计算机科学最新文献_第5页

IEEE Internet of Things Journal Society Information

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-01-10 DOI: 10.1109/jiot.2024.3517279

引用次数: 0

Acoustic Resolution Photoacoustic Microscopy Imaging Enhancement: Integration of Group Sparsity with Deep Denoiser Prior

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2025-01-10 DOI: 10.1109/tip.2025.3526065

Zhengyuan Zhang, Zuozhou Pan, Zhuoyi Lin, Arunima Sharma, Chia-Wen Lin, Manojit Pramanik, Yuanjin Zheng

引用次数: 0

The Impact of Cloaking Digital Footprints on User Privacy and Personalization.

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-01-10 DOI: 10.1089/big.2024.0036

Sofie Goethals, Sandra Matz, Foster Provost, David Martens, Yanou Ramon

{"title":"The Impact of Cloaking Digital Footprints on User Privacy and Personalization.","authors":"Sofie Goethals, Sandra Matz, Foster Provost, David Martens, Yanou Ramon","doi":"10.1089/big.2024.0036","DOIUrl":"https://doi.org/10.1089/big.2024.0036","url":null,"abstract":"Our online lives generate a wealth of behavioral records-digital footprints-which are stored and leveraged by technology platforms. These data can be used to create value for users by personalizing services. At the same time, however, it also poses a threat to people's privacy by offering a highly intimate window into their private traits (e.g., their personality, political ideology, sexual orientation). We explore the concept of cloaking: allowing users to hide parts of their digital footprints from predictive algorithms, to prevent unwanted inferences. This article addresses two open questions: (i) can cloaking be effective in the longer term, as users continue to generate new digital footprints? And (ii) what is the potential impact of cloaking on the accuracy of desirable inferences? We introduce a novel strategy focused on cloaking \"metafeatures\" and compare its efficacy against just cloaking the raw footprints. The main findings are (i) while cloaking effectiveness does indeed diminish over time, using metafeatures slows the degradation; (ii) there is a tradeoff between privacy and personalization: cloaking undesired inferences also can inhibit desirable inferences. Furthermore, the metafeature strategy-which yields more stable cloaking-also incurs a larger reduction in desirable inferences.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Multi-level adaptive feature representation based on task augmentation for Cross-Domain Few-Shot learning

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-01-10 DOI: 10.1007/s10489-024-06110-9

Ling Yue, Lin Feng, Qiuping Shuai, Zihao Li, Lingxiao Xu

{"title":"Multi-level adaptive feature representation based on task augmentation for Cross-Domain Few-Shot learning","authors":"Ling Yue, Lin Feng, Qiuping Shuai, Zihao Li, Lingxiao Xu","doi":"10.1007/s10489-024-06110-9","DOIUrl":"10.1007/s10489-024-06110-9","url":null,"abstract":"<div>Cross-Domain Few-Shot Learning (CDFSL) is one of the most cutting-edge fields in machine learning. It not only addresses the traditional few-shot problem but also allows for different distributions between base classes and novel classes. However, most current CDFSL models only focus on the generalization performance of high-level features during training and testing, which hinders their ability to generalize well to domains with significant gaps. To overcome this problem, we propose a CDFSL method based on Task Augmentation and Multi-Level Adaptive features representation(TA-MLA). At the feature representation level, we introduce a meta-learning strategy for multi-level features and adaptive features. The former come from different layers of network. They jointly participate in image prediction to fully explore transferable features suitable for cross-domain scenarios. The latter is based on a feature adaptation module of feed-forward attention, aiming to learn domain-adaptive features to improve the generalization of the model. At the training task level, we employ a plug-and-play Task Augmentation(TA) module to generate challenging tasks with adaptive inductive biases, thereby expanding the distribution of the source domain and further bridging domain gaps. Extensive experiments conducted on multiple datasets. The results demonstrate that our method based on meta-learning can effectively improves few-shot classification performance, especially in cases with significant domain shift.</div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 4","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938749","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

Digital Twin for Autonomous Guided Vehicles Based on Integrated Sensing and Communications

IF 6.8 2区计算机科学

IEEE Transactions on Vehicular Technology Pub Date : 2025-01-10 DOI: 10.1109/tvt.2025.3528113

Van-Phuc Bui, Pedro Maia de Sant Ana, Soheil Gherekhloo, Shashi Raj Pandey, Petar Popovski

引用次数: 0

An Enhanced Augmented Coprime Array With Low Mutual Coupling and High DOFs

IF 6.8 2区计算机科学

IEEE Transactions on Vehicular Technology Pub Date : 2025-01-10 DOI: 10.1109/tvt.2025.3525627

Liang Han, Yijiu Zhao, Sitao Mei, Xinyi Zhao, Houjun Wang, Bo Zhang

引用次数: 0

A Hybrid Adaptive Dynamic Programming for Optimal Tracking Control of USVs

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-01-10 DOI: 10.1109/tnnls.2024.3512539

Shan Xue, Ning Zhao, Weidong Zhang, Biao Luo, Derong Liu

引用次数: 0

A Reliable Federated Learning Server Rotation Algorithm in IoV

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-01-10 DOI: 10.1109/jiot.2025.3528013

Xuelian Cai, Junyi Yang, Tianyu Chang, Yuchuan Fu, F. Richard Yu, Nan Cheng, Changle Li, Yilong Hui

引用次数: 0

LogNotion: Highlighting Massive Logs to Assist Human Reading and Decision Making

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-10 DOI: 10.1109/tsc.2025.3528327

Guojun Chu, Jingyu Wang, Tao Sun, Qi Qi, Haifeng Sun, Zirui Zhuang, Jianxin Liao

引用次数: 0

Robust Dynamic Corrective Consensus over Lossy Networks with Gilbert-Elliott Channels

IF 6.8 1区计算机科学

IEEE Transactions on Automatic Control Pub Date : 2025-01-10 DOI: 10.1109/tac.2025.3528353

Sabato Manfredi

引用次数: 0