no code implementations • 15 Mar 2023 • Xingyu Zhao, Simos Gerasimou, Radu Calinescu, Calum Imrie, Valentin Robu, David Flynn
We develop a novel Bayesian learning framework that enables the runtime verification of autonomous robots performing critical missions in uncertain environments.
no code implementations • 6 Feb 2023 • Corina S. Pasareanu, Ravi Mangal, Divya Gopinath, Sinem Getir Yaman, Calum Imrie, Radu Calinescu, Huafeng Yu
We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set.
no code implementations • 7 Feb 2022 • Radu Calinescu, Calum Imrie, Ravi Mangal, Corina Păsăreanu, Misael Alpizar Santana, Gricel Vázquez
We use the method in simulation to synthesise controllers for mobile-robot collision avoidance, and for maintaining driver attentiveness in shared-control autonomous driving.
no code implementations • 2 Mar 2021 • Colin Paterson, Haoze Wu, John Grese, Radu Calinescu, Corina S. Pasareanu, Clark Barrett
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, and changes in image contrast.
no code implementations • 5 Feb 2021 • Radu Calinescu, Naif Alasmari, Mario Gleirscher
We present a work-in-progress approach to improving driver attentiveness in cars provided with automated driving systems.
Autonomous Driving Robotics Systems and Control Systems and Control
no code implementations • 2 Feb 2021 • Xinwei Fang, Radu Calinescu, Simos Gerasimou, Faisal Alhwikem
Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.)
Software Engineering Formal Languages and Automata Theory Robotics
1 code implementation • 2 Feb 2021 • Richard Hawkins, Colin Paterson, Chiara Picardi, Yan Jia, Radu Calinescu, Ibrahim Habli
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance.
no code implementations • 28 Nov 2019 • Colin Paterson, Radu Calinescu, Chiara Picardi
Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems.
no code implementations • 10 May 2019 • Rob Ashmore, Radu Calinescu, Colin Paterson
Our paper provides a comprehensive survey of the state-of-the-art in the assurance of ML, i. e. in the generation of evidence that ML is sufficiently safe for its intended use.