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Titlebook: Web Information Systems and Applications; 19th International C Xiang Zhao,Shiyu Yang,Jianxin Li Conference proceedings 2022 The Editor(s) (

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61#
發(fā)表于 2025-4-1 05:45:54 | 只看該作者
62#
發(fā)表于 2025-4-1 09:57:34 | 只看該作者
Yanting Jiang,Di Wu systematic description of a cyclic load. Then, the books use two probabilistic fatigue theories toestablish the limit state function of a component under cyclic load, and further to present how to calculate the reliability of a component under a cyclic loading spectrum. Finally, the book presents h
63#
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發(fā)表于 2025-4-1 18:02:58 | 只看該作者
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發(fā)表于 2025-4-2 02:43:36 | 只看該作者
Shuning Hou,Xueqing Zhao,Ning Liu,Xin Shi,Yun Wang,Guigang Zhang rise to non-equivalent double fault mutants, and hence, cannot be discarded. Moreover, we have developed several supplementary test case selection strategies to detect double faults that cannot be detected by existing test case selection strategies which aim at single-fault detection.
67#
發(fā)表于 2025-4-2 05:16:25 | 只看該作者
68#
發(fā)表于 2025-4-2 08:33:24 | 只看該作者
Temporal Knowledge Graph Embedding for Link Predictioncing the position embedding characterizing the dynamic information of temporal knowledge graph, TKGE can generate the evolutional embedding of entities and relations for downstream applications, such as link prediction, recommender system, and so on. We conduct experiments on several real datasets.
69#
發(fā)表于 2025-4-2 11:16:50 | 只看該作者
Temporal Knowledge Graph Embedding for Link Predictioncing the position embedding characterizing the dynamic information of temporal knowledge graph, TKGE can generate the evolutional embedding of entities and relations for downstream applications, such as link prediction, recommender system, and so on. We conduct experiments on several real datasets.
70#
發(fā)表于 2025-4-2 16:53:07 | 只看該作者
Fusion of Natural Language and Knowledge Graph for Multi-hop Reasoninghe natural language. We tested the performance of the NLKGF model on two datasets requiring multi-hop reasoning. The experimental results show that NLKGF beats advanced benchmark models in multi-hop reasoning tasks, which proves superiority of our model.
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