Zobrazeno 1 - 10
of 217
pro vyhledávání: '"PATERSON, COLIN"'
Autor:
Vardal, Ozan, Hawkins, Richard, Paterson, Colin, Picardi, Chiara, Omeiza, Daniel, Kunze, Lars, Habli, Ibrahim
For machine learning components used as part of autonomous systems (AS) in carrying out critical tasks it is crucial that assurance of the models can be maintained in the face of post-deployment changes (such as changes in the operating environment o
Externí odkaz:
http://arxiv.org/abs/2406.16220
Autor:
Yaman, Sinem Getir, Cavalcanti, Ana, Calinescu, Radu, Paterson, Colin, Ribeiro, Pedro, Townsend, Beverley
Autonomous agents are increasingly being proposed for use in healthcare, assistive care, education, and other applications governed by complex human-centric norms. To ensure compliance with these norms, the rules they induce need to be unambiguously
Externí odkaz:
http://arxiv.org/abs/2307.03697
Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental or internal changes trigger a system response only after a violation of
Externí odkaz:
http://arxiv.org/abs/2205.03628
Autor:
Getir Yaman, Sinem, Ribeiro, Pedro, Cavalcanti, Ana, Calinescu, Radu, Paterson, Colin, Townsend, Beverley
Publikováno v:
In The Journal of Systems & Software February 2025 220
Stochastic models are widely used to verify whether systems satisfy their reliability, performance and other nonfunctional requirements. However, the validity of the verification depends on how accurately the parameters of these models can be estimat
Externí odkaz:
http://arxiv.org/abs/2109.02984
Autor:
Gleirscher, Mario, Calinescu, Radu, Douthwaite, James, Lesage, Benjamin, Paterson, Colin, Aitken, Jonathan, Alexander, Rob, Law, James
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 manufacturing processes that use collaborative robots. In human-robot collaborati
Externí odkaz:
http://arxiv.org/abs/2106.06604
Autor:
Weyns, Danny, Schmerl, Bradley, Kishida, Masako, Leva, Alberto, Litoiu, Marin, Ozay, Necmiye, Paterson, Colin, Tei, Kenji
Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-bas
Externí odkaz:
http://arxiv.org/abs/2103.10847
Autor:
Paterson, Colin, Wu, Haoze, Grese, John, Calinescu, Radu, Pasareanu, Corina S., Barrett, Clark
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. While the robustness of DNN classifie
Externí odkaz:
http://arxiv.org/abs/2103.01629
Autor:
Hawkins, Richard, Paterson, Colin, Picardi, Chiara, Jia, Yan, Calinescu, Radu, Habli, Ibrahim
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 such as healthcare , automotive and manufacturing, exhibit high degrees o
Externí odkaz:
http://arxiv.org/abs/2102.01564
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. When these regions b
Externí odkaz:
http://arxiv.org/abs/1911.12780