Outputs (2147)

“They’re not going to do all the tasks we do”: Understanding Trust and Reassurance towards a UV-C Disinfection Robot (2023)
Presentation / Conference Contribution
Trigo, M. J. G., Reyes-Cruz, G., Maior, H. A., Pepper, C., Price, D., Leonard, P., Tochia, C., Hyde, R., Watson, N., & Fischer, J. E. (2023, August). “They’re not going to do all the tasks we do”: Understanding Trust and Reassurance towards a UV-C Disinfection Robot. Presented at IEEE RO-MAN 2023, Busan, South Korea

Increasingly, robots are adopted for routine tasks such as cleaning and disinfection of public spaces, raising questions about attitudes and trust of professional cleaners who might in future have robots as teammates, and whether the general public f... Read More about “They’re not going to do all the tasks we do”: Understanding Trust and Reassurance towards a UV-C Disinfection Robot.

Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data (2023)
Presentation / Conference Contribution
Dave, R., Angarita-Zapata, J. S., & Triguero, I. (2023, August). Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data. Presented at 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy

The emergence of Machine Learning (ML) has altered how researchers and business professionals value data. Applicable to almost every industry, considerable amounts of time are wasted creating bespoke applications and repetitively hand-tuning models t... Read More about Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data.

Automatically Labeling Cyber Threat Intelligence reports using Natural Language Processing (2023)
Presentation / Conference Contribution
Abdi, H., Bagley, S. R., Furnell, S., & Twycross, J. (2023, August). Automatically Labeling Cyber Threat Intelligence reports using Natural Language Processing. Presented at DocEng 2023 - Proceedings of the 2023 ACM Symposium on Document Engineering, Limerick, Ireland

Attribution provides valuable intelligence in the face of Advanced Persistent Threat (APT) attacks. By accurately identifying the culprits and actors behind the attacks, we can gain more insights into their motivations, capabilities, and potential fu... Read More about Automatically Labeling Cyber Threat Intelligence reports using Natural Language Processing.

A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study (2023)
Journal Article
Horne, K. L., Viramontes-Hörner, D., Packington, R., Monaghan, J., Shaw, S., Akani, A., Reilly, T., Trimble, T., Figueredo, G., & Selby, N. M. (2023). A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study. Kidney International, 104(6), 1185-1193. https://doi.org/10.1016/j.kint.2023.08.005

Acute kidney injury (AKI) is associated with adverse long-term outcomes, but many studies are retrospective, focused on specific patient groups or lack adequate comparators. The ARID (AKI Risk in Derby) Study was a five-year prospective parallel-grou... Read More about A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study.

LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems (2023)
Presentation / Conference Contribution
Fatehi, K., & Kucukyilmaz, A. (2023, August). LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems. Presented at Interspeech 2023, Dublin, Ireland

With advances in deep learning methodologies, Automatic Speech Recognition (ASR) systems have seen impressive results. However, ASR in Low-Resource Environments (LREs) are challenged by a lack of training data for the specific target domain. We propo... Read More about LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems.

The Design and Implementation of a Constrained Interval Type-2 Fuzzy System for Credit Card Fraud Detection (2023)
Presentation / Conference Contribution
Wang, X., Li, M., Chen, C., & Garibaldi, J. M. (2023, August). The Design and Implementation of a Constrained Interval Type-2 Fuzzy System for Credit Card Fraud Detection. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Songdo Incheon, Korea

Fuzzy systems with type-1, interval type-2 and general type-2 fuzzy sets have been widely applied in various fields. Constrained Interval Type-2 (CIT2) fuzzy sets and systems are an approach designed to improve the interpretability of type-2 fuzzy in... Read More about The Design and Implementation of a Constrained Interval Type-2 Fuzzy System for Credit Card Fraud Detection.

Towards Causal Fuzzy System Rules Using Causal Direction (2023)
Presentation / Conference Contribution
Zhang, T., Ying, J., Wagner, C., & Garibaldi, J. (2023, August). Towards Causal Fuzzy System Rules Using Causal Direction. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Incheon, Korea

Generating (fuzzy) rule bases from data can provide a rapid pathway to constructing (fuzzy) systems. However, direct rule generation approaches tend to generate very large numbers of rules. One reason for this is that such techniques are not designed... Read More about Towards Causal Fuzzy System Rules Using Causal Direction.

Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation (2023)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., Pekaslan, D., & Garibaldi, J. M. (2023, August). Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Songdo Incheon, Korea

Deep learning models have achieved high performance in numerous semantic segmentation tasks. However, when the input data at test time do not resemble the training data, deep learning models can not handle them properly and will probably produce poor... Read More about Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation.

Digitally Un/Free: the everyday impact of social media on the lives of young people (2023)
Journal Article
Bibizadeh, R. E., Procter, R., Girvan, C., Webb, H., & Jirotka, M. (in press). Digitally Un/Free: the everyday impact of social media on the lives of young people. Learning, Media and Technology, 1-14. https://doi.org/10.1080/17439884.2023.2237883

This article offers an original contribution to the crucial question of how digital media impacts children and young people’s everyday lives. Focus groups with young people aged 11–21 years, and interviews with teachers in schools in England revealed... Read More about Digitally Un/Free: the everyday impact of social media on the lives of young people.

Apartness, sharp elements, and the Scott topology of domains (2023)
Journal Article
de Jong, T. (2023). Apartness, sharp elements, and the Scott topology of domains. Mathematical Structures in Computer Science, 33(7), 573-604. https://doi.org/10.1017/S0960129523000282

Working constructively, we study continuous directed complete posets (dcpos) and the Scott topology. Our two primary novelties are a notion of intrinsic apartness and a notion of sharp elements. Being apart is a positive formulation of being unequal,... Read More about Apartness, sharp elements, and the Scott topology of domains.