Fault management involves actions to restore interrupted customers as quickly as possible after a fault occurrence, which is facilitated by optimal switch placement. However, the switch optimization problem requires s...
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Synthetic data generation via generative artificial intelligence (GenAI) is essential for enhancing cybersecurity and safeguarding privacy in the Internet of Medical Things (IoMT) and healthcare. We introduce multifea...
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This review examines the methods, determinants, and forecasting horizons used in electricity demand forecasting in Türkiye. The study investigates how Türkiye's electricity demand is influenced by econom...
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Due to recent expansion of wireless communications, it has become impossible to cope with the allotment of the precious spectrum while resources for wireless communication are bounded and finite. Hence, the cognitive ...
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This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
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The majority of biomedical studies use limited datasets that may not generalize over large heterogeneous datasets that have been collected over several decades. The current paper develops and validates several multimo...
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Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distri...
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Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...
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Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal *** this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for ***,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect ***,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature ***,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and *** on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
作者：
Abreu, MiguelReis, Luís PauloLau, NunoLIACC/LASI/FEUP
Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI
Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal
The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase ...
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