Abstract Context: Engineering Artificial Intelligence (AI) software is a relatively new area with
many challenges, unknowns, and limited proven best practices. Big companies such as …

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in
requirements engineering for artificial intelligence (RE4AI), eg, the difficulty in specifying and …

Countless domains rely on Machine Learning (ML) models, including safety-critical
domains, such as autonomous driving, which this paper focuses on. While the black box …

Automatic control of the greenhouse environmental conditions is among the most important
necessities of modern agriculture. In order to better control and manage the conditions …

In the recent years, many software systems have adopted AI techniques, especially deep
learning techniques. Due to their black-box nature, AI-based systems brought challenges to …

Although many real-world applications, such as disease prediction, and fault detection suffer
from class imbalance, most existing graph-based classification methods ignore the …

ABSTRACT [Context] Engineering Artificial Intelligence (AI) software is a relatively new area
with many challenges, unknowns, and limited proven best practices. Big companies such as …

The world is evolving in a pace faster than ever, in terms of the ways in which human can
interact with the rest of the world virtually or physically. Our exploration together with Carl …

Abstract Machine Learning (ML) models have been gaining popularity in recent years in a
wide variety of domains, including safety-critical domains. While ML models have shown …