Artificial intelligence (AI) systems are trained to solve complex problems and learn to
perform specific tasks by using large volumes of data, such as prediction, classification …

Most machine learning and data analytics applications, including performance engineering
in software systems, require a large number of annotations and labelled data, which might …

Annotated driving scenario trajectories are crucial for verification and validation of
autonomous vehicles. However, annotation of such trajectories based only on explicit rules …

Data-centric artificial intelligence (DC-AI) is a modern paradigm that gives more priority to
data quality enhancement, rather than only optimizing the complex codes of AI models. The …

Despite substantial advances in network architecture performance, the susceptibility of
adversarial attacks makes deep learning challenging to implement in safety-critical …

Abstract The Artificial Intelligence has evolved and is now associated with Deep Learning,
driven by availability of vast amount of data and computing power. Traditionally, researchers …

In many domains, effectively applying machine learning models requires a large number of
annotations and labelled data, which might not be available in advance. Acquiring …

Quality control allows companies to verify the products' conformance to requirements and
specifications and thus build customer satisfaction and the brand's reputation. Artificial …

A variety of hierarchical domain taxonomies exist in the medical domain for describing
medical concepts such as laboratory tests, medications, and procedures. The structural …

Acquiring labelled data for machine learning tasks, for example, for software performance
prediction, remains a resource-intensive task. This study extends our previous work by …