro-defect detection method for small data volume 5

Research on Faster R-CNN material micro-defect detection met...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Wang, Zhili Yan, Hairong Zhou, Weiyu Beijing University of Technology Software Engineering Faculty of Information Technology Beijing China

ISBN: (纸本)9798350366174

Defect detection in material micro-images has a significant impact on the study of the relationship between the micro-structure and macro-properties, however, material microdefects are usually relatively small and span a wide range of scales, which increases the difficulty of defect detection. Meanwhile, since defects exist in a small number, overfitting becomes another challenge. In this paper, based on the Faster R-CNN algorithm, automated data enhancement is used to solve the overfitting, and a feature pyramid model is proposed for the defects multi-scale problem, and finally, the feasibility of the above viewpoint is verified by experiments. © 2024 IEEE.

关键词： defect detection Faster R-CNN feature pyramid model small data volume

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Secure Collaborative Computation Offloading and Resource Allocation in Cache-Assisted Ultradense IoT Networks With Multislope Channels

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IEEE Internet of Things Journal 2025年第11期12卷 17828-17843页

作者： Zhou, Tianqing Wang, Bobo Qin, Dong Nie, Xuefang Jiang, Nan Li, Chunguo East China Jiaotong University School of Information and Software Engineering Nanchang330013 China Huaibei Institute of Technology College of Electronic and Information Engineering Huaibei235000 China Nanchang University School of Information Engineering Nanchang330031 China Southeast University School of Information Science and Engineering Nanjing210096 China

Cache-assisted ultradense mobile-edge computing (MEC) networks are a promising solution for meeting the increasing demands of numerous Internet of Things mobile devices (IMDs). To address the complex interferences caused by small base stations (SBSs) deployed densely in such networks, this article exploits the combination of orthogonal frequency-division multiple access (OFDMA), nonorthogonal multiple access (NOMA), and base station (BS) clustering. Additionally, security measures are introduced to protect IMDs’ tasks offloaded to BSs from potential eavesdropping and malicious attacks. Within this network framework, a computation offloading scheme is proposed to minimize IMDs’ energy consumption while considering constraints, such as delay, power, computing resources, and security costs, optimizing channel selections, task execution decisions, device associations, power controls, security service assignments, and computing resource allocations. To solve the formulated problem efficiently, we develop a further improved hierarchical adaptive search (FIHAS) algorithm, providing some insights into its parallel implementation, computation complexity, and convergence. Simulation results demonstrate that the proposed algorithms can achieve lower total energy consumption and delay compared to other algorithms when strict latency and cost constraints are imposed. © 2014 IEEE.

关键词： Energy utilization

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Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance

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Journal of Systems Science & Complexity 2024年第3期37卷 965-984页

作者： NIU Ben WANG Xiaoan WANG Xiaomei WANG Xinjun LI Tao Faculty of Electronic Information and Electrical Engineering Dalian University of TechnologyDalian 116024China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Mathematics Southeast UniversityNanjing 211189China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China

In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state *** to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered *** to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be ***,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear *** presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to ***,it is shown that the constraint requirement on the system will not be violated during the ***,two simulation examples are provided to show the effectiveness of the proposed control scheme.

关键词： Asymptotic tracking control barrier Lyapunov functions full state constraints nonlinear pure-feedback systems

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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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Single-photon scattering and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system

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Chinese Physics B 2024年第5期33卷 337-342页

作者：黄劲松黄红武李艳玲徐中辉 School of Information Engineering Jiangxi University of Science and TechnologyGanzhou 341000China Faculty of Science Jiangxi University of Science and TechnologyGanzhou 341000China

We theoretically investigate coherent scattering of single photons and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide *** the real-space Hamiltonian,analytical expressions are derived for the transport spectra scattered by these two giant atoms with four azimuthal ***-like resonance can be exhibited in the scattering spectra by adjusting the azimuthal angle *** concurrence of the entangled state for two atoms can be implemented in a wide angle-difference range,and the entanglement of the atomic states can be switched on/off by modulating the additional azimuthal angle differences from the giant *** suggests a novel handle to effectively control the single-photon scattering and quantum entanglement.

关键词： quantum transport quantum entanglement scattering theory optical waveguide

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Data Security and Privacy Protection Scheme Based on EC-ElGamal in Federal Learning

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SN Computer Science 2025年第2期6卷 1-10页

作者： Liu, Bin Blancaflor, Eric B. School of Information Technology Mapua University Manila Philippines School of Software Engineering Xiamen University of Technology Xiamen China

Federated learning is a new distributed learning framework that can train multi-party user data without knowing the original user data, which protects user privacy to a certain extent. However, the application of federated learning in the Internet of Things still faces some problems, such as the high cost of client-server communication, frequent communication failures, and lack of high-quality data to guarantee the training process. To solve the problems of communication costs affected by different encryption schemes, dynamic user joining and exiting, and low user data quality affecting accuracy, this paper proposes a data security and privacy protection method based on EC-ElGamal in federated learning. EC-ElGamal is an additive homomorphic encryption scheme based on ElGamal elliptic curves, which has a shorter key length and less traffic than the ElGamal scheme. Firstly, the scheme uses lightweight EC-ElGamal and improved multi-key EC-ElGamal cryptosystem in the Internet of Things environment to realize cooperative training of multi-key users under federated learning and reduce communication costs. Secondly, FSOA(Federated sum optimization algorithm) is proposed to obtain high-quality data for training, which can avoid the problem of low data quality affecting accuracy. At the same time, a dynamic user joining and exiting algorithm is proposed, which can realize the dynamic user update without affecting the training accuracy. The experiment results shows that by selecting high-quality training data and a short key length encryption scheme for model training, The total encryption and decryption time of this scheme is lower than the other three encryption and decryption time of Paillier, ElGamal and LWE, the communication cost of the scheme is reduced, and the training accuracy is ensured under the condition of dynamic updating of users. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Dynamic update EC-ElGamal Federated learning Internet of things Privacy protection

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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A Quantum Tanimoto Coefficient Fidelity for Entanglement Measurement

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 439-450页

作者： Yangyang Zhao Fuyuan Xiao Masayoshi Aritsugi Weiping Ding the School of Big Data and Software Engineering Chongqing UniversityChongqing 401331China IEEE the Faculty of Advanced Science and Technology Kumamoto UniversityKumamoto 860-8555Japan the School of Information and Technology Nantong UniversityNantong 223019China

Fidelity plays an important role in quantum information processing,which provides a basic scale for comparing two quantum *** present,one of the most commonly used fidelities is Uhlmann-Jozsa(U-J)***,U-J fidelity needs to calculate the square root of the matrix,which is not trivial in the case of large or infinite density ***,U-J fidelity is a measure of overlap,which has limitations in some cases and cannot reflect the similarity between quantum states ***,a novel quantum fidelity measure called quantum Tanimoto coefficient(QTC)fidelity is proposed in this *** other existing fidelities,QTC fidelity not only considers the overlap between quantum states,but also takes into account the separation between quantum states for the first time,which leads to a better performance of ***,we discuss the properties of the proposed QTC *** fidelity is compared with some existing fidelities through specific examples,which reflects the effectiveness and advantages of QTC *** addition,based on the QTC fidelity,three discrimination coefficients d_(1)^(QTC),d_(2)^(QTC),and d_^(3)^(QTC)are defined to measure the difference between quantum *** is proved that the discrimination coefficient d_(3)^(QTC)is a true ***,we apply the proposed QTC fidelity-based discrimination coefficients to measure the entanglement of quantum states to show their practicability.

关键词： Distance measure entanglement measurement fidelity measure quantum Tanimoto coefficient(QTC) similarity measure uncertainty

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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