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All Outputs (88)

On difunctions (2023)
Journal Article
Backhouse, R., & Oliveira, J. N. (2023). On difunctions. Journal of Logical and Algebraic Methods in Programming, 134, Article 100878. https://doi.org/10.1016/j.jlamp.2023.100878

The notion of a difunction was introduced by Jacques Riguet in 1948. Since then it has played a prominent role in database theory, type theory, program specification and process theory. The theory of difunctions is, however, less known in computing t... Read More about On difunctions.

Components and acyclicity of graphs. An exercise in combining precision with concision (2021)
Journal Article
Backhouse, R., Doornbos, H., Glück, R., & van der Woude, J. (2022). Components and acyclicity of graphs. An exercise in combining precision with concision. Journal of Logical and Algebraic Methods in Programming, 124, Article 100730. https://doi.org/10.1016/j.jlamp.2021.100730

Central to algorithmic graph theory are the concepts of acyclicity and strongly connected components of a graph, and the related search algorithms. This article is about combining mathematical precision and concision in the presentation of these conc... Read More about Components and acyclicity of graphs. An exercise in combining precision with concision.

Public health messaging by political leaders: a corpus linguistic analysis of COVID-19 speeches delivered by Boris Johnson (2021)
Report
McClaughlin, E., Nichele, E., Adolphs, S., Barnard, P., Clos, J., Knight, D., …Lang, A. (2021). Public health messaging by political leaders: a corpus linguistic analysis of COVID-19 speeches delivered by Boris Johnson. Nottingham: UKRI/AHRC

This study analyses the language of speeches about COVID-19 delivered by Boris Johnson, the Prime Minister of the United Kingdom between 3rd March 2020 and 5th April 2021. We use transcribed speeches to construct a digitised body of texts called a... Read More about Public health messaging by political leaders: a corpus linguistic analysis of COVID-19 speeches delivered by Boris Johnson.

Defence against the dark artefacts: Smart home cybercrimes and cybersecurity standards (2021)
Journal Article
Piasecki, S., Urquhart, L., & McAuley, D. (2021). Defence against the dark artefacts: Smart home cybercrimes and cybersecurity standards. Computer Law and Security Review, 42, Article 105542. https://doi.org/10.1016/j.clsr.2021.105542

This paper analyses the assumptions underpinning a range of emerging EU and UK smart home cybersecurity standards. We use internet of things (IoT) case studies (such as the Mirai Botnet affair) and the criminological concept of 'routine activity theo... Read More about Defence against the dark artefacts: Smart home cybercrimes and cybersecurity standards.

A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms. Machine Vision and Applications, 32(3), Article 75. https://doi.org/10.1007/s00138-021-01196-4

Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available.... Read More about A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms.

Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning (2021)
Journal Article
Maciel-Guerra, A., Esener, N., Giebel, K., Lea, D., Green, M. J., Bradley, A. J., & Dottorini, T. (2021). Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning. Scientific Reports, 11(1), Article 7736. https://doi.org/10.1038/s41598-021-87300-0

Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate th... Read More about Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning.

Solving the Rubik’s Cube with Stepwise Deep Learning (2021)
Journal Article
JOHNSON, C. (2021). Solving the Rubik’s Cube with Stepwise Deep Learning. Expert Systems, Article e12665. https://doi.org/10.1111/exsy.12665

This paper explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hillclimbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function) from a se... Read More about Solving the Rubik’s Cube with Stepwise Deep Learning.

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning (2020)
Journal Article
Khan, F. A., Voß, U., Pound, M. P., & French, A. P. (2020). Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning. Frontiers in Plant Science, 11, Article 1275. https://doi.org/10.3389/fpls.2020.01275

© Copyright © 2020 Khan, Voß, Pound and French. Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development—a process referred to as plant phenotyping—is increasing... Read More about Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning.

Choosing Sample Sizes for Statistical Measures on Interval-Valued Data (2020)
Conference Proceeding
McCulloch, J., Ellerby, Z., & Wagner, C. (2020). Choosing Sample Sizes for Statistical Measures on Interval-Valued Data. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-8). https://doi.org/10.1109/FUZZ48607.2020.9177745

Intervals have frequently been used in the literature to represent uncertainty in data, from eliciting uncertain judgements from experts to representing uncertainty in sensor measurements. This widespread use of intervals has led to research on inter... Read More about Choosing Sample Sizes for Statistical Measures on Interval-Valued Data.

An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities (2020)
Journal Article
Standen, P., Brown, D. J., Taheri, M., Galvez Trigo, M. J., Boulton, H., Burton, A., …Hortal, E. (2020). An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities. British Journal of Educational Technology, 51(5), 1748-1765. https://doi.org/10.1111/bjet.13010

Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a... Read More about An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities.

Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI) (2020)
Conference Proceeding
D'Alterio, P., Garibaldi, J. M., & John, R. I. (2020). Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI). In Proceedings of IEEE World Congress on Computational Intelligence (WCCI) 2020

In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the eme... Read More about Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI).

Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java (2020)
Conference Proceeding
D'Alterio, P., Garibaldi, J. M., John, R. I., & Wagner, C. (2020). Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java. In Proceedings of IEEE World Congress on Computational Intelligence (WCCI) 2020

Constrained interval type-2 (CIT2) fuzzy sets are a class of type-2 fuzzy sets that has been recently proposed as a way to extend type-1 membership functions to interval type-2 (IT2) while keeping a semantic connection between the IT2 fuzzy set and t... Read More about Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java.

Developing a measure of online wellbeing and user trust (2020)
Book Chapter
DOWTHWAITE, L., Perez Vallejos, E., Creswick, H., Portillo, V., Patel, M., & Zhao, J. (2020). Developing a measure of online wellbeing and user trust. In M. Arias-Oliva, J. Pelegrín-Borondo, K. Murata, & A. María Lara Palma (Eds.), Societal Challenges in the Smart Society (21-33). Logrono: Universidad de La Rioja

Intention-Aware Multiagent Scheduling (2020)
Conference Proceeding
Dann, M., Thangarajah, J., Yao, Y., & Logan, B. (2020). Intention-Aware Multiagent Scheduling. In B. An, N. Yorke-Smith, A. El Fallah Seghrouchni, & G. Sukthankar (Eds.), Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)

The Belief Desire Intention (BDI) model of agency is a popular and mature paradigm for designing and implementing multiagent systems. There are several agent implementation platforms that follow the BDI model. In BDI systems, the agents typically hav... Read More about Intention-Aware Multiagent Scheduling.

Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks (2020)
Journal Article
Soltaninejad, M., Sturrock, C. J., Griffiths, M., Pridmore, T. P., & Pound, M. P. (2020). Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks. IEEE Transactions on Image Processing, 29, 6667-6679. https://doi.org/10.1109/TIP.2020.2992893

© 1992-2012 IEEE. We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on enco... Read More about Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks.

Objective Assessment of Subjective Tasks in Crowdsourcing Applications (2020)
Conference Proceeding
Haralabopoulos, G., Tsikandilakis, M., Torres, M. T., & Mcauley, D. (2020). Objective Assessment of Subjective Tasks in Crowdsourcing Applications. In Proceedings of the 12th Language Resources and Evaluation Conference. , (15-25)

Labelling, or annotation, is the process by which we assign labels to an item with regards to a task. In some Artificial Intelligence problems, such as Computer Vision tasks, the goal is to obtain objective labels. However, in problems such as text a... Read More about Objective Assessment of Subjective Tasks in Crowdsourcing Applications.

Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo (2020)
Journal Article
Rostam, H. M., Fisher, L. E., Hook, A. L., Burroughs, L., Luckett, J. C., Figueredo, G. P., …Ghaemmaghami, A. M. (2020). Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo. Matter, 2(6), 1564-1581. https://doi.org/10.1016/j.matt.2020.03.018

© 2020 The Author(s) Implantation of medical devices can result in inflammation. A large library of polymers is screened, and a selection found to promote macrophage differentiation towards pro- or anti-inflammatory phenotypes. The bioinstructive pro... Read More about Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo.

Sensitizing Scenarios: Sensitizing Designer Teams to Theory (2020)
Conference Proceeding
Waern, A., Rajkowska, P., Johansson, K. B., Back, J., Spence, J., & Løvlie, A. S. (2020). Sensitizing Scenarios: Sensitizing Designer Teams to Theory. In CHI 20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (1–13). https://doi.org/10.1145/3313831.3376620

Concepts and theories that emerge within the social sciences tend to be nuanced, dealing with complex social phenomena. While their relevance to design could be high, it is difficult to make sense of them in design projects, especially when participa... Read More about Sensitizing Scenarios: Sensitizing Designer Teams to Theory.

Parameterised Resource-Bounded ATL (2020)
Conference Proceeding
Alechina, N., Demri, S., & Logan, B. (2020). Parameterised Resource-Bounded ATL. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (7040-7046). https://doi.org/10.1609/aaai.v34i05.6189

It is often advantageous to be able to extract resource requirements in resource logics of strategic ability, rather than to verify whether a fixed resource requirement is sufficient for achieving a goal. We study Parameterised Resource-Bounded Alter... Read More about Parameterised Resource-Bounded ATL.