Stereotypes constitute a widely used technique for creating user models. This paper explores the potential of stereotype-based models in virtual environments in order to enhance user engagement and learning outcomes. ...
详细信息
The development of Artificial Intelligence (AI) technology is used to minimize the risk of maternal disorders during pregnancy. Maternal health needs to be monitored so as not to cause problems during the baby's b...
详细信息
Decision Support systems have been widely implemented in various problem areas. This is due to the advantages of decision support systems in providing alternative solutions based on data and established criteria. Vari...
详细信息
The exploitation of sustainable distributed energy sources is associated with the energy resilience and power optimisation of power grids. This study divides the energy sector of urban areas into isolated and non-isol...
详细信息
The text's legibility can dramatically influence any device's usability, and a wealth of research has examined the ideal font characteristics for various displays regarding legibility and readability. However,...
详细信息
Adolescent individuals who take a step towards adulthood need important education about morals and religion, such as fomo that can lead to bad behavior. Fomo has an impact on the success of adolescents' lives in t...
详细信息
Though many deep attributed graph clustering approaches have been developed in recent years, most still suffer from two limitations. First, in the input space, they primarily rely on the original topology structure as...
详细信息
Though many deep attributed graph clustering approaches have been developed in recent years, most still suffer from two limitations. First, in the input space, they primarily rely on the original topology structure as the input (to some graph network), lacking the ability to jointly leverage local and global topology information to refine the graph. Second, in the learning process, they usually employ a single graph learning pipeline (with a single input graph), overlooking the opportunities in the joint optimization of multiple graph learning pipelines (with multiple topology structures). In view of this, this paper presents a Global and Local Topology-Aware Contrastive Graph Clustering Network (GLAC-GCN) for attributed graph clustering. Specifically, the local topology structure and global semantic information are simultaneously utilized to refine the graph. Then a paralleled graph convolutional network (GCN) learning mechanism is designed, where (i) both the original graph and the globally and locally refined graph are treated as input graphs, and (ii) two pipelines of GCNs are jointly and interactively utilized. Furthermore, a self-adaptive learning mechanism is devised to ensure consistency between multiple learning pipelines via the Kullback-Leibler (KL)-divergence. Meanwhile, the contrastive learning is enforced by minimizing the mismatch of the cluster distributions obtained from different GCN pipelines. Extensive experiments are conducted on seven real-world datasets. Notably, GLAC-GCN achieves the best ACC (or NMI) scores on all (or five) of the seven datasets, demonstrating its superiority over the state-of-the-art approaches. Code available: https://***/xuyuankun631/GLAC-GCN. IEEE
In the business process of a payment system using web applications, it requires a container as a place to run a program or what is commonly referred to as a server with services such as a web server, database server, ...
详细信息
Influence and influence diffusion have been studied widely in social networks. Influence maximization is the problem of detecting a set of influential nodes in a social network, which represents relationships among in...
详细信息
This paper presents a novel approach to automatically summarize lecture slides using eye-tracking data. The tool generates personalized summaries by analyzing learners' visual attention patterns, with the aim of r...
详细信息
暂无评论