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Titlebook: Artificial Intelligence and Security; 6th International Co Xingming Sun,Jinwei Wang,Elisa Bertino Conference proceedings 2020 Springer Natu

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樓主: risky-drinking
41#
發(fā)表于 2025-3-28 17:14:55 | 只看該作者
42#
發(fā)表于 2025-3-28 21:57:05 | 只看該作者
43#
發(fā)表于 2025-3-29 01:14:42 | 只看該作者
https://doi.org/10.1007/88-470-0426-8imental results show that the mAP value of the method is 22.9 on the public dataset Charades. And the results show that the proposed method has better robustness than other network models and improves the short video action recognition effect.
44#
發(fā)表于 2025-3-29 03:12:51 | 只看該作者
Il futuro dell’ingegneria industrialee points, and obtain high recognition rate by using few feature points. It is robust to rigid changes, illumination and rotations changes of ear image, provides a new approach to the research for ear recognition.
45#
發(fā)表于 2025-3-29 10:37:44 | 只看該作者
Fabbriche, sistemi, organizzazionis a lattice-based PECKS scheme which can resist inside KGA. Its security can be reduced to the hardness of LWE problem and ISIS problem, thus it can resist quantum computing attack. We also give a comparison with other searchable encryption schemes on the computational cost of the main algorithms and the size of related parameters.
46#
發(fā)表于 2025-3-29 11:49:24 | 只看該作者
47#
發(fā)表于 2025-3-29 19:19:20 | 只看該作者
Verso la matematica dell’organizzazione our method achieves dimensionality reduction skillfully, which greatly improves the efficiency of the algorithm. Taking the GT circuit and the NCV circuit as examples, when the number of quantum lines is as large as 8, our method is hundreds of thousands of times faster than the method proposed in the previous paper.
48#
發(fā)表于 2025-3-29 22:52:07 | 只看該作者
49#
發(fā)表于 2025-3-30 03:45:19 | 只看該作者
Computing Sentence Embedding by Merging Syntactic Parsing Tree and Word Embeddingmpared to the traditional sentence embedding weighting method, our method achieves better or comparable performance on various text similarity tasks, especially with the low dimension of the data set.
50#
發(fā)表于 2025-3-30 08:02:51 | 只看該作者
Deep Learning Video Action Recognition Method Based on Key Frame Algorithmimental results show that the mAP value of the method is 22.9 on the public dataset Charades. And the results show that the proposed method has better robustness than other network models and improves the short video action recognition effect.
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