Outputs (1552)

FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training (2024)
Presentation / Conference Contribution
Wang, Z., Lin, H., Liu, Q., Zhang, Y., & Liu, X. (2024, July). FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training. Presented at The 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers u... Read More about FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training.

A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation (2024)
Presentation / Conference Contribution
Ji, R., Liu, Q., Zhang, Y., & Liu, X. (2024, July). A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation. Presented at 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, United Kingdom

Precipitation forecasting has long been a prominent topic in meteorology, as accurate predictions of impending rainfall are crucial for daily life and travel planning. Currently, radar echo extrapolation serves as the primary method for precipitation... Read More about A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation.

High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure (2024)
Presentation / Conference Contribution
Tao, S., Liu, Q., Zhang, Y., & Liu, X. (2024, July). High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure. Presented at 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK

In recent years, the phenomenon of severe convective disaster weather has increased significantly in the world. Due to the characteristics of small spatial scale, short occurrence period, great destructiveness and drastic changes, severe convective d... Read More about High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure.

Applying the Right UX based on Users' Needs and Future Trends of UX (2024)
Presentation / Conference Contribution
Cameron, J. (2024, October). Applying the Right UX based on Users' Needs and Future Trends of UX. Presented at BINUS Business School International Lecture Week 2024, Jakarta, Indonesia and online

This lecture by Dr Jackie Cameron, an experienced digital marketing lecturer at Edinburgh Napier University, provides a comprehensive overview of user experience (UX) principles and their application in digital marketing contexts. Covering key defini... Read More about Applying the Right UX based on Users' Needs and Future Trends of UX.

Automated Human-Readable Label Generation in Open Intent Discovery (2024)
Presentation / Conference Contribution
Anderson, G., Hart, E., Gkatzia, D., & Beaver, I. (2024, September). Automated Human-Readable Label Generation in Open Intent Discovery. Presented at Interspeech 2024, Kos, Greece

The correct determination of user intent is key in dialog systems. However, an intent classifier often requires a large, labelled training dataset to identify a set of known intents. The creation of such a dataset is a complex and time-consuming task... Read More about Automated Human-Readable Label Generation in Open Intent Discovery.

Unregulated Futures: Scenario Planning for the Unknown (2024)
Presentation / Conference Contribution
Crawford, M., Roubelat, F., & Marchais-Roubelat, A. (2024, September). Unregulated Futures: Scenario Planning for the Unknown. Presented at Anticipation 2024, Lancaster, UK

Building from years of piloting novel methods for future visioning through group engagement, this curated session will take attendees through a fast-tracked, yet fully- immersive scenario planning workshop. The prompt is, “What does 2034 look like if... Read More about Unregulated Futures: Scenario Planning for the Unknown.

A method for constrained energy-maximising control of heaving wave-energy converters via a nonlinear frequency response (2024)
Presentation / Conference Contribution
Guiver, C. (2024, August). A method for constrained energy-maximising control of heaving wave-energy converters via a nonlinear frequency response. Presented at The 8th IEEE Conference on Control Technology and Applications (CCTA) 2024, Newcastle Upon Tyne

A theoretical grounding is presented for justifying how frequency domain methods may be applied in the determination of constrained extracted-energy maximising controls in wave-energy conversion applications subject to nonlinear models. A computation... Read More about A method for constrained energy-maximising control of heaving wave-energy converters via a nonlinear frequency response.

Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances (2024)
Presentation / Conference Contribution
Hart, E., Renau, Q., Sim, K., & Alissa, M. (2024, September). Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances. Presented at 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenburg, Austria

Deep neural networks (DNN) are increasingly being used to perform algorithm-selection in combinatorial optimisation domains, particularly as they accommodate input representations which avoid designing and calculating features. Mounting evidence fro... Read More about Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances.

Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy (2024)
Presentation / Conference Contribution
Li, Q., Gao, Y., Wang, F., Wang, C., Babaagba, K. O., & Tan, Z. (2024, October). Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy. Presented at 20th EAI International Conference on Security and Privacy in Communication Networks, Dubai, United Arab Emirates

Graph Neural Networks (GNNs) exhibit promise in the domains of network analysis and recommendation systems. Notwithstanding , these networks are susceptible to node injection attacks. To mitigate this vulnerability, the academic community has put for... Read More about Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy.

A Framework for Speech Enhancement based on Audio Signal and Speaker Embeddings (2024)
Presentation / Conference Contribution
Nazemi, A., Sami, A., Sami, M., & Hussain, A. (2024, September). A Framework for Speech Enhancement based on Audio Signal and Speaker Embeddings. Presented at 3rd COG-MHEAR Workshop on Audio-Visual Speech Enhancement (AVSEC), Kos Island, Greece

This study addresses the challenge of speech enhancement within an audio-only context. Our proposed framework extracts speaker embeddings and voice signals, subsequently integrating these components to synthesise a voice based on the extracted data.... Read More about A Framework for Speech Enhancement based on Audio Signal and Speaker Embeddings.

VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography (2024)
Presentation / Conference Contribution
Khan, M. S., Ahmad, J., Ali, M., Al Dubai, A., Pitropakis, N., & Buchanan, W. J. (2024, July). VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography. Presented at 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP), Sousse, Tunisia

In this digital era, ensuring the security of data transmission is critically important. Digital data, especially image data, needs to be secured against unauthorized access. In this regards, this paper presents a robust image encryption scheme named... Read More about VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography.

Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture (2024)
Presentation / Conference Contribution
Le Goff, L., & Hart, E. (2024, July). Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture. Presented at GECCO 2024 Embodied and Evolved Artificial Intelligence Workshop, Melbourne, Australia

Algorithmic frameworks for the joint optimisation of a robot's design and controller often utilise a learning loop nested within an evolutionary algorithm to refine the controller associated with a newly generated robot design. Intuitively, it is rea... Read More about Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture.

Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration (2024)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2024, June). Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration. Presented at The 39th International Conference on ICT Systems Security and Privacy Protection (SEC 2024), Edinburgh

The perpetual evolution of cyberattacks, especially in the realm of Internet of Things (IoT) networks, necessitates advanced, adaptive, and intelligent defence mechanisms. The integration of expert knowledge can drastically enhance the efficacy of Io... Read More about Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration.

Investigating Markers and Drivers of Gender Bias in Machine Translations (2024)
Presentation / Conference Contribution
Barclay, P., & Sami, A. (2024, March). Investigating Markers and Drivers of Gender Bias in Machine Translations. Presented at IEEE International Conference on Software Analysis, Evolution and Reengineering, Rovaniemi, Finland

Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such b... Read More about Investigating Markers and Drivers of Gender Bias in Machine Translations.

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 GECCO '24: Genetic and Evolutionary Computation Conference, 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.

Virtual Rehabilitation: XR Design for Senior Users in Immersive Exergame Environments (2024)
Presentation / Conference Contribution
Charisis, V., Khan, S., AlTarteer, S., & Lagoo, R. (2024, June). Virtual Rehabilitation: XR Design for Senior Users in Immersive Exergame Environments. Presented at 2024 IEEE Gaming, Entertainment, and Media Conference (GEM), Turin, Italy

The global ageing population presents significant challenges, with healthcare systems strained to meet the needs of an increasingly elderly demographic. Societies face issues related to healthcare costs, caregiving, and maintaining quality of life fo... Read More about Virtual Rehabilitation: XR Design for Senior Users in Immersive Exergame Environments.

Participatory Design with Domain Experts: A Delphi Study for a Career Support Chatbot (2024)
Presentation / Conference Contribution
Wilson, M., Brazier, D., Gkatzia, D., & Robertson, P. (2024, July). Participatory Design with Domain Experts: A Delphi Study for a Career Support Chatbot. Presented at ACM Conversational User Interfaces 2024 (CUI ’24), Luxembourg, Luxembourg

We present a study of collaboration with expert participants for the purpose of the responsible design of a conversational agent. The Delphi study was used to identify and develop design and evaluation criteria for an automated career support interve... Read More about Participatory Design with Domain Experts: A Delphi Study for a Career Support Chatbot.

AI Literacy Framework for Marketing Education and Assessment Design. (2024)
Presentation / Conference Contribution
Kurtzke, S. (2024, June). AI Literacy Framework for Marketing Education and Assessment Design. Presented at Marketing Professional Advisory Group Meeting, Edinburgh Napier University, UK

This talk aims to collect feedback from marketing practitioners on an evidence-based AI Literacy Framework that shows how higher-order human and applied AI skills can be embedded in marketing curricula and through assessment. It sets out debates on A... Read More about AI Literacy Framework for Marketing Education and Assessment Design..

Employability attributes: Meeting deadlines, time management (2024)
Presentation / Conference Contribution
Cameron, J., Gutu, M., & Kurtzke, S. (2024, June). Employability attributes: Meeting deadlines, time management. Presented at Marketing Professional Advisory Group Meeting, Edinburgh Napier University, UK

This talk aims to excavate marketing practitioner insights on whether meeting deadlines and time management are important graduate attributes that should be carefully considered in an employability-focused curriculum. The presentation sets out debat... Read More about Employability attributes: Meeting deadlines, time management.

Efficient building retrofitting towards carbon-neutral built environment in developing economies: A scoping review (2024)
Presentation / Conference Contribution
Ejidike, C. C., Mewomo, M. C., Agbajor, F. D., Olawumi, T. O., & Luo, J. (2024, June). Efficient building retrofitting towards carbon-neutral built environment in developing economies: A scoping review. Paper presented at 1st International Conference on Net-Zero Built Environment, Oslo, Norway

The construction industry is widely recognized for its high energy consumption and carbon emissions in the built environment. This recognition is attributable to the underperformance of existing buildings within the built environment. Retrofitting bu... Read More about Efficient building retrofitting towards carbon-neutral built environment in developing economies: A scoping review.