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Outputs (3461)

Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios (2025)
Presentation / Conference Contribution
Huang, Z., Liu, X., Romdhani, I., & Shih, C.-S. (2024, August). Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios. Presented at The 7th International Conference on Information Science and Systems (ICISS 2024), Edinburgh, UK

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies betwee... Read More about Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios.

Molecular dynamics simulations and experimental measurements of density and viscosity of phase change material based on stearic acid with graphene nanoplatelets (2025)
Journal Article
Gonzalez, M. A., Tenorio, M. J., Bismilla, A. Z., D'Oliveira, E. J., Costa Pereira, S. C., & Sanchez-Vicente, Y. (2025). Molecular dynamics simulations and experimental measurements of density and viscosity of phase change material based on stearic acid with graphene nanoplatelets. Fluid Phase Equilibria, 593, Article 114361. https://doi.org/10.1016/j.fluid.2025.114361

Understanding the thermophysical properties of nano-enhanced phase change materials (NEPCMs) is crucial for developing thermal energy storage technologies. Thermal conductivity of NEPCMs is the most studied property, but investigations on density and... Read More about Molecular dynamics simulations and experimental measurements of density and viscosity of phase change material based on stearic acid with graphene nanoplatelets.

Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection (2025)
Journal Article
Kalutharage, C. S., Mohan, S., Liu, X., & Chrysoulas, C. (2025). Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection. Electronics, 14(3), 474. https://doi.org/10.3390/electronics14030474

The rapid integration of connected technologies in modern vehicles has introduced significant cybersecurity challenges, particularly in securing critical systems against advanced threats such as IP spoofing and rule manipulation. This study investiga... Read More about Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection.

Dynamic Caching Dependency-Aware Task Offloading in Mobile Edge Computing (2025)
Journal Article
Zhao, L., Zhao, Z., Hawbani, A., Liu, Z., Tan, Z., & Yu, K. (online). Dynamic Caching Dependency-Aware Task Offloading in Mobile Edge Computing. IEEE Transactions on Computers, https://doi.org/10.1109/TC.2025.3533091

Mobile Edge Computing (MEC) is a distributed computing paradigm that provides computing capabilities at the periphery of mobile cellular networks. This architecture empowers Mobile Users (MUs) to offload computation-intensive applications to large-sc... Read More about Dynamic Caching Dependency-Aware Task Offloading in Mobile Edge Computing.

Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response (2025)
Journal Article
Kalutharage, C. S., Liu, X., & Chrysoulas, C. (2025). Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response. Computers and Security, 151, Article 104318. https://doi.org/10.1016/j.cose.2025.104318

In the dynamic landscape of network security, where cyberattacks continuously evolve, robust and adaptive detection mechanisms are essential, particularly for safeguarding Internet of Things (IoT) networks. This paper introduces an advanced anomaly d... Read More about Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response.

Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing (2025)
Presentation / Conference Contribution
Sim, K., Hart, E., & Renau, Q. (2025, April). Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing. Presented at EvoSTAR 2025, Trieste, Italy

Coupling Large Language Models (LLMs) with Evolutionary Algorithms has recently shown significant promise as a technique to design new heuristics that outperform existing methods, particularly in the field of combinatorial optimisation. An escalating... Read More about Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing.

Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model (2025)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2025, April). Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model. Presented at EvoSTAR 2025, Trieste, Italy

Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is 'algorithm-centric' in order to encapsulate information about how an algorithm performs on an instance, rather... Read More about Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model.

IoT Authentication Protocols: Challenges, and Comparative Analysis (2025)
Journal Article
Alsheavi, A., Hawbani, A., Othman, W., Wang, X., Qaid, G. R. S., Zhao, L., Al-Dubai, A., Liu, Z., Ismail, A., Haveri, R. H., Alsamhi, S. H., & Al-Qaness, M. A. A. (2025). IoT Authentication Protocols: Challenges, and Comparative Analysis. ACM computing surveys, 57(5), Article 116. https://doi.org/10.1145/3703444

In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a sub... Read More about IoT Authentication Protocols: Challenges, and Comparative Analysis.

A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing (2025)
Journal Article
Lin, H., Xiao, B., Zhou, X., Zhang, Y., & Liu, X. (2025). A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2025.3527043

In the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for... Read More about A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing.

Audio Occlusion Experiment Data (2025)
Data
McSeveney, S., Tamariz, M., McGregor, I., Koniaris, B., & Mitchell, K. (2025). Audio Occlusion Experiment Data. [Data]

This dataset comprises anonymous user study participant responses of audio occlusion to investigate presence response of body occlusions in the presence of sound sources in the direct path between the person and the audio driver speaker.

Computational Investigation of Laminar Premixed Hydrogen/Ammonia Flames at Sub-Atmospheric Conditions (2025)
Presentation / Conference Contribution
Tule, D., & Tingas, E.-A. (2025, January). Computational Investigation of Laminar Premixed Hydrogen/Ammonia Flames at Sub-Atmospheric Conditions. Presented at AIAA SCITECH 2025 Forum, Orlando, Florida

The increasing urgency to address global warming has highlighted the need to explore alternative fuels for the transport sector, a significant contributor to carbonemissions. Hydrogen and ammonia have emerged as promising candidates, each with distin... Read More about Computational Investigation of Laminar Premixed Hydrogen/Ammonia Flames at Sub-Atmospheric Conditions.

An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation (2025)
Presentation / Conference Contribution
Stone, C., Renau, Q., Miguel, I., & Hart, E. (2024, June). An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation. Presented at 18th Learning and Intelligent Optimization Conference, Ischia, Italy

We address the question of multi-task algorithm selection in combinatorial optimisation domains. This is motivated by a desire to simplify the algorithm-selection pipeline by developing a more general classifier that does not require specialised info... Read More about An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation.

Three ways to develop students’ AI literacy (2025)
Newspaper / Magazine
Gonsalves, C., & Illingworth, S. (2025). Three ways to develop students’ AI literacy

Is higher education prepared for a future defined by AI, or do we need to do more to align education with technology’s changing landscape? Here are three ways to get your students to engage with it critically.

How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction (2024)
Presentation / Conference Contribution
Orme, M., Yu, Y., & Tan, Z. (2024, May). How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction. Presented at The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy

This paper concerns the pressing need to understand and manage inappropriate language within the evolving human-robot interaction (HRI) landscape. As intelligent systems and robots transition from controlled laboratory settings to everyday households... Read More about How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction.

An investigation of the rotation patterns of international association meetings and events (2024)
Presentation / Conference Contribution
Drake, C., Lockstone-Binney, L., Robertson, M., & Thi Phuong Dung, L. (2024, February). An investigation of the rotation patterns of international association meetings and events. Presented at The 34th Annual Council for Australasian Tourism and Hospitality Education (CAUTHE) Conference, Hobart, Tasmania

International association meetings and events (IAMEs) are a significant segment of the business events sector. Noting the dearth of longitudinal research to confirm how these events rotate globally, regionally and over time, this study conducted an a... Read More about An investigation of the rotation patterns of international association meetings and events.

Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode (2024)
Presentation / Conference Contribution
Buchanan, W., Grierson, S., & Uribe, D. (2024, February). Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode. Presented at 10th International Conference on Information Systems Security and Privacy, Rome, Italy

Biometric data is often highly sensitive, and a leak of this data can lead to serious privacy breaches. Some of the most sensitive of this type of data relates to the usage of DNA data on individuals. A leak of this type of data without consent could... Read More about Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode.

Library catalogue’s search interface: Making the most of subject metadata (2024)
Journal Article
Gnoli, C., Golub, K., Haynes, D., Salaba, A., Shiri, A., & Slavic, A. (2024). Library catalogue’s search interface: Making the most of subject metadata. Knowledge Organization, 51, 169-186. https://doi.org/10.5771/0943-7444-2024-3-169

This article addresses the underutilization of knowledge organization systems (KOS) elements in online library catalogues, hindering effective subject-based search and discovery. It highlights the International Society for Knowledge Organization's in... Read More about Library catalogue’s search interface: Making the most of subject metadata.

Convex neural network synthesis for robustness in the 1-norm (2024)
Presentation / Conference Contribution
Drummond, R., Guiver, C., & Turner, M. C. (2024, July). Convex neural network synthesis for robustness in the 1-norm. Presented at 6th Annual Learning for Dynamics & Control Conference, Oxford, England

With neural networks being used to control safety-critical systems, they increasingly have to be both accurate (in the sense of matching inputs to outputs) and robust. However, these two properties are often at odds with each other and a trade-off ha... Read More about Convex neural network synthesis for robustness in the 1-norm.

Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations (2024)
Presentation / Conference Contribution
Almaini, A., Koßmann, T., Folz, J., Schramm, M., Heigl, M., & Al-Dubai, A. (2024, June). Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations. Presented at UNet24: The International Conference on Ubiquitous Networking, Marrakesh, Morocco

Recent advancements in Software-Defined Networking (SDN) have facilitated its deployment across diverse network types, including edge networks. Given the broad applicability of SDN and the complexity of large-scale environments, establishing a compre... Read More about Integrating Reality: A Hybrid SDN Testbed for Enhanced Realism in Edge Computing Simulations.