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Yalu WANG1, Jie LI2, Zhijie HAN3, Pu CHENG3, Roshan KUMAR4. FedSTGCN: a novel federated spatiotemporal graph learning-based network intrusion detection for the Internet of Things[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="FedSTGCN: a novel federated spatiotemporal graph learning-based network intrusion detection for the Internet of Things",
author="Yalu WANG1, Jie LI2, Zhijie HAN3, Pu CHENG3, Roshan KUMAR4",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400932"
}
%0 Journal Article
%T FedSTGCN: a novel federated spatiotemporal graph learning-based network intrusion detection for the Internet of Things
%A Yalu WANG1
%A Jie LI2
%A Zhijie HAN3
%A Pu CHENG3
%A Roshan KUMAR4
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400932
TY - JOUR
T1 - FedSTGCN: a novel federated spatiotemporal graph learning-based network intrusion detection for the Internet of Things
A1 - Yalu WANG1
A1 - Jie LI2
A1 - Zhijie HAN3
A1 - Pu CHENG3
A1 - Roshan KUMAR4
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400932
Abstract: Image generation models have made remarkable progress, and image evaluation is crucial for explaining and driving the development of these models. Previous studies have extensively explored human and automatic evaluations of image generation. Herein, these studies are comprehensively surveyed, specifically for two main parts: evaluation protocols and evaluation methods. First, 10 image generation tasks are summarized by focusing on their differences in evaluation aspects. Based on this, a novel protocol is proposed to cover human and automatic evaluation aspects required for various image generation tasks. Second, the review of automatic evaluation methods in the past five years is highlighted. To our knowledge, this paper presents the first comprehensive summary of human evaluation, encompassing evaluation methods, tools, details, and data analysis methods. Finally, this the challenges and potential directions for image generation evaluation are discussed. We hope that this survey will help researchers to develop a systematic understanding of image generation evaluation, stay updated with the latest advancements in the field, and encourage further research.
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