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Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito

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21#
發(fā)表于 2025-3-25 04:56:50 | 只看該作者
22#
發(fā)表于 2025-3-25 09:48:45 | 只看該作者
23#
發(fā)表于 2025-3-25 15:01:10 | 只看該作者
24#
發(fā)表于 2025-3-25 19:19:40 | 只看該作者
Markets, Freedom and Responsibility, the number of required airplanes and devise several optimizations to balance their workload and enhance scalability. Moreover, we provide theoretical guarantees on both optimization goals. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and scalability of our solution.
25#
發(fā)表于 2025-3-25 22:09:04 | 只看該作者
Flight Planning at?Scale: A Bipartite Matching Based Approach the number of required airplanes and devise several optimizations to balance their workload and enhance scalability. Moreover, we provide theoretical guarantees on both optimization goals. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and scalability of our solution.
26#
發(fā)表于 2025-3-26 02:07:40 | 只看該作者
27#
發(fā)表于 2025-3-26 08:02:30 | 只看該作者
Ethnicity, Racism and Social Theory,om various roads, including highways and mountainous local roads in Japan, and compared them with the VCVR-EBM to verify its accuracy-all of which achieved good results. Furthermore, we showed how the VCVR-EBM can be utilized for analysis of EV acceleration methods, route selection for travel, and charging planning for long-distance trips.
28#
發(fā)表于 2025-3-26 11:58:57 | 只看該作者
https://doi.org/10.1007/978-3-642-66787-9 is likely to be beneficial for their annotations. The results of a large-scale experiment show that our algorithm manages to request human experts at times of need and to significantly improve annotation compared to fixed, non-interactive, requesting approaches.
29#
發(fā)表于 2025-3-26 12:48:28 | 只看該作者
30#
發(fā)表于 2025-3-26 18:27:30 | 只看該作者
AdaShifter: An Online Data Annotation Framework Under Human-Machine Collaboration is likely to be beneficial for their annotations. The results of a large-scale experiment show that our algorithm manages to request human experts at times of need and to significantly improve annotation compared to fixed, non-interactive, requesting approaches.
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