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Logic mining method via hybrid discrete hopfield neural network

computers and Industrial Engineering 2025年 206卷

作者： Guo, Yueling Kasihmuddin, Mohd Shareduwan Mohd Zamri, Nur Ezlin Li, Jia Romli, Nurul Atiqah Mansor, Mohd. Asyraf Ruzai, Wan Nur Aqlili School of Science Hunan Institute of Technology Hengyang421002 China School of Mathematical Sciences Universiti Sains Malaysia Penang11800 USM Malaysia Department of Mathematics and Statistics Faculty of Science Universiti Putra Malaysia Selangor Serdang43400 UPM Malaysia School of Computer Science and Engineering Hunan Institute of Technology Hengyang421002 China School of Distance Education Universiti Sains Malaysia Penang11800 USM Malaysia

The growing interest in logic mining as a knowledge extraction tool in data mining has attracted considerable attention from researchers. Despite the success, the limitations of existing logic mining methods are often overlooked, hindering the search for optimal solutions in binary classification tasks. To address these challenges, this paper introduces a novel logic mining approach using the Y-type Random 2 Satisfiability logical rule, combined with hybrid mechanisms within the Discrete Hopfield Neural Network. The first contribution involves the incorporation of a Hybrid Differential Evolution Algorithm to accelerate the optimization of synaptic weights during the training phase. Additionally, the retrieval phase is enhanced by proposing a swarm mutation operator, which diversifies the final neuron states, thereby broadening the solution space. Furthermore, an improved reverse analysis method is applied to optimize attribute selection and generate the most effective training logic. To demonstrate the efficacy of the proposed logic mining approach, experiments were conducted using both simulated and real-world datasets. The results indicate that the proposed model significantly outperforms baseline models across all performance metrics. The study concludes that the enhanced logic mining technique effectively captures the knowledge of datasets and facilitates transparent decision-making, making it a valuable tool for both researchers and practitioners. © 2025 Elsevier Ltd

关键词： Cellular automata

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MULSAM: Multidimensional Attention with Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, He Shi, Xiaokang Liu, Hansheng Wang, Yanwen Lu, Jiwu Zeng, Haibo Li, Renfa Wu, Di Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24061 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China

Controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic Control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle Intrusion Detection System (IDS) is developed. The challenge is that existing techniques of Intrusion Detection Systems (IDS) rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges Multi-dimensional Long Short-Term Memory (MD-LSTM) and Self-Attention Mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including Stacked LSTM, MD-LSTM, etc. Experiments show that our approach has the best detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high detection accuracy of 98.81% and a low latency of 1.88 ms. © 1982-2012 IEEE.

关键词： Intrusion detection

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Cloud Computing: Innovations and Impacts on Global Data Infrastructure

Cloud Computing: Innovations and Impacts on Global Data Infr...

引用

Computational, Communication and Information technology (ICCCIT), International Conference on

作者： S. Banumathi E Sivajothi Indumathi.G. S Enku Yosef Kefyalew J. Sivakumar V. Banu Priya Department of computer science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of MBA St Joseph's College of Engineering OMR Chennai Department of Electrical and Electronics Technology Technical and Vocational Training Institute Addis Ababa Department of Electronics and Communication Engineering St.Joseph's College of Engineering Chennai Department of Mathematics R.M.K. College of Engineering and Technology Thiruvallur District

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Cloud computing has transformed global data infrastructure with unparalleled scale, flexibility, and accessibility. This study examines how cloud computing innovations have changed data storage, processing, and management worldwide. By examining the progression of cloud services from simple storage and computing to sophisticated platforms and SaaS, we examine how they have transformed corporate operations, economic growth, and societal development. Virtualization, containerization, serverless architectures, and edge computing are the main cloud computing advancements, which we investigate through a literature study and case studies. Data security, privacy, and regulatory frameworks are also considered while adopting the cloud. Cloud computing promotes digital transformation in banking, healthcare, education, and government, according to study. The industry's sustainability and green computing efforts and data centers' environmental impacts are also covered. We found that cloud computing continues to spur innovation by enabling new business models, global connectivity, and data-driven application growth. Cloud technologies’ integration with AI, IoT, and 5G networks is likely to change the global data infrastructure, opening new research and development options. This report covers the cloud computing landscape, its advancements, and its potential to shape global data management and digital ecosystems.

关键词： Cloud computing Technological innovation Data centers Digital transformation Software as a service Ecosystems Internet of Things Virtualization Research and development Edge computing

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Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wu, Linshan Zhong, Zhun Ma, Jiayi Wei, Yunchao Chen, Hao Fang, Leyuan Li, Shutao Hunan University College of Electrical and Information Engineering Changsha410082 China Hefei University of Technology School of Computer Science and Information Engineering China Wuhan University Electronic Information School Wuhan430072 China Beijing Jiaotong University Institute of Information Science Beijing10044 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Clear Water Bay Hong Kong Hong Kong

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models by weak labels, which is receiving significant attention due to its low annotation cost. Existing approaches focus on generating pseudo labels for supervision while largely ignoring to leverage the inherent semantic correlation among different pseudo labels. We observe that pseudo-labeled pixels that are close to each other in the feature space are more likely to share the same class, and those closer to the distribution centers tend to have higher confidence. Motivated by this, we propose to model the underlying label distributions and employ cross-label constraints to generate more accurate pseudo labels. In this paper, we develop a unified WSSS framework named Adaptive Gaussian Mixtures Model, which leverages a GMM to model the label distributions. Specifically, we calculate the feature distribution centers of pseudo-labeled pixels and build the GMM by measuring the distance between the centers and each pseudo-labeled pixel. Then, we introduce an Online Expectation-Maximization (OEM) algorithm and a novel maximization loss to optimize the GMM adaptively, aiming to learn more discriminative decision boundaries between different class- wise Gaussian mixtures. Based on the label distributions, we leverage the GMM to generate high-quality pseudo labels for more reliable supervision. Our framework is capable of solving different forms of weak labels: image-level labels, points, scribbles, blocks, and bounding-boxes. Extensive experiments on PASCAL, COCO, Cityscapes, and ADE20 K datasets demonstrate that our framework can effectively provide more reliable supervision and outperform the state-of-the-art methods under all settings. Code will be available at https://***/Luffy03/AGMM-SASS. © 1979-2012 IEEE.

关键词： Semantic Segmentation

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Simultaneous Wireless Information and Power Transfer for STAR-RIS Assisted UAV Networks

引用

IEEE Internet of Things Journal 2025年

作者： Xie, Wenwu Qin, Lijuan Wang, Ji Wu, Weimin Li, Xingwang Xu, Hongbo Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Central China Normal University Department of Electronics and Information Engineering Wuhan430079 China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This paper explores the benefits of deploying simultaneously transmitting and reflecting reconfigurable intelligence surfaces (STAR-RIS) in unmanned aerial vehicle (UAV) networks with simultaneous wireless information and power transfer. In the proposed system, the UAV utilizes STAR-RIS to radiate energy-carrying information signals (ECISs) to multiple outdoor energy receivers and multiple indoor information receivers without flying over indoor no-fly zone. Based on the UAV propulsion power formula, we successively introduce fly-hover-broadcast (FHB) and path discretization (PD) protocols to minimize the total UAV energy consumption by jointly using the extended penalty function and optimization algorithm based on the expected value of channel status information, where the UAV flight constraints, no-fly constraint, minimized energy or information threshold constraints, and STAR-RIS phase-shift constraints are met. The FHB protocol, which requires the UAV to radiate the ECISs to the users at only a few hovering positions, provides a lower-bound performance of UAV energy consumption, while the PD protocol is used to discuss the general situation of the ECISs during UAV flight. Simulation results demonstrate that the utilization of the STAR-RIS in UAV networks outperforms the traditional RIS in improving energy efficiency and extending UAV flight time. © 2014 IEEE.

关键词： Energy utilization

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Integrative Multi-Source Mapping of Coastal Environments Using Hy-perspectral Data Fusion Platforms

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Han, Huimin Por, Lip Yee Talpur, Bandeh Ali Hashim, Muhammad Yang, Jing Saidani, Taoufik Djumaniyazova, Mukhayya Xusinovna Madrakhimovich, Khudoynazarov Egambergan Talpur, Kazim Raza Hainan Vocational University of Science and Technology Mechanical and Electrical Engineering Col-lege Haikou571126 China Uni-versiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Trinity Col-lege Dublin School of Computer Science and Statistics Ireland National Textile Uni-versity Faisalabad Faisalabad Business School Pakistan Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur50603 Malaysia Northern Border University Center for Scientific Research and Entrepreneurship Arar73213 Saudi Arabia Urganch State University Pedagogy and Psychology Department Uzbekistan Mamun University Uzbekistan Jamshoro Institute of Health Management and Re-search Sciences Sindh Pakistan

Hyperspectral images (HSIs) not only have a large number of spectral features, but also show a comprehensive spa-tial distribution of land cover and offer substantial advantages in the fine classification of ground materials. However, due to the high dimensionality and redundancy of HSIs and class imbalance in hyperspectral datasets, improving classification performance is still a huge challenge. Recently, graph-based learning algorithms are proven to be better classifiers for high dimensional data. Learning graph data and modeling spatial topological links be-tween features are significantly more accessible and more effective with graph-based deep learning, which has been demonstrated to be a promising approach. In the past few years, convolutional neu-ral networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs strug-gle to achieve good accuracy in low samples, while GCNs have a substantial computational cost. To resolve these issues, this paper proposes a feature fusion model using a 3D-CNN and hybrid-GAT named FFCHG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in im-proving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classifica-tion time with accuracy of more than 90% in all datasets and kappa with more than 91% for each dataset. © 2025 IEEE.

关键词： Convolutional neural networks

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GBFKAN: An Adaptive Multi-layer Interpretable Architecture for Intrusion Detection in Various Internet of Things Scenarios

引用

IEEE Internet of Things Journal 2025年

作者： Zhang, Zhiqiang Zeng, Liyi Zhu, Dong Tan, Haonan Wang, Le Li, Zhaohua Gu, Zhaoquan Harbin Institute of Technology (Shenzhen) School of Computer Science and Technology Guangdong Shenzhen China Peng Cheng Laboratory Department of New Networks Guangdong Shenzhen China National University of Defense Technology College of Computer Science and Technology Hunan Changsha China Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Guangzhou China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen China

As Internet of Things (IoT) technologies continue to evolve and gain widespread integration across various sectors, IoT devices have become increasingly interconnected and ubiquitous. However, this surge has also led to more sophisticated, diverse, and covert attacks, posing severe security challenges to IoT ecosystems and their defense mechanisms. Intrusion Detection Systems (IDS) are instrumental in securing the IoT by uncovering and mitigating malicious behaviors in real-time. Nevertheless, the growing complexity of network traffic presents significant challenges for IDS, often resulting in a higher false alarm rate and a lower detection rate, due to the intricate characteristics of space and notable temporal dependencies present in network traffic. We conduct an in-depth dimensionality reduction analysis using the t-distributed Stochastic Neighbor Embedding methodology to examine the relationships among attack examples. Then, to enhance the generalization and robustness of IDS, we propose GBFKAN, a novel interpretable three-layer network architecture. The structured design of GBFKAN can comprehensively extract and perceive key information from multiple abstract levels in the raw traffic, enhancing the architecture’s generalization ability and robustness. Furthermore, we utilize the SHapley Additive ExPlanations method to provide detailed explanations of the detection outcomes, augmenting its credibility. Building on this, we successfully identify the erroneous decision-making paradigms within GBFKAN by incorporating empirical knowledge. Extensive simulation experiments conducted on the four widely recognized intrusion detection datasets have conclusively demonstrated that our proposed GBFKAN model surpasses existing methods in terms of intrusion detection performance. © 2014 IEEE.

关键词： Intrusion detection Kolmogorov-Arnold Networks SHapley Additive ExPlanations Structured design T-distributed Stochastic Neighbor Embedding

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Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach

arXiv

引用

arXiv 2025年

作者： Ruzicka, Laurenz Spenke, Alexander Bergmann, Stephan Nolden, Gerd Kohn, Bernhard Heitzinger, Clemens Department of Digital Safety and Security Austrian Institute of Technology Vienna Austria Federal Office for Information Security Bundesamt für Sicherheit in der Informationstechnik Bonn Germany Department of Computer Science TU Wien Vienna Austria

Fingerprint mosaicking, which is the process of combining multiple fingerprint images into a single master fingerprint, is an essential process in modern biometric systems. However, it is prone to errors that can significantly degrade fingerprint image quality. This paper proposes a novel deep learning-based approach to detect and score mosaicking artifacts in fingerprint images. Our method leverages a self-supervised learning framework to train a model on large-scale unlabeled fingerprint data, eliminating the need for manual artifact annotation. The proposed model effectively identifies mosaicking errors, achieving high accuracy on various fingerprint modalities, including contactless, rolled, and pressed fingerprints and furthermore proves to be robust to different data sources. Additionally, we introduce a novel mosaicking artifact score to quantify the severity of errors, enabling automated evaluation of fingerprint images. By addressing the challenges of mosaicking artifact detection, our work contributes to improving the accuracy and reliability of fingerprint-based biometric systems. © 2025, CC BY-NC-SA.

关键词： Self-supervised learning

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Optimal Parameter Adaptation for safety-Critical Control via Safe Barrier Bayesian Optimization

arXiv

引用

arXiv 2025年

作者： Wang, Shengbo Li, Ke Yan, Zheng Guo, Zhenyuan Zhu, Song Wen, Guanghui Wen, Shiping School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science University of Exeter ExeterEX4 4RN United Kingdom Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia College of Mathematics and Econometrics Hunan University Changsha410082 China School of Mathematics China University of Mining and Technology Xuzhou221116 China Department of Systems Science School of Mathematics Southeast University Nanjing China

safety is of paramount importance in control systems to avoid costly risks and catastrophic damages. The control barrier function (CBF) method, a promising solution for safety-critical control, poses a new challenge of enhancing control performance due to its direct modification of original control design and the introduction of uncalibrated parameters. In this work, we shed light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization. Based on that, we propose a novel framework combining the CBF method with Bayesian optimization (BO) to optimize the safe control performance. Considering feasibility/safety-critical constraints, we develop a safe version of BO using the barrier-based interior method to efficiently search for promising feasible configurable parameters. Furthermore, we provide theoretical criteria of our framework regarding safety and optimality. An essential advantage of our framework lies in that it can work in model-agnostic environments, leaving sufficient flexibility in designing objective and constraint functions. Finally, simulation experiments on swing-up control and high-fidelity adaptive cruise control are conducted to demonstrate the effectiveness of our framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Adaptive cruise control

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Towards Fair Federated Learning via Unbiased Feature Aggregation

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： He, Zeqing Wang, Zhibo Dong, Xiaowei Sun, Peng Ren, Ju Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security China Institute of Blockchain and Data Security Hangzhou310027 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Federated learning (FL) is a distributed machine learning framework that enables multiple clients to collaboratively train models without raw data exchange. Prior studies on FL mainly focus on optimizing learning performance, enhancing privacy preservation, and improving attack resilience. However, little work studies how to mitigate the unfairness of federated trained models while unfair models would make discriminatory decisions toward certain groups or populations (e.g., favoring males over females), leading to serious ethical concerns. Thus, it is crucial to mitigate model unfairness in FL, yet challenging as this requires centralized access to each data point's fairness-sensitive information (e.g., race, gender), which is prohibited in FL. In this work, we propose a novel fair FL framework FedUFA, where the server can aggregate clients' learned knowledge in an unbiased manner, to obtain fair and high-usability federated trained models. Specifically, to unearth the bias in clients' local data and account for potentially heterogeneous local models, we propose a knowledge distillation-based FL scheme, where clients' knowledge of learned features on a public dataset is amalgamated to the server for aggregation. We train an unbiased feature mapper at the server to remove fairness-sensitive latent features and extract fair representations from clients' submitted raw features. In particular, we design an adversarial training method to train the mapper, which involves a predictor aiming to maximize the prediction accuracy on the FL task and a discriminator intending to help identify fairness-sensitive features. Extensive experiments on real-world datasets demonstrate the effectiveness of FedUFA. © 2025 IEEE. All rights reserved,

关键词： Federated learning

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