All Outputs (791)

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.

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). A Model-based RCM Analysis Method. In IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C) (301-307). https://doi.org/10.1109/QRS-C51114.2020.00059

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.

An age-associated reduction in Nav1.5 protein expression correlates with an increase in Nav1.8 protein expression in the right atrial appendage of the human heart. (2020)
Presentation / Conference Contribution
Isaac, E., Cooper, S., Lancaster, M., Loubani, M., & Jones, S. An age-associated reduction in Nav1.5 protein expression correlates with an increase in Nav1.8 protein expression in the right atrial appendage of the human heart. Presented at Basic Cardiovascular Sciences Scientific Sessions 2020, Online

Background: Nav1.5 is the predominantly expressed voltage-gated sodium channel (VGSC) isoform in the heart, responsible for phase 0 of the action potential. Our rodent studies have shown expression of neuronal isoform Nav1.8 protein in the adult hear... Read More about An age-associated reduction in Nav1.5 protein expression correlates with an increase in Nav1.8 protein expression in the right atrial appendage of the human heart..

Deep reinforcement learning for maintenance planning of offshore vessel transfer (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020). Deep reinforcement learning for maintenance planning of offshore vessel transfer. In C. Guedes Soares (Ed.), Developments in Renewable Energies Offshore Proceedings of the 4th International Conference on Renewable Energies Offshore (RENEW 2020, 12 - 15 October 2020, Lisbon, Portugal) (435-443)

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.

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). Comparative review of pipelines monitoring and leakage detection techniques. In 2nd International Conference on Computer and Information Sciences (ICCIS). https://doi.org/10.1109/ICCIS49240.2020.9257602

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.

In-situ measurement and prediction of traffic noise transmission loss across a residential flat unit façade installed with two plenum windows (2020)
Presentation / Conference Contribution
Tang, S. K., & Li, X. (2020). In-situ measurement and prediction of traffic noise transmission loss across a residential flat unit façade installed with two plenum windows. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings 2020 (1368-1375)

In the present study, the traffic noise transmission loss across a residential flat unit façade installed with two plenum windows are measured in-situ in a 30-storey high-rise building located in an opened environment next to a very busy and noisy ma... Read More about In-situ measurement and prediction of traffic noise transmission loss across a residential flat unit façade installed with two plenum windows.

Taboo Or Not Taboo: (In)visibilities Of Death, Dying And Bereavement (2020)
Presentation / Conference Contribution
Hård Af Segerstad, Y., Bell, J., Giaxoglou, K., Pitsillides, S., & Yeshua-Katz, D. Taboo Or Not Taboo: (In)visibilities Of Death, Dying And Bereavement. Presented at Annual Conference of the Association of Internet Researchers., 2020

The notion that ‘death is a taboo’ pervades private, public and academic discourses around death, dying and bereavement in contemporary Western societies. The rise of digital media within the last decades further complicates the appreciation of the s... Read More about Taboo Or Not Taboo: (In)visibilities Of Death, Dying And Bereavement.

Soundscape assessment: Towards a validated translation of perceptual attributes in different languages (2020)
Presentation / Conference Contribution
Aletta, F., Oberman, T., Axelsson, Ö., Xie, H., Zhang, Y., Lau, S. K., …Kang, J. (2020). Soundscape assessment: Towards a validated translation of perceptual attributes in different languages. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings 2020 ( 3137-3146)

The recently published ISO/TS 12913-2:2018 standard aims to provide researchers and practitioners around the world with a reliable questionnaire for soundscape characterization. The ISO Technical Specifications report protocols and attributes grounde... Read More about Soundscape assessment: Towards a validated translation of perceptual attributes in different languages.

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.

Advance care planning: exploration of the public’s understanding. In Abstracts from the 11th EAPC World Research Congress Online, 7th – 9th October 2020 (2020)
Presentation / Conference Contribution
Mcllfatrick, S., Hasson, F., Slater, P., Beck, E., McCloskey, S., Carr, K., Muldrew, D., Bamidele, O., & Hanna-Trainor, L. Advance care planning: exploration of the public’s understanding. In Abstracts from the 11th EAPC World Research Congress Online, 7th – 9th October 2020. Presented at 11th EAPC World Research Congress, Online

Background/aims: Advance care planning, a voluntary process whereby an individual outline their preferences and beliefs, to aid in planning for end of life care, is widely considered an essential step for achieving a `good death”. Raising awareness o... Read More about Advance care planning: exploration of the public’s understanding. In Abstracts from the 11th EAPC World Research Congress Online, 7th – 9th October 2020.

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.

Realisation of a biocompatible diffraction grating using an ArF excimer laser (2020)
Presentation / Conference Contribution
Aesa, A. A., & Walton, C. D. Realisation of a biocompatible diffraction grating using an ArF excimer laser. Presented at First International Conference of Pure and Engineering Sciences (ICPES2020), Karbala, Iraq

We report the fabrication of a bio-compatible diffraction grating made out chitosan, a derivative of chitin. The diffraction grating has been realised by laser ablation using 193 nm excimer laser. Thin spun coated chitosan films 520 nm thick were use... Read More about Realisation of a biocompatible diffraction grating using an ArF excimer laser.

Use of Digital and 3D Visualisation Technology in Planning for Woodland Expansion. EGU2020-1243 (2020)
Presentation / Conference Contribution
Wang, C., Gimona, A., Compagnucci, A. B., & Jiang, Y. (2020, May). Use of Digital and 3D Visualisation Technology in Planning for Woodland Expansion. EGU2020-1243. Paper presented at EGU General Assembly 2020, Online

Forests and woodlands offer many benefits to people. They can provide timber and food, store carbon to help deal with the effects of climate change, decrease flooding and soil erosion, and provide recreation for people and habitat for a multitude of... Read More about Use of Digital and 3D Visualisation Technology in Planning for Woodland Expansion. EGU2020-1243.