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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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發(fā)表于 2025-3-28 17:37:53 | 只看該作者
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發(fā)表于 2025-3-28 21:29:40 | 只看該作者
Improving Zero-Shot Information Retrieval with?Mutual Validation of?Generative and?Pseudo-Relevance zing various prompting strategies. The mutual validation process incorporates a comprehensive scoring mechanism?that considers both consistency and relevance dimensions, facilitating alignment between GRF and PRF while retaining query-relevant information. Lastly, our fine-grained dual-filtering app
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發(fā)表于 2025-3-29 01:30:21 | 只看該作者
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發(fā)表于 2025-3-29 06:23:44 | 只看該作者
Entity Semantic Feature Fusion Network for?Remote Sensing Image-Text Retrievalntic in the text into our textual feature extractor, so that it can have a good entity perception of remote sensing text. We designed a Text Phrase Enhancement module (TPE) to further extract and enhance entity semantic and alignment visual information in text. In addition, ESFN’s experimental resul
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發(fā)表于 2025-3-29 07:21:06 | 只看該作者
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發(fā)表于 2025-3-29 13:15:15 | 只看該作者
Semantic Preservation and?Hash Fusion Network for?Unsupervised Cross-Modal Retrievalonstruct a joint similarity matrix to generate high-quality unified binary hash codes?by leveraging the interaction among continuous codes from different modalities. Experimental results on multiple datasets show that?our method significantly outperforms existing state-of-the-art approaches in cross
47#
發(fā)表于 2025-3-29 18:07:13 | 只看該作者
48#
發(fā)表于 2025-3-29 20:38:13 | 只看該作者
ECHO: Adaptive Correction for?Subgraph-Wise Sampling with Lightweight Hyperparameter Searche,?ECHO adaptively incorporates neighbor sampling epochs into the training process as correction according to the training loss.?Our theoretical analysis and extensive experiments demonstrate that?ECHO achieves fast convergence with high accuracy. Specifically,?ECHO achieves up to 3.5. training time
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發(fā)表于 2025-3-30 03:27:03 | 只看該作者
50#
發(fā)表于 2025-3-30 06:16:37 | 只看該作者
A Parallel and Distributed Data Management Approach for MEC Using the Improved Parameterized Deep Q-s data management and analysis across multiple edge servers and it addresses the dual challenges of parallel?and distributed data management by ensuring data consistency?and availability across distributed nodes, while optimizing computational resources to reduce latency and energy consumption. Expe
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