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Titlebook: Web Information Systems Engineering – WISE 2021; 22nd International C Wenjie Zhang,Lei Zou,Lu Chen Conference proceedings 2021 Springer Nat

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41#
發(fā)表于 2025-3-28 15:49:59 | 只看該作者
Md. Nurul Ahad Tawhid,Siuly Siuly,Kate Wang,Hua Wangd Asia. Cultural Heritage has become one of the most successful application domains of Linked Data and Semantic Web technologies.This book gives an overview on why, when, and how Linked (Open) Data and Semantic Web technologies can be employed in practice in publishing CH collections and other conte
42#
發(fā)表于 2025-3-28 22:28:20 | 只看該作者
43#
發(fā)表于 2025-3-29 01:25:54 | 只看該作者
Transaction Confirmation Time Estimation in the Bitcoin Blockchain effectiveness and efficiency of our proposed approaches. Each of our approaches can finish training and estimation within one block interval, demonstrating that our approaches can process real-time cases.
44#
發(fā)表于 2025-3-29 04:40:05 | 只看該作者
A Blockchain-Based Approach for Trust Management in Collaborative Business Processesginally designed according to a centralised BPM strategy. The methodology and the tool are grounded on a set of criteria, properly enforced with metrics, to identify trust-demanding elements to be considered for their deployment on the blockchain. The approach has been validated on a real case study
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發(fā)表于 2025-3-29 09:07:23 | 只看該作者
46#
發(fā)表于 2025-3-29 11:23:00 | 只看該作者
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發(fā)表于 2025-3-29 17:43:22 | 只看該作者
48#
發(fā)表于 2025-3-29 22:41:44 | 只看該作者
XTuning: Expert Database Tuning System Based on Reinforcement Learningdiverse workloads. The models define the importance and correlations among these configuration knobs for the user’s specified target. Then we implement the models as Progressive Expert Knowledge Tuning (PEKT) algorithm with an abstracted architectural optimization integrated into XTuning. Experiment
49#
發(fā)表于 2025-3-30 02:02:34 | 只看該作者
XTuning: Expert Database Tuning System Based on Reinforcement Learningdiverse workloads. The models define the importance and correlations among these configuration knobs for the user’s specified target. Then we implement the models as Progressive Expert Knowledge Tuning (PEKT) algorithm with an abstracted architectural optimization integrated into XTuning. Experiment
50#
發(fā)表于 2025-3-30 04:52:06 | 只看該作者
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