Recently, deep learning models have made great breakthroughs in the field of computer
vision, relying on large-scale class-balanced datasets. However, most of them do not …

In autonomous driving, the perception module typically utilizes a combination of millimeter-
wave radar and LiDAR. However, when driving in challenging environmental conditions, this …

The field of cysec is evolving fast. Security professionals are in need of intelligence on past,
current and—ideally—upcoming threats, because attacks are becoming more advanced and …

Graph neural networks have triggered a resurgence of graph-based text classification
methods, defining today's state of the art. We show that a wide multi-layer perceptron (MLP) …

The exponential growth of digital data in recent years has spurred a significant interest in
natural language processing (NLP) and sentiment analysis. However, the effectiveness of …

Abstract Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …

Machine learning-based automatic scoring can be challenging if students' responses are
unbalanced across scoring categories, as it introduces uncertainty in the machine training …

Data augmentation is a commonly-used technique to avoid over-fitting in deep learning.
However, the mechanism behind effective data augmentation methods is unclear. To …

Natural language processing (NLP) techniques are becoming increasingly popular in
industrial and organizational psychology. One promising area for NLP-based applications is …

Generative adversarial networks (GANs) are deep generative models (GMs) that have
recently attracted attention owing to their impressive performance in generating completely …