IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-04-25 DOI: 10.1109/jiot.2025.3564526

Aroosa Hameed, Syed Muhammad Danish, Ali Ranjha, Gautam Srivastava

引用次数: 0

Prescribed-Performance Green Dynamic Positioning for Fully Actuated Vessels under Input Magnitude and Rate Saturations

IF 5.6 2区计算机科学

IEEE Transactions on Automation Science and Engineering Pub Date : 2025-04-25 DOI: 10.1109/tase.2025.3564323

Zhihao Yu, Jialu Du, Jingyao Wang, Ze Lin

引用次数: 0

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

Directly Attention loss adjusted prioritized experience replay

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-25 DOI: 10.1007/s40747-025-01852-6

Zhuoying Chen, Huiping Li, Zhaoxu Wang

引用次数: 0

SMG-MATSM: Scene Memory Generation Based on Motion-Aware Temporal Style Modulation

IF 2 4区计算机科学

IET Image Processing Pub Date : 2025-04-25 DOI: 10.1049/ipr2.70083

Liang Wang, Zhao Wang, Shaokang Zhang, Meng Wang, Haibo Liu

{"title":"SMG-MATSM: Scene Memory Generation Based on Motion-Aware Temporal Style Modulation","authors":"Liang Wang, Zhao Wang, Shaokang Zhang, Meng Wang, Haibo Liu","doi":"10.1049/ipr2.70083","DOIUrl":"https://doi.org/10.1049/ipr2.70083","url":null,"abstract":"<p>Scene memory generation (SMG) refers to training AI agents to recall scene memories similarly to the human brain. This is the key work to realize the artificial memory system. The challenge is to generate scenes rich in motion and keep it realistic while ensuring temporal consistency. Inspired by the principles of memory function in brain neuroscience, this paper proposes a motion-aware scene generation model named SMG based on motion-aware temporal style modulation (SMG-MATSM), which ensures temporal consistency by redesigning the temporal latent representation and constructing a motion matrix to guide the motion of intermediate latent variables. The motion matrix preserves motion consistency in the scene memory through both the cosine similarity and the Mahalanobis distance of intermediate latent variables of adjacent frames. Additionally, SMG-MATSM uses a style-based approach and enhances conditional features through the motion matrix during the scene memory synthesis process. Experimental results show that SMG-MATSM has better effect of action-enriched scene memory generation, and has varying degrees of efficiency improvement on different datasets with Frechet video distance and Frechet inception distance evaluation metrics.</p>","PeriodicalId":56303,"journal":{"name":"IET Image Processing","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ipr2.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871821","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

Experimental demonstration of local AI-Agents for lifecycle management and control automation of optical networks

IF 4 2区计算机科学

Journal of Optical Communications and Networking Pub Date : 2025-04-25 DOI: 10.1364/JOCN.550286

Chenyu Sun;Xin Yang;Nicola Di Cicco;Reda Ayassi;Venkata Virajit Garbhapu;Photios A. Stavrou;Massimo Tornatore;Gabriel Charlet;Yvan Pointurier

{"title":"Experimental demonstration of local AI-Agents for lifecycle management and control automation of optical networks","authors":"Chenyu Sun;Xin Yang;Nicola Di Cicco;Reda Ayassi;Venkata Virajit Garbhapu;Photios A. Stavrou;Massimo Tornatore;Gabriel Charlet;Yvan Pointurier","doi":"10.1364/JOCN.550286","DOIUrl":"https://doi.org/10.1364/JOCN.550286","url":null,"abstract":"This paper presents an innovative approach to automating the full lifecycle management of optical networks using locally fine-tuned large language models (LLMs) and digital twin technologies. We experimentally demonstrate the integration of generative AI and digital twins to create powerful AI-Agents capable of handling the design, deployment, maintenance, and upgrade phases in the lifecycle of optical networks. By deploying and fine-tuning LLMs locally, our framework eliminates the need for public cloud services, thereby ensuring data privacy and security. The experimental setup includes a commercial-product-based testbed with eight optical multiplex sections in the C-band, showcasing the effectiveness of the AI-Agents in various automation tasks, such as API-calling for service establishment and periodic power equalization, as well as log analysis for troubleshooting. The results highlight significant improvements in operational accuracy and efficiency, underscoring the feasibility of this approach in real-world scenarios. This work represents a significant advancement toward intent-based networking, showcasing the transformative potential of AI in automating and optimizing optical network operations.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"17 8","pages":"C82-C92"},"PeriodicalIF":4.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875246","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

IEEE Control Systems Society Publication Information

IF 6.2 1区计算机科学

IEEE Transactions on Automatic Control Pub Date : 2025-04-25 DOI: 10.1109/TAC.2025.3556577

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

计算机科学最新文献