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

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,TASML: Two-Stage Adaptive Semi-supervised Meta-learning for?Few-Shot Learning,y and efficiency, and interrelated hierarchies for integrated processing of visual inputs is a signature of human vision intelligence. Similar to the process of human recognition of unknown objects, meta-learning enables models to “l(fā)earn to learn" by providing a small number of “support" samples to

極為憤怒

Fillet,Filet

博識(shí)

,MHNA: Multi-Hop Neighbors Aware Index for?Accelerating Subgraph Matching,the subgraph matching problem has been proven to be an NP-complete problem. While specific approaches aim to accelerate queries by leveraging favorable matching orders and pruning rules, they face limitations in handling large-scale graph data due to the exponential search space. Conversely, other m

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,Epidemic Source Identification Based on?Infection Graph Learning, enables tracing propagation processes and implementing effective measures to block transmission. However, most algorithms employed to identify propagation sources in networks heavily rely on prior knowledge of the underlying propagation models and associated parameters, as different propagation pat

情愛

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,Hierarchical Retrieval of?Ancient Chinese Character Images Based on?Region Saliency and?Skeleton Mar image retrieval techniques when applied to ancient Chinese characters, this paper proposes a hierarchical retrieval method for ancient Chinese characters images based on region saliency and skeleton matching (RSSM). The proposed method utilizes saliency joint weighting algorithm to effectively int

四目在模仿

,MHNA: Multi-Hop Neighbors Aware Index for?Accelerating Subgraph Matching,the subgraph matching problem has been proven to be an NP-complete problem. While specific approaches aim to accelerate queries by leveraging favorable matching orders and pruning rules, they face limitations in handling large-scale graph data due to the exponential search space. Conversely, other m

規(guī)范要多

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,Locality Sensitive Hashing for?Data Placement to?Optimize Parallel Subgraph Query Evaluation, vertices, thereby reducing redundant communication and computation across multiple workers during parallel subgraph query evaluation. Extensive experimental studies conducted on both large real and synthetic graphs demonstrate that our proposed techniques lead to significant improvements in query performance compared to existing methods.