It is true to say that the technique of animated character designing is a vital component of demonstrating visual storytelling and emotion evocation. If we talk about earlier times in the context of the traditional me...
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This paper delves into the existing routing schemes in Vehicular Ad hoc Networks (VANETs), critically examines their limitations, and proposes an efficient routing scheme for software-Defined Vehicular Networks (SDVN)...
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Cross-Domain Recommendation can significantly mitigate the challenges posed by sparse data for recommendation systems. Relevant studies indicate domain-specific preferences negatively impact the recommendation perform...
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ISBN:
(纸本)9789819755547;9789819755554
Cross-Domain Recommendation can significantly mitigate the challenges posed by sparse data for recommendation systems. Relevant studies indicate domain-specific preferences negatively impact the recommendation performance of target domains based on domain-shared information. Recent research considers domain-invariant and domain-specific features. Nevertheless, these intricately entangled features are hardly discerned for differentiation and the semantic diversity in heterogeneous relationships tends to be understated. In light of this, a novel model entitled Disentangled Representations for Cross-Domain Recommendation via Heterogeneous Graph Contrastive Learning (DHCL) is proposed. We derive domain-invariant and domain-specific representations, capturing both commonalities and unique features across diverse domains. Heterogeneous graph and meta-path are used to assist in enhancing the amount of information. We formulate dual contrastive learning tasks to further obtain optimal disentangled representations. Comprehensive experiments on three pairs of authentic review datasets highlight the superiority of DHCL over SOTA recommendation methods.
software-defined networking (SDN) is a transformative technology that systematically centralises and manages network resources. This paradigm shift allows for greater flexibility, agility, and efficiency in network ma...
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This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to te...
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Open-source softwaresystems have proliferated over the past few decades, with increasing penetration across domains. The wide availability of development data from such systems has led to studies on various aspects o...
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Machine learning (ML) has been widely used in computer system development and optimization levels, boosting computer design and optimization improvement. With the increase of computer system design complexity and the ...
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With the rapid development of intelligent systems, Multi-Agent systems (MAS) have shown unique advantages in solving complex decision-making problems. Particularly in the field of Multi-Agent Reinforcement Learning (M...
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The rotating joint is an important part of the mechanical scanning antenna, which ensures the effective transmission of electromagnetic wave signals during the rotation of the antenna. In this paper, the foundational ...
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The advent of large language models (LLMs) represents a significant paradigm shift in autonomous threat detection within IoT networks. This paper explores the application of Large Language Models (LLMs) for autonomous...
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