Outputs (276)

A Collaboratively-Derived Research Agenda for E-assessment in Undergraduate Mathematics (2022)
Journal Article
Kinnear, G., Jones, I., Sangwin, C., Alarfaj, M., Davies, B., Fearn, S., Foster, C., Heck, A., Henderson, K., Hunt, T., Iannone, P., Kontorovich, I., Larson, N., Lowe, T., Meyer, J. C., O’Shea, A., Rowlett, P., Sikurajapathi, I., & Wong, T. (online). A Collaboratively-Derived Research Agenda for E-assessment in Undergraduate Mathematics. International Journal of Research in Undergraduate Mathematics Education, https://doi.org/10.1007/s40753-022-00189-6

This paper describes the collaborative development of an agenda for research on e-assessment in undergraduate mathematics. We built on an established approach to develop the agenda from the contributions of 22 mathematics education researchers, unive... Read More about A Collaboratively-Derived Research Agenda for E-assessment in Undergraduate Mathematics.

Developing ‘deep mathematical thinking’ in geometry with 3- and 4-year-olds: A collaborative study between early years teachers and University-based mathematicians (2022)
Journal Article
Oughton, R., Nichols, K., Bolden, D. S., Dixon-Jones, S., Fearn, S., Darwin, S., Mistry, M., Peyerimhoff, N., & Townsend, A. (2024). Developing ‘deep mathematical thinking’ in geometry with 3- and 4-year-olds: A collaborative study between early years teachers and University-based mathematicians. Mathematical Thinking and Learning, 26(3), 306-325. https://doi.org/10.1080/10986065.2022.2119497

Mathematics in early years settings is often restricted to learning to count and identifying simple shapes. This is partly due to the narrow scope of many early years curricula and insufficient teacher training for exploring deeper mathematical conce... Read More about Developing ‘deep mathematical thinking’ in geometry with 3- and 4-year-olds: A collaborative study between early years teachers and University-based mathematicians.

The evolutionary drivers of primate scleral coloration (2022)
Journal Article
Mearing, A. S., Burkart, J. M., Dunn, J., Street, S. E., & Koops, K. (2022). The evolutionary drivers of primate scleral coloration. Scientific Reports, 12, Article 14119. https://doi.org/10.1038/s41598-022-18275-9

The drivers of divergent scleral morphologies in primates are currently unclear, though white sclerae are often assumed to underlie human hyper-cooperative behaviours. Humans are unusual in possessing depigmented sclerae whereas many other extant pri... Read More about The evolutionary drivers of primate scleral coloration.

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., Al Moubayed, N., Zeze, D. A., & 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.

Reaction–Diffusion Models for a Class of Infinite-Dimensional Nonlinear Stochastic Differential Equations (2022)
Journal Article
da Costa, C., Freitas Paulo da Costa, B., & Valesin, D. (2023). Reaction–Diffusion Models for a Class of Infinite-Dimensional Nonlinear Stochastic Differential Equations. Journal of Theoretical Probability, 36, 1059–1087. https://doi.org/10.1007/s10959-022-01187-9

We establish the existence of solutions to a class of nonlinear stochastic differential equations of reaction–diffusion type in an infinite-dimensional space, with diffusion corresponding to a given transition kernel. The solution obtained is the sca... Read More about Reaction–Diffusion Models for a Class of Infinite-Dimensional Nonlinear Stochastic Differential Equations.

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.

Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task (2022)
Presentation / Conference Contribution
Ampomah, I., Burton, J., Enshaei, A., & Al Moubayed, N. (2022, June). Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task. Presented at 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, France

Numerical tables are widely employed to communicate or report the classification performance of machine learning (ML) models with respect to a set of evaluation metrics. For non-experts, domain knowledge is required to fully understand and interpret... Read More about Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task.

MuLD: The Multitask Long Document Benchmark (2022)
Presentation / Conference Contribution
Hudson, G. T., & Al Moubayed, N. (2022, June). MuLD: The Multitask Long Document Benchmark. Presented at 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, France

The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been exciting work in designing efficien... Read More about MuLD: The Multitask Long Document Benchmark.

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.