Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

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Ejaculate

Robust Federated Learning with?Realistic Corruptionf the noise is large, while those from benign clients are never filtered throughout the training process. For realistic gradient noise, our approach significantly outperforms existing methods, while the performance under the worst-case attack (i.e. the Byzantine attack) remains nearly the same. Expe

分解

BAIL

0302-9743 t Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30–September 1, 2024...The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions...The papers are organized in the following topical sections:.Volume I:?Natural

船員

SAM: A Spatial-Aware Learned Index for?Disk-Based Multi-dimensional Searchonsumption. To address these issues, we propose a spatial-aware learned index for disk-based multi-dimensional search (SAM for short). Its core idea is to use a data transformation technique based on dual-distance metric to map more similar data in space into compact regions and the mapped values ar

Antagonist

BIVXDB: A Bottom Information Invert Index to?Speed up?the?Query Performance of?LSM-Treerange query process involves filtering and sorting SSTables at each level, resulting in significant performance overhead. To enhance range query performance, we traverse all KV pairs during the SSTable creation phase when compaction, and construct an efficient global index named BIVX with LSM-tree’s

斜谷

SAM: A Spatial-Aware Learned Index for?Disk-Based Multi-dimensional Searchonsumption. To address these issues, we propose a spatial-aware learned index for disk-based multi-dimensional search (SAM for short). Its core idea is to use a data transformation technique based on dual-distance metric to map more similar data in space into compact regions and the mapped values ar

sinoatrial-node

Dual-Contrastive Multi-view Clustering Under the Guidance of Global Similarity and Pseudo-labeling studies focus on the selection of contrastive learning method in the feature space, while the selection of positive and negative samples in the contrast process is too arbitrary and often ignores the global relationship among data samples, which may lead to samples from the same clusters having

喃喃而言

BIVXDB: A Bottom Information Invert Index to?Speed up?the?Query Performance of?LSM-Treerange query process involves filtering and sorting SSTables at each level, resulting in significant performance overhead. To enhance range query performance, we traverse all KV pairs during the SSTable creation phase when compaction, and construct an efficient global index named BIVX with LSM-tree’s

Explosive