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Titlebook: Big Data; 6th CCF Conference, Zongben Xu,Xinbo Gao,Jiajun Bu Conference proceedings 2018 Springer Nature Singapore Pte Ltd. 2018 artificia

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樓主: 灰塵
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發(fā)表于 2025-3-28 17:26:16 | 只看該作者
42#
發(fā)表于 2025-3-28 22:23:51 | 只看該作者
43#
發(fā)表于 2025-3-28 23:51:47 | 只看該作者
44#
發(fā)表于 2025-3-29 05:47:41 | 只看該作者
Intimate Investments in Drag King Cultureshe proposed correlation filter, we design an efficient ADMM (alternation direction of multipliers) solver. Extensive experimental results on the OTB-2013 dataset show that the proposed approach performs favorably against state-of-the-art trackers.
45#
發(fā)表于 2025-3-29 07:15:37 | 只看該作者
The Influence of Online Community Interaction on Individual User Behaviorl media community. We then expound the result from our analysis on data gathered and reach certain conclusions which may be useful to the general public who participate in social media communities and may of value to the regulatory agencies and commercial users of social media as well.
46#
發(fā)表于 2025-3-29 14:17:18 | 只看該作者
An Optimized Artificial Bee Colony Based Parameter Training Method for Belief Rule-Baseraining was implemented in combination with the constraint conditions of the Belief rule-base. By fitting the multi-peak function and the leakage detection experiment of oil pipelines, the experimental error were compared with the traditional and existing parameter training methods to verify its effectiveness.
47#
發(fā)表于 2025-3-29 15:58:12 | 只看該作者
Search of , Community in Large Graphsnd online search algorithms (SingleQuery and MultiQuery) which support efficient search of . community in optimal time. Extensive experiments on four real-world large networks demonstrate the efficiency and effectiveness of our methods.
48#
發(fā)表于 2025-3-29 23:44:32 | 只看該作者
49#
發(fā)表于 2025-3-30 03:14:58 | 只看該作者
50#
發(fā)表于 2025-3-30 05:27:08 | 只看該作者
Multiple Meta Paths Combined for Vertex Embedding in Heterogeneous Networksctural information in the network. We conduct experiments on two real world datasets. The experimental results demonstrate the efficacy and efficiency of the proposed method in heterogeneous network mining tasks. Compare to the previous method, our model can cover a wider range of semantic information and be more flexible and scalable.
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