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Outputs (156)

An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment (2024)
Journal Article
Liu, Q., Jin, Y., Cao, X., Liu, X., Zhou, X., Zhang, Y., Xu, X., & Qi, L. (2024). An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment. IEEE Transactions on Computational Social Systems, 11(4), 5308 - 5318. https://doi.org/10.1109/TCSS.2023.3342873

Fake news is a prevalent issue in modern society, leading to misinformation and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awa... Read More about An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment.

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing (2024)
Journal Article
Liu, Q., Sun, J., Zhang, Y., & Liu, X. (2024). DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing. Journal of cloud computing: advances, systems and applications, 13, Article 32. https://doi.org/10.1186/s13677-024-00607-x

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric re... Read More about DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing.

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms (2024)
Journal Article
Mantovani, R. G., Horváth, T., Rossi, A. L. D., Cerri, R., Barbon Junior, S., Vanschoren, J., & de Carvalho, A. C. P. L. F. (2024). Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, 38, 1364–1416. https://doi.org/10.1007/s10618-024-01002-5

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex i... Read More about Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms.

Image Forgery Detection using Cryptography and Deep Learning (2024)
Presentation / Conference Contribution
Oke, A., & Babaagba, K. O. (2023, August). Image Forgery Detection using Cryptography and Deep Learning. Presented at EAI BDTA 2023 - 13th EAI International Conference on Big Data Technologies and Applications, Edinburgh

The advancement of technology has undoubtedly exposed everyone to a remarkable array of visual imagery. Nowadays, digital technology is eating away the trust and historical confidence people have in the integrity of imagery. Deep learning is often us... Read More about Image Forgery Detection using Cryptography and Deep Learning.

Evolving Behavior Allocations in Robot Swarms (2024)
Presentation / Conference Contribution
Hallauer, S., Nitschke, G., & Hart, E. (2023, December). Evolving Behavior Allocations in Robot Swarms. Presented at IEEE Symposium Series on Computational Intelligence (SSCI 2023), Mexico City, Mexico

Behavioral diversity is known to benefit problem-solving in biological social systems such as insect colonies and human societies, as well as in artificial distributed systems including large-scale software and swarm-robotics systems. We investigate... Read More about Evolving Behavior Allocations in Robot Swarms.

The stuff we swim in: Regulation alone will not lead to justifiable trust in AI (2023)
Journal Article
Powers, S. T., Linnyk, O., Guckert, M., Hannig, J., Pitt, J., Urquhart, N., Ekart, A., Gumpfer, N., Han, A., Lewis, P. R., Marsh, S., & Weber, T. (2023). The stuff we swim in: Regulation alone will not lead to justifiable trust in AI. IEEE technology & society magazine, 42(4), 95-106. https://doi.org/10.1109/MTS.2023.3341463

Information technology is used ubiquitously and has become an integral part of everyday life. With the ever increasing pervasiveness and persuasiveness of Artificial Intelligence (AI), the function of socio-technical systems changes and must be consi... Read More about The stuff we swim in: Regulation alone will not lead to justifiable trust in AI.

Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces (2023)
Journal Article
Li, W., Buchanan, E., Goff, L. K. L., Hart, E., Hale, M. F., Wei, B., Carlo, M. D., Angus, M., Woolley, R., Gan, Z., Winfield, A. F., Timmis, J., Eiben, A. E., & Tyrrell, A. M. (2024). Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces. IEEE Transactions on Evolutionary Computation, 28(6), 1561 - 1574. https://doi.org/10.1109/tevc.2023.3316363

Jointly optimising both the body and brain of a robot is known to be a challenging task, especially when attempting to evolve designs in simulation that will subsequently be built in the real world. To address this, it is increasingly common to combi... Read More about Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces.

The Opaque Nature of Intelligence and the Pursuit of Explainable AI (2023)
Presentation / Conference Contribution
Thomson, S. L., van Stein, N., van den Berg, D., & van Leeuwen, C. (2023, November). The Opaque Nature of Intelligence and the Pursuit of Explainable AI. Presented at NCTA 2023: 15th International Conference on Neural Computation Theory and Applications, Rome, Italy

When artificial intelligence is used for making decisions, people are more likely to accept those decisions if they can be made intelligible to the public. This understanding has led to the emerging field of explainable artificial intelligence. We re... Read More about The Opaque Nature of Intelligence and the Pursuit of Explainable AI.

Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. (2023)
Presentation / Conference Contribution
Verduin, K., Thomson, S. L., & van den Berg, D. (2023, November). Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. Presented at ECTA 2023 15th International Conference on Evolutionary Computation Theory and Applications, Rome, Italy

Unlike other NP-hard problems, the constraints on the traveling tournament problem are so pressing that it’s hardly possible to randomly generate a valid solution, for example, to use in a genetic algorithm’s initial population. In this study, we ran... Read More about Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem..

Can HP-protein folding be solved with genetic algorithms? Maybe not (2023)
Presentation / Conference Contribution
Jansen, R., Horn, R., van Eck, O., Version, K., Thomson, S. L., & van den Berg, D. (2023, November). Can HP-protein folding be solved with genetic algorithms? Maybe not. Presented at ECTA 2023 15th International Conference on Evolutionary Computation Theory and Applications, Rome, Italy

Genetic algorithms might not be able to solve the HP-protein folding problem because creating random individuals for an initial population is very hard, if not impossible. The reason for this, is that the expected number of constraint violations incr... Read More about Can HP-protein folding be solved with genetic algorithms? Maybe not.

Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction (2023)
Journal Article
Aziz, A., Hossain, M. A., Chy, A. N., Ullah, M. Z., & Aono, M. (2023). Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction. Natural Language Processing Journal, 5, Article 100039. https://doi.org/10.1016/j.nlp.2023.100039

Lexical complexity prediction (LCP) determines the complexity level of words or phrases in a sentence. LCP has a significant impact on the enhancement of language translations, readability assessment, and text generation. However, the domain-specific... Read More about Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction.

Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity (2023)
Preprint / Working Paper
Pringle, S., Davies, Z. G., Goddard, M. A., Dallimer, M., Hart, E., Le Goff, L., & Langdale, S. J. Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity

Welcome to the UK-RAS White paper Series on Robotics and Autonomous Systems (RAS). This is one of the core activities of UK-RAS Network, funded by the Engineering and Physical Sciences Research Council (EPSRC). By Bringing together academic centres o... Read More about Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity.

DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar (2023)
Journal Article
Liu, Q., Sun, J., & Liu, X. (in press). DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar. Journal on Artificial Intelligence,

In the realm of meteorological research, extensive global radar networks serve to detect and provide early warnings for a diverse array of weather phenomena. However, the inherently discontinuous nature of radar observations often results in the pres... Read More about DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar.

Building a dual dataset of text-and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic) (2023)
Presentation / Conference Contribution
Howcroft, D. M., Lamb, W., Groundwater, A., & Gkatzia, D. (2023, September). Building a dual dataset of text-and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic). Presented at The 16th International Natural Language Generation Conference

Gàidhlig (Scottish Gaelic; gd) is spoken by about 57k people in Scotland, but remains an under-resourced language with respect to natural language processing in general and natural language generation (NLG) in particular. To address this gap, we deve... Read More about Building a dual dataset of text-and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic).

enunlg: a Python library for reproducible neural data-to-text experimentation (2023)
Presentation / Conference Contribution
Howcroft, D. M., & Gkatzia, D. (2023, September). enunlg: a Python library for reproducible neural data-to-text experimentation. Presented at 16th International Natural Language Generation Conference, Prague, Czechia

Over the past decade, a variety of neural ar-chitectures for data-to-text generation (NLG) have been proposed. However, each system typically has its own approach to pre-and post-processing and other implementation details. Diversity in implementatio... Read More about enunlg: a Python library for reproducible neural data-to-text experimentation.

A Comparative Study of Assessment Metrics for Imbalanced Learning (2023)
Presentation / Conference Contribution
Farou, Z., Aharrat, M., & Horváth, T. (2023, September). A Comparative Study of Assessment Metrics for Imbalanced Learning. Presented at European Conference on Advances in Databases and Information Systems (ADBIS 2023), Barcelona, Spain

There are several machine learning algorithms addressing class imbalance problem, requiring standardized metrics for adequete performance evaluation. This paper reviews several metrics for imbalanced learning in binary and multi-class problems. We em... Read More about A Comparative Study of Assessment Metrics for Imbalanced Learning.

Selective Query Processing: A Risk-Sensitive Selection of Search Configurations (2023)
Journal Article
Mothe, J., & Ullah, M. Z. (2024). Selective Query Processing: A Risk-Sensitive Selection of Search Configurations. ACM transactions on information systems, 42(1), https://doi.org/10.1145/3608474

In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches and these optimized parameters are then used as the system configuration for all subsequent queries. A better approach, how... Read More about Selective Query Processing: A Risk-Sensitive Selection of Search Configurations.

Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality (2023)
Presentation / Conference Contribution
Graham, K. C., Thomson, S. L., & Brownlee, A. E. I. (2023, July). Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality. Presented at GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal

We study the behaviour of particle swarm optimisation (PSO) with increasing problem dimension for the Alpine 1 function as an exploratory and preliminary case study. Performance trends are analysed and the tuned population size for PSO across dimensi... Read More about Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality.

From Fitness Landscapes to Explainable AI and Back (2023)
Presentation / Conference Contribution
Thomson, S. L., Adair, J., Brownlee, A. E. I., & van den Berg, D. (2023, July). From Fitness Landscapes to Explainable AI and Back. Presented at GECCO '23, Lisbon, Portugal

We consider and discuss the ways in which search landscapes might contribute to the future of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to gain insight into algorithm search dynamics on optimisation prob... Read More about From Fitness Landscapes to Explainable AI and Back.