Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1...
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Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1], [2].
X-ray security inspection for detecting prohibited items is widely used to maintain social order and ensure the safety of people’s lives and property. Due to the large number of parameters and high computational comp...
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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.
Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of softwareengineering theo...
Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of softwareengineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.
In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenge...
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This paper introduces an advanced road damage detection algorithm that effectively addresses the shortcomings of existing models, including limited detection performance and large parameter sizes, by utilizing the YOL...
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Diabetes retinopathy (DR) is one of the complications of diabetes. Early diagnosis of retinopathy is helpful to avoid vision loss or blindness. The difficulty of this task lies in the significant differences in the si...
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The Traveling Salesman Problem (TSP) seeks the shortest closed tour that visits each city once and returns to the starting city. This problem is NP-hard, so it is not easy to solve using conventional methods. The grey...
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Medical image classification plays a pivotal role in modern healthcare, aiding in accurate disease diagnosis, treatment planning, and patient management. With the advent of deep learning techniques, significant advanc...
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In the electronic manufacturing industry, accurate detection of PCB defects is crucial as it directly impacts product quality and reliability. The primary challenges in PCB defect detection include missed detections a...
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