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Titlebook: Service Science; CCF 17th Internation Jianping Wang,Bin Xiao,Xuanzhe Liu Conference proceedings 2024 The Editor(s) (if applicable) and The

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31#
發(fā)表于 2025-3-26 21:32:01 | 只看該作者
1865-0929 in Hong Kong, China, in May 11–12, 2024...?..The?12 full papers and 2 short papers presented here were carefully reviewed and selected from 21 submissions. These papers have been categorized into the following sections: Cloud service and Edge service; Knowledge-inspired service; Trustworthy service
32#
發(fā)表于 2025-3-27 01:29:31 | 只看該作者
33#
發(fā)表于 2025-3-27 06:35:03 | 只看該作者
34#
發(fā)表于 2025-3-27 12:55:07 | 只看該作者
Crawling and Exploring RESTful Web APIs from RapidAPIpidAPI (.) and provide rich visualizations and statistical analysis for the crawled dataset. The crawling code and the crawled dataset are open for public access (.) so that researchers and users can acquire them with no effort. Moreover, the use cases of RESTful Web APIs are provided as a usage reference for users and developers.
35#
發(fā)表于 2025-3-27 16:30:42 | 只看該作者
FIPO: Software-Defined Packet Scheduling Primitive for Time-Sensitive Networkinghanges to the network architecture. However, with the development of metaverse as well as holographic technology, applications require the network to provide different levels of deterministic transmission guarantees including data rate, delay, and jitter. In addition, these deterministic requirement
36#
發(fā)表于 2025-3-27 18:29:28 | 只看該作者
37#
發(fā)表于 2025-3-28 00:32:19 | 只看該作者
38#
發(fā)表于 2025-3-28 04:11:46 | 只看該作者
DRT: A Deterministic Computing and?Network Resources Tradeoff Mechanism for?Holographic-Type Communiion. While the HTC system can deliver an immersive experience, it demands stringent real-time processing, substantial computing resources, and high bandwidth. However, current HTC systems struggle with ensuring ultra-low latency and finding a tradeoff between network and computing resources. In this
39#
發(fā)表于 2025-3-28 09:09:51 | 只看該作者
The State of?Charge Predication of?Lithium-Ion Battery Using Contrastive Learninglue of SOC is challenging due to it being a hidden state quantity. Existing neural network models commonly employ an end-to-end prediction paradigm for SOC estimation, which fails to fully exploit the rich information present in the time-series battery data. To address this limitation, this paper de
40#
發(fā)表于 2025-3-28 11:26:08 | 只看該作者
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