Recent work has demonstrated that pretrained transformers are overconfident in text
classification tasks, which can be calibrated by the famous post-hoc calibration method …

Computer Emergency Response Teams (CERTs) provide advisory, preventive and reactive
cybersecurity services for authorities, citizens, and businesses. However, their responsibility …

Classification of long-tailed distributed data is a challenging problem, which suffers from
serious class imbalance and hence poor performance on tail classes, which have only a few …

Abstract Counterfactually-Augmented Data (CAD)–minimal editing of sentences to flip the
corresponding labels–has the potential to improve the Out-Of-Distribution (OOD) …

Learning a directed acyclic graph (DAG) from observational data plays a crucial role in
causal inference and machine learning. However, the scarcity of observational data is a …

The application of clustering has always been an important method for problem-solving. In
the era of big data, most classical clustering methods suffer from the curse of dimensionality …

The task of grading answers ranging from one phrase to one paragraph using computational
techniques is known as Automated Short Answer Grading (ASAG). The performance of …

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

Mining user-generated content on e-commerce platforms and social media is timely and
more objective compared with other information access channels for gaining competitive …

Electric submersible pump (ESP) in offshore oilfields is one of the important artificial lifting
methods to achieve high and stable production. The complexity of the ESP system and the …