Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Artificial intelligence systems are increasingly being used in industrial applications, security
and military contexts, disaster response complexes, policing and justice practices, finance …

Although deep neural networks (DNNs) have achieved tremendous performance in diverse
vision challenges, they are surprisingly susceptible to adversarial examples, which are born …

Background The healthcare sector demands a higher degree of responsibility,
trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems …

Deep neural network (deepnet) applications play a crucial role in safety-critical systems
such as autonomous vehicles (AVs). An AV must drive safely towards its destination …

Context. The problem of optimizing the resilience of artificial intelligence systems to
destructive disturbances has not yet been fully solved and is quite relevant for safety-critical …

Many commercial anti-virus software already use some form of machine learning to help
with detection. However, there has been little research on ways in which multiple algorithms …

The broad learning system (BLS) framework gives an efficient solution for training flat-
structured feedforward networks and flat structured deep neural networks. However, the …

We are currently experiencing the Noise Intermediate-Scale Quantum (NISQ) era where
quantum algorithms are forced to be small in terms of qubits and gates used. This limitation …

Machine Learning (ML) has become indispensable for real-world complex systems, such as
perception systems of autonomous systems and vehicles. However, ML-based systems are …