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All Outputs (4445)

Building an Embodied Musicking Dataset for co-creative music-making (2024)
Presentation / Conference Contribution
Vear, C., Poltronieri, F., Di Donato, B., Zhang, Y., Benerradi, J., Hutchinson, S., Turowski, P., Shell, J., & Malekmohamadi, H. (2024, April). Building an Embodied Musicking Dataset for co-creative music-making. Presented at Evostar 2024: The Leading European Event on Bio‑Inspired Computation, Aberystwyth, Wales, United Kingdom

In this paper, we present our findings of the design, development and deployment of a proof-of-concept dataset that captures some of the physiological, musicological, and psychological aspects of embodied musicking. After outlining the conceptual ele... Read More about Building an Embodied Musicking Dataset for co-creative music-making.

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.

Enhancing Mac OS Malware Detection through Machine Learning and Mach-O File Analysis (2024)
Presentation / Conference Contribution
Thaeler, A., Yigit, Y., Maglaras, L. A., Buchanan, B., Moradpoor, N., & Russell, G. (2023, November). Enhancing Mac OS Malware Detection through Machine Learning and Mach-O File Analysis. Presented at IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMDAD) 2023, Edinburgh

Can we predict QPP? An approach based on multivariate outliers (2024)
Presentation / Conference Contribution
Chifu, A., Déjean, S., Garouani, M., Mothe, J., Ortiz, D., & Ullah, M. Z. (2024, March). Can we predict QPP? An approach based on multivariate outliers. Presented at 46th European Conference on Information Retrieval, ECIR 2024, Glasgow

Query performance prediction (QPP) aims to predict the success and failure of a search engine on a collection of queries and documents. State of the art predictors can enable this prediction with a degree of accuracy; however, it is far from being pe... Read More about Can we predict QPP? An approach based on multivariate outliers.

Design Considerations of Voice Articulated Generative AI Virtual Reality Dance Environments (2024)
Presentation / Conference Contribution
Casas, L., Mitchell, K., Tamariz, M., Hannah, S., Sinclair, D., Koniaris, B., & Kennedy, J. (2024, May). Design Considerations of Voice Articulated Generative AI Virtual Reality Dance Environments. Presented at CHI24 - Generative AI in User-Generated Content, Honolulu, Hawaii

We consider practical and social considerations of collaborating verbally with colleagues and friends, not confined by physical distance, but through seamless networked telepres-ence to interactively create shared virtual dance environments. In respo... Read More about Design Considerations of Voice Articulated Generative AI Virtual Reality Dance Environments.

Towards Building a Smart Water Management System (SWAMS) in Nigeria (2024)
Presentation / Conference Contribution
Bamgboye, O., Chrysoulas, C., Liu, X., Watt, T., Sodiya, A., Oyeleye, M., & Kalutharage, S. (2024, June). Towards Building a Smart Water Management System (SWAMS) in Nigeria. Presented at The 22nd IEEE Mediterranean Electrotechnical Conference, Porto, Portugal

The water management landscape in Nigeria struggles with formidable obstacles characterized by a lack of adequate infrastructure, an uneven distribution of resources, and insufficient access to clean water, particularly in rural areas. These challeng... Read More about Towards Building a Smart Water Management System (SWAMS) in Nigeria.

Understanding fitness landscapes in morpho-evolution via local optima networks (2024)
Presentation / Conference Contribution
Thomson, S. L., Le Goff, L., Hart, E., & Buchanan, E. (2024, July). Understanding fitness landscapes in morpho-evolution via local optima networks. Presented at Genetic and Evolutionary Computation Conference (GECCO 2024), Melbourne, Australia

Morpho-Evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings have been proposed which are capable of representing design and control. Pre... Read More about Understanding fitness landscapes in morpho-evolution via local optima networks.

On the Utility of Probing Trajectories for Algorithm-Selection (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, April). On the Utility of Probing Trajectories for Algorithm-Selection. Presented at EvoStar 2024, Aberystwyth, UK

Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance description or fitness landscape , or can be a direct representation of the ins... Read More about On the Utility of Probing Trajectories for Algorithm-Selection.

Wide-Scan/High-Gain Phased Array Antenna for 5G/6G Cellular Networks (2024)
Presentation / Conference Contribution
Basherlou, H. J., Ojaroudi Parchin, N., Alibakhshikenari, M., Kouhalvandi, L., & See, C. H. (2024, June). Wide-Scan/High-Gain Phased Array Antenna for 5G/6G Cellular Networks. Presented at 2024 IEEE 22nd Mediterranean Electrotechnical Conference- IEEE MELECON 2024, Porto, Portugal

Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, July). Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples. Presented at GECCO 2024, Melbourne, USA

The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajec-tories obtained from running a solver as input... Read More about Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples.

A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control (2024)
Presentation / Conference Contribution
Montague, K., Hart, E., & Paechter, B. (2024, April). A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control. Presented at EvoStar 2024, Aberystwyth

Behaviour trees (BTs) are commonly used as controllers in robotic swarms due their modular composition and to the fact that they can be easily interpreted by humans. From an algorithmic perspective, an additional advantage is that extra modules can e... Read More about A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control.

Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution (2024)
Presentation / Conference Contribution
Marrero, A., Segredo, E., León, C., & Hart, E. (2024, July). Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. Presented at ACM GECCO 2024, Melbourne, Australia

The ability to generate example instances from a domain is important in order to benchmark algorithms and to generate data that covers an instance-space in order to train machine-learning models for algorithm selection. Quality-Diversity (QD) algorit... Read More about Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution.

Evolving Staff Training Schedules using an Extensible Fitness Function and a Domain Specific Language (2024)
Presentation / Conference Contribution
Urquhart, N., & Hunter, K. (2024, April). Evolving Staff Training Schedules using an Extensible Fitness Function and a Domain Specific Language. Presented at 27th European Conference, EvoApplications 2024, Aberystwyth, UK

When using a meta-heuristic based optimiser in some industrial scenarios, there may be a need to amend the objective function as time progresses to encompass constraints that did not exist during the development phase of the software. We propose a me... Read More about Evolving Staff Training Schedules using an Extensible Fitness Function and a Domain Specific Language.

Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective (2024)
Presentation / Conference Contribution
Rodriguez, C. J., Thomson, S. L., Alderliesten, T., & Bosman, P. A. N. (2024, July). Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective. Presented at Genetic and Evolutionary Computation Conference (GECCO 2024), Melbourne, Australia

Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such problems. Despite this, literature about analysing the fitness landscapes... Read More about Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective.

PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme (2024)
Presentation / Conference Contribution
Yaqub, Z., Yigit, Y., Maglaras, L., Tan, Z., & Wooderson, P. (2024, April). PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme. Presented at The 20th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2024), Abu Dhabi, UAE

In the rapidly evolving landscape of Intelligent Transportation Systems (ITS), Vehicular Ad-hoc Networks (VANETs) play a critical role in enhancing road safety and traffic flow. However, VANETs face significant security and privacy challenges due to... Read More about PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme.

Using Frequency B-Splines for an accurate and faster calculation of adaptive transforms for electric machines diagnosis (2024)
Presentation / Conference Contribution
Pons-Llinares, J., Quijano-López, A., Bonet-Jara, J., & Vedreño-Santos, F. (2024, May). Using Frequency B-Splines for an accurate and faster calculation of adaptive transforms for electric machines diagnosis. Presented at Electrimacs 2024, Castellón de la Plana, Spain

Early detection of faults in electric motors is crucial to prevent unplanned downtime and expensive repairs. Transient analysis through time-frequency transforms reveals important information on the motor condition. Computational time of these transf... Read More about Using Frequency B-Splines for an accurate and faster calculation of adaptive transforms for electric machines diagnosis.

A Novel RFID Tag's Antenna for Mounting on Metallic Objects (2024)
Presentation / Conference Contribution
Gharbia, I., Aldelemy, A., Elmegri, F., Ismail, . A. S., Darwish, M., See, C. H., & Abd-Alhameed, R. A. (2024, May). A Novel RFID Tag's Antenna for Mounting on Metallic Objects. Presented at 14th International Conference on Electrical Engineering (ICEENG), Cairo, Egypt

Gain-Enhanced/End-Fire Phased Array Antenna for Future Cellular Networks (2024)
Presentation / Conference Contribution
Basherlou, H. J., Ojaroudi Parchin, N., Manjakkal, L., See, C. H., Amar, A., & Salama, A. (2024, May). Gain-Enhanced/End-Fire Phased Array Antenna for Future Cellular Networks. Presented at 14th International Conference on Electrical Engineering (ICEENG), Cairo, Egypt