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 …

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

Formal methods have been widely used to support software testing to guarantee correctness
and reliability. For example, model checking technology attempts to ensure that the …

The unprecedented advancement of artificial intelligence (AI) in recent years has altered our
perspectives on software engineering and systems engineering as a whole. Nowadays …

The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …

Safety requirements are among the main barriers to the industrialization of machine learning
based on deep learning architectures. In this work, a new metric of data coverage is …

Performance of trained neural network (NN) models, in terms of testing accuracy, has
improved remarkably over the past several years, especially with the advent of deep …

Deep Learning has enabled machines to learn computational models (ie, Deep Neural
Networks-DNNs) that can perform certain complex tasks with claims to be close to human …

Over the past years, numerous studies demonstrated the vulnerability of deep neural
networks (DNNs) to make correct classifications in the presence of small noise. This …

The immunity of multilayer perceptron (MLP) is less effective toward input noise. In this
article, we have focused on the robustness of MLP with respect to input noise where noise …