All Outputs (6)

An Exploration of Dropout with RNNs for Natural Language Inference (2018)
Presentation / Conference Contribution
Gajbhiye, A., Jaf, S., Al-Moubayed, N., McGough, A. S., & Bradley, S. (2018, December). An Exploration of Dropout with RNNs for Natural Language Inference. Presented at ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In thi... Read More about An Exploration of Dropout with RNNs for Natural Language Inference.

Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems (2017)
Presentation / Conference Contribution
McGough, A. S., Al Moubayed, N., & M, F. (2017, April). Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems. Presented at ENERGY-SIM 2017, L'Aqua

When performing a trace-driven simulation of a High Throughput Computing system we are limited to the knowledge which should be available to the system at the current point within the simulation. However, the trace-log contains information we would n... Read More about Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems.

Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints' (2016)
Presentation / Conference Contribution
Bonner, S., Brennan, J., Theodoropoulos, G., Kureshi, I., & McGough, A. (2016, August). Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints'. Presented at Twelfth Workshop on Mining and Learning with Graphs (MLG) at KDD'16., San Francisco, USA

The problem of how to compare empirical graphs is an area of great interest within the field of network science. The ability to accurately but efficiently compare graphs has a significant impact in such areas as temporal graph evolution, anomaly dete... Read More about Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints'.

SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder (2016)
Presentation / Conference Contribution
Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016, August). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of labelled data samples. Features are extracted using topi... Read More about SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder.

Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain (2015)
Presentation / Conference Contribution
Bonner, S., McGough, S., Kureshi, I., Brennan, J., Theodoropoulos, G., Moss, L., Corsar, D., & Antoniou, G. (2023, October). Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain. Presented at IEEE International Conference on Big Data, Santa Clara

Recent technological advances in modern healthcare have lead to the ability to collect a vast wealth of patient monitoring data. This data can be utilised for patient diagnosis but it also holds the potential for use within medical research. However,... Read More about Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain.

Insider Threats: Identifying Anomalous Human Behaviour in Heterogeneous Systems Using Beneficial Intelligent Software (Ben-ware) (2015)
Presentation / Conference Contribution
McGough, A. S., Wall, D., Brennan, J., Theodoropoulos, G., Ruck-Keene, E., Arief, B., Gamble, C., Fitzgerald, J., & van Moorsel, A. (2015, December). Insider Threats: Identifying Anomalous Human Behaviour in Heterogeneous Systems Using Beneficial Intelligent Software (Ben-ware). Presented at Proceedings of the 7th ACM CCS International Workshop on Managing Insider Security Threats - MIST '15, Denver, USA

In this paper, we present the concept of "Ben-ware" as a beneficial software system capable of identifying anomalous human behaviour within a 'closed' organisation's IT infrastructure. We note that this behaviour may be malicious (for example, an emp... Read More about Insider Threats: Identifying Anomalous Human Behaviour in Heterogeneous Systems Using Beneficial Intelligent Software (Ben-ware).