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Outputs (3497)

The intersection of datafication and data justice in higher education (2025)
Presentation / Conference Contribution
Fabian, K. (2025, October). The intersection of datafication and data justice in higher education. Presented at Connecting Space, Data and Society: Interdisciplinary Pathways, London

There has been an increasing uptake of technology in the classroom, either for teaching or for classroom management purposes. Along with this uptake is the increasing datafication of education. Datafication is the process of rendering social and natu... Read More about The intersection of datafication and data justice in higher education.

Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption (2025)
Journal Article
Buchanan, W. J., & Ali, H. (2025). Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption. Cryptography, 9(2), Article 33. https://doi.org/10.3390/cryptography9020033

The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. To overcome the existing privacy issues, we can apply fully homomorphic encryption (FHE) to encrypt... Read More about Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption.

Perceptually Adaptive Alarm Systems Using Doppler-Based Pitch Modulation (2025)
Report
McGregor, I. (2025). Perceptually Adaptive Alarm Systems Using Doppler-Based Pitch Modulation. Edinburgh Napier University

This technical note presents a novel alarm system design that uses Doppler-based pitch and amplitude modulation to help people better understand emergency situations through sound alone. By embedding intuitive rising and falling pitch cues into famil... Read More about Perceptually Adaptive Alarm Systems Using Doppler-Based Pitch Modulation.

Perceptual Calibration and Passive Adaptation in Under-Pillow Audio Systems for Sleep and Care Environments (2025)
Report
McGregor, I. (2025). Perceptual Calibration and Passive Adaptation in Under-Pillow Audio Systems for Sleep and Care Environments

This technical report outlines a perceptually grounded calibration model for under-pillow and near-field audio systems. The approach enables real-time tuning based on how sound is actually perceived through materials like foam and fabric. It supports... Read More about Perceptual Calibration and Passive Adaptation in Under-Pillow Audio Systems for Sleep and Care Environments.

Perceptually Adaptive Speech Clarity System for Reverberant Environments (2025)
Report
McGregor, I. (2025). Perceptually Adaptive Speech Clarity System for Reverberant Environments. Not commissioned – self-initiated research and disclosure

This document outlines a perceptually adaptive audio system designed to improve the intelligibility of spoken announcements in highly reverberant environments. Using speech buffering, pacing modification, spectral shaping, and real-time feedback, the... Read More about Perceptually Adaptive Speech Clarity System for Reverberant Environments.

FedBT: Effective and Robust Federated Unlearning via Bad Teacher Distillation for Secure Internet of Things (2025)
Journal Article
Wang, F., Huo, J., Wang, W., Zhang, X., Liu, Y., Tan, Z., & Wang, C. (online). FedBT: Effective and Robust Federated Unlearning via Bad Teacher Distillation for Secure Internet of Things. IEEE Internet of Things Journal, https://doi.org/10.1109/JIOT.2025.3571432

Smart Internet of Things (IoT) devices generate vast, distributed data, and their limited computational and storage capacities complicate data protection. Federated Learning (FL) enables collaborative model training across clients, enhancing performa... Read More about FedBT: Effective and Robust Federated Unlearning via Bad Teacher Distillation for Secure Internet of Things.

A Perceptual Approach to HRTF Personalisation: From Localisation to Spatial Literacy (2025)
Report
Mcgregor, I. (2025). A Perceptual Approach to HRTF Personalisation: From Localisation to Spatial Literacy

Efforts to personalise Head-Related Transfer Functions (HRTFs) have traditionally focused on anatomical accuracy, modelling the head, ears and torso to predict how sound should reach an individual’s ears. While valuable, such approaches often require... Read More about A Perceptual Approach to HRTF Personalisation: From Localisation to Spatial Literacy.

NeFT-Net: N-window extended frequency transformer for rhythmic motion prediction (2025)
Journal Article
Ademola, A., Sinclair, D., Koniaris, B., Hannah, S., & Mitchell, K. (online). NeFT-Net: N-window extended frequency transformer for rhythmic motion prediction. Computers and Graphics, https://doi.org/10.1016/j.cag.2025.104244

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.

Discipline-Sensitive Predictive Analytics for IPA-Driven Building Maintenance Management: Material Stock Quantity Modeling (2025)
Journal Article
Huang, Z., Liu, X., & Romdhani, I. (in press). Discipline-Sensitive Predictive Analytics for IPA-Driven Building Maintenance Management: Material Stock Quantity Modeling. Journal of Data Science and Intelligent Systems,

The study introduces a machine learning model for BMM (Building Maintenance Management), which utilized IPA (Intelligent Process Automation) to predict the material stock required in a period, to manage the cost efficiency. Traditional BMM approaches... Read More about Discipline-Sensitive Predictive Analytics for IPA-Driven Building Maintenance Management: Material Stock Quantity Modeling.

MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing (2025)
Journal Article
Wu, M., Xiao, B., Yang, Z., Sun, J., Liu, Q., Zhang, Y., & Liu, X. (online). MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/TCE.2025.3566725

With the increasing frequency of extreme weather events, there is a growing demand from the public for rapid and accurate short-term heavy precipitation forecasts. This study proposes a lightweight deep learning model, MMST-LSTM, which integrates Mul... Read More about MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing.

Global Work Arrangements and Outsourcing in the Age of AI (2025)
Book
Yadav, M., Pandey, A., & Huzooree, G. (Eds.). (2025). Global Work Arrangements and Outsourcing in the Age of AI. IGI Global. https://doi.org/10.4018/979-8-3373-1270-5

The rise of AI has reshaped outsourcing and work arrangements in global businesses, transforming how businesses operate and allocate tasks across borders. The use of AI in automation and intelligent workflow management, which enables companies to str... Read More about Global Work Arrangements and Outsourcing in the Age of AI.

Future-Proofing Business: Aligning Sustainability Goals with Workforce and Digital Transformation (2025)
Presentation / Conference Contribution
Cameron, J. (2025, April). Future-Proofing Business: Aligning Sustainability Goals with Workforce and Digital Transformation. Presented at Future-Proofing Business: Aligning Sustainability Goals with Workforce and Digital Transformation, Jakarta, Indonesia [and online]

This roundtable convenes leading academics and industry experts to discuss strategic alignment of digital transformation, sustainability imperatives, and workforce preparedness—particularly in light of emerging technologies and Gen Z’s workplace expe... Read More about Future-Proofing Business: Aligning Sustainability Goals with Workforce and Digital Transformation.

Exploring DTrace as an Incident Response Tool for Unix Systems (2025)
Presentation / Conference Contribution
Duin, J., Mckeown, S., & Abubakar, M. (2024, June). Exploring DTrace as an Incident Response Tool for Unix Systems. Presented at Cyber Science 2024, Edinburgh, Scotland

Critical National Infrastructure (CNI) is often the target of sophisticated and sustained cyber attacks perpetrated by advanced threat actors with considerable resources. These attacks can lead to interruptions in core services such as energy and wat... Read More about Exploring DTrace as an Incident Response Tool for Unix Systems.

LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts (2025)
Journal Article
Kumar, G., Saha, R., Conti, M., & Buchanan, W. J. (online). LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts. IEEE Transactions on Services Computing, https://doi.org/10.1109/tsc.2025.3562323

Smart contracts are integral to decentralized systems like blockchains and enable the automation of processes through programmable conditions. However, their immutability, once deployed, poses challenges when addressing errors or bugs. Existing solut... Read More about LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts.

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.

Determination of singular control in the optimal management of natural resources (2025)
Journal Article
Guiver, C., & Opmeer, M. R. (2025). Determination of singular control in the optimal management of natural resources. Applied Mathematics and Optimization, 91(3), https://doi.org/10.1007/s00245-025-10257-3

A method is presented to simplify the determination of solutions of certain optimal control problems which commonly arise in natural resource management and bioeconomic contexts. The method, termed the resource-value balance method, essentially lever... Read More about Determination of singular control in the optimal management of natural resources.