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Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation (2023)
Journal Article
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI open, 4, 19-32. https://doi.org/10.1016/j.aiopen.2023.05.001

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence... Read More about Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation.

Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data (2023)
Journal Article
Kluvanec, D., McCaffrey, K. J., Phillips, T. B., & Al Moubayed, N. (2023). Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data. IEEE Transactions on Geoscience and Remote Sensing, 61, https://doi.org/10.1109/tgrs.2023.3273329

We can represent the orientation of a plane in 3D by its normal vector. However, every plane has two normal vectors that are negatives of each other. We propose four novel representations of vectors in 3D that are negation invariant and can be used b... Read More about Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data.

Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data (2022)
Journal Article
Watson, M., Awwad Shekh Hasan, B., & Al Moubayed, N. (2022). Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data. Scientific Reports, 12(19899), Article 19899. https://doi.org/10.1038/s41598-022-24356-6

It has been shown that identical Deep Learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and... Read More about Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data.

In-Materio Extreme Learning Machines (2022)
Book Chapter
Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). In-Materio Extreme Learning Machines. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (505-519). Springer Verlag. https://doi.org/10.1007/978-3-031-14714-2_35

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as... Read More about In-Materio Extreme Learning Machines.

Towards Intelligently Designed Evolvable Processors (2022)
Journal Article
Jones, B. A., Chouard, J. L., Branco, B. C., Vissol-Gaudin, E. G., Pearson, C., Petty, M. C., …Groves, C. (2022). Towards Intelligently Designed Evolvable Processors. Evolutionary Computation, 30(4), 479-501. https://doi.org/10.1162/evco_a_00309

Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material’s properties to achieve a specific computational function. This paper addresses the question of how successful and well performing Evolution-in-Materio pro... Read More about Towards Intelligently Designed Evolvable Processors.

A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification (2022)
Journal Article
Winterbottom, T., Leone, A., & Al Moubayed, N. (2022). A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification. Scientific Reports, 12(1), Article 13468. https://doi.org/10.1038/s41598-022-15965-2

We approach the task of detecting the illicit movement of cultural heritage from a machine learning perspective by presenting a framework for detecting a known artefact in a new and unseen image. To this end, we explore the machine learning problem o... Read More about A deep learning approach to fight illicit trafficking of antiquities using artefact instance classification.

Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels (2022)
Journal Article
Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2022). Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels. PeerJ Computer Science, 8(e974), Article e974. https://doi.org/10.7717/peerj-cs.974

Bilinear pooling (BLP) refers to a family of operations recently developed for fusing features from different modalities predominantly for visual question answering (VQA) models. Successive BLP techniques have yielded higher performance with lower co... Read More about Bilinear Pooling in Video-QA: Empirical Challenges and Motivational Drift from Neurological Parallels.

ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR (2022)
Journal Article
Shakeel, A., Walters, R. J., Ebmeier, S. K., & Moubayed, N. A. (2022). ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR. IEEE Transactions on Geoscience and Remote Sensing, 60, https://doi.org/10.1109/tgrs.2022.3169455

In this study, we address the challenging problem of automatic detection of transient deformation of the Earth’s crust in time series of differential satellite radar [interferometric synthetic aperture radar (InSAR)] images. The detection of these ev... Read More about ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR.

Ask me in your own words: paraphrasing for multitask question answering (2021)
Journal Article
Hudson, G. T., & Al Moubayed, N. (2021). Ask me in your own words: paraphrasing for multitask question answering. PeerJ Computer Science, 7, Article e759. https://doi.org/10.7717/peerj-cs.759

Multitask learning has led to significant advances in Natural Language Processing, including the decaNLP benchmark where question answering is used to frame 10 natural language understanding tasks in a single model. In this work we show how models tr... Read More about Ask me in your own words: paraphrasing for multitask question answering.

Curvature-based feature selection with application in classifying electronic health records (2021)
Journal Article
Zuo, Z., Li, J., Xu, H., & Al Moubayed, N. (2021). Curvature-based feature selection with application in classifying electronic health records. Technological Forecasting and Social Change, 173, Article 121127. https://doi.org/10.1016/j.techfore.2021.121127

Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML) techniques. As a powerful tool, ML... Read More about Curvature-based feature selection with application in classifying electronic health records.

ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference (2021)
Book Chapter
Gajbhiye, A., Al Moubayed, N., & Bradley, S. (2021). ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V (460-472). Springer Verlag. https://doi.org/10.1007/978-3-030-86383-8_37

Neural language representation models such as BERT, pretrained on large-scale unstructured corpora lack explicit grounding to real-world commonsense knowledge and are often unable to remember facts required for reasoning and inference. Natural Langua... Read More about ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference.

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records (2021)
Journal Article
Alhassan, Z., Watson, M., Budgen, D., Alshammari, R., Alessa, A., & Al Moubayed, N. (2021). Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records. JMIR Medical Informatics, 9(5), Article e25237. https://doi.org/10.2196/25237

Background: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems such as diabetes. Early preventive interventions based upon advanced predictive mode... Read More about Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records.

Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models (2020)
Book Chapter
Gajbhiye, A., Winterbottom, T., Al Moubayed, N., & Bradley, S. (2020). Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020 (633-646). Springer Verlag. https://doi.org/10.1007/978-3-030-61609-0_50

We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical-level knowledge and lack generalization across NLI mode... Read More about Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models.

Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm (2020)
Journal Article
Alhassan, Z., Budgen, D., Alshammari, R., & Moubayed, N. A. (2020). Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm. Journal of Medical Internet Research, 8(7), Article e18963. https://doi.org/10.2196/18963

Background: Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate the effect of glycated hemoglobin (HbA1c) elevation... Read More about Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm.

Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling (2020)
Journal Article
Al Moubayed, N., McGough, S., & Awwad Shiekh Hasan, B. (2020). Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. PeerJ Computer Science, 6, Article e252. https://doi.org/10.7717/peerj-cs.252

The article presents a discriminative approach to complement the unsupervised probabilistic nature of topic modelling. The framework transforms the probabilities of the topics per document into class-dependent deep learning models that extract highly... Read More about Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling.

Identifying Changes in the Cybersecurity Threat Landscape using the LDA-Web Topic Modelling Data Search Engine (2017)
Book Chapter
Al Moubayed, N., Wall, D., & McGough, A. (2017). Identifying Changes in the Cybersecurity Threat Landscape using the LDA-Web Topic Modelling Data Search Engine. In T. Tryfonas (Ed.), Human aspects of information security, privacy and trust : 5th International Conference, HAS 2017, held as part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, proceedings (287-295). Springer Verlag. https://doi.org/10.1007/978-3-319-58460-7_19

Successful Cybersecurity depends on the processing of vast quantities of data from a diverse range of sources such as police reports, blogs, intelligence reports, security bulletins, and news sources. This results in large volumes of unstructured tex... Read More about Identifying Changes in the Cybersecurity Threat Landscape using the LDA-Web Topic Modelling Data Search Engine.

Multi-objective particle swarm optimisation: methods and applications (2014)
Thesis
Al Moubayed, N. (2014). Multi-objective particle swarm optimisation: methods and applications. (Thesis). Robert Gordon University. Retrieved from https://durham-repository.worktribe.com/output/1618029

Solving real life optimisation problems is a challenging engineering venture. Since the early days of research on optimisation it was realised that many problems do not simply have one optimisation objective. This led to the development of multi-obje... Read More about Multi-objective particle swarm optimisation: methods and applications.

D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces (2014)
Journal Article
Al Moubayed, N., Petrovski, A., & McCall, J. (2014). D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces. Evolutionary Computation, 22(1), 47-77. https://doi.org/10.1162/evco_a_00104

This paper improves a recently developed multi-objective particle swarm optimizer () that incorporates dominance with decomposition used in the context of multi-objective optimization. Decomposition simplifies a multi-objective problem (MOP) by trans... Read More about D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces.

Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data
Presentation / Conference Contribution
Alhassan, Z., McGough, A. S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018, December). Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data. Presented at IEEE 17th International Conference on Machine Learning and Applications (ICMLA 2018)., Orlando, Fl, USA

Clinical data, such as evaluations, treatments, vital sign and lab test results, are usually observed and recorded in hospital systems. Making use of such data to help physicians to evaluate the mortality risk of in-hospital patients provides an inva... Read More about Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data.