Titlebook: Web and Big Data; 7th International Jo Xiangyu Song,Ruyi Feng,Geyong Min Conference proceedings 2024 The Editor(s) (if applicable) and The

interference

reflection

,A Dual−Population Strategy Based Multi−Objective Yin−Yang−Pair Optimization for Cloud Computing,per proposes a novel Dual−Population strategy based Multi−Objective Yin−Yang−Pair Optimization which is termed as DP−MOYYPO. The proposed DP−MOYYPO algorithm makes the following three improvements to Front−based Yin−Yang−Pair Optimization (F−YYPO). First, a population of the same size to explore non

POINT

Lipoprotein

,Heterogeneous Graph Contrastive Learning with Dual Aggregation Scheme and Adaptive Augmentation,etworks (HGNNs) have been widely used to capture rich semantic information on graph data, showing strong potential for application in real-world scenarios. However, the semantic information is not fully exploited by existing heterogeneous graph models in the following two aspects: (1) Most HGNNs use

surmount

Multiview Subspace Clustering of Hyperspectral Images Based on Graph Convolutional Networks,to be an effective approach for addressing this problem. However, current subspace clustering algorithms are mainly designed for a single view and do not fully exploit spatial or texture feature information in HSI. This study proposed a multiview subspace clustering of HSI based on graph convolution

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,Ultra-DPC: Ultra-scalable and Index-Free Density Peak Clustering,m density within a predefined sphere, plays a critical role. However, Density Peak Estimation (DPE), the process of identifying the nearest denser relation for each data object, is extremely expensive. The state-of-the-art accelerating solutions that utilize the index are still resource-consuming fo

CRAMP

,Heterogeneous Graph Contrastive Learning with Dual Aggregation Scheme and Adaptive Augmentation,etworks (HGNNs) have been widely used to capture rich semantic information on graph data, showing strong potential for application in real-world scenarios. However, the semantic information is not fully exploited by existing heterogeneous graph models in the following two aspects: (1) Most HGNNs use

vasculitis

,Lifelong Hierarchical Topic Modeling via Non-negative Matrix Factorization,shot scenario since they do not use the identified topic information to guide the subsequent mining of topics. By storing and exploiting the previous knowledge, we propose a lifelong hierarchical topic model based on Non-negative Matrix Factorization (NMF) for boosting the topic quality over a text

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