Collision risk assessment aims to provide a subjective cognitive comprehension of the risk
level in driving scenarios, which is critical for the safety of autonomous driving systems …

Miscalibrated models tend to be unreliable and insecure for downstream applications. In this
work, we attempt to highlight and remedy miscalibration in current scene graph generation …

Many real-world applications of the Internet of Things (IoT) employ machine learning (ML)
algorithms to analyze time series information collected by interconnected sensors. However …

Scene understanding is the process of analysing and interpreting various perceptual
information in the environment to understand and reason about people, objects, events, and …

Scene Graph Generation (SGG) is a powerful tool for autonomous vehicles to understand
their environment. In this paper, a novel one-stage neuro-symbolic architecture called nEuro …

Deep learning has been widely adopted for human activity recognition (HAR) while
generalizing a trained model across diverse users and scenarios remains challenging due …

Since the appearance of microprocessors, miscellaneous categories of computer systems,
including Embedded and Cyber-Physical Systems (ECPS), have become an integral part of …

The 2022 study on traffic fatalities in Italy by the Italian National Institute of Statistics (ISTAT)
reports 454 daily fatalities and 561 injuries, primarily due to distractions. Then, the success …

We propose a novel approach to control a swarm of drones. Leveraging Graph Neural
Networks (GNNs), our approach aims to improve the robustness of the drone swarm system …