In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these chal...
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This paper presents MACPGANA, a novel multimodal agriculture commodity price prediction model leveraging Generative Adversarial Networks (GAN) and Autoencoders (AEs). The model integrates multimodal data to extract an...
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Cancer remains a leading cause of mortality worldwide, with early detection and accurate diagnosis critical to improving patient outcomes. While computer-aided diagnosis systems powered by deep learning have shown con...
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The core task in natural language processing (NLP) is text summarization, which condenses important information from large volumes of text into brief summaries. This study reviews text summarization strategies using N...
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作者:
Mahapatra, AbhijeetPradhan, RosyMajhi, Santosh K.Mishra, Kaushik
Department of Computer Science & Engineering Odisha Burla768018 India Sikkim Manipal University
Sikkim Manipal Institute of Technology Department of Artificial Intelligence and Data Science Sikkim India
Department of Electrical Engineering Odisha Burla768018 India
Department of Computer Science and Information Technology Chhattisgarh Bilaspur495009 India Manipal Academy of Higher Education
Manipal Institute of Technology Bengaluru Department of Computer Science and Engineering Manipal India
The rapid proliferation of IoT devices like smartphones, smartwatches, etc. has significantly elevated the quantity of data requiring execution. It poses challenges for centralized Cloud computing servers, such as lat...
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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.
Agriculture is backbone of many economies but still farmers often face many challenges in accessing financial services or available resources for them, making informed crop choices and participating in government init...
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Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations bet...
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Sadri is the most widely used language of the Chotanagpur Plateau region of India. This is primarily a spoken language and developing an automatic speech recognition (ASR) system in Sadri is extremely important. When ...
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The surrounding environmental and climatic conditions have a significant impact on the utilisation of ecosystem services for recreational purposes. Climate change poses a threat to future natural leisure opportunities...
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