Utilizing machine learning (ML)-based approaches for network intrusion detection systems
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …

Advancements in technology have led to the widespread adoption of machine learning (ML)
algorithms in almost all areas of society (eg, shaping customer experiences through …

Clinicians increasingly pay attention to Artificial Intelligence (AI) to improve the quality and
timeliness of their services. There are converging opinions on the need for Explainable AI …

Relation extraction methods are currently dominated by deep neural models, which capture
complex statistical patterns while being brittle and vulnerable to perturbations in data and …

With the growing interest in underwater exploration and monitoring, Autonomous
Underwater Vehicles (AUVs) have become essential. The recent interest in onboard Deep …

This paper presents several challenges when developing AI-based software systems for
potentially safety-critical domains in the European jurisdiction. Starting with the legal …

Autonomous vehicles rely on accurate trajectory prediction to inform decision-making
processes related to navigation and collision avoidance. However, current trajectory …

EXplainable machine learning (XML) has recently emerged as a promising approach to
address the inherent opacity of machine learning (ML) systems by providing insights into …

Regulatory frameworks for the use of AI are emerging. However, they trail behind the fast-
evolving malicious AI technologies that can quickly cause lasting societal damage. In …

Graph neural networks (GNNs) are powerful graph-based machine-learning models that are
popular in various domains, eg, social media, transportation, and drug discovery. However …