计算机科学最新文献

{"title":"Integrated Sensing and Communication for Low Altitude Economy: Opportunities and Challenges","authors":"Yihang Jiang, Xiaoyang Li, Guangxu Zhu, Hang Li, Jing Deng, Kaifeng Han, Chao Shen, Qingjiang Shi, Rui Zhang","doi":"10.1109/mcom.001.2400685","DOIUrl":"https://doi.org/10.1109/mcom.001.2400685","url":null,"abstract":"","PeriodicalId":55030,"journal":{"name":"IEEE Communications Magazine","volume":"37 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797766","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}

{"title":"Revolutionizing QoE-Driven Network Management with Digital Agents in 6G","authors":"Xuemin Sherman Shen, Xinyu Huang, Jianzhe Xue, Conghao Zhou, Xiufang Shi, Weihua Zhuang","doi":"10.1109/mcom.001.2400679","DOIUrl":"https://doi.org/10.1109/mcom.001.2400679","url":null,"abstract":"","PeriodicalId":55030,"journal":{"name":"IEEE Communications Magazine","volume":"27 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797811","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}

{"title":"Input Excitation Disturbance Separation-Based Energy-Saving Attitude Control for Flexible Spacecraft With Actuator Degradation","authors":"Yukai Zhu, Yan Meng, Hao Teng, Yangyang Cui, Haipeng Chen","doi":"10.1109/tie.2025.3552260","DOIUrl":"https://doi.org/10.1109/tie.2025.3552260","url":null,"abstract":"","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"13 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Susceptibility risk assessment of oil and gas pipeline geological hazards in mountainous areas based on data-driven model","authors":"Yuxiang Yang ,&nbsp;Benji Wang ,&nbsp;Xiao Cen ,&nbsp;Bowen Shao ,&nbsp;Baikang Zhu ,&nbsp;Jin Yang ,&nbsp;Bingyuan Hong","doi":"10.1016/j.engappai.2025.110732","DOIUrl":"10.1016/j.engappai.2025.110732","url":null,"abstract":"<div><div>Geological hazards are recognized as causing significant damage to oil and gas pipelines, often resulting in catastrophic loss of life and property and hindering societal progress. In this study, a data-driven evaluation model is developed by integrating the Information Value method (IVM) with a Back Propagation Neural Network (BPNN) to assess the susceptibility of geological hazards in mountainous oil and gas pipelines. The IVM is used to identify non-hazardous areas, optimizing sample selection and reducing training errors, while the BPNN is employed to determine the weights of evaluation indicators, enhancing accuracy. First, an evaluation index system is proposed that comprehensively considers the natural geographical conditions and main disaster types. Next, non-disaster areas are located using the IVM and combined with disaster-prone areas to form the sample data. The sample data is then input into a BPNN for training, and the weights of each evaluation index are obtained from the trained network. Finally, a susceptibility risk assessment model is developed based on the derived weights and information values to accurately evaluate the susceptibility of pipeline geological hazards. A pipeline in China's Zhejiang Province's mountainous region is used as an illustration. Compared to the single IVM model and the single BPNN model, the receiver operator characteristic curve shows that the proposed method achieves significant improvements in the area under the curve by 9.8 % and 11.2 %, respectively, indicating a high level of evaluation accuracy. This study provides a reliable approach for assessing geological hazard susceptibility, offering scientific support for pipeline planning and hazard mitigation in oil and gas operations.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"151 ","pages":"Article 110732"},"PeriodicalIF":7.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792474","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}

{"title":"Hierarchical multi-scale Mamba generative adversarial network for multi-modal medical image synthesis","authors":"Liwei Jin,&nbsp;Yanjun Peng,&nbsp;Jiao Wang,&nbsp;Yuxin Jiang,&nbsp;Kai Zhang","doi":"10.1016/j.eswa.2025.127451","DOIUrl":"10.1016/j.eswa.2025.127451","url":null,"abstract":"<div><div>In recent years, the rapid advancement of medical imaging technology has placed higher demands on diagnostic accuracy, making multi-modal medical image synthesis an essential pathway for comprehensive diagnosis. Although Generative Adversarial Networks (GANs) have achieved some progress in the field of medical image synthesis, their reliance on single-modality mapping often hampers the effective capture of complex contextual relationships between modalities, thereby limiting the quality and precision of the synthesized images. We propose a novel multi-modal medical image synthesis model, HMS-MambaGAN, which adopts a hierarchical multi-scale structure that effectively captures and fuses both global and local features through the innovative design of channel ConvNext Mamba (ConvMamba) blocks, Episodic Bottleneck, HMSModule, and a dual-decoder structure. Additionally, we have designed a loss function based on the Gray-Level Gradient Co-occurrence Matrix (GLGCM), incorporating gradient information to enhance the texture and structural details of the synthesized images, while a diffusion model is utilized as an auxiliary component to synthesize additional target domain images, further improving overall image quality and detail representation. Our results demonstrate that HMS-MambaGAN significantly outperforms current state-of-the-art models on multi-contrast MRI, MRI-CT, and CT-PET datasets. Our code is publicly available at <span><span>https://github.com/jlw9999/HMS-MambaGAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"280 ","pages":"Article 127451"},"PeriodicalIF":7.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convergence analysis of the Friedkin–Johnsen model with multiple topics","authors":"Lingling Yao ,&nbsp;Dongmei Xie ,&nbsp;Aming Li","doi":"10.1016/j.automatica.2025.112272","DOIUrl":"10.1016/j.automatica.2025.112272","url":null,"abstract":"<div><div>In this paper, we study the convergence and consensus of the Friedkin–Johnsen (F–J) model with <span><math><mi>n</mi></math></span> individuals/agents and <span><math><mi>m</mi></math></span> interdependent topics, where the interpersonal influence among <span><math><mi>n</mi></math></span> individuals is represented by an interpersonal influence matrix <span><math><mi>W</mi></math></span> and the interdependencies among <span><math><mi>m</mi></math></span> topics are described by a set of “logic matrices” <span><math><mrow><msub><mrow><mi>C</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>,</mo><mo>…</mo><mo>,</mo><msub><mrow><mi>C</mi></mrow><mrow><mi>n</mi></mrow></msub></mrow></math></span>. Each topic is described by a topic subnetwork and the whole F–J model can be regarded as a large network with <span><math><mi>m</mi></math></span> isomorphic subnetworks as well as the interactions among them. For the F–J model with homogeneous logic matrices (i.e., <span><math><mrow><msub><mrow><mi>C</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mo>⋯</mo><mo>=</mo><msub><mrow><mi>C</mi></mrow><mrow><mi>n</mi></mrow></msub></mrow></math></span>), our results show that it can achieve global topic consensus (consensus on each topic) if and only if all individuals are non-oblivious with only one stubborn individual and all topics are independent. For the F–J model with heterogeneous logic matrices (i.e., <span><math><mrow><mo>∃</mo><msub><mrow><mi>C</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>≠</mo><msub><mrow><mi>C</mi></mrow><mrow><mi>j</mi></mrow></msub></mrow></math></span>), we not only construct the integrated digraph <span><math><mrow><mi>G</mi><mrow><mo>(</mo><msub><mrow><mi>C</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>,</mo><msub><mrow><mi>C</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>,</mo><mo>…</mo><mo>,</mo><msub><mrow><mi>C</mi></mrow><mrow><mi>n</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span> but also establish the connections between this digraph and the logic networks <span><math><mrow><mi>G</mi><mrow><mo>(</mo><msub><mrow><mi>C</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>)</mo></mrow><mo>,</mo><mo>…</mo><mo>,</mo><mi>G</mi><mrow><mo>(</mo><msub><mrow><mi>C</mi></mrow><mrow><mi>n</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span>. Taking advantage of these connections, we establish the convergence and topic consensus criteria for the F–J model with heterogeneous logic matrices, where the heterogeneity is reflected in the structure, weight, and sign of <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>i</mi></mrow></msub></math></span>. Finally, some simulations are provided to illustrate the results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"177 ","pages":"Article 112272"},"PeriodicalIF":4.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792106","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}

{"title":"AIoMT-Driven Secure and Green Medical Image Processing for Sustainable Healthcare Supply Chains","authors":"Yang Guo, Liang Dong, Shanmuganathan Manimurugan, Xiaohong Lyu","doi":"10.1109/jiot.2025.3558241","DOIUrl":"https://doi.org/10.1109/jiot.2025.3558241","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"59 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FHECAP: An Encrypted Control System with Piecewise Continuous Actuation","authors":"Song Bian, Yunhao Fu, Dong Zhao, Haowen Pan, Yuexiang Jin, Jiayue Sun, Hui Qiao, Zhenyu Guan","doi":"10.1109/tifs.2025.3558580","DOIUrl":"https://doi.org/10.1109/tifs.2025.3558580","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"87 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieval-Augmented Fine-Tuning for Improving Retrieve-and-Edit Based Assertion Generation","authors":"Hongyan Li, Weifeng Sun, Meng Yan, Ling Xu, Qiang Li, Xiaohong Zhang, Hongyu Zhang","doi":"10.1109/tse.2025.3558403","DOIUrl":"https://doi.org/10.1109/tse.2025.3558403","url":null,"abstract":"","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"87 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GoalGrasp: Grasping Goals in Partially Occluded Scenarios Without Grasp Training","authors":"Shun Gui, Kai Gui, Yan Luximon","doi":"10.1109/tii.2025.3552653","DOIUrl":"https://doi.org/10.1109/tii.2025.3552653","url":null,"abstract":"","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"37 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}