All Outputs (147)

Why the Educational Metaverse Is Not All About Virtual Reality Apps (2023)
Presentation / Conference Contribution
Gordon, N., Brayshaw, M., Kambili-Mzembe, F., & Al Jaber, T. (2023). Why the Educational Metaverse Is Not All About Virtual Reality Apps. Lecture notes in computer science, 14041 LNCS, 22-32. https://doi.org/10.1007/978-3-031-34550-0_2

This paper explores how the Metaverse can be used in the context of learning and collaboration. In it we seek to dispel the story that the Metaverse is just another synonym for Virtual Reality and future technology. Instead we will argue that the Met... Read More about Why the Educational Metaverse Is Not All About Virtual Reality Apps.

A Reinforcement Learning-based Assignment Scheme for EVs to Charging Stations (2022)
Presentation / Conference Contribution
Aljaidi, M., Aslam, N., Chen, X., Kaiwartya, O., Al-Gumaei, A., & Khalid, M. (2022). A Reinforcement Learning-based Assignment Scheme for EVs to Charging Stations. In IEEE 95th Vehicular Technology Conference: VTC2022-Spring. https://doi.org/10.1109/VTC2022-Spring54318.2022.9860535

Due to recent developments in electric mobility, public charging infrastructure will be essential for modern transportation systems. As the number of electric vehicles (EVs) increases, the public charging infrastructure needs to adopt efficient charg... Read More about A Reinforcement Learning-based Assignment Scheme for EVs to Charging Stations.

Synchronous Multi-User Cross-Platform Virtual Reality for School Teachers (2022)
Presentation / Conference Contribution
Kambili-Mzembe, F., & Gordon, N. A. (2022, May). Synchronous Multi-User Cross-Platform Virtual Reality for School Teachers. Presented at 8th International Conference of the Immersive Learning Research Network (iLRN), Vienna, Austria

Motivated by a desire to apply Computer Science and Virtual Reality (VR) technology due to the need for improving secondary school education in Malawi, this paper presents a prototype of a synchronous multi-user cross-platform real-time 3D VR applica... Read More about Synchronous Multi-User Cross-Platform Virtual Reality for School Teachers.

Flexible Ontology-Driven Educational Apps and Social Media for Learners with a Disability (2022)
Presentation / Conference Contribution
Nganji, J. T., Brayshaw, M., & Gordon, N. (2022). Flexible Ontology-Driven Educational Apps and Social Media for Learners with a Disability. In P. Zaphiris, & A. Ioannou (Eds.), Learning and Collaboration Technologies. Designing the Learner and Teacher Experience. HCII 2022 (361-375). https://doi.org/10.1007/978-3-031-05657-4_26

This paper explores how to build ontology-driven learning systems from a flexible disability-aware mentality and augment them into a learning blend that embraces social media. The approach emphasizes the use of user centered flexible software in a bl... Read More about Flexible Ontology-Driven Educational Apps and Social Media for Learners with a Disability.

The Role of Gamification in a Software Development Lifecycle (2021)
Presentation / Conference Contribution
Gordon, N., Brayshaw, M., Dixon, J., Grey, S., & Parker, D. (2021). The Role of Gamification in a Software Development Lifecycle. In INSPIRE XXVI . Delivering Global Education and Impact in Emergencies Using E-Learning (81-94)

Teaching Software Engineering students raises a number of challenges; in particular that student developers typically demonstrate behaviours that run counter to good software development. These include failing to plan properly, failing to develop the... Read More about The Role of Gamification in a Software Development Lifecycle.

Terminal sliding mode control of an anthropomorphic manipulator with friction based observer (2021)
Presentation / Conference Contribution
Ali, K., Mehmood, A., & Iqbal, J. (2021). Terminal sliding mode control of an anthropomorphic manipulator with friction based observer. In 2021 International Conference on Robotics and Automation in Industry (ICRAI). https://doi.org/10.1109/ICRAI54018.2021.9651323

The role of modern control techniques has been instrumental in today’s robotic applications because of their increasing requirements for reliability, accuracy, productivity and repeatability. Robotic manipulators are highly non-linear systems with co... Read More about Terminal sliding mode control of an anthropomorphic manipulator with friction based observer.

Distributed Leader-Follower Formation Control of Quadrotors Swarm Subjected to Disturbances (2021)
Presentation / Conference Contribution
Mechali, O., Iqbal, J., Wang, J., Xie, X., & Xu, L. (2021). Distributed Leader-Follower Formation Control of Quadrotors Swarm Subjected to Disturbances. In 2021 IEEE International Conference on Mechatronics and Automation (1442-1447). https://doi.org/10.1109/ICMA52036.2021.9512623

This paper proposes a novel control scheme for a group of quadrotors aircrafts that form a leader-follower configuration and are subjected to nonlinear behavior with lumped disturbances. For each aircraft, a distributed formation control law is desig... Read More about Distributed Leader-Follower Formation Control of Quadrotors Swarm Subjected to Disturbances.

Finite-Time Attitude Control of Uncertain Quadrotor Aircraft via Continuous Terminal Sliding-Mode-Based Active Anti-Disturbance Approach (2021)
Presentation / Conference Contribution
Mechali, O., Iqbal, J., Mechali, A., Xie, X., & Xu, L. (2021). Finite-Time Attitude Control of Uncertain Quadrotor Aircraft via Continuous Terminal Sliding-Mode-Based Active Anti-Disturbance Approach. In IEEE International Conference on Mechatronics and Automation, ICMA 2021 (1170-1175). https://doi.org/10.1109/ICMA52036.2021.9512751

This research addresses the problem of robust attitude control for a quadrotor operating in an environment polluted with lumped disturbances. A new continuous terminal sliding mode-based active anti-disturbance control (CTSMBAADC) is proposed by inno... Read More about Finite-Time Attitude Control of Uncertain Quadrotor Aircraft via Continuous Terminal Sliding-Mode-Based Active Anti-Disturbance Approach.

Research on College Computer-Computing and Information Literacy online course based on MOOC: Taking the North Minzu University as an example (2021)
Presentation / Conference Contribution
Mian, Z., Bai, Y., & Ur, R. K. (2021). Research on College Computer-Computing and Information Literacy online course based on MOOC: Taking the North Minzu University as an example. In IEEE 3rd International Conference on Computer Science and Educational Informatization (CSEI) (300-306). https://doi.org/10.1109/CSEI51395.2021.9477751

In 2015, North Minzu University (NMU) introduced MOOC teaching method into the teaching practice of computer major. It is the first experiment of this teaching method in Ningxia Hui Autonomous Region. In the three years of teaching practice, NMU has... Read More about Research on College Computer-Computing and Information Literacy online course based on MOOC: Taking the North Minzu University as an example.

Intelligent Predictive Maintenance Model for Rolling Components of a Machine based on Speed and Vibration (2021)
Presentation / Conference Contribution
Ahmad, B., Mishra, B. K., Ghufran, M., Pervez, Z., & Ramzan, N. (2021). Intelligent Predictive Maintenance Model for Rolling Components of a Machine based on Speed and Vibration. In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (459-464). https://doi.org/10.1109/ICAIIC51459.2021.9415249

Machines have come a long way, from the industrial revolution to a modern-day industry 4.0. In this massive transition, one thing that has never changed within a machine is the moving part. Most industries use rotating machine with different load cap... Read More about Intelligent Predictive Maintenance Model for Rolling Components of a Machine based on Speed and Vibration.

Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J. (2020). Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines. In J. M. Alonso, & U. Cortés (Eds.), Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI2020-proceedings) (53-54)

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.

Identifying Gaps in Cybersecurity Teaching and Learning (2020)
Presentation / Conference Contribution
Brayshaw, M., Gordon, N., & Karatazgianni, A. (2020). Identifying Gaps in Cybersecurity Teaching and Learning. In INSPIRE XXV : e-Learning as a solution during unprecedented times in the 21st Century (165-178)

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.

Refining the Teaching of Programming (2020)
Presentation / Conference Contribution
Gordon, N., Cargill, M., Grey, S., & Brayshaw, M. (2020). Refining the Teaching of Programming. In INSPIRE XXV : e-Learning as a solution during unprecedented times in the 21st Century (97-107)

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.

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). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. In 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) (9-18). https://doi.org/10.1109/BDCAT50828.2020.00013

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.

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.

A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020). A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9206839

© 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. (2020). Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI. Journal of Physics: Conference Series, 1618(2), Article 022022. https://doi.org/10.1088/1742-6596/1618/2/022022

© 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. (2020). SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. Lecture notes in computer science, 12297, 197-211. https://doi.org/10.1007/978-3-030-58920-2_13

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.