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4th International Conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Meng, Hao Liu, Zijie Meng, Hui System Engineering Research Institute of China State Shipbuilding Corporation Limited Beijing China Northwestern Polytechnical University Xi'an China School of Software Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9798331531409

This paper investigates an adaptive dynamic sliding mode control algorithm based on Chebyshev neural networks, applied to the formation control problem of multiple unmanned surface vehicles (USVs). The algorithm combines the self-learning capability of neural networks with the robustness of sliding mode control, enabling stable formation control under uncertain environments, external disturbances, and actuator hysteresis. The Chebyshev neural network is utilized to estimate nonlinear functions in the system, reducing computational complexity and improving real-time performance. Additionally, the dynamic sliding mode control method effectively suppresses chattering. Simulation results demonstrate that the proposed method excels in control accuracy, real-time performance, and robustness, providing an effective solution for the formation control of multiple USVs. © 2024 IEEE.

关键词： Sliding mode control

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Multimodal representation reconstruction for video localization with natural language

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Multimedia Tools and Applications 2025年 1-16页

作者： Wang, Qin Jiang, Libiao Ma, Le Jiang, Hailan Hu, Xizhi Jiang, Siyu School of Software Engineering Xi’an Jiaotong University Xi’an710049 China School of Mechanical Engineering and School of Robotics Engineering Guangzhou City University of Technology Guangdong Guangzhou510030 China Engineering Research Institute Guangzhou City University of Technology Guangdong Guangzhou510030 China Shandong Polytechnic Shandong Jinan250104 China School of Mechanical and Automotive Engineering South China University of Technology Guangdong Guangzhou510641 China School of Information Science and Technology Guangdong University of Foreign Studies Guangdong Guangzhou510000 China

We focus on localizing a specific video moment given a description query within a raw and untrimmed video. Accurate localization is challenging due to the requirement for distinction among similar candidates. Generally, though existing approaches consider the verbs and nouns in the query, they fail to pay attention to the inferential and key words that play crucial roles in a query and contribute a lot to video localization with natural language. To improve localization, we proposed a Multimodal Representation Reconstruction for Video Localization (MRRVL) model to jointly model visual and textual representations. To be specific, the model transforms the query into a semantic graph according to semantic roles. The detailed textual representations are updated by graph reasoning, which can guide visual representations to be reconstructed. As the specific and context information is mined from both the video and query sufficiently, it is very discriminative for similar segments. Comprehensive experimental results demonstrate that our MRRVL outperforms related approaches on two benchmark datasets significantly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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Cybersecurity of SMART Technologies Using Deep Learning Methods 8

Cybersecurity of SMART Technologies Using Deep Learning Meth...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Milov, Oleksandr Rahimova, Irada Havrylova, Alla Golovashych, Sergii Aksonova, Iryna Pecherytsia, Dmytro National Technical University 'Kharkiv Polytechnic Institute' Department of Cyber Security Kharkiv Ukraine Azerbaijan Technical University Department of 'Computer Technologies' Baku Azerbaijan National Technical University 'Kharkiv Polytechnic Institute' Department of Software Engineering and Intelligent Control Technologies Kharkiv Ukraine

ISBN: (纸本)9798350354423

The concept of 'smart', the concept of smart technology, and its main elements are considered. An analysis of the factors influencing the formation and development of the concept of smart technology is provided. Aspects of the development of smart technology are highlighted. A description of deep learning methods is given and their classification is proposed. The areas of application of the described methods in various cybersecurity applications are identified, including detection of intrusions and malware, analysis of network traffic and some other tasks related to smart technologies. © 2024 IEEE.

关键词： Network intrusion

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Machine Learning Algorithms Application for Fixed-Income Market Analysis: Cross Countries Comparisons 14

Machine Learning Algorithms Application for Fixed-Income Mar...

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14th International Conference on Advanced Computer Information Technologies, ACIT 2024

作者： Chernova, Natalia Serhiienko, Olena Mashchenko, Maryna Lisna, Iryna Haponenko, Olha National Technical University «kharkiv Polytechnic Institute» Department of Software Engineering and Management Intelligent Technologies Kharkiv Ukraine National Technical University «kharkiv Polytechnic Institute» Business Trade and Logistics Department Kharkiv Ukraine University of Southampton Business School Southampton United Kingdom

ISBN: (纸本)9798350350036

The research aim is to distinguish the global fixed income market structure and dynamics basing on such core indicator as yields to maturity of government bonds. The processes taken part within or linked with fixed income market are of core importance for the global world economic system as a whole, thus have to be analyzed and researched constantly to ensure decision-making processes on all levels of hierarchy. The following tasks have been implemented to achieve the aim: initial observation set of yields data for world government bonds of different maturities has been formed;initial dataset dimension has been reduced based on principal components analysis technique;fixed-income market structure has been analyzed in the obtained three-dimensional level-slope-curvature space for each researched country;set of countries has been classified onto homogenous groups in multi-dimensional space basing on k-means algorithm;the resulting groupings composition and structure have been analyzed. © 2024 IEEE.

关键词： Principal component analysis

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Tomato leaf diseases classification with convolutional neural network 4

Tomato leaf diseases classification with convolutional neura...

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2023 4th International Conference on Computer engineering and Intelligent Control, ICCEIC 2023

作者： Fu, Dandan Chen, Zishuo Liao, Xuejiao Feng, Jing Liu, Xun Zhang, Zhenfei Software Engineering Institute of Guangzhou Department of Electronics Guangzhou China Guangdong Polytechnic Normal University School of Electronic and Information Guangzhou China

ISBN: (纸本)9798350308877

In recent years, convolutional neural network (CNN) has developed rapidly and been widely used in plant disease detection. Tomato leaf disease is an important diseases of plant diseases. Therefore, it is necessary to improve the classification results of tomato leaf diseases. In this paper, a new CNN framework is proposed to classify tomato leaf diseases. The proposed framework includes several key deep-learning-based techniques: dilated convolution, batch normalization (BN), and the dropout after each convolutional block. Specifically, we apply dilated convolution to tomato leaf disease classification, which enlarges the receptive field without the reduction of image size. In addition, BN and the dropout are used to accelerate model convergence and reduce the redundant features generated by each convolutional block. Experiments show that the proposed model achieved the best results in both classification accuracy and the number of parameters compared with different CNN models. © 2023 IEEE.

关键词： Convolution

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Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation 38

Addressing Asynchronicity in Clinical Multimodal Fusion via ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yao, Wenfang Liu, Chen Yin, Kejing Cheung, William K. Qin, Jing School of Nursing The Hong Kong Polytechnic University Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong School of Software Engineering South China University of Technology China

Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous. EHR can be continuously collected but CXR is generally taken with a much longer interval due to its high cost and radiation dose. When clinical prediction is needed, the last available CXR image might have been outdated, leading to suboptimal predictions. To address this challenge, we propose DDL-CXR, a method that dynamically generates an up-to-date latent representation of the individualized CXR images. Our approach leverages latent diffusion models for patient-specific generation strategically conditioned on a previous CXR image and EHR time series, providing information regarding anatomical structures and disease progressions, respectively. In this way, the interaction across modalities could be better captured by the latent CXR generation process, ultimately improving the prediction performance. Experiments using MIMIC datasets show that the proposed model could effectively address asynchronicity in multimodal fusion and consistently outperform existing methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing Energy Efficiency in WSNs with Hybrid LEACH-D and ANN

Enhancing Energy Efficiency in WSNs with Hybrid LEACH-D and ...

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2024 Multimedia university engineering Conference, MECON 2024

作者： Qamar, Muhammad Salman Haq, Ihsan Ul Munir, Muhammad Fahad Roslee, Mardeni Bin Awan, Adnan Anwar Waseem, Athar International Islamic University Department of Electrical and Computer Engineering Islamabad Pakistan Multimedia University Centre for Wireless Technology Faculty of Engineering Cyberjaya63100 Malaysia School of Engineering California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798331530747

This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. The proposed methodology is precisely crafted to optimize energy consumption (EC) while preserving the overall effectiveness of WSNs. LEACH (Low Energy Adaptive Cluster Hierarchy) is a cluster-based protocol where non-cluster head nodes deactivate their RF until their allocated time slots. Nevertheless, a drawback of traditional LEACH is its random rotation of local Cluster Heads (CH), leading to unequal cluster distribution. To overcome this issue, we proposed a novel hybrid technique called LEACH-D protocol and Artificial Neural Network (ANN). The LEACH-D algorithm enhances the duration of transmission tasks while ensuring uniform battery energy consumption. On the other hand, the ANN plays a pivotal role in coordinating optimal routing decisions and CH placement, refining data aggregation and transmission mechanisms to restrain idle listening. The findings indicate that the suggested approach manifests as substantial reductions in energy consumption and prolonged network lifespan of WSN. © 2024 IEEE.

关键词： Sensor nodes

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Privacy-Preserving Edge-Aided Eigenvalue Decomposition in Internet of Things

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IEEE Internet of Things Journal 2025年第12期12卷 19901-19914页

作者： Zhao, Xiaotong Zhang, Hanlin Lin, Jie Liang, Fan Kong, Fanyu Xu, Hansong Hua, Kun Qingdao University College of Computer Science and Technology Qingdao266071 China Xian Jiaotong University School of Electronic and Information Engineering Xian710049 China Sam Houston State University Department of Computer Science HuntsvilleTX77340 United States Shandong University School of Software Jinan250101 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China California Polytechnic State University Department of Electrical Engineering San Luis ObispoCA93407 United States

Eigenvalue decomposition (EVD) is a fundamental yet time-consuming operation with extensive applications in IoT. When the matrix dimension reaches millions, resource-limited IoT devices struggle to perform such computationally expensive operations. Edge computing, with its plentiful computing resources, offers an effective solution to this problem. However, privacy concerns arise because outsourced tasks may contain sensitive user data. In this paper, we propose the first privacy-preserving, edge-assisted EVD outsourcing scheme that securely enables users to outsource EVD tasks to edge servers. We design a privacy-preserving matrix transformation method to encode the original data, ensuring that edge servers cannot access users' private information. Additionally, we design a verification scheme that enables the user to verify the correctness of the results returned by the edge servers. Our protocol supports parallel computation by multiple edge servers, thus enhancing the efficiency of EVD. The feasibility of our proposed scheme is demonstrated through both theoretical and experimental perspectives. © 2025 IEEE.

关键词： Differential privacy

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Modeling and Analysis for the Propagation of DDoS Attacks in Microgrid System

Modeling and Analysis for the Propagation of DDoS Attacks in...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Xiong, Lei Xu, Qichao Fang, Dongfeng Zeng, Hui School of Mechatronic Engineering and Automation Shanghai University Shanghai China California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798350304602

With the development and progress of electric power technology and information technology, microgrid has become an important and indispensable part of smart grid. The cyber-security of microgrids has a significant impact on the stability and security of smart grids. Distributed denial-of-service (DDoS) attack as one of the common types of cyberattacks in microgrids has formed a considerable threat to the network security of microgrids. In a DDoS event, attackers first use malware to infect few nodes in the microgrid and the infected nodes can generate large-scale malware propagation in the system. These infection lays the foundation for the flood attacks. Therefore, this paper establishes a model to analyse the DDoS attack propagation in microgrid. Firstly, this paper establishs a dynamic model based on the epidemiological theory to describe the propagation dynamics of DDoS attack in microgrid. Then we go on to compute the attack threshold $(R_{0})$ of the model and prove the stability of equilibrium point. Finally we carry out the experimental simulation based on the model, and the experimental results show that the proposed model can reflect the propagation of DDoS attack in microgrid effectively. © 2023 IEEE.

关键词： Denial-of-service attack

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Light field super-resolution using complementary-view feature attention

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Computational Visual Media 2023年第4期9卷 843-858页

作者： Wei Zhang Wei Ke Da Yang Hao Sheng Zhang Xiong Faculty of Applied Sciences Macao Polytechnic UniversityMacao SAR 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191Chinaand Beihang Hangzhou Innovation Institute YuhangXixi Octagon CityYuhang DistrictHangzhou 310023China

Light field(LF)cameras record multiple perspectives by a sparse sampling of real scenes,and these perspectives provide complementary *** information is beneficial to LF super-resolution(LFSR).Compared with traditional single-image super-resolution,LF can exploit parallax structure and perspective correlation among different LF ***,the performance of existing methods are limited as they fail to deeply explore the complementary information across LF *** this paper,we propose a novel network,called the light field complementary-view feature attention network(LF-CFANet),to improve LFSR by dynamically learning the complementary information in LF ***,we design a residual complementary-view spatial and channel attention module(RCSCAM)to effectively interact with complementary information between complementary ***,RCSCAM captures the relationships between different channels,and it is able to generate informative features for reconstructing LF images while ignoring redundant ***,a maximum-difference information supplementary branch(MDISB)is used to supplement information from the maximum-difference angular positions based on the geometric structure of LF *** branch also can guide the process of *** results on both synthetic and real-world datasets demonstrate the superiority of our *** proposed LF-CFANet has a more advanced reconstruction performance that displays faithful details with higher SR accuracy than state-of-the-art methods.

关键词： light field(LF) super-resolution(SR) attention

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