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A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction (2023)
Journal Article
Huang, H., Zhao, B., Gao, F., Chen, P., Wang, J., & Hussain, A. (2023). A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction. Sensors, 23(10), Article 4828. https://doi.org/10.3390/s23104828

Reconstruction-based and prediction-based approaches are widely used for video anomaly detection (VAD) in smart city surveillance applications. However, neither of these approaches can effectively utilize the rich contextual information that exists i... Read More about A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction.

Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research (2023)
Journal Article
Borg, J. M., Buskell, A., Kapitany, R., Powers, S. T., Reindl, E., & Tennie, C. (2024). Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research. Artificial Life, 30(3), 417-438. https://doi.org/10.1162/artl_a_00406

The goal of Artificial Life research, as articulated by Chris Langton, is “to contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be” (1989, p. 1). The study and pursuit of open-ended evoluti... Read More about Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research.

An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors (2023)
Journal Article
Mohammad, Z., Anwary, A. R., Mridha, M. F., Shovon, M. S. H., & Vassallo, M. (2023). An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors. Sensors, 23(10), Article 4774. https://doi.org/10.3390/s23104774

Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict... Read More about An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors.

MhaGNN: A novel framework for wearable sensor-based human activity recognition combining multi-head attention and graph neural networks (2023)
Journal Article
Wang, Y., Wang, X., Yang, H., Geng, Y., Yu, H., Zheng, G., & Liao, L. (2023). MhaGNN: A novel framework for wearable sensor-based human activity recognition combining multi-head attention and graph neural networks. IEEE Transactions on Instrumentation and Measurement, 72, Article 2514314. https://doi.org/10.1109/tim.2023.3276004

Obtaining robust feature representations from multi-position wearable sensory data is challenging in human activity recognition (HAR) since data from different positions can have unordered implicit correlations. Graph neural networks (GNNs) represent... Read More about MhaGNN: A novel framework for wearable sensor-based human activity recognition combining multi-head attention and graph neural networks.

Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing (2023)
Data
Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., Corney, J., & Sherlock, A. (2023). Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing. [Dataset]. https://doi.org/10.17869/enu.2023.3100035

This data contains the worker position datasets (including the event logs) and the source codes of the discrete event simulation that are used in the research article titled "Using Worker Position Data for Human-Driven Decision Support in Labour-inte... Read More about Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing.

Machine Learning and the Optimal Choice of Asset Pricing Model (2023)
Book Chapter
Bielinski, A., & Broby, D. (2023). Machine Learning and the Optimal Choice of Asset Pricing Model. In S. Hasan Jafar, H. K, H. El-Chaarani, S. Moturi, & N. Gupta (Eds.), Artificial Intelligence for Capital Markets (91-127). Taylor & Francis. https://doi.org/10.1201/9781003327745-7

This chapter evaluates the traditional methods for price prediction and examines what we believe are the most promising machine learning techniques for that task. Asset price forecasting is one of the fundamental problems in the financial field. Trad... Read More about Machine Learning and the Optimal Choice of Asset Pricing Model.

Road space reallocation in Scotland: A health impact assessment (2023)
Journal Article
Douglas, M. J., Teuton, J., Macdonald, A., Whyte, B., & Davis, A. L. (2023). Road space reallocation in Scotland: A health impact assessment. Journal of transport & health, 30, Article 101625. https://doi.org/10.1016/j.jth.2023.101625

Introduction
Road space reallocation involves re-distributing space away from motor vehicles, including car parking and carriageway space, towards other uses. This can promote a shift to more sustainable travel modes and is likely to affect health t... Read More about Road space reallocation in Scotland: A health impact assessment.

Channel Modeling for 6G Programmable Wireless Environment (2023)
Book Chapter
Karadimas, P., Hossain, M. S., & Tariq, F. (2023). Channel Modeling for 6G Programmable Wireless Environment. In F. Tariq, M. Khandaker, & I. Shafique Ansari (Eds.), 6G Wireless: The Communication Paradigm Beyond 2030 (59-72). CRC Press. https://doi.org/10.1201/9781003282211-4

In 6G wireless networks [1], the requirements for high data rates and low power consumption can be met by addressing the impact of wireless propagation. This can be achieved through diversity combining and multiple input multiple output (MIMO) techni... Read More about Channel Modeling for 6G Programmable Wireless Environment.

Decentralised Fully Probabilistic Design for Stochastic Networks with Multiplicative Noise (2023)
Journal Article
Zhou, Y., Herzallah, R., & Zhang, Q. (2023). Decentralised Fully Probabilistic Design for Stochastic Networks with Multiplicative Noise. International Journal of Systems Science, 54(8), 1841-1854. https://doi.org/10.1080/00207721.2023.2210568

In this paper, we present an innovative decentralised control framework, designed to address stochastic dynamic complex systems that are influenced by multiple multiplicative noise factors. Our advanced approach builds upon the foundation of conventi... Read More about Decentralised Fully Probabilistic Design for Stochastic Networks with Multiplicative Noise.

Numerical Experimental Investigations Into Force Chain Networks In Soils: A Grading Entropy Approach (2023)
Presentation / Conference Contribution
Leak, J., Barreto, D., & Imre, E. (2023, May). Numerical Experimental Investigations Into Force Chain Networks In Soils: A Grading Entropy Approach. Paper presented at Numerical Analysis of Geomaterials Conference (NANGE), Assisi, Italy

These slides were presented at the numerical analysis of geomaterials conference in Assisi in 2023. They detail an experimental and numerical investigation into the effect of particle size distribution (PSD) on soil fabric. Grading entropy was used t... Read More about Numerical Experimental Investigations Into Force Chain Networks In Soils: A Grading Entropy Approach.

The Career Information Podcast (2023)
Digital Artefact
(2023). The Career Information Podcast. [Podcast]

This series of podcasts explores the topic of career information. It is the product of a collaboration between staff and students in the School of Computing, Engineering and the Built Environment (SCEBE) and the School of Applied Sciences (SAS).... Read More about The Career Information Podcast.

Advanced simulation methodologies for smart soft multifunctional polymeric composites (2023)
Presentation / Conference Contribution
Kadapa, C., & Hossain, M. (2023, May). Advanced simulation methodologies for smart soft multifunctional polymeric composites. Poster presented at RubberCon 2023, Edinburgh

Smart multifunctional polymeric composites such as magnetoactive polymers, electroac-tive polymers and photopolymers are increasingly being explored for various applications in soft robotics, energy harvesting, flexible electronic devices, precision... Read More about Advanced simulation methodologies for smart soft multifunctional polymeric composites.

Wasserstein GAN-based Digital Twin Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks (2023)
Journal Article
Hasan, M. N., Jan, S. U., & Koo, I. (2023). Wasserstein GAN-based Digital Twin Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks. IEEE Sensors Journal, 23(12), 13327-13339. https://doi.org/10.1109/JSEN.2023.3272908

In this Internet of Things (IoT) era, the number of devices capable of sensing their surroundings is increasing day by day. Based on the data from these devices, numerous services and systems are now offered where critical decisions depend on the dat... Read More about Wasserstein GAN-based Digital Twin Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks.

A CNN-Transformer Hybrid Recognition Approach for sEMG-based Dynamic Gesture Prediction (2023)
Journal Article
Liu, Y., Li, X., Yang, L., Bian, G., & Yu, H. (2023). A CNN-Transformer Hybrid Recognition Approach for sEMG-based Dynamic Gesture Prediction. IEEE Transactions on Instrumentation and Measurement, 72, Article 2514816. https://doi.org/10.1109/tim.2023.3273651

As a unique physiological electrical signal in the human body, surface electromyography (sEMG) signals always include human movement intention and muscle state. Through the collection of sEMG signals, different gestures can be effectively recognized.... Read More about A CNN-Transformer Hybrid Recognition Approach for sEMG-based Dynamic Gesture Prediction.

Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning (2023)
Journal Article
Basabain, S., Cambria, E., Alomar, K., & Hussain, A. (2023). Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning. Expert Systems, 40(8), Article e13329. https://doi.org/10.1111/exsy.13329

A growing amount of research use pre-trained language models to address few/zero-shot text classification problems. Most of these studies neglect the semantic information hidden implicitly beneath the natural language names of class labels and develo... Read More about Enhancing Arabic-text Feature Extraction Utilizing Label-semantic Augmentation in Few/Zero-shot Learning.

Eight Element MIMO Antenna Array with Tri-Band Response for Modern Smartphones (2023)
Journal Article
Kiani, S. H., Savci, H. S., Abubakar, H. S., Parchin, N. O., Rimli, H., & Hakim, B. (2023). Eight Element MIMO Antenna Array with Tri-Band Response for Modern Smartphones. IEEE Access, 11, 44244-44253. https://doi.org/10.1109/access.2023.3271716

This article presents an eight-element tri-band Multiple Input Multiple Output (MIMO) antenna system for future handheld devices. The suggested antenna system consists of a main and sideboards. The feed lines are connected on the main board while the... Read More about Eight Element MIMO Antenna Array with Tri-Band Response for Modern Smartphones.