Acetaminophen
发表于 2025-3-28 15:50:29
1876-1100 vel applications of new processing techniques in engineering, science and other disciplines related to ubiquitous computing.. 978-981-99-1254-4978-981-99-1252-0Series ISSN 1876-1100 Series E-ISSN 1876-1119
过剩
发表于 2025-3-28 20:03:57
https://doi.org/10.1007/978-981-99-1252-0Ubiquitous Communication and Networking; Ubiquitous Software Technology; Ubiquitous Systems and Applic
勾引
发表于 2025-3-29 00:56:21
978-981-99-1254-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
停止偿付
发表于 2025-3-29 03:47:02
Sorin Istrail,Cristian Masalagiuthods, an agent must collect all data to train the model, potentially leading to data leakage. Thus, the security challenges have encouraged the use of federated learning to address this issue by anomaly detection while improving the efficiency and privacy of the training models. However, in an exte
广口瓶
发表于 2025-3-29 07:57:01
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柏树
发表于 2025-3-29 15:23:03
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新义
发表于 2025-3-29 18:01:22
https://doi.org/10.1007/978-981-10-8675-5ipate in the FL network. Even though there are many advantages of adopting FL architecture on their system, this is still suffered from security concerns because of various components. Mainly, a poisoning attack can be applied in the FL network to compromise the gradient update of the global model.
Microaneurysm
发表于 2025-3-29 23:31:54
Variantenerzeugung durch Modellmodifikation,an encoder to learn the embedding of the neural architecture, called Graphormer-NAS-X, where X is related to the specific search algorithm. Specifically, the centrality encoding is improved by adding an eigenvector centrality that can capture the importance of adjacent nodes. And the position encodi
停止偿付
发表于 2025-3-30 02:17:34
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Dysarthria
发表于 2025-3-30 07:17:40
Automatisierung des Konstruktionsprozesses,sed Re-ID method has gained great progress by conducting contrastive learning with memory dictionaries. However, most existing methods ignored the importance of positive and negative samples. In this work, we proposed a novel mixup cluster contrastive (MCC) learning framework for unsupervised person