All Outputs (36)

The inadvertently revealing statistic: A systemic gap in statistical training? (2024)
Journal Article
Derrick, B., Green, E., Ritchie, F., Smith, J., & White, P. (2024). The inadvertently revealing statistic: A systemic gap in statistical training?. Significance, 21(1), 24-27. https://doi.org/10.1093/jrssig/qmae009

While concerns around data privacy are well-known, there's a lack of awareness and training when it comes to the confidentiality risk of published statistics, argue Ben Derrick, Elizabeth Green, Felix Ritchie, Jim Smith, Paul White

Machine learning models in trusted research environments - Understanding operational risks (2023)
Journal Article
Ritchie, F., Tilbrook, A., Cole, C., Jefferson, E., Krueger, S., Mansouri-Benssassi, E., …Smith, J. (2023). Machine learning models in trusted research environments - Understanding operational risks. International Journal of Population Data Science, 8(1), Article 2165. https://doi.org/10.23889/ijpds.v8i1.2165

IntroductionTrusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amou... Read More about Machine learning models in trusted research environments - Understanding operational risks.

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.

Automatic Checking of Research Outputs (ACRO): A tool for dynamic disclosure checks (2021)
Journal Article
Green, E., Ritchie, F., & Smith, J. (2021). Automatic Checking of Research Outputs (ACRO): A tool for dynamic disclosure checks. ESS Statistical Working Papers, 2021 Edition, https://doi.org/10.2785/75954

This paper discusses the issues surrounding the creation of an automatic tool to reduce the burden of output checking in research environments. It describes ACRO (Automatic Checking of Research Outputs), a Stata tool written as a proof-of-concept, an... Read More about Automatic Checking of Research Outputs (ACRO): A tool for dynamic disclosure checks.

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.

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.

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.

Interactive ant colony optimization (iACO) for early lifecycle software design (2014)
Journal Article
Simons, C., Smith, J., & White, P. (2014). Interactive ant colony optimization (iACO) for early lifecycle software design. Swarm Intelligence, 8(2), 139-157. https://doi.org/10.1007/s11721-014-0094-2

Finding good designs in the early stages of the software development lifecycle is a demanding multi-objective problem that is crucial to success. Previously, both interactive and non-interactive techniques based on evolutionary algorithms (EAs) have... Read More about Interactive ant colony optimization (iACO) for early lifecycle software design.

A genetic algorithm for the one-dimensional cutting stock problem with setups (2014)
Journal Article
de Araujo, S. A., Poldi, K. C., & Smith, J. (2014). A genetic algorithm for the one-dimensional cutting stock problem with setups. Pesquisa Operacional, 34(2), 165-187. https://doi.org/10.1590/0101-7438.2014.034.02.0165

This paper investigates the one-dimensional cutting stock problem considering two conflicting objective functions: minimization of both the number of objects and the number of different cutting patterns used. A new heuristic method based on the conce... Read More about A genetic algorithm for the one-dimensional cutting stock problem with setups.

A comparison of meta-heuristic search for interactive software design (2013)
Journal Article
Smith, J. E., Simons, C. L., Simons, C., & Smith, J. (2013). A comparison of meta-heuristic search for interactive software design. Soft Computing, 17(11), 2147-2162. https://doi.org/10.1007/s00500-013-1039-1

Advances in processing capacity, coupled with the desire to tackle problems where a human subjective judgment plays an important role in determining the value of a proposed solution, has led to a dramatic rise in the number of applications of Interac... Read More about A comparison of meta-heuristic search for interactive software design.

A preprocessing optimization applied to the cell suppression problem in statistical disclosure control (2013)
Journal Article
Staggemeier, A., Serpell, M., Smith, J., Clark, A., & Staggemeier, A. T. (2013). A preprocessing optimization applied to the cell suppression problem in statistical disclosure control. Information Sciences, 238, 22-32. https://doi.org/10.1016/j.ins.2013.02.006

As organizations start to publish the data that they collect, either internally or externally, in the form of statistical tables they need to consider the protection of the confidential information held in those tables. The algorithms used to protect... Read More about A preprocessing optimization applied to the cell suppression problem in statistical disclosure control.

Estimating meme fitness in adaptive memetic algorithms for combinatorial problems (2012)
Journal Article
Smith, J. (2012). Estimating meme fitness in adaptive memetic algorithms for combinatorial problems. Evolutionary Computation, 20(2), 165-188. https://doi.org/10.1162/EVCO_a_00060

Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement op... Read More about Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.

A genetic approach to statistical disclosure control (2012)
Journal Article
Smith, J., Clark, A., Staggemeier, A. T., & Serpell, M. (2012). A genetic approach to statistical disclosure control. IEEE Transactions on Evolutionary Computation, 16(3), 431-441. https://doi.org/10.1109/TEVC.2011.2159271

Statistical disclosure control is the collective name for a range of tools used by data providers such as government departments to protect the confidentiality of individuals or organizations. When the published tables contain magnitude data such as... Read More about A genetic approach to statistical disclosure control.

Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms (2010)
Journal Article
Serpell, M., & Smith, J. (2010). Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms. Evolutionary Computation, 18(3), 491-514. https://doi.org/10.1162/EVCO_a_00006

The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is... Read More about Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms.

Assessment of the influence of adaptive components in trainable surface inspection systems (2010)
Journal Article
Van Brussel, H., Eitzinger, C., Heidl, W., Lughofer, E., Raiser, S., Smith, J., …Sannen, D. (2010). Assessment of the influence of adaptive components in trainable surface inspection systems. Machine Vision and Applications, 21(5), 613-626. https://doi.org/10.1007/s00138-009-0211-1

In this paper, we present a framework for the classification of images in surface inspection tasks and address several key aspects of the processing chain from the original image to the final classification result. A major contribution of this paper... Read More about Assessment of the influence of adaptive components in trainable surface inspection systems.

Impact of object extraction methods on classification performance in surface inspection systems (2010)
Journal Article
Raiser, S., Lughofer, E., Eitzinger, C., & Smith, J. (2010). Impact of object extraction methods on classification performance in surface inspection systems. Machine Vision and Applications, 21(5), 627-641. https://doi.org/10.1007/s00138-009-0205-z

In surface inspection applications, the main goal is to detect all areas which might contain defects or unacceptable imperfections, and to classify either every single 'suspicious' region or the investigated part as a whole. After an image is acquire... Read More about Impact of object extraction methods on classification performance in surface inspection systems.

Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection (2010)
Journal Article
Tahir, M. A., & Smith, J. (2010). Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection. Pattern Recognition Letters, 31(11), 1470-1480. https://doi.org/10.1016/j.patrec.2010.01.030

The nearest-neighbour (1NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and corresp... Read More about Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection.