Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem.
Conference Proceeding
Verduin, K., Thomson, S. L., & van den Berg, D. (2023)
Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. In Proceedings of the 15th International Joint Conference on Computational Intelligence (246-257). https://doi.org/10.5220/0012192100003595
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...
Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction
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...
Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity
Working Paper
Pringle, S., Davies, Z. G., Goddard, M. A., Dallimer, M., Hart, E., Le Goff, L., & Langdale, S. J. (2023)
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...
enunlg: a Python library for reproducible neural data-to-text experimentation
Conference Proceeding
Howcroft, D. M., & Gkatzia, D. (2023)
enunlg: a Python library for reproducible neural data-to-text experimentation. In Proceedings of the 16th International Natural Language Generation Conference: System Demonstrations (4-5
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 p...
Building a dual dataset of text-and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic)
Conference Proceeding
Howcroft, D. M., Lamb, W., Groundwater, A., & Gkatzia, D. (2023)
Building a dual dataset of text-and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic). In Proceedings of the 16th International Natural Language Generation Conference (443-448
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 na...
A Comparative Study of Assessment Metrics for Imbalanced Learning
Conference Proceeding
Farou, Z., Aharrat, M., & Horváth, T. (2023)
A Comparative Study of Assessment Metrics for Imbalanced Learning. In New Trends in Database and Information Systems: ADBIS 2023 Short Papers, Doctoral Consortium and Workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, Proceedings (119-129). https://doi.org/10.1007/978-3-031-42941-5_11
There are several machine learning algorithms addressing class imbalance problem, requiring standardized metrics for adequete performance evaluation. This paper reviews severa...
PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images
Journal Article
Liu, Q., Zhang, Z., Liu, X., Zhang, Y., & Du, Z. (in press)
PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images. Intelligent Automation and Soft Computing,
Automatic extraction of water body information from high-resolution remote sensing images is one of the core tasks of remote sensing image interpretation. Since the complex mu...
Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids
Conference Proceeding
Since the advent of deep learning (DL), speech enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic...
NCC: Neural concept compression for multilingual document recommendation
Journal Article
Tashu, T. M., Lenz, M., & Horváth, T. (2023)
NCC: Neural concept compression for multilingual document recommendation. Applied Soft Computing, 142, Article 110348. https://doi.org/10.1016/j.asoc.2023.110348
In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to i...
Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features
Journal Article
Horváth, T., Mantovani, R. G., & de Carvalho, A. C. (2023)
Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features. Applied Soft Computing, 134, Article 109969. https://doi.org/10.1016/j.asoc.2022.109969
Meta-learning, a concept from the area of automated machine learning, aims at providing decision support for data scientists by recommending a suitable setting (a machine lear...