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A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data

Author

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  • Chao Yu

    (School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China
    Key Laboratory of Counter-Terrorism Command & Information Engineering, Ministry of Education, Engineering University of PAP, Xi’an 710086, China)

  • Yang Zhou

    (School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Xiaolong Cui

    (Key Laboratory of Counter-Terrorism Command & Information Engineering, Ministry of Education, Engineering University of PAP, Xi’an 710086, China)

Abstract

With continuous progress in science and technology, a large amount of data are produced in all fields of the world at anytime and anywhere. These data are unmarked and lack marking information, while manual marking is time-consuming and laborious. Herein, this paper introduces a distributed semi-supervised labeling framework. This framework addresses the issue of missing data by proposing an attribute-filling method based on subspace learning. Furthermore, this paper presents a distributed semi-supervised learning strategy that trains sub-models (private models) within each sub-system. Finally, this paper develops a distributed graph convolutional neural network fusion technique with enhanced interpretability grounded on the attention mechanism. This paper assigns weights of importance to the edges of each layer in the graph neural network based on sub-models and public data, thereby enabling distributed and interpretable graph convolutional attention. Extensive experimentation using public datasets demonstrates the superiority of the proposed scheme over other state-of-the-art baselines, achieving a reduction in loss of 50% compared to the original approach.

Suggested Citation

  • Chao Yu & Yang Zhou & Xiaolong Cui, 2023. "A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data," Mathematics, MDPI, vol. 11(21), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4543-:d:1273909
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    References listed on IDEAS

    as
    1. Li, Yang & Zhang, Meng & Chen, Chen, 2022. "A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems," Applied Energy, Elsevier, vol. 308(C).
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