Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems to underpin network edge intelligence smart cities applications. However, most of the data collected by Internet Things devices in such applications unlabeled, necessitating use semi-supervised learning. Existing studies have introduced solutions run FL; however, they overlooked inherent critical impact...

Change detection is a crucial but extremely challenging task in remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing learning-based change methods try to elaborately design complicated neural networks powerful feature representations. they ignore universal domain shift induced by time-varying land cover changes, inclu...

A large number of accuracy measures for crisp supervised classification have been developed in supervised image classification literature. Overall accuracy, Kappa index, Kappa location, Kappa histo and user accuracy are some well-known examples. In this work, we will extend and analyze some of these measures in a fuzzy framework to be able to measure the goodness of a given classifier in a supe...

This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedding learning is based on the idea of two-view semi-supervised learning, which...

To deal with the problem of sensitivity to noise in semi-supervised learning for biometrics, this paper proposes a robust Gaussian-Laplacian Regularized (GLR) framework based on maximum correntropy criterion (MCC), called GLR-MCC, along with its convergence analysis. The half quadratic (HQ) optimization technique is used to simplify the correntropy optimization problem to a standard semi-superv...