87 citations · 294 across the 41 of their papers we have counts for
8 papers · 1 filter
High-Availability Clusters: A Taxonomy, Survey, and Future Directions
Premathas Somasekaram, Radu Calinescu, Rajkumar Buyya
The delivery of key services in domains ranging from finance and manufacturing to healthcare and transportation is underpinned by a rapidly growing number of mission-critical enter…
Quantitative Verification with Adaptive Uncertainty Reduction
Naif Alasmari, Radu Calinescu, Colin Paterson +1
Stochastic models are widely used to verify whether systems satisfy their reliability, performance and other nonfunctional requirements. However, the validity of the verification d…
Verified Synthesis of Optimal Safety Controllers for Human-Robot Collaboration
Mario Gleirscher, Radu Calinescu, James Douthwaite +5
We present a tool-supported approach for the synthesis, verification and validation of the control software responsible for the safety of the human-robot interaction in manufacturi…
Uncertainty in Self-Adaptive Systems: A Research Community Perspective
Sara M. Hezavehi, Danny Weyns, Paris Avgeriou +3
One of the primary drivers for self-adaptation is ensuring that systems achieve their goals regardless of the uncertainties they face during operation. Nevertheless, the concept of…
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers
Colin Paterson, Haoze Wu, John Grese +3
We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze…
Maintaining driver attentiveness in shared-control autonomous driving
Radu Calinescu, Naif Alasmari, Mario Gleirscher
We present a work-in-progress approach to improving driver attentiveness in cars provided with automated driving systems. The approach is based on a control loop that monitors the…
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)
Richard Hawkins, Colin Paterson, Chiara Picardi +3
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains suc…
Fast Parametric Model Checking through Model Fragmentation
Xinwei Fang, Radu Calinescu, Simos Gerasimou +1
Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the sys…