activity
20182022
most citedAssuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

87 citations · 151 across the 10 of their papers we have counts for

collaborators

12 papers

cs.SE2022

Specification Architectural Viewpoint for Benefit-Cost-Risk-Aware Decision-Making in Self-Adaptive Systems

Danny Weyns, Paris Avegriou, Radu Calinescu +3

Over the past two decades, researchers and engineers have extensively studied the problem of how to enable a software system to deal with uncertain operating conditions. One promin…

cs.RO20225 cited

Towards Adaptive Planning of Assistive-care Robot Tasks

Jordan Hamilton, Ioannis Stefanakos, Radu Calinescu +1

This 'research preview' paper introduces an adaptive path planning framework for robotic mission execution in assistive-care applications. The framework provides a graph-based envi…

cs.LO20226 cited

Scheduling of Missions with Constrained Tasks for Heterogeneous Robot Systems

Gricel Vázquez, Radu Calinescu, Javier Cámara

We present a formal tasK AllocatioN and scheduling apprOAch for multi-robot missions (KANOA). KANOA supports two important types of task constraints: task ordering, which requires…

cs.SE2022

PRESTO: Predicting System-level Disruptions through Parametric Model Checking

Xinwei Fang, Radu Calinescu, Colin Paterson +1

Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental o…

cs.SE20214 cited

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…

cs.LG2021

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…