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Outputs (136)

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

Analyst-driven XAI for time series forecasting: Analytics for telecoms maintenance (2024)
Conference Proceeding
Barrett, J., Legg, P., Smith, J., & Boyle, C. (in press). Analyst-driven XAI for time series forecasting: Analytics for telecoms maintenance.

Time series forecasting facilitates real-time anomaly detection in telecom networks, predicting events that disrupt security and service. Current research efforts have been found to focus on new forecasting libraries, more rigorous data cleaning meth... Read More about Analyst-driven XAI for time series forecasting: Analytics for telecoms maintenance.

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 decision-making architecture for human-robot collaboration: Model transferability (2023)
Conference Proceeding
Sobhani, M., Smith, J., Pipe, A., & Peer, A. (2023). A decision-making architecture for human-robot collaboration: Model transferability. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1 (719-726). https://doi.org/10.5220/0012210600003543

In this paper, we aim to demonstrate the potential for wider-ranging capabilities and ease of transferability of our recently developed decision-making architecture for human-robot collaboration. To this end, a somewhat related but different applicat... Read More about A decision-making architecture for human-robot collaboration: Model transferability.

SACRO guide to statistical output checking (2023)
Other
Ritchie, F., Green, E., Smith, J., Tilbrook, A., & White, P. (2023). SACRO guide to statistical output checking. [web]

This guide for output SDC is the first report from the SACRO project. It covers, theory of output SDC, including the new statbarns model, practicalities, operational considerations, and FAQs for output checking teams.

Disclosure control issues in complex medical data (2023)
Presentation / Conference
Green, E., Ritchie, F., Smith, J., Western, D., & White, P. (2023, September). Disclosure control issues in complex medical data. Paper presented at UNECE/Eurostat Expert Group on Statisticial Data Confidentiality, Wiesbaden

The covid19 pandemic assisted the acceleration of routine access to medical records for research. In the UK platforms including OpenSafely and NHSDigital, alongside emerging hospital trust based Trusted Research Environments (TREs), demonstrate the u... Read More about Disclosure control issues in complex medical data.

SACRO: Semi-Automated Checking Of Research Outputs (2023)
Presentation / Conference
Smith, J., Preen, R., Albashir, M., Ritchie, F., Green, E., Davy, S., …Bacon, S. (2023, September). SACRO: Semi-Automated Checking Of Research Outputs. Paper presented at UNECE Expert meeting on Statistical Data Confidentiality, Wiesbaden, Germany

Output checking can require significant resources, acting as a barrier to scaling up the research use of confidential data. We report on a project, SACRO, that is developing a general-purpose, semi-automatic output checking systems that works across... Read More about SACRO: Semi-Automated Checking Of Research Outputs.

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.

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.

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 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.

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.

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.

The influence of search components and problem characteristics in early life cycle class modelling (2014)
Journal Article
Smith, J., & Simons, C. (2015). The influence of search components and problem characteristics in early life cycle class modelling. Journal of Systems and Software, 103, 440-451. https://doi.org/10.1016/j.jss.2014.11.034

© 2014 Elsevier Inc. All rights reserved. This paper examines the factors affecting the quality of solution found by meta-heuristic search when optimising object-oriented software class models. From the algorithmic perspective, we examine the effect... Read More about The influence of search components and problem characteristics in early life cycle class modelling.

Using evolutionary computation to shed light on the effect of scale and complexity on object-orientedsoftware design (2014)
Presentation / Conference
Simons, C., & Smith, J. (2014, October). Using evolutionary computation to shed light on the effect of scale and complexity on object-orientedsoftware design. Paper presented at 2014 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, USA

Early lifecycle software design is an intensely human activity in which design scale and complexity can place a high cognitive load on the software designer. Recently, the use of evolutionary search has been suggested to yield insights in the natur... Read More about Using evolutionary computation to shed light on the effect of scale and complexity on object-orientedsoftware design.

The Baldwin effect hinders self-adaptation (2014)
Conference Proceeding
Smith, J. (2014). The Baldwin effect hinders self-adaptation. In J. Branke, B. Filipic, J. Smith, & T. Bartz-Beielstein (Eds.), Parallel Problem Solving from Nature – PPSN XIII. , (120-129). https://doi.org/10.1007/978-3-319-10762-2_12

The “end-game” of evolutionary optimisation is often largely governed by the efficiency and effectiveness of searching regions of space known to contain high quality solutions. In a traditional EA this role is done via mutation, which creates a tensi... Read More about The Baldwin effect hinders self-adaptation.

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.

Initial application of ant colony optimisation to statistical disclosure control (2013)
Presentation / Conference
Serpell, M., & Smith, J. (2013, July). Initial application of ant colony optimisation to statistical disclosure control. Paper presented at Fifteenth annual conference on Genetic and evolutionary computation, Amsterdam

In this paper Ant Colony Optimisation (ACO) is applied in the field of Statistical Disclosure Control (SDC) for the first time. It has been applied to a permutation problem found in Cell Suppression. ACO has successfully improved the suppression patt... Read More about Initial application of ant colony optimisation to statistical disclosure control.

A comparison of two memetic algorithms for software class modelling (2013)
Presentation / Conference
Smith, J., & Simons, C. (2013, July). A comparison of two memetic algorithms for software class modelling. Paper presented at Genetic and Evolutionary Computation Conference 2013 (GECCO 2013), Amsterdam, Netherlands

Recent research has demonstrated that the problem of class modelling within early cycle object orientated software engineering can be successfully tackled by posing it as a search problem to be tackled with meta-heuristics. This “Search Based Softwa... Read More about A comparison of two memetic algorithms for software class modelling.

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.

Aspect oriented software fault tolerance and analytically redundant design framework (2010)
Conference Proceeding
Hameed, K., Williams, R., & Smith, J. (2010). Aspect oriented software fault tolerance and analytically redundant design framework. In 2010 International Conference on Dependable Systems and Networks Workshops (DSN-W) (38-44). https://doi.org/10.1109/DSNW.2010.5542623

Diversity or redundancy based software fault tolerance does not come for free; rather it introduces additional complexity to the core functionality in the form of redundancy development, management and controlled execution. This results in tangling o... Read More about Aspect oriented software fault tolerance and analytically redundant design framework.

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.

Teaching problem solving and AI with PacMan (2010)
Presentation / Conference
Smith, J., & Cayzer, S. (2010, August). Teaching problem solving and AI with PacMan. Paper presented at Proceedings of the 11th Annual Conference on the Teaching of Computing, Higher Education Academy Information and Computer Sciences Subject Centre (HEA-ICS), Durham University, UK

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.

User-centric image segmentation using an interactive parameter adaptation tool (2010)
Journal Article
Pauplin, O., Caleb-Solly, P., & Smith, J. (2010). User-centric image segmentation using an interactive parameter adaptation tool. Pattern Recognition, 43(2), 519-529. https://doi.org/10.1016/j.patcog.2009.03.007

Creating successful machine vision systems often begins a process of developing customised reliable image segmentation algorithms for the detection, and possibly categorisation of regions of interest within images. This can require significant invest... Read More about User-centric image segmentation using an interactive parameter adaptation tool.

Evolutionary algorithms (2010)
Book Chapter
Eiben, A. E., & Smith, J. (2010). Evolutionary algorithms. In F. Neri, C. Cotta, & P. Moscato (Eds.), Handbook of Memetic Algorithms (9-27). Springer

What are evolutionary algorithms? (2010)
Book Chapter
Eiben, A. E., & Smith, J. (2010). What are evolutionary algorithms?. In C. Cotta, & F. Neri (Eds.), Handbook of Memetic Algorithms (9-27). Berlin, Heidelberg, New York: Springer

Self-adaptive and coevolving MAs (2010)
Book Chapter
Smith, J. (2010). Self-adaptive and coevolving MAs. In C. Cotta, F. Neri, & P. Moscato (Eds.), Handbook of Memetic Algorithms. Berlin, Heidelberg, New York: Springer

Human-machine interaction issues in quality control based on online image classification (2009)
Journal Article
Lughofer, E., Smith, J., Tahir, M. A., Caleb-Solly, P., Eitzinger, C., Sannen, D., & Nuttin, M. (2009). Human-machine interaction issues in quality control based on online image classification. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39(5), 960-971. https://doi.org/10.1109/TSMCA.2009.2025025

This paper considers on a number of issues that arise when a trainable machine vision system learns directly from humans. We contrast this to the "normal" situation where machine learning (ML) techniques are applied to a "cleaned" data set which is c... Read More about Human-machine interaction issues in quality control based on online image classification.

Human-machine interaction issues in quality control based on on-line image classification (2009)
Journal Article
Lughofer, E., Smith, J., Tahir, M., Caleb-Solly, P., Eitzinger, C., Sannen, D., & Nuttin, M. (2009). Human-machine interaction issues in quality control based on on-line image classification. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39(5), 960-971. https://doi.org/10.1109/TSMCA.2009.2025025

This paper considers on a number of issues that arise when a trainable machine vision system learns directly from humans. We contrast this to the ldquonormalrdquo situation where machine learning (ML) techniques are applied to a ldquocleanedrdquo dat... Read More about Human-machine interaction issues in quality control based on on-line image classification.

Aspect oriented software fault tolerance (2009)
Presentation / Conference
Hameed, K., Williams, R., & Smith, J. (2009, July). Aspect oriented software fault tolerance. Paper presented at 4th International Conference on Computer Science & Education (WCE09), Nanning, China

Learning through programming games: Teaching AI with pacman and netlogo (2009)
Presentation / Conference
Smith, J. (2009, June). Learning through programming games: Teaching AI with pacman and netlogo. Paper presented at Proceedings of the 5th UK Conference on AI in Education, Higher Education Academy Information and Computer Sciences Subject Centre (HEA-ICS)

On human-machine interaction during online image classifier training (2008)
Presentation / Conference
Lughofer, E., Smith, J., Tahir, M., Caleb-Solly, P., Eitzinger, C., Sannen, D., & van Brussels, H. (2008, December). On human-machine interaction during online image classifier training. Paper presented at Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA), Vienna, Austria

Interactive parameter adaptation tool for image segmentation (2008)
Presentation / Conference
Pauplin, O., Caleb-Solly, P., & Smith, J. (2008, July). Interactive parameter adaptation tool for image segmentation. Paper presented at IADIS Computer Graphics and Visualization 2008 (CGV 2008) Conference, Amsterdam, The Netherlands

An on-line interactive self-adaptive image classification framework (2008)
Journal Article
Sannen, D., Nuttin, M., Smith, J., Tahir, M. A., Caleb-Solly, P., Lughofer, E., & Eitzinger, C. (2008). An on-line interactive self-adaptive image classification framework. Lecture Notes in Artificial Intelligence, 5008 LNCS, 171-180. https://doi.org/10.1007/978-3-540-79547-6_17

In this paper we present a novel image classification framework, which is able to automatically re-configure and adapt its feature-driven classifiers and improve its performance based on user interaction during on-line processing mode. Special emphas... Read More about An on-line interactive self-adaptive image classification framework.

Memetic algorithms: The polynomial local search complexity theory perspective (2008)
Journal Article
Krasnogor, N., & Smith, J. (2008). Memetic algorithms: The polynomial local search complexity theory perspective. Journal of Mathematical Modelling and Algorithms, 7(1), 3-24. https://doi.org/10.1007/s10852-007-9070-9

In previous work (Krasnogor, http://www.cs.nott.ac.uk/~nxk/papers.html . In: Studies on the Theory and Design Space of Memetic Algorithms. Ph.D. thesis, University of the West of England, Bristol, UK, 2002; Krasnogor and Smith, IEEE Trans Evol Algori... Read More about Memetic algorithms: The polynomial local search complexity theory perspective.

Pre-processing optimisation applied to the classical integer programming model for statistical disclosure control (2008)
Conference Proceeding
Staggemeier, A., Serpell, M., Clark, A., & Smith, J. (2008). Pre-processing optimisation applied to the classical integer programming model for statistical disclosure control. . https://doi.org/10.1007/978-3-540-87471-3_3

A pre-processing optimisation is proposed that can be applied to the integer and mixed integer linear programming models that are used to solve the cell suppression problem in statistical disclosure control. In this paper we report our initial findin... Read More about Pre-processing optimisation applied to the classical integer programming model for statistical disclosure control.

Computer vision applications - Special issue (2007)
Journal Article
Smith, L. N., Smith, M. L., Caleb-Solly, P., & Smith, J. (2007). Computer vision applications - Special issue. Image and Vision Computing, 25(7), 1035-1036. https://doi.org/10.1016/j.imavis.2007.04.001

This peer-reviewed international journal paper arose from a research collaboration which formed part of the PhD of a colleague and builds on previous conferences works [proceedings of "Adaptative Computing in Design and Manufacturing" 2002, 2005, pro... Read More about Computer vision applications - Special issue.

Parameter control in evolutionary algorithms (2007)
Journal Article
Eiben, A., Michalewicz, Z., Schoenauer, M., & Smith, J. (2007). Parameter control in evolutionary algorithms. Studies in Computational Intelligence, 54, 19-46. https://doi.org/10.1007/978-3-540-69432-8_2

The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed... Read More about Parameter control in evolutionary algorithms.

On replacement strategies in steady state evolutionary algorithms (2007)
Journal Article
Smith, J. (2007). On replacement strategies in steady state evolutionary algorithms. Evolutionary Computation, 15(1), 29-59. https://doi.org/10.1162/evco.2007.15.1.29

Steady State models of Evolutionary Algorithms are widely used, yet surprisingly little attention has been paid to the effects arising from different replacement strategies. This paper explores the use of mathematical models to characterise the selec... Read More about On replacement strategies in steady state evolutionary algorithms.

Coevolving memetic algorithms: A review and progress report (2007)
Journal Article
Smith, J. (2007). Coevolving memetic algorithms: A review and progress report. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(1), 6-17. https://doi.org/10.1109/TSMCB.2006.883273

Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems h... Read More about Coevolving memetic algorithms: A review and progress report.

Gene libraries: Coverage, efficiency and diversity (2006)
Conference Proceeding
Cayzer, S., & Smith, J. (2006). Gene libraries: Coverage, efficiency and diversity. In H. Bersini, & J. Carneiro (Eds.), In Artificial Immune Systems. ICARIS 2006. , (136-149). https://doi.org/10.1007/11823940_11

Gene libraries are a biological mechanism for generating combinatorial diversity in the immune system. However, they also bias the antibody creation process, so that they can be viewed as a way of guiding lifetime learning mechanisms. In this paper w... Read More about Gene libraries: Coverage, efficiency and diversity.

A tutorial for competent memetic algorithms: Model, taxonomy and design issues (2005)
Journal Article
Krasnogor, N., & Smith, J. (2005). A tutorial for competent memetic algorithms: Model, taxonomy and design issues. IEEE Transactions on Evolutionary Computation, 9(5), 474-488. https://doi.org/10.1109/TEVC.2005.850260

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learni... Read More about A tutorial for competent memetic algorithms: Model, taxonomy and design issues.

A tutorial for competent memetic algorithms: Model, taxonomy, and design issues (2005)
Journal Article
Krasnogor, N., & Smith, J. (2005). A tutorial for competent memetic algorithms: Model, taxonomy, and design issues. IEEE Transactions on Evolutionary Computation, 9(5), 474-488. https://doi.org/10.1109/TEVC.2005.850260

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learni... Read More about A tutorial for competent memetic algorithms: Model, taxonomy, and design issues.

Memetic evolutionary algorithms (2005)
Book Chapter
Hart, W., Krasnogor, N., & Smith, J. (2005). Memetic evolutionary algorithms. In W. Hart, N. Krasnogor, & J. Smith (Eds.), Recent Advances in Memetic Algorithms (3-30). Berlin, Heidelberg, New York: Springer

Multimodal problems and spatial distribution (2003)
Book Chapter
Eiben, A. E., Smith, J., & Computing-slides, C. (2003). Multimodal problems and spatial distribution. In A. Eiben, & J. Smith (Eds.), Introduction to Evolutionary Computing (155). Springer

The co-evolution of memetic algorithms for protein structure prediction (2002)
Presentation / Conference
Smith, J. (2002, September). The co-evolution of memetic algorithms for protein structure prediction. Paper presented at Advances in Nature-Inspired Computation: The PPSN VII Workshops, PEDAL (Parallel, Emergent & Distributed Architectures Lab), University of Reading, Reading, UK

Alignment of protein structures with a memetic evolutionary algorithm (2002)
Presentation / Conference
Carr, B., Hart, W., Krasnogor, N., Hirst, J., Burke, E., & Smith, J. (2002, June). Alignment of protein structures with a memetic evolutionary algorithm. Paper presented at Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), Morgan Kaufmann, San Francisco

Strategy parameter variety in self-adaption of mutation rates (2002)
Presentation / Conference
Stone, C., & Smith, J. (2002, June). Strategy parameter variety in self-adaption of mutation rates. Paper presented at Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), Morgan Kaufmann, San Francisco, USA

Co-evolving memetic algorithms: Initial investigations (2002)
Conference Proceeding
Smith, J. (2002). Co-evolving memetic algorithms: Initial investigations. In J. J. M. Guervós, P. Adamidis, H. Beyer, H. Schwefel, & J. Fernández-Villacañas (Eds.), In Parallel Problem Solving from Nature — PPSN VII (537-546). https://doi.org/10.1007/3-540-45712-7_52

This paper presents and examines the behaviour of a system whereby the rules governing local search within a Memetic Algorithm are co-evolved alongside the problem representation. We describe the rationale for such a system, and the implementation of... Read More about Co-evolving memetic algorithms: Initial investigations.

Genetic algorithms (2002)
Book Chapter
Smith, J. (2002). Genetic algorithms. In P. M. Pardalos, & H. E. Romeijn (Eds.), Handbook of Global Optimization (275-362). Boston, USA: Kluwer Academic Publishers

A modified neocognitron network for medical signal classification (2001)
Presentation / Conference
Steuer, M., Caleb-Solly, P., & Smith, J. (2001, June). A modified neocognitron network for medical signal classification. Paper presented at Neural Networks and Expert Systems in Medicine and HealthCare, Milos Island, Greece

Modelling GAs with self-adaptive mutation rates (2001)
Presentation / Conference
Smith, J. (2001, June). Modelling GAs with self-adaptive mutation rates. Paper presented at Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Morgan Kaufmann, San Francisco, USA

New methods for tunable, random landscapes (2001)
Presentation / Conference
Smith, R. E., & Smith, J. (2001, June). New methods for tunable, random landscapes. Paper presented at Foundations of Genetic Algorithms 6, Morgan Kaufmann, San Francisco, USA

MAFRA: A java memetic algorithms framework (2000)
Presentation / Conference
Krasnogor, N., & Smith, J. (2000, July). MAFRA: A java memetic algorithms framework. Paper presented at Workshops Proceedings of the 2000 International Genetic and Evolutionary Computation Conference (GECCO2000), Las Vegas, Nevada, USA

An examination of tuneable, random search landscapes (1999)
Presentation / Conference
Smith, R., & Smith, J. (1999, June). An examination of tuneable, random search landscapes. Paper presented at Foundations of Genetic Algorithms 5, Morgan Kaufmann, San Francisco, USA

Protein structure prediction with evolutionary algorithms (1999)
Presentation / Conference
Krasnogor, N., Hart, W., Smith, J., & Pelta, D. (1999, June). Protein structure prediction with evolutionary algorithms. Paper presented at Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), Morgan Kaufmann, San Francisco, USA

Self adaptation in evolutionary algorithms (1998)
Thesis
Smith, J. Self adaptation in evolutionary algorithms. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/1099661

Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the... Read More about Self adaptation in evolutionary algorithms.

Genetic selection of features for clustering and classification (1997)
Presentation / Conference
Smith, J., Fogarty, T. C., & Johnson, I. R. (1997, June). Genetic selection of features for clustering and classification. Paper presented at Genetic Algorithms in Image Processing and Vision, IEE Colloquium on, Houston, USA

Operator and parameter adaptation in genetic algorithms (1997)
Journal Article
Smith, J., & Fogarty, T. (1997). Operator and parameter adaptation in genetic algorithms. Soft Computing, 1(2), 81-87. https://doi.org/10.1007/s005000050009

Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the... Read More about Operator and parameter adaptation in genetic algorithms.

Recombination strategy adaptation via evolution of gene linkage (1996)
Presentation / Conference
Smith, J., & Fogarty, T. (1996, June). Recombination strategy adaptation via evolution of gene linkage. Paper presented at Proceedings of the 1996 IEEE Conference on Evolutionary Computation, IEEE Press, Piscataway, NJ, USA

Visualising state space representations of LSTM networks
Presentation / Conference
Smith, E. M., Smith, J., Legg, P., & Francis, S. Visualising state space representations of LSTM networks. Presented at Workshop on Visualization for AI Explainability, Berlin, Germany

Long Short-Term Memory (LSTM) networks have proven to be one of the most effective models for making predictions on sequence-based tasks. These models work by capturing, remembering, and forgetting information relevant to their future predictions. Th... Read More about Visualising state space representations of LSTM networks.

Credit assignment in adaptive memetic algorithms
Presentation / Conference
Smith, J. Credit assignment in adaptive memetic algorithms. Paper presented at Proceedings of Gecco, the ACM-SIGEVO Conference on Evolutionary Computation, Springer, Berlin, Heidelberg, New York

What have gene libraries done for AIS?
Presentation / Conference
Cayzer, S., Smith, J., Marshall, J., & Kovacs, T. What have gene libraries done for AIS?. Paper presented at Proceedings of ICARIS 2005: 4th International Conference on Artificial Immune Systems, Springer, Berlin, Heidelberg, New York