Outputs (87)

Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features (2022)
Presentation / Conference Contribution
Horváth, T., Mantovani, R. G., & de Carvalho, A. C. Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features

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 learning algorithm or its hyper-parameters) to be used for a given dataset. Suc... Read More about Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features.

Most NLG is Low-Resource: here's what we can do about it (2022)
Presentation / Conference Contribution
Howcroft, D. M., & Gkatzia, D. (2022, December). Most NLG is Low-Resource: here's what we can do about it. Presented at Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), Abu Dhabi, UAE

Many domains and tasks in natural language generation (NLG) are inherently 'low-resource', where training data, tools and linguistic analyses are scarce. This poses a particular challenge to researchers and system developers in the era of machine-lea... Read More about Most NLG is Low-Resource: here's what we can do about it.

Universally Hard Hamiltonian Cycle Problem Instances (2022)
Presentation / Conference Contribution
Sleegers, J., Thomson, S. L., & van den Berg, D. (2022, November). Universally Hard Hamiltonian Cycle Problem Instances. Presented at ECTA 2022 : 14th International Conference on Evolutionary Computation Theory and Applications, Valletta, Malta

In 2021, evolutionary algorithms found the hardest-known yes and no instances for the Hamiltonian cycle problem. These instances, which show regularity patterns, require a very high number of recursions for the best exact backtracking algorithm (Vand... Read More about Universally Hard Hamiltonian Cycle Problem Instances.

Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations (2022)
Presentation / Conference Contribution
Nguyen, J., Powers, S., Urquhart, N., Farrenkopf, T., & Guckert, M. (2022, November). Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations. Presented at 24th International Conference on Principles and Practice of Multi-Agent Systems, Valencia, Spain

Cause-effect graphs have been applied in non agent-based simulations, where they are used to model chained causal relations between input parameters and system behaviour measured by appropriate indicators. This can be useful for the analysis and inte... Read More about Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations.

Structural Complexity and Performance of Support Vector Machines (2022)
Presentation / Conference Contribution
Olorisade, B. K., Brereton, P., & Andras, P. (2022, July). Structural Complexity and Performance of Support Vector Machines. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy

Support vector machines (SVM) are often applied in the context of machine learning analysis of various data. Given the nature of SVMs, these operate always in the sub-interpolation range as a machine learning method. Here we explore the impact of str... Read More about Structural Complexity and Performance of Support Vector Machines.

Fractal Dimension and Perturbation Strength: A Local Optima Networks View (2022)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., & Verel, S. (2022, September). Fractal Dimension and Perturbation Strength: A Local Optima Networks View

We study the effect of varying perturbation strength on the fractal dimensions of Quadratic Assignment Problem (QAP) fitness landscapes induced by iterated local search (ILS). Fitness landscapes are represented as Local Optima Networks (LONs), which... Read More about Fractal Dimension and Perturbation Strength: A Local Optima Networks View.

On funnel depths and acceptance criteria in stochastic local search (2022)
Presentation / Conference Contribution
Thomson, S. L., & Ochoa, G. (2022, July). On funnel depths and acceptance criteria in stochastic local search. Presented at GECCO '22: Genetic and Evolutionary Computation Conference, Boston Massachusetts

We propose looking at the phenomenon of fitness landscape funnels in terms of their depth. In particular, we examine how the depth of funnels in Local Optima Networks (LONs) of benchmark Quadratic Assignment Problem instances relate to metaheuristic... Read More about On funnel depths and acceptance criteria in stochastic local search.

Compounding barriers to fairness in the digital technology ecosystem (2021)
Presentation / Conference Contribution
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021, October). Compounding barriers to fairness in the digital technology ecosystem. Presented at 2021 IEEE International Symposium on Technology and Society (ISTAS), Waterloo, ON, Canada

A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology inno... Read More about Compounding barriers to fairness in the digital technology ecosystem.

What happens if you treat ordinal ratings as interval data? Human evaluations in {NLP} are even more under-powered than you think (2021)
Presentation / Conference Contribution
Howcroft, D. M., & Rieser, V. (2021, November). What happens if you treat ordinal ratings as interval data? Human evaluations in {NLP} are even more under-powered than you think. Presented at 2021 Conference on Empirical Methods in Natural Language Processing

Previous work has shown that human evaluations in NLP are notoriously under-powered. Here, we argue that there are two common factors which make this problem even worse: NLP studies usually (a) treat ordinal data as interval data and (b) operate unde... Read More about What happens if you treat ordinal ratings as interval data? Human evaluations in {NLP} are even more under-powered than you think.

Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters (2020)
Presentation / Conference Contribution
Briggs, C., Fan, Z., & Andras, P. (2020, December). Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. Presented at NeurIPS 2020 Workshop: Tackling Climate Change with Machine Learning, Online

In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer’s ho... Read More about Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters.

Federated learning with hierarchical clustering of local updates to improve training on non-IID data (2020)
Presentation / Conference Contribution
Briggs, C., Fan, Z., & Andras, P. (2020, July). Federated learning with hierarchical clustering of local updates to improve training on non-IID data. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow

Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion - as is typic... Read More about Federated learning with hierarchical clustering of local updates to improve training on non-IID data.

Composition of Games as a Model for the Evolution of Social Institutions (2020)
Presentation / Conference Contribution
Andras, P. (2020, July). Composition of Games as a Model for the Evolution of Social Institutions. Presented at ALIFE 2020: The 2020 Conference on Artificial Life, Online

The evolution of social institutions (e.g. institutions of political decision making or joint resource administration) is an important question in the context of understanding of how societies develop and evolve. In principle, social institutions can... Read More about Composition of Games as a Model for the Evolution of Social Institutions.

The Local Optima Level in Chemotherapy Schedule Optimisation (2020)
Presentation / Conference Contribution
Thomson, S. L., & Ochoa, G. (2020, April). The Local Optima Level in Chemotherapy Schedule Optimisation. Presented at EvoCOP 2020: Evolutionary Computation in Combinatorial Optimization, Seville, Spain

In this paper a multi-drug Chemotherapy Schedule Optimisation Problem (CSOP) is subject to Local Optima Network (LON) analysis. LONs capture global patterns in fitness landscapes. CSOPs have not previously been subject to fitness landscape analysis.... Read More about The Local Optima Level in Chemotherapy Schedule Optimisation.

Environmental Harshness and Fitness Improving Innovations (2019)
Presentation / Conference Contribution
Andras, P. (2019, July). Environmental Harshness and Fitness Improving Innovations. Presented at ALIFE 2019: The 2019 Conference on Artificial Life, Newcastle-upon-Tyne

Fitness improving innovations occur in populations of organisms as genetic changes (mutations) that allow better fit with the environmental niche of the organisms. Similarly, fitness improving innovations may occur in the context of human communities... Read More about Environmental Harshness and Fitness Improving Innovations.

Clarifying the Difference in Local Optima Network Sampling Algorithms (2019)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., & Verel, S. (2019, April). Clarifying the Difference in Local Optima Network Sampling Algorithms. Presented at 19th European Conference, EvoCOP 2019, Leipzig, Germany

We conduct the first ever statistical comparison between two Local Optima Network (LON) sampling algorithms. These methodologies attempt to capture the connectivity in the local optima space of a fitness landscape. One sampling algorithm is based on... Read More about Clarifying the Difference in Local Optima Network Sampling Algorithms.

Measuring and testing the scalability of cloud-based software services (2019)
Presentation / Conference Contribution
Al-Said Ahmad, A., & Andras, P. (2018, October). Measuring and testing the scalability of cloud-based software services. Presented at 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT), Amman, Jordan

Performance and scalability testing and measurements of cloud-based software services are critically important in the context of rapid growth of cloud computing and supporting the delivery of these services. Cloud-based software services performance... Read More about Measuring and testing the scalability of cloud-based software services.

Cooperation in Repeated Rock-Paper-Scissors Games in Uncertain Environments (2018)
Presentation / Conference Contribution
Andras, P. (2018, July). Cooperation in Repeated Rock-Paper-Scissors Games in Uncertain Environments. Presented at ALIFE 2018: The 2018 Conference on Artificial Life, Tokyo, Japan

Cooperation among selfish individuals provides the fundamentals for social organization among animals and humans. Cooperation games capture this behavior at an abstract level and provide the tools for the analysis of the evolution of cooperation. Her... Read More about Cooperation in Repeated Rock-Paper-Scissors Games in Uncertain Environments.

Random projection neural network approximation (2018)
Presentation / Conference Contribution
Andras, P. (2018, July). Random projection neural network approximation. Presented at 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil

Neural networks are often used to approximate functions defined over high-dimensional data spaces (e.g. text data, genomic data, multi-sensor data). Such approximation tasks are usually difficult due to the curse of dimensionality and improved method... Read More about Random projection neural network approximation.

New Trends in Databases and Information Systems: ADBIS 2018 Short Papers and Workshops, AI*QA, BIGPMED, CSACDB, M2U, BigDataMAPS, ISTREND, DC, Budapest, Hungary, September, 2-5, 2018, Proceedings (2018)
Presentation / Conference Contribution
(2018, September). New Trends in Databases and Information Systems: ADBIS 2018 Short Papers and Workshops, AI*QA, BIGPMED, CSACDB, M2U, BigDataMAPS, ISTREND, DC, Budapest, Hungary, September, 2-5, 2018, Proceedings. Presented at 22th European Conference on Advances in Databases and Information Systems, ADBIS 2018, Budapest, Hungary

Advances in Databases and Information Systems: 22nd European Conference, ADBIS 2018, Budapest, Hungary, September 2–5, 2018, Proceedings (2018)
Presentation / Conference Contribution
(2018, September). Advances in Databases and Information Systems: 22nd European Conference, ADBIS 2018, Budapest, Hungary, September 2–5, 2018, Proceedings. Presented at 22nd European Conference on Advances in Databases and Information Systems, ADBIS 2018, Budapest, Hungary