Outputs (124)

Tracing the Local Breeds in an Outdoor System – A Hungarian Example with Mangalica Pig Breed (2022)
Book Chapter
Alexy, M., & Horváth, T. Tracing the Local Breeds in an Outdoor System – A Hungarian Example with Mangalica Pig Breed. In Tracing the Domestic Pig. IntechOpen. https://doi.org/10.5772/intechopen.101615

Pig farming is largely characterized by closed, large-scale housing technology. These systems are driven by resource efficiency. In intensive technologies, humans control almost completely. However, there are pig farming systems where humans have jus... Read More about Tracing the Local Breeds in an Outdoor System – A Hungarian Example with Mangalica Pig Breed.

Scalability resilience framework using application-level fault injection for cloud-based software services (2022)
Journal Article
Al-Said Ahmad, A., & Andras, P. (2022). Scalability resilience framework using application-level fault injection for cloud-based software services. Journal of cloud computing: advances, systems and applications, 11(1), Article 1. https://doi.org/10.1186/s13677-021-00277-z

This paper presents an investigation into the effect of faults on the scalability resilience of cloud-based software services. The study introduces an experimental framework using the Application-Level Fault Injection (ALFI) to investigate how the fa... Read More about Scalability resilience framework using application-level fault injection for cloud-based software services.

A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things (2021)
Book Chapter
Farhad, A., Woolley, S. I., & Andras, P. (2021). A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things. In J. Mantas, L. Stoicu-Tivadar, C. Chronaki, A. Hasman, P. Weber, P. Gallos, M. Crişan-Vida, E. Zoulias, & O. Sorina Chirila (Eds.), Public Health and Informatics (504-505). IOS Press. https://doi.org/10.3233/SHTI210216

This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has potential to improve patient care. Federated Learning and IoMT – as standal... Read More about A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things.

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.

Multimodal Emotion Recognition from Art Using Sequential Co-Attention (2021)
Journal Article
Tashu, T. M., Hajiyeva, S., & Horvath, T. (2021). Multimodal Emotion Recognition from Art Using Sequential Co-Attention. Journal of Imaging, 7(8), Article 157. https://doi.org/10.3390/jimaging7080157

In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the... Read More about Multimodal Emotion Recognition from Art Using Sequential Co-Attention.

A review of privacy-preserving federated learning for the Internet-of-Things (2021)
Book Chapter
Briggs, C., Fan, Z., & Andras, P. (2021). A review of privacy-preserving federated learning for the Internet-of-Things. In M. Habib ur Rehman, & M. Medhat Gaber (Eds.), Federated Learning Systems: Towards Next-Generation AI (21-50). Springer. https://doi.org/10.1007/978-3-030-70604-3_2

The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is attributable to human activities and behavior. Collecting personal data and executing machine learning tasks on this data in a central location presents a significan... Read More about A review of privacy-preserving federated learning for the Internet-of-Things.

Where do successful populations originate from? (2021)
Journal Article
Andras, P., & Stanton, A. (2021). Where do successful populations originate from?. Journal of Theoretical Biology, 524, Article 110734. https://doi.org/10.1016/j.jtbi.2021.110734

In order to understand the dynamics of emergence and spreading of socio-technical innovations and population moves it is important to determine the place of origin of these populations. Here we focus on the role of geographical factors, such as land... Read More about Where do successful populations originate from?.

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

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.

The fractal geometry of fitness landscapes at the local optima level (2020)
Journal Article
Thomson, S. L., Ochoa, G., & Verel, S. (2022). The fractal geometry of fitness landscapes at the local optima level. Natural Computing, 21(2), 317-333. https://doi.org/10.1007/s11047-020-09834-y

A local optima network (LON) encodes local optima connectivity in the fitness landscape of a combinatorial optimisation problem. Recently, LONs have been studied for their fractal dimension. Fractal dimension is a complexity index where a non-integer... Read More about The fractal geometry of fitness landscapes at the local optima level.

Inferring Future Landscapes: Sampling the Local Optima Level (2020)
Journal Article
Thomson, S. L., Ochoa, G., Verel, S., & Veerapen, N. (2020). Inferring Future Landscapes: Sampling the Local Optima Level. Evolutionary Computation, 28(4), 621-641. https://doi.org/10.1162/evco_a_00271

Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size comp... Read More about Inferring Future Landscapes: Sampling the Local Optima Level.

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). Composition of Games as a Model for the Evolution of Social Institutions. In Artificial Life Conference Proceedings (171-179). https://doi.org/10.1162/isal_a_00264

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.

Scalability analysis comparisons of cloud-based software services (2019)
Journal Article
Ahmad, A. A., & Andras, P. (2019). Scalability analysis comparisons of cloud-based software services. Journal of cloud computing: advances, systems and applications, 8, Article 10. https://doi.org/10.1186/s13677-019-0134-y

Performance and scalability testing and measurements of cloud-based software services are necessary for future optimizations and growth of cloud computing. Scalability, elasticity, and efficiency are interrelated aspects of cloud-based software servi... Read More about Scalability analysis comparisons of cloud-based software services.

Environmental Harshness and Fitness Improving Innovations (2019)
Presentation / Conference Contribution
Andras, P. (2019). Environmental Harshness and Fitness Improving Innovations. In Artificial Life Conference Proceedings (300-307). https://doi.org/10.1162/isal_a_00179

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.

Cloud-based software services delivery from the perspective of scalability (2019)
Journal Article
Al-Said Ahmad, A., & Andras, P. (2021). Cloud-based software services delivery from the perspective of scalability. International Journal of Parallel, Emergent and Distributed Systems, 36(2), 53-68. https://doi.org/10.1080/17445760.2019.1617864

Measuring and testing the scalability and performance of cloud-based software services is critical for the delivery of such services, and the development of cloud computing. There are three interconnected Cloud-based software services’ performance as... Read More about Cloud-based software services delivery from the perspective of scalability.

The use of bibliography enriched features for automatic citation screening (2019)
Journal Article
Olorisade, B. K., Brereton, P., & Andras, P. (2019). The use of bibliography enriched features for automatic citation screening. Journal of Biomedical Informatics, 94, Article 103202. https://doi.org/10.1016/j.jbi.2019.103202

Context
Citation screening (also called study selection) is a phase of systematic review process that has attracted a growing interest on the use of text mining (TM) methods to support it to reduce time and effort. Search results are usually imbalan... Read More about The use of bibliography enriched features for automatic citation screening.

Swarm intelligence techniques in recommender systems - A review of recent research (2019)
Journal Article
Peška, L., Tashu, T. M., & Horváth, T. (2019). Swarm intelligence techniques in recommender systems - A review of recent research. Swarm and Evolutionary Computation, 48, 201-219. https://doi.org/10.1016/j.swevo.2019.04.003

One of the main current applications of Intelligent Systems are Recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS.... Read More about Swarm intelligence techniques in recommender systems - A review of recent research.

Clarifying the Difference in Local Optima Network Sampling Algorithms (2019)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., & Verel, S. (2019). Clarifying the Difference in Local Optima Network Sampling Algorithms. In Evolutionary Computation in Combinatorial Optimization. EvoCOP 2019 (163-178). https://doi.org/10.1007/978-3-030-16711-0_11

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.