All Outputs (16)

Refining the Teaching of Programming (2020)
Presentation / Conference Contribution
Gordon, N., Cargill, M., Grey, S., & Brayshaw, M. (2020, July). Refining the Teaching of Programming. Presented at INSPIRE XXV: e-Learning as a Solution during Unprecedented Times in the 21st Century, Online

This paper considers issues around the teaching of programming, a critical yet challenging part of the computing education at all levels. This paper begins by outlining some of the key concerns around computing education-from secondary school, throug... Read More about Refining the Teaching of Programming.

Identifying Gaps in Cybersecurity Teaching and Learning (2020)
Presentation / Conference Contribution
Brayshaw, M., Gordon, N., & Karatazgianni, A. (2020, July). Identifying Gaps in Cybersecurity Teaching and Learning. Presented at INSPIRE XXV : e-Learning as a solution during unprecedented times in the 21st Century

This paper explores perceptions and expectations of privacy when using computer-mediated communication and social media. In this paper we present the results of an empirical survey into this topic and explore the pedagogic implications for the teachi... Read More about Identifying Gaps in Cybersecurity Teaching and Learning.

Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J. (2020, August). Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines. Presented at 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), Online

As global efforts in transitioning to sustainable energy sources rise, wind energy has become a leading renewable energy resource. However, turbines are complex engineering systems and rely on effective operations & maintenance (O&M) to prevent catas... Read More about Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines.

A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease (2020)
Presentation / Conference Contribution
Rana, S. S., Ma, X., Pang, W., & Wolverson, E. (2020, December). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. Presented at 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Leicester, United Kingdom

Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease - many people however remain stable or even demonstrate improvement in cognition. Early detection of progressive MCI (pMCI) therefore can be utili... Read More about A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease.

A Model-based RCM Analysis Method (2020)
Presentation / Conference Contribution
Mian, Z., Jia, S., Shi, X., Tang, C., Chen, J., & Gao, Y. (2020, December). A Model-based RCM Analysis Method. Presented at 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), Macau, China

The reliability-centered maintenance (RCM) is one of the most advanced maintenance plan generating technologies for equipments. At present, the key technologies such as FMEA and FMECA supporting the RCM analysis remains in the manual stage in some en... Read More about A Model-based RCM Analysis Method.

Comparative review of pipelines monitoring and leakage detection techniques (2020)
Presentation / Conference Contribution
Aljuaid, K. G., Albuoderman, M. A., Alahmadi, E. A., & Iqbal, J. (2020, October). Comparative review of pipelines monitoring and leakage detection techniques. Presented at 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia

The oil and gas industry owns expensive and widely-spread assets. Any fault in this complex transportation network may result in accidents and/or huge losses thereby triggering various environmental and economic issues. Thus, real-time monitoring and... Read More about Comparative review of pipelines monitoring and leakage detection techniques.

Deep reinforcement learning for maintenance planning of offshore vessel transfer (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020, October). Deep reinforcement learning for maintenance planning of offshore vessel transfer. Presented at 4th International Conference on Renewable Energies Offshore (RENEW 2020), Lisbon, Portugal

Offshore wind farm operators need to make short-term decisions on planning vessel transfers to turbines for preventive or corrective maintenance. These decisions can play a pivotal role in ensuring maintenance actions are carried out in a timely and... Read More about Deep reinforcement learning for maintenance planning of offshore vessel transfer.

A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020, July). A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK

© 2020 IEEE. Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines an... Read More about A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines.

Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI. Presented at The Science of Making Torque from Wind (TORQUE 2020), Online, Netherlands

© 2020 Published under licence by IOP Publishing Ltd. Machine learning techniques have been widely used for condition-based monitoring of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many machine learning models, inclu... Read More about Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI.

SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures (2020)
Presentation / Conference Contribution
Aslansefat, K., Sorokos, I., Whiting, D., Tavakoli Kolagari, R., & Papadopoulos, Y. SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. Presented at IMBSA: International Symposium on Model-Based Safety and Assessment, Lisbon

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with... Read More about SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures.

An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems (2020)
Presentation / Conference Contribution
Bressan, L., de Oliveira, A. L., Campos, F., Papadopoulos, Y., & Parker, D. An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems. Presented at Model-Based Safety and Assessment 7th International Symposium, IMBSA 2020, Lisbon, Portugal

© 2020, Springer Nature Switzerland AG. Component-based approaches and software product lines have been adopted by industry to manage the diversity of configurations on safety-critical software. Safety certification demands compliance with standards.... Read More about An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems.

Failure Mode Reasoning in Model Based Safety Analysis (2020)
Presentation / Conference Contribution
Jahanian, H., Parker, D., Zeller, M., McIver, A., & Papadopoulos, Y. Failure Mode Reasoning in Model Based Safety Analysis. Presented at International Symposium on Model-Based Safety and Assessment, Lisbon, Portugal

© 2020, Springer Nature Switzerland AG. Failure Mode Reasoning (FMR) is a novel approach for analyzing failure in a Safety Instrumented System (SIS). The method uses an automatic analysis of an SIS program to calculate potential failures in parts of... Read More about Failure Mode Reasoning in Model Based Safety Analysis.

The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020, August). The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines. Paper presented at Fragile Earth: Data Science for a Sustainable Planet. KDD 2020, Virtual Conference

The global pursuit towards sustainable development is leading to increased adaptation of renewable energy sources. Wind turbines are promising sources of clean energy, but regularly suffer from failures and down-times, primarily due to the complex en... Read More about The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines.

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease (2020)
Presentation / Conference Contribution
Alabed, A., Kambhampati, C., & Gordon, N. Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Presented at Computing 2020, London

A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by rem... Read More about Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease.

Semantic Mapping for Model Transformation between AADL2 and HiP-HOPS (2020)
Presentation / Conference Contribution
Mian, Z., Gao, Y., Shi, X., & Tang, C. (2019, November). Semantic Mapping for Model Transformation between AADL2 and HiP-HOPS. Presented at 2019 4th International Conference on System Reliability and Safety, ICSRS 2019, Rome, Italy

Currently, AADL has gradually become as one of the standards for the architecture design of complex embedded system. It is widely used in aerospace, automotive electronics and other fields for the design and analysis of high dependability-critical sy... Read More about Semantic Mapping for Model Transformation between AADL2 and HiP-HOPS.

A Cost Modeling Method Based on AADL2 (2020)
Presentation / Conference Contribution
Mian, Z., Tang, C., Gao, Y., Jia, S., Shi, X., & Chen, J. (2019, November). A Cost Modeling Method Based on AADL2. Presented at 2019 4th International Conference on System Reliability and Safety, ICSRS 2019, Rome, Italy

The Architecture Analysis and Design Language (AADL) is widely used in the modeling, analysis and verification of the dependability-critical system. Previously, we have implemented the multi-objective (based on dependability and cost) architecture op... Read More about A Cost Modeling Method Based on AADL2.