The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated
applications. While it shows great potential to provide transformational capabilities, DL also …

The increasing use of Machine Learning (ML) components embedded in autonomous
systems—so-called Learning-Enabled Systems (LESs)—has resulted in the pressing need …

With the continuous popularization and deepening of the Internet-of-Things (IoT)
technologies, trillions of IoT Devices (IoTD) are connected to the network. The huge growth …

Context Assessing the accuracy in operation of a Machine Learning (ML) system for image
classification on arbitrary (unlabeled) inputs is hard. This is due to the oracle problem, which …

Deep Neural Networks (DNN) are nowadays largely adopted in many application domains
thanks to their human-like, or even superhuman, performance in specific tasks. However …

Machine learning has seen wide adoptions, although its deployment is resource-intensive
and time-consuming with interoperability and performance concerns. Cloud deployment and …

Software reliability stands as a cornerstone in the development and deployment of
dependable applications, with the advent of AI intensifying its significance. The intricacies …

Deep neural networks (DNNs) are now widely used in many sectors of our society. This
phenomenon also means that if these DNNs contain faults, they will have profound adverse …

Past few years have witnessed tremendous progress on machine learning (ML) models,
especially deep neural networks. The great achievement in human-level intelligence …

Abstract Machine Learning (ML) systems are nowadays largely adopted in many application
domains. In the field of Image Classification (IC), where Convolutional Neural Networks …