Annotations of Virus Data for Knowledge Enrichment

Vizza, Patrizia; Tradigo, Giuseppe; Guzzi, Pietro Hiram; Puccio, Barbara; Prosperi, Mattia; Torti, Carlo; Veltri, Pierangelo

doi:10.1109/ichi54592.2022.00084

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…In recent years, there has been a lot of research on data marking, with most work using the traditional machine learning method to complete the centralized data annotation. Visa et al [19] proposed a data integration and annotation model through the Web-based application integration virus data source and then completed data annotation. Vindas et al [20] undertook the comprehensive use of an automatic encoder and K-Means algorithm, using the semi-supervised method to realize the rapid annotation of images.…”

Section: Related Workmentioning

confidence: 99%

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

Yu,

Zhou,

Cui

2023

Mathematics

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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.

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Section: Related Workmentioning

confidence: 99%

“…Then, in order to strengthen the node relationship, this paper added the loss function of missing labels on the basis of model 1 as (19) and obtained model 2 as (20) to learn the structural information of D L,M data. min…”

Section: Model Building Within the Datasetmentioning

confidence: 99%