The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Formal approaches can provide strict correctness guarantees for the development of both
hardware and software systems. In this work, we examine state-of-the-art formal methods for …

Neural networks as a machine learning technique are increasingly deployed in various
domains. Despite their performance and their continuous improvement, the deployment of …

Established approaches to assuring safety-critical systems and software are difficult to apply
to systems employing ML where there is no clear, pre-defined specification against which to …

Insider threats pose a critical challenge for securing computer networks and systems. They
are malicious activities by authorised users that can cause extensive damage, such as …

Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on
deep neural networks for perception and control. This paper proposes a method to repair …

Recently, human–robot interaction technology has been considered as a key solution for
smart factories. Surface electromyography signals obtained from hand gestures are often …

Complete verification of deep neural networks (DNNs) can exactly determine whether the
DNN satisfies a desired trustworthy property (eg, robustness, fairness) on an infinite set of …

Artificial Intelligence (AI) is transforming our daily life with many applications in healthcare,
space exploration, banking, and finance. This rapid progress in AI has brought increasing …

Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-
world problems. However, like human-written software, DNNs can have bugs and can be …