All Outputs (126)

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities (2023)
Journal Article
Mansouri-Benssassi, E., Rogers, S., Reel, S., Malone, M., Smith, J., Ritchie, F., & Jefferson, E. (2023). Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities. Heliyon, 9(4), Article e15143. https://doi.org/10.1016/j.heliyon.2023.e15143

Introduction: Artificial intelligence (AI) applications in healthcare and medicine have increased in recent years. To enable access to personal data, Trusted Research Environments (TREs) (otherwise known as Safe Havens) provide safe and secure enviro... Read More about Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities.

A novel mirror neuron inspired decision-making architecture for human–robot interaction (2023)
Journal Article
Sobhani, M., Smith, J., Pipe, A., & Peer, A. (in press). A novel mirror neuron inspired decision-making architecture for human–robot interaction. International Journal of Social Robotics, https://doi.org/10.1007/s12369-023-00988-0

Inspired by the role of mirror neurons and the importance of predictions in joint action, a novel decision-making structure is proposed, designed and tested for both individual and dyadic action. The structure comprises models representing individual... Read More about A novel mirror neuron inspired decision-making architecture for human–robot interaction.

Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue (2022)
Journal Article
Duran, N., Battle, S., & Smith, J. (2022). Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue. Communication Methods and Measures, 16(3), 182-214. https://doi.org/10.1080/19312458.2021.2020229

We present the Conversation Analysis Modeling Schema (CAMS), a novel dialogue labeling schema that combines the Conversation Analysis concept of Adjacency Pairs, with Dialogue Acts. The aim is to capture both the semantic and syntactic structure of d... Read More about Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue.

Sentence encoding for dialogue act classification (2021)
Journal Article
Duran, N., Battle, S., & Smith, J. (2023). Sentence encoding for dialogue act classification. Natural Language Engineering, 29(3), 794-823. https://doi.org/10.1017/S1351324921000310

In this study, we investigate the process of generating single-sentence representations for the purpose of Dialogue Act (DA) classification, including several aspects of text pre-processing and input representation which are often overlooked or under... Read More about Sentence encoding for dialogue act classification.

Statistical disclosure controls for machine learning models (2021)
Conference Proceeding
Krueger, S., Mansouri-Benssassi, E., Ritchie, F., & Smith, J. (2021). Statistical disclosure controls for machine learning models

Artificial Intelligence (AI) models are trained on large datasets. Where the training data is sensitive, the data holders need to consider risks posed by access to the training data and risks posed by the models that are released. The first problem c... Read More about Statistical disclosure controls for machine learning models.

Protein structured reservoir computing for spike-based pattern recognition (2021)
Journal Article
Tsakalos, K., Ch. Sirakoulis, G., Adamatzky, A., & Smith, J. (2022). Protein structured reservoir computing for spike-based pattern recognition. IEEE Transactions on Parallel and Distributed Systems, 33(2), 322 - 331. https://doi.org/10.1109/TPDS.2021.3068826

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computin... Read More about Protein structured reservoir computing for spike-based pattern recognition.

Using active learning to understand the videoconference experience: A case study (2020)
Conference Proceeding
Llewellyn, S., Simons, C., & Smith, J. (2020). Using active learning to understand the videoconference experience: A case study. https://doi.org/10.1007/978-3-030-63799-6_30

Videoconferencing is becoming ubiquitous, especially so during the COVID-19 pandemic. However, user experience of a videoconference call can be variable. To better understand and classify the performance of videoconference call systems, this paper re... Read More about Using active learning to understand the videoconference experience: A case study.

Understanding output checking (2020)
Report
Green, E., Ritchie, F., & Smith, J. (2020). Understanding output checking. Luxembourg: European Commission (Eurostat - Methodology Directorate)

This report for Eurostat (Methodology) considers the conceptual and practical issues that need to be addressed in designing and implementing automatic disclosure control checking for statistical research outputs. The report covers - The basic theo... Read More about Understanding output checking.

Visual analytics for collaborative human-machine confidence in human-centric active learning tasks (2019)
Journal Article
Legg, P., Smith, J., & Downing, A. (2019). Visual analytics for collaborative human-machine confidence in human-centric active learning tasks. Human-Centric Computing and Information Sciences, 9, Article 5. https://doi.org/10.1186/s13673-019-0167-8

Active machine learning is a human-centric paradigm that leverages a small labelled dataset to build an initial weak classifier, that can then be improved over time through human-machine collaboration. As new unlabelled samples are observed, the mach... Read More about Visual analytics for collaborative human-machine confidence in human-centric active learning tasks.

Evolutionary n-level hypergraph partitioning with adaptive coarsening (2019)
Journal Article
Preen, R., & Smith, J. (2019). Evolutionary n-level hypergraph partitioning with adaptive coarsening. IEEE Transactions on Evolutionary Computation, 23(6), 962-971. https://doi.org/10.1109/TEVC.2019.2896951

Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computationally tractable sub-problems. Current techniques use a multilevel appro... Read More about Evolutionary n-level hypergraph partitioning with adaptive coarsening.

Confidentiality and linked data (2018)
Book Chapter
Ritchie, F., & Smith, J. Confidentiality and linked data. In G. Roarson (Ed.), Privacy and Data Confidentiality Methods – a National Statistician’s Quality Review (1-34). Newport: Office for National Statistics

This chapter considers the confidentiality issues around linked data. It notes that the use and availability of secondary (adminstrative or social media) data, allied to powerful processing and machine learning techniques, in theory means that re-ide... Read More about Confidentiality and linked data.

E-assessment of computer programming (2018)
Presentation / Conference
Gwynllyw, R., & Smith, J. (2018, September). E-assessment of computer programming. Paper presented at 12th International Symposium on Advances in Technology Education Nurturing Professionals for Smart Cities: Way Forward for Technology Education, Hong Kong

This paper demonstrates how we have used Dewis, an algorithmic open source e-assessment system, to automatically assess programming skills, in particular, in the C programming language. Teaching and assessing programming skills is challenging; prior... Read More about E-assessment of computer programming.

Predicting user confidence during visual decision making (2018)
Journal Article
Smith, J., Legg, P., Matovis, M., & Kinsey, K. (2018). Predicting user confidence during visual decision making. ACM Transactions on Interactive Intelligent Systems, 8(2), Article 10. https://doi.org/10.1145/3185524

© 2018 ACM People are not infallible consistent “oracles”: their confidence in decision-making may vary significantly between tasks and over time. We have previously reported the benefits of using an interface and algorithms that explicitly captured... Read More about Predicting user confidence during visual decision making.

The internet of flying things (2018)
Book Chapter
Pigatto, D. F., Rodrigues, M., de Carvalho Fontes, J. V., Pinto, A. S. R., Smith, J., & Branco, K. R. L. J. C. (2018). The internet of flying things. In Q. Hassan (Ed.), Internet of Things A to Z: Technologies and Applications (529-562). Wiley. https://doi.org/10.1002/9781119456735.ch19

Popularly known as drones, unmanned aerial vehicles (UAVs) have been applied in several fields, usually operating in cooperative and collaborative swarms to enable the execution of more dynamic missions. Thus, the new Flying Ad Hoc Networks (FANETs)... Read More about The internet of flying things.

Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models (2017)
Book Chapter
Smith, E. M., Smith, J., Legg, P., & Francis, S. (2017). Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models. In F. Chao, S. Schockaert, & Q. Zhang (Eds.), Advances in Computational Intelligence Systems: UKCI 2017 (191-202). Springer Cham

The ability to predict future states is fundamental for a wide variety of applications, from weather forecasting to stock market analysis. Understanding the related data attributes that can influence changes in time series is a challenging task that... Read More about Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models.

Exploiting antipheromone in ant colony optimisation for interactive search-based software design and refactoring (2016)
Presentation / Conference
Simons, C., & Smith, J. (2016, July). Exploiting antipheromone in ant colony optimisation for interactive search-based software design and refactoring. Poster presented at ACM-SIGEVO Genetic and Evolutionary Computation Conference, GECCO ’16, Denver, CO, USA

Preventing user-fatigue in interactive meta-heuristic search places as great an emphasis on efficiency as it does on ef- fectiveness. Engagement may also be boosted if the system provides a sense of “responsiveness” - for example, avoiding unpopular... Read More about Exploiting antipheromone in ant colony optimisation for interactive search-based software design and refactoring.

Exploiting diverse distance metrics for surrogate-based optimisation of ordering problems (2016)
Presentation / Conference
Smith, J., Stone, C., & Serpell, M. (2016, July). Exploiting diverse distance metrics for surrogate-based optimisation of ordering problems. Paper presented at ACM-SIGEVO Genetic and Evolutionary Computation Conference, GECCO ’16, Denver, CO, USA

Surrogate-assisted optimisation has proven success in the continuous domain, but only recently begun to be explored for other representations, in particular permutations. The use of Gaussian kernel-based models has been proposed, but only tested on s... Read More about Exploiting diverse distance metrics for surrogate-based optimisation of ordering problems.

Evolving atomic aesthetics and dynamics (2016)
Journal Article
Davies, E., Tew, P., Glowacki, D., Smith, J., & Mitchell, T. (2016). Evolving atomic aesthetics and dynamics. Lecture Notes in Artificial Intelligence, 9596, 17-33. https://doi.org/10.1007/978-3-319-31008-4_2

© Springer International Publishing Switzerland 2016. The depiction of atoms and molecules in scientific literature owes as much to the creative imagination of scientists as it does to scientific theory and experimentation. danceroom Spectroscopy (dS... Read More about Evolving atomic aesthetics and dynamics.

Sphere: A novel platform for increasing safety & security on unmanned systems (2015)
Presentation / Conference
Pigatto, D., Smith, J., & Branco, K. (2015, June). Sphere: A novel platform for increasing safety & security on unmanned systems. Paper presented at 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Denver, Colorado, USA

The Healthy, Mobility and Security-based Data Communication Architecture, also known as HAMSTER, is provided with a special platform for safety & security: Sphere. It concentrates all the safety & security aspects of the main ar- chitecture and all d... Read More about Sphere: A novel platform for increasing safety & security on unmanned systems.

From evolutionary computation to the evolution of things (2015)
Journal Article
Eiben, A. E., & Smith, J. (2015). From evolutionary computation to the evolution of things. Nature, 521(7553), 476-482. https://doi.org/10.1038/nature14544

© 2015 Macmillan Publishers Limited. All rights reserved . Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering... Read More about From evolutionary computation to the evolution of things.