Addressing real-world challenges in battery diagnostics, particularly under incomplete or
inconsistent boundary conditions, has proven difficult with traditional methodologies such as …

The arrival of the intelligent manufacturing and industrial internet era brings more and more
opportunities and challenges to modern industry. Specifically, the revolution of the …

This article discusses four fundamental topics for future Distributed Computing Continuum
Systems: their representation, model, lifelong learning, and business model. Further, it …

Antifragility is one of the terms that have recently emerged with the aim of indicating a
direction that should be pursued toward the objective of designing Information and …

Abstract Industry 4.0 and 5.0 are currently pushing towards a reconciliation between
humans and the concurrent evolution of cyber-physical systems of systems. This constitutes …

The collection of network data poses a significant challenge for machine/deep learning-
driven network defense systems. This paper proposes a new paradigm, namely In-network …

This paper focuses on the problem of optimizing system utility of Machine-Learning (ML)
based systems in the presence of ML mispredictions. This is achieved via the use of self …

Machine Learning (ML) models are now commonly used as components in systems. As any
other component, ML components can produce erroneous outputs that may penalize system …

Recently, machine learning (ML) has become a popular approach to support self-
adaptation. ML has been used to deal with several problems in self-adaptation, such as …

Artifacts support evaluating new research results and help comparing them with the state of
the art in a field of interest. Over the past years, several artifacts have been introduced to …