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Outputs (153)

NeFT-Net: N-window Extended Frequency Transformer for Rhythmic Motion Prediction (2025)
Journal Article
Ademola, A., Sinclair, D., Koniaris, B., Hannah, S., & Mitchell, K. (in press). NeFT-Net: N-window Extended Frequency Transformer for Rhythmic Motion Prediction. Computers and Graphics,

Advancements in prediction of human motion sequences are critical for enabling online virtual reality (VR) users to dance and move in ways that accurately mirror real-world actions, delivering a more immersive and connected experience. However, laten... Read More about NeFT-Net: N-window Extended Frequency Transformer for Rhythmic Motion Prediction.

HoloJig: Interactive Spoken Prompt Specified Generative AI Environments (2025)
Journal Article
Casas, L., Hannah, S., & Mitchell, K. (online). HoloJig: Interactive Spoken Prompt Specified Generative AI Environments. IEEE Computer Graphics and Applications, https://doi.org/10.1109/mcg.2025.3553780

HoloJig offers an interactive, speech-to-VR, virtual reality experience that generates diverse environments in real-time based on live spoken descriptions. Unlike traditional VR systems that rely on pre-built assets, HoloJig dynamically creates perso... Read More about HoloJig: Interactive Spoken Prompt Specified Generative AI Environments.

GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios (2025)
Journal Article
Liu, Q., Wang, Z., Zhou, X., Zhang, Y., Liu, X., & Lin, H. (online). GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios. ACM transactions on autonomous and adaptive systems, https://doi.org/10.1145/3725221

The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to n... Read More about GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios.

Subfunction Structure Matters: A New Perspective on Local Optima Networks (2025)
Presentation / Conference Contribution
Thomson, S. L., & Przewozniczek, M. W. (2025, July). Subfunction Structure Matters: A New Perspective on Local Optima Networks. Presented at Genetic and Evolutionary Computation Conference (GECCO 2025), Málaga, Spain

Local optima networks (LONs) capture fitness landscape information. They are typically constructed in a black-box manner; information about the problem structure is not utilised. This also applies to the analysis of LONs: knowledge about the problem,... Read More about Subfunction Structure Matters: A New Perspective on Local Optima Networks.

Machine learning for animatronic development and optimization (2025)
Patent
Mitchell, K., Castellon, J., Bacher, M., McCrory, M., Stolarz, J., & Ayala, A. (2025). Machine learning for animatronic development and optimization. US12236168B2

Techniques for animatronic design are provided. A plurality of simulated meshes is generated using a physics simulation model, where the plurality of simulated meshes corresponds to a plurality of actuator configurations for an animatronic mechanical... Read More about Machine learning for animatronic development and optimization.

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.

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.

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.

Into the Black Box: Mining Variable Importance with XAI (2025)
Presentation / Conference Contribution
Hunter, K., Thomson, S. L., & Hart, E. (2025, April). Into the Black Box: Mining Variable Importance with XAI. Presented at Evostar 2025, Trieste, Italy

Recent works have shown that the idea of mining search spaces to train machine learning models can facilitate increasing understanding of variable importance in optimisation problems. However , so far, the problems studied have typically either been... Read More about Into the Black Box: Mining Variable Importance with XAI.

Stalling in Space: Attractor Analysis for any Algorithm (2025)
Presentation / Conference Contribution
Thomson, S. L., Renau, Q., Vermetten, D., Hart, E., van Stein, N., & Kononova, A. V. (2025, April). Stalling in Space: Attractor Analysis for any Algorithm. Paper presented at EvoStar 2025, Trieste, Italy

Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisation algorithms. Local optima networks (LONs) have been especially dominant in... Read More about Stalling in Space: Attractor Analysis for any Algorithm.

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.

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.

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.

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.

The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes (2024)
Presentation / Conference Contribution
Pijning, I., Koppenhol, L., Dijkzeul, D., Brouwer, N., Thomson, S. L., & van den Berg, D. (2024, November). The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes. Presented at ECTA 2024: 16th International Conference on Evolutionary Computation Theory and Applications, Porto, Portugal

The Frequency Fitness Assignment (FFA) method steers evolutionary algorithms by objective rareness instead of objective goodness. Does this mean the size of the combinatorial search space influences its performance when compared to more traditional e... Read More about The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes.

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.

Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem (2024)
Presentation / Conference Contribution
Chen, J., Wu, Z., Thomson, S. L., & Weise, T. (2024, November). Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. Presented at ECTA 2024: 16th International Conference on Evolutionary Computation Theory and Applications, Porto, Portugal

The Quadratic Assignment Problem (QAP) is one of the classical N P-hard tasks from operations research with a history of more than 65 years. It is often approached with heuristic algorithms and over the years, a multitude of such methods has been app... Read More about Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem.