计算机科学最新文献_第8页

Event-Triggered Model-Free Adaptive Formation Constrained Control for Nonlinear Heterogeneous Multiagent Systems

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-25 DOI: 10.1109/tcyb.2025.3557383

Weiming Zhang, Dezhi Xu, Yujian Ye, Wei Hua, Bin Jiang

引用次数: 0

Breaking barriers in hotspot mining: a novel approach to reflecting domain characteristics and correlations

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-25 DOI: 10.1007/s10489-024-06136-z

Wei Chen, Zhengtao Yu, Shengxiang Gao, Yantuan Xian

{"title":"Breaking barriers in hotspot mining: a novel approach to reflecting domain characteristics and correlations","authors":"Wei Chen, Zhengtao Yu, Shengxiang Gao, Yantuan Xian","doi":"10.1007/s10489-024-06136-z","DOIUrl":"10.1007/s10489-024-06136-z","url":null,"abstract":"<div><p>Hotspot mining is essential for acquiring information on hotspots and knowledge in a given domain, and it is also of great value for improving the efficiency and quality of scientific research work in the profession. Previous literature on hotspot mining did not take into account the domain characteristics of the literature and the diverse associations of the domain-specific literature itself. It is a challenging task to reflect the domain characteristics of the literature and use multiple correlations among the literature in the model. In this study, we depict each association link using a heterogeneous network of metallurgical literature and simultaneously fuse metallurgical domain-specific knowledge by aggregating the knowledge graph data of the neighbors into the term nodes of the heterogeneous network of the literature. A proposed heterogeneous academic network metallurgical literature hotspot mining method incorporates domain-specific knowledge. This method reflects various types of associational relation information in the literature via the heterogeneous network. In the meantime, it weights and analyzes the paths in the heterogeneous network, identifies the most critical paths for vectorized representation, and highlights the impact of essential paths and domain knowledge on representation learning, enhancing the information representation of diverse data in the model and improving its accuracy. The suggested model is compared with GCN, the MAGNN standard model, and its ablation model as applied to public and metallurgical literature datasets. The findings on the public dataset show that the proposed method is superior to the other two approaches. In contrast, the results for the metallurgical literature dataset are more conspicuous, with the proposed method exhibiting a more remarkable improvement in HR and NGCC.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised detail and color restorer for Retinex-based low-light image enhancement

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-25 DOI: 10.1016/j.engappai.2025.110867

Yue Sun , Yutao Jin , Xiaoyan Chen , Yanbin Xu , Xiaoning Yan , Zefu Liu

{"title":"Unsupervised detail and color restorer for Retinex-based low-light image enhancement","authors":"Yue Sun , Yutao Jin , Xiaoyan Chen , Yanbin Xu , Xiaoning Yan , Zefu Liu","doi":"10.1016/j.engappai.2025.110867","DOIUrl":"10.1016/j.engappai.2025.110867","url":null,"abstract":"<div><div>Retinex-based methods have demonstrated promising results in restoring low-light images to their natural, normal-light appearance. However, existing approaches often inevitably amplify hidden artifacts because the Retinex theory does not consider the various uncertain degradation patterns in dark regions. Without modeling degradations, an algorithm may easily deviate from the original color and details of regions. To address this issue, we propose a novel detail and color modeling for Retinex-based low-light image enhancement. The modeling mechanism assists our Retinex-based solution in learning rich and diverse information hidden in the dark. In addition, we develop an unsupervised loss function to reduce the solution space of Retinex decomposition. It encourages all components to mutually constrain each other, further improving the adaptiveness in unknown complex scenarios. Extensive experiments demonstrate that our approach performs favorably against state-of-the-art methods. On the SICE dataset, our method achieves 19.71 Peak Signal-to-Noise Ratio (PSNR) and 0.773 Structural Similarity Index Measure (SSIM), surpassing all compared methods in PSNR and SSIM. Our framework also generalizes robustly to the LSRW-Huawei and LSRW-Nikon benchmarks, outperforming unsupervised approaches while maintaining competitive results against supervised counterparts. The code can be accessed via: <span><span>https://github.com/starsky68/DCRetinex</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"153 ","pages":"Article 110867"},"PeriodicalIF":7.5,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RT-A3C: Real-time Asynchronous Advantage Actor–Critic for optimally defending malicious attacks in edge-enabled Industrial Internet of Things

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-04-25 DOI: 10.1016/j.jisa.2025.104073

Wenyi Zhu , Xiaolong Liu , Yimeng Liu , Yizhou Shen , Xiao-Zhi Gao , Shigen Shen

{"title":"RT-A3C: Real-time Asynchronous Advantage Actor–Critic for optimally defending malicious attacks in edge-enabled Industrial Internet of Things","authors":"Wenyi Zhu , Xiaolong Liu , Yimeng Liu , Yizhou Shen , Xiao-Zhi Gao , Shigen Shen","doi":"10.1016/j.jisa.2025.104073","DOIUrl":"10.1016/j.jisa.2025.104073","url":null,"abstract":"<div><div>The existing Asynchronous Advantage Actor–Critic (A3C) open-source training model can effectively recommend defense strategies for the edge-enabled Industrial Internet of Things (IIoT) under malware attacks. However, it faces challenges in rapidly countering large-scale IIoT network attacks. To address this issue, we develop an enhanced algorithm, RT-A3C, by innovatively integrating the A3C model into a real-time Markov game framework. This approach involves three key enhancements: incorporating prediction models, integrating adversary models, and optimizing state transition and action selection strategies. Such contributions collectively enhance the practicality and efficiency of IIoT security simulation training. The core innovation lies in converting the traditional turn-based Markov game into a real-time reactive one, showing the potential for policy optimization and strategic development in advanced IIoT network security. Through simulations, we demonstrate that the proposed RT-A3C algorithm surpasses the performance of the state-of-the-art actor–critic models. Our research clarifies that we can develop a more resilient and responsive IIoT security training model by merging real-time components with Markov games and A3C technology. This advancement significantly improves real-time monitoring and defense capabilities against large-scale IIoT network attacks, thereby strengthening the overall security of IIoT network systems.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"91 ","pages":"Article 104073"},"PeriodicalIF":3.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Through the static: Demystifying malware visualization via explainability

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-04-25 DOI: 10.1016/j.jisa.2025.104063

Matteo Brosolo, Vinod P., Mauro Conti

{"title":"Through the static: Demystifying malware visualization via explainability","authors":"Matteo Brosolo, Vinod P., Mauro Conti","doi":"10.1016/j.jisa.2025.104063","DOIUrl":"10.1016/j.jisa.2025.104063","url":null,"abstract":"<div><div>Security researchers face growing challenges in rapidly identifying and classifying malware strains for effective protection. While Convolutional Neural Networks (CNNs) have emerged as powerful visual classifiers for this task, critical issues of robustness and explainability, well-studied in domains like medicine, remain underaddressed in malware analysis. Although these models achieve strong performance without manual feature engineering, their replicability and decision-making processes remain poorly understood. Two technical barriers have limited progress: first, the lack of obvious methods for selecting and evaluating explainability techniques due to their inherent complexity, and second the substantial computational resources required for replicating and tuning these models across diverse environments, which requires extensive computational power and time investments often beyond typical research constraints. Our study addresses these gaps through comprehensive replication of six CNN architectures, evaluating both performance and explainability using Class Activation Maps (CAMs) including GradCAM and HiResCAM. We conduct experiments across standard datasets (MalImg, Big2015) and our new VX-Zoo collection, systematically comparing how different models interpret inputs. Our analysis reveals distinct patterns in malware family identification while providing concrete explanations for CNN decisions. Furthermore, we demonstrate how these interpretability insights can enhance Visual Transformers, achieving F1-score yielding substantial improvements in F1 score, ranging from 2% to 8%, across the datasets compared to benchmark values.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"91 ","pages":"Article 104063"},"PeriodicalIF":3.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariable QFT control of the direction flip problem in wire arc additive manufacturing

IF 2.2 4区计算机科学

IET Control Theory and Applications Pub Date : 2025-04-25 DOI: 10.1049/cth2.12765

Manuel Masenlle, Jorge Elso, J. Xabier Ostolaza

{"title":"Multivariable QFT control of the direction flip problem in wire arc additive manufacturing","authors":"Manuel Masenlle, Jorge Elso, J. Xabier Ostolaza","doi":"10.1049/cth2.12765","DOIUrl":"https://doi.org/10.1049/cth2.12765","url":null,"abstract":"<p>Additive metal manufacturing (AM), particularly Wire Arc Additive Manufacturing (WAAM), offers a compelling alternative to traditional machining methods. While AM presents advantages such as reduced material waste and lower production costs, challenges remain in effectively controlling the process to prevent defects and optimise material deposition. This article proposes a multivariable control system for WAAM utilising Quantitative Feedback Theory (QFT) to maintain the shape of the heat-affected zone (HAZ) during transitions in direction flips during layer deposition. By modelling these direction flips as predictable disturbances, the full potential of QFT to integrate feedback and feedforward actions is exploited. The resulting multivariable control laws seek to minimise temperature variation in two critical points around the welding pool by adequately manipulating the power and speed of the heat source. A benchmark system is established to evaluate the effectiveness of the proposed control system. The results demonstrate significant improvement in temperature control, leading to enhanced layer construction quality and reduced need for height corrections or cooling pauses.</p>","PeriodicalId":50382,"journal":{"name":"IET Control Theory and Applications","volume":"19 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cth2.12765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Jamming Type Selection and Power Allocation for Countering Multifunctional Radar Network Based on IMAHPPO Algorithm

IF 4.4 2区计算机科学

IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2025-04-25 DOI: 10.1109/taes.2025.3564286

Tianjian Yang, You Chen, Siyi Cheng, Xi Zhang, Xing Wang

引用次数: 0

Accuracy Enhancement of Multipath Navigation Using Reflected Solar Oscillation: A Geometric Perspective

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-04-25 DOI: 10.1109/jiot.2025.3564507

Yang Yuqing, Huang Yueqing, Yang Haonan

引用次数: 0

WMMSE-Based Joint Transceiver Design for Multi-RIS Assisted Cell-free Networks Using Hybrid CSI

IF 10.4 1区计算机科学

IEEE Transactions on Wireless Communications Pub Date : 2025-04-25 DOI: 10.1109/twc.2025.3562138

Xuesong Pan, Zhong Zheng, Xueqing Huang, Zesong Fei

引用次数: 0

Hierarchical Coded Caching With Low Subpacketization and Coding Delay Using Combinatorial t-Designs

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-04-25 DOI: 10.1109/jiot.2025.3564393

Rashid Ummer N.T., B. Sundar Rajan

引用次数: 0