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Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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樓主: Flange
11#
發(fā)表于 2025-3-23 13:02:34 | 只看該作者
Binxia Yang,Xudong Luo,Kaili Sun,Michael Y. Luorchase, and avoidance when risks are simply too high. Software failure risk assessment can provide the information to analyze functionality, financial, and project risks associated with failure due to faults in software. This information can modify the development process or suggest changes to syste
12#
發(fā)表于 2025-3-23 16:20:49 | 只看該作者
13#
發(fā)表于 2025-3-23 21:46:58 | 只看該作者
Hu Zhang,Kunrui Li,Guangjun Zhang,Yong Guan,Ru Liat we can only claim to have a truly sCientific approach. and so justify the description software engineering. when we are able to measure the attributes of process and product. It is still common to find software development methods recommended to users on purely anecdotal grounds. This is not good enough. R978-3-540-55212-3978-3-642-84725-7
14#
發(fā)表于 2025-3-24 02:10:45 | 只看該作者
15#
發(fā)表于 2025-3-24 02:52:55 | 只看該作者
16#
發(fā)表于 2025-3-24 08:54:42 | 只看該作者
17#
發(fā)表于 2025-3-24 12:08:56 | 只看該作者
Event-Aware Document-Level Event Extraction via?Multi-granularity Event Encoderhen utilized for subsequent event record generation, thereby improving the accuracy of argument classification. Our proposed model’s effectiveness is demonstrated through experimental results obtained from a large Chinese financial dataset.
18#
發(fā)表于 2025-3-24 18:05:02 | 只看該作者
A Deep Joint Model of?Multi-scale Intent-Slots Interaction with?Second-Order Gate for?SLUaluable information through the gate with second-order weights, reduces the noise information of fusion, accelerates model convergence, and alleviates model overfitting. Experiments on two public datasets demonstrate that our model outperforms the baseline and achieves state-of-the-art performance c
19#
發(fā)表于 2025-3-24 21:47:50 | 只看該作者
Graph Attention Network Knowledge Graph Completion Model Based on?Relational Aggregationl parameters, so that the model can extract richer relational information. Finally, the decoder chooses the convolutional network ConvR. We conduct experiments on standard datasets such as FB15k-237 and WN18RR, and the experimental results confirm the effectiveness of the model, while also achieving
20#
發(fā)表于 2025-3-25 00:43:10 | 只看該作者
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