Shuteng Niu

TL;DR: Transfer Learning (TL) as discussed by the authors can be classified into four classes: transductive learning, inductive, unsupervised learning and negative learning, and each category can be organized into four learning types: learning on instances, learning on features, learningon parameters, and learning on relations.

TL;DR: A lightweight Blockchain-based secure routing algorithm for swarm UAS networking based on 5G NR cellular networking that can avoid the malicious connections from attackers, recognize the malicious UASs and mitigate the attacks from malicious U ASs is proposed.

TL;DR: In this article, an enhanced deep learning framework for IoT device identification using physical-layer signals is proposed to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability.

TL;DR: Wang et al. as discussed by the authors proposed a novel transfer learning framework for medical image classification, which can effectively handle the distribution shift between the training data and the testing data, and achieved 96% classification accuracy, which is 13% higher classification accuracy than non-transfer and 8% higher than existing transfer and distant transfer algorithms.

TL;DR: A novel transfer learning framework for medical image classification using unlabeled Office-31, Caltech-256, and chest X-ray image data sets as the source data, and a small set of labeled COVID-19 lung CT as the target data is developed.