Web service clustering technique can effectively improves the service retrieval efficiency. Service networks offer new possibilities to handle the huge growth of Web services; Community detection is... Show More

Web service clustering technique can effectively improves the service retrieval efficiency. Service networks offer new possibilities to handle the huge growth of Web services; Community detection is one of the important tasks in Web data mining to efficiently analyze and understand the structural properties and group characteristics of various networks. In view of this, this paper proposes a service clustering method based on unsupervised community detection. First, a structure center update strategy is used to overcome the dependence on the initial structure center; Second, the label propagation model is based on the GCN model as a base module, which can utilize both the network topology and node attributes. In order to improve the model's label propagation capability, the method extends the pseudo-label set as supervisory information to train the model and is used to infer the community labels of the remaining nodes. Finally, experiments conducted on 4 real networks show that the method has better community detection performance.

This paper explores the evolving relationship between new media technologies and the preservation of Intangible Cultural Heritage (ICH), focusing on how digital platforms reshape cultural... Show More

This paper explores the evolving relationship between new media technologies and the preservation of Intangible Cultural Heritage (ICH), focusing on how digital platforms reshape cultural communication patterns in the post-pandemic context. Through a case study of Xinjiang Uygur Muqam art, it examines the role of Geographic Information System (GIS) analysis and Virtual Reality (VR) technology in revitalizing traditional practices and engaging diverse age groups and cultural backgrounds. The research highlights the transformative potential of new media in ensuring the continued relevance and accessibility of ICH in the digital age, contributing to ongoing discussions on the role of technology in cultural preservation. By integrating GIS and VR technologies, this study provides insights into practical applications, outcomes, and cutting-edge methods for digitalization, analyzing the effectiveness of engaging younger generations and ensuring the enduring legacy of ICH.

Large-scale language models have demonstrated robust language understanding and generation capabilities, enabling them to tackle various complex natural language processing tasks. However, for... Show More

Large-scale language models have demonstrated robust language understanding and generation capabilities, enabling them to tackle various complex natural language processing tasks. However, for domain-specific tasks like healthcare that require specialized expertise, relying solely on large language models for dialogue generation is insufficient. Moreover, this paper aims to improve the performance of models in medical conversations and enhance the interpretability of the intermediary processes. It argues that leveraging diverse knowledge and agent-based architecture can significantly address the challenges. We introduce an agent-based adaptive medical dialogue service (AMDS) for personalized healthcare. This service utilizes large language models as its cognitive core and integrates medical knowledge extracted from knowledge graph and process knowledge. Extensive experiments show that AMDS outperforms baselines in multi-turn medical dialogue generation tasks.