All Outputs (65)

Structural Complexity and Performance of Support Vector Machines (2022)
Conference Proceeding
Olorisade, B. K., Brereton, P., & Andras, P. (2022). Structural Complexity and Performance of Support Vector Machines. In 2022 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn55064.2022.9892368

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.

Compounding barriers to fairness in the digital technology ecosystem (2021)
Conference Proceeding
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021). Compounding barriers to fairness in the digital technology ecosystem. In 2021 IEEE International Symposium on Technology and Society (ISTAS). https://doi.org/10.1109/istas52410.2021.9629166

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.

Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters (2020)
Conference Proceeding
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.

Federated learning with hierarchical clustering of local updates to improve training on non-IID data (2020)
Conference Proceeding
Briggs, C., Fan, Z., & Andras, P. (2020). Federated learning with hierarchical clustering of local updates to improve training on non-IID data. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9207469

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)
Conference Proceeding
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.

Environmental Harshness and Fitness Improving Innovations (2019)
Conference Proceeding
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.

Measuring and testing the scalability of cloud-based software services (2019)
Conference Proceeding
Al-Said Ahmad, A., & Andras, P. (2019). Measuring and testing the scalability of cloud-based software services. In 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT) (67-74). https://doi.org/10.1109/ISIICT.2018.8613297

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)
Conference Proceeding
Andras, P. (2018). Cooperation in Repeated Rock-Paper-Scissors Games in Uncertain Environments. In ALIFE 2018: The 2018 Conference on Artificial Life (404-411). https://doi.org/10.1162/isal_a_00078

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)
Conference Proceeding
Andras, P. (2018). Random projection neural network approximation. In 2018 International Joint Conference on Neural Networks (IJCNN) (1-8). https://doi.org/10.1109/IJCNN.2018.8489215

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.

Reproducibility in machine Learning-Based studies: An example of text mining (2017)
Conference Proceeding
Olorisade, B. K., Brereton, P., & Andras, P. (2017). Reproducibility in machine Learning-Based studies: An example of text mining. In ICML 2017 RML Workshop: Reproducibility in Machine Learning

Reproducibility is an essential requirement for computational studies including those based on machine learning techniques. However, many machine learning studies are either not reproducible or are difficult to reproduce. In this paper, we consider... Read More about Reproducibility in machine Learning-Based studies: An example of text mining.

A robust data-driven approach to the decoding of pyloric neuron activity (2017)
Conference Proceeding
dos Santos, F., Andras, P., Collins, D., & Lam, K. (2017). A robust data-driven approach to the decoding of pyloric neuron activity. In 2017 IEEE International Workshop on Signal Processing Systems (SiPS). https://doi.org/10.1109/SiPS.2017.8110017

The combination of intra and extra-cellular recording of small neuronal circuits such as stomatogastric nervous systems of the crab (Cancer borealis) is well documented and routinely practised. Voltage sensitive dye imaging (VSDi) is a promising tech... Read More about A robust data-driven approach to the decoding of pyloric neuron activity.

Towards an Accurate Identification of Pyloric Neuron Activity with VSDi (2017)
Conference Proceeding
dos Santos, F., Andras, P., & Lam, K. (2017). Towards an Accurate Identification of Pyloric Neuron Activity with VSDi. In Artificial Neural Networks and Machine Learning – ICANN 2017 (121-128). https://doi.org/10.1007/978-3-319-68600-4_15

Voltage-sensitive dye imaging (VSDi) which enables simultaneous optical recording of many neurons in the pyloric circuit of the stomatogastric ganglion is an important technique to supplement electrophysiological recordings. However, utilising the te... Read More about Towards an Accurate Identification of Pyloric Neuron Activity with VSDi.

A multiresolution approach to the extraction of the pyloric rhythm (2017)
Conference Proceeding
dos Santos, F., Andras, P., & Lam, K. (2017). A multiresolution approach to the extraction of the pyloric rhythm. In 2017 40th International Conference on Telecommunications and Signal Processing (TSP) (403-406). https://doi.org/10.1109/TSP.2017.8076015

This paper describes our work toward the development of a computationally robust methodology to identify the pyloric neurons in the stomatogastric ganglion of Cancer pagurus using voltage-sensitive dye imaging. The multi-resolution signal decompositi... Read More about A multiresolution approach to the extraction of the pyloric rhythm.

Open-ended evolution in cellular automata worlds (2017)
Conference Proceeding
Andras, P. (2017). Open-ended evolution in cellular automata worlds. In ECAL 2017, the Fourteenth European Conference on Artificial Life (438-445). https://doi.org/10.1162/isal_a_073

Open-ended evolution is a fundamental issue in artificial life research. We consider biological and social systems as a flux of interacting components that transiently participate in interactions with other system components as part of these systems.... Read More about Open-ended evolution in cellular automata worlds.

A systematic mapping study of empirical studies on software cloud testing methods (2017)
Conference Proceeding
Ahmad, A. A., Brereton, P., & Andras, P. (2017). A systematic mapping study of empirical studies on software cloud testing methods. In 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) (555-562). https://doi.org/10.1109/QRS-C.2017.94

Context: Software has become more complicated, dynamic, and asynchronous than ever, making testing more challenging. With the increasing interest in the development of cloud computing, and increasing demand for cloud-based services, it has become ess... Read More about A systematic mapping study of empirical studies on software cloud testing methods.

Using supervised machine learning algorithms to detect suspicious URLs in online social networks (2017)
Conference Proceeding
Al-Janabi, M., Quincey, E. D., & Andras, P. (2017). Using supervised machine learning algorithms to detect suspicious URLs in online social networks. In ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (1104-1111). https://doi.org/10.1145/3110025.3116201

The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of m... Read More about Using supervised machine learning algorithms to detect suspicious URLs in online social networks.

A systematic analysis of random forest based social media spam classification (2017)
Conference Proceeding
Al-Janabi, M., & Andras, P. (2017). A systematic analysis of random forest based social media spam classification. In Network and System Security: 11th International Conference, NSS 2017, Helsinki, Finland, August 21–23, 2017, Proceedings (427-438). https://doi.org/10.1007/978-3-319-64701-2_31

Recently random forest classification became a popular choice machine learning applications aimed to detect spam content in online social networks. In this paper, we report a systematic analysis of random forest classification for this purpose. We as... Read More about A systematic analysis of random forest based social media spam classification.

Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies (2017)
Conference Proceeding
Olorisade, B. K., Brereton, P., & Andras, P. (2017). Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies. In EASE'17: Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (128-133). https://doi.org/10.1145/3084226.3084283

Background:: Statistical validity and model complexity are both important concepts to enhanced understanding and correctness assessment of computational models. However, information about these are often missing from publications applying machine lea... Read More about Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies.

The Infiniteness of Open Ended Evolution (2016)
Conference Proceeding
Andras, P. (2016). The Infiniteness of Open Ended Evolution. In OEE2: The Second Workshop on Open-Ended Evolution

Biological and social systems are considered as a flux of interacting components that transiently participate in interactions with other system components as part of the system. This suggests that any simulated system undergoing open ended evolution... Read More about The Infiniteness of Open Ended Evolution.

Modelling the restoration of activity in a biological neural network (2016)
Conference Proceeding
Dos Santos, F., Steyn, J. S., & Andras, P. (2016). Modelling the restoration of activity in a biological neural network. In 2016 International Joint Conference on Neural Networks (IJCNN) (4672-4679). https://doi.org/10.1109/IJCNN.2016.7727813

Understanding the mechanisms of restoration of activity in biological neural systems following exposure to damage is key for design of future neuro-prosthetic devices and restorative treatments. The pyloric rhythm network within the crustacean stomat... Read More about Modelling the restoration of activity in a biological neural network.