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ORCIDA. Stephen McGough
Andrew Stephen McGough
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- affiliation: Newcastle University, UK
- affiliation (former): Durham University, School of Engineering and Computing Sciences
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2020 – today
- 2024
[c78]Rob Geada, David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, A. Stephen McGough:
Insights from the Use of Previously Unseen Neural Architecture Search Datasets. CVPR 2024: 22541-22550- [i41]Rob Geada, David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, A. Stephen McGough:
Insights from the Use of Previously Unseen Neural Architecture Search Datasets. CoRR abs/2404.02189 (2024) - [i40]Matthew Kent Myers, Nick Wright, A. Stephen McGough, Nicholas G. Martin:
O-TALC: Steps Towards Combating Oversegmentation within Online Action Segmentation. CoRR abs/2404.06894 (2024) - 2023
- [c77]Mohamad Elhadi Abushofa
, Amir Atapour Abarghouei, Matthew Forshaw, Andrew Stephen McGough:
FEGR: Feature Enhanced Graph Representation Method for Graph Classification. ASONAM 2023: 371-378 - [c76]Ayse Betul Cengiz, A. Stephen McGough:
How much data do I need? A case study on medical data. IEEE Big Data 2023: 3688-3697 - [c75]Atif Khan, Conor Lawless, Amy E. Vincent, Charlotte Warren, Valeria Di Leo, Tiago Gomes, A. Stephen McGough:
NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies. IEEE Big Data 2023: 3704-3710 - [c74]Mohamad Khalil, A. Stephen McGough, Hussain Kazmi, Sara Walker:
The Forecastability of Underlying Building Electricity Demand from Time Series Data. IEEE Big Data 2023: 3785-3793 - [c73]Samuel Appleby, Kirsten Crane, Giacomo Bergami, A. Stephen McGough:
SWiMM DEEPeR: A Simulated Underwater Environment for Tracking Marine Mammals Using Deep Reinforcement Learning and BlueROV2. CoG 2023: 1-8 - [c72]Matthew Kent Myers, Nick Wright, A. Stephen McGough, Nicholas G. Martin:
Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies. WACV (Workshops) 2023: 1-10 - [c71]Mehmet Cengiz, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Predicting the Performance of a Computing System with Deep Networks. ICPE 2023: 91-98 - [i39]Mehmet Cengiz, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Predicting the Performance of a Computing System with Deep Networks. CoRR abs/2302.13638 (2023) - [i38]Atif Khan, Conor Lawless, Amy E. Vincent, Charlotte Warren, Valeria Di Leo, Tiago Gomes, A. Stephen McGough:
Introducing NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies. CoRR abs/2311.11099 (2023) - [i37]Atif Khan, Conor Lawless, Amy E. Vincent, Charlotte Warren, Valeria Di Leo, Tiago Gomes, A. Stephen McGough:
NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies. CoRR abs/2311.15113 (2023) - [i36]Ayse Betul Cengiz, A. Stephen McGough:
How much data do I need? A case study on medical data. CoRR abs/2311.15331 (2023) - [i35]Mohamad Khalil, A. Stephen McGough, Hussain Kazmi, Sara Walker:
The Forecastability of Underlying Building Electricity Demand from Time Series Data. CoRR abs/2311.18078 (2023) - [i34]Georgia Atkinson, Nick Wright, A. Stephen McGough, Per Berggren:
The Effects of Signal-to-Noise Ratio on Generative Adversarial Networks Applied to Marine Bioacoustic Data. CoRR abs/2312.14806 (2023) - 2022
- [j23]Mohamad Khalil, A. Stephen McGough, Zoya Pourmirza, Mehdi Pazhoohesh, Sara Walker:
Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption - A systematic review. Eng. Appl. Artif. Intell. 115: 105287 (2022) - [j22]Kenan Koc, Andrew Stephen McGough, Sara Johansson Fernstad:
PeaGlyph: Glyph design for investigation of balanced data structures. Inf. Vis. 21(1): 74-92 (2022) - [j21]Becky Allen, Andrew Stephen McGough, Marie Devlin:
Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience. ACM Trans. Comput. Educ. 22(2): 15:1-15:29 (2022) - [c70]Cameron Trotter, Nick Wright, A. Stephen McGough, Matthew Sharpe, Barbara Cheney, Mònica Arso Civil, Reny Tyson Moore, Jason Allen, Per Berggren:
Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology. IEEE Big Data 2022: 1942-1949 - [c69]Michael Luke Battle, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets. IEEE Big Data 2022: 4346-4355 - [c68]Atif Khan, Conor Lawless, Amy E. Vincent, Satish Pilla, Sushanth Ramesh, A. Stephen McGough:
Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Data. IEEE Big Data 2022: 4375-4384 - [c67]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning. IEEE Big Data 2022: 4495-4502 - [c66]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
A systematic literature review on machine learning for electricity market agent-based models. IEEE Big Data 2022: 4503-4512 - [c65]Rob Geada, Andrew Stephen McGough:
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics. CVPR Workshops 2022: 1961-1969 - [c64]Andrew Stephen McGough, Matthew Forshaw:
Analysis of Reinforcement Learning for Determining Task Replication in Workflows. EPEW 2022: 117-132 - [i33]Rob Geada, Andrew Stephen McGough:
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics. CoRR abs/2204.09320 (2022) - [i32]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Machine learning applications for electricity market agent-based models: A systematic literature review. CoRR abs/2206.02196 (2022) - [i31]David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Long-term Reproducibility for Neural Architecture Search. CoRR abs/2207.04821 (2022) - [i30]Andrew Stephen McGough, Matthew Forshaw:
Analysis of Reinforcement Learning for determining task replication in workflows. CoRR abs/2209.13531 (2022) - [i29]Atif Khan, Conor Lawless, Amy E. Vincent, Satish Pilla, Sushanth Ramesh, A. Stephen McGough:
Explainable Deep Learning to Profile Mitochondrial Disease Using High Dimensional Protein Expression Data. CoRR abs/2210.17360 (2022) - [i28]Matthew Kent Myers, Nick Wright, A. Stephen McGough, Nicholas G. Martin:
Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies. CoRR abs/2211.13694 (2022) - [i27]Cameron Trotter, Nick Wright, A. Stephen McGough, Matthew Sharpe, Barbara Cheney, Mònica Arso Civil, Reny Tyson Moore, Jason Allen, Per Berggren:
Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology. CoRR abs/2212.03646 (2022) - [i26]Michael Luke Battle, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets. CoRR abs/2212.06130 (2022) - 2021
- [j20]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets. Sustain. Comput. Informatics Syst. 30: 100532 (2021) - [c63]Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough:
Rank over Class: The Untapped Potential of Ranking in Natural Language Processing. IEEE BigData 2021: 3950-3959 - [c62]Ryan Curry, Cameron Trotter, A. Stephen McGough:
Application of deep learning to camera trap data for ecologists in planning / engineering - Can captivity imagery train a model which generalises to the wild? IEEE BigData 2021: 4011-4020 - [c61]Becky Allen, Marie Devlin, A. Stephen McGough:
Using the One Minute Paper to Gain Insight into Potential Threshold Concepts in Artificial Intelligence Courses. CEP 2021: 21-24 - [i25]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets. CoRR abs/2103.04327 (2021) - [i24]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning. CoRR abs/2109.05024 (2021) - [i23]Ryan Curry, Cameron Trotter, Andrew Stephen McGough:
Application of deep learning to camera trap data for ecologists in planning / engineering - Can captivity imagery train a model which generalises to the wild? CoRR abs/2111.12805 (2021) - 2020
- [j19]Noura Al Moubayed, Andrew Stephen McGough, Bashar Awwad Shiekh Hasan:
Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. PeerJ Comput. Sci. 6: e252 (2020) - [c60]John Brennan, Stephen Bonner, Amir Atapour Abarghouei, Philip T. G. Jackson, Boguslaw Obara, Andrew Stephen McGough:
Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks. IEEE BigData 2020: 2725-2734 - [c59]Amir Atapour Abarghouei, A. Stephen McGough, David S. Wall:
Resolving the cybersecurity Data Sharing Paradox to scale up cybersecurity via a co-production approach towards data sharing. IEEE BigData 2020: 3867-3876 - [c58]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Exploring market power using deep reinforcement learning for intelligent bidding strategies. IEEE BigData 2020: 4402-4411 - [c57]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Long-term electricity market agent based model validation using genetic algorithm based optimization. e-Energy 2020: 1-13 - [c56]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Optimizing carbon tax for decentralized electricity markets using an agent-based model. e-Energy 2020: 454-460 - [c55]Mohammad Samawat Ullah, A. Stephen McGough:
Distributed Disk Store. ICCA 2020: 50:1-50:10 - [i22]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Long-term electricity market agent based model validation using genetic algorithm based optimization. CoRR abs/2005.10346 (2020) - [i21]Cameron Trotter, Georgia Atkinson, Matthew Sharpe, Kirsten Richardson, A. Stephen McGough, Nick Wright, Ben Burville, Per Berggren:
NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation. CoRR abs/2005.13359 (2020) - [i20]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Optimizing carbon tax for decentralized electricity markets using an agent-based model. CoRR abs/2006.01601 (2020) - [i19]Rob Geada, Dennis Prangle, Andrew Stephen McGough:
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners. CoRR abs/2006.09264 (2020) - [i18]Amir Atapour Abarghouei, Stephen Bonner, Andrew Stephen McGough:
Rank over Class: The Untapped Potential of Ranking in Natural Language Processing. CoRR abs/2009.05160 (2020) - [i17]John Brennan, Stephen Bonner, Amir Atapour Abarghouei, Philip T. G. Jackson, Boguslaw Obara, Andrew Stephen McGough:
Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks. CoRR abs/2010.12635 (2020) - [i16]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Exploring market power using deep reinforcement learning for intelligent bidding strategies. CoRR abs/2011.04079 (2020) - [i15]Amir Atapour Abarghouei, Andrew Stephen McGough, David Stanley Wall:
Resolving the cybersecurity Data Sharing Paradox to scale up cybersecurity via a co-production approach towards data sharing. CoRR abs/2011.12709 (2020)
2010 – 2019
- 2019
- [j18]Stephen Bonner, Ibad Kureshi, John Brennan, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara:
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study. Data Sci. Eng. 4(3): 269-289 (2019) - [j17]Bowen Li, Denis Taniguchi, Pahala Gedara Jayathilake, Valentina Gogulancea, Rebeca Gonzalez-Cabaleiro, Jinju Chen, Andrew Stephen McGough, Irina Dana Ofiteru, Thomas P. Curtis, Paolo Zuliani:
NUFEB: A massively parallel simulator for individual-based modelling of microbial communities. PLoS Comput. Biol. 15(12) (2019) - [c54]Mahnaz Mohammadi, Sardar F. Jaf, Andrew Stephen McGough, Toby P. Breckon, Peter Matthews, Georgios Theodoropoulos, Boguslaw Obara:
On the Use of Neural Text Generation for the Task of Optical Character Recognition. AICCSA 2019: 1-8 - [c53]Amir Atapour Abarghouei, Stephen Bonner, Andrew Stephen McGough:
A King's Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation. IEEE BigData 2019: 1601-1606 - [c52]Amir Atapour Abarghouei, Stephen Bonner, Andrew Stephen McGough:
Volenti non fit injuria: Ransomware and its Victims. IEEE BigData 2019: 4701-4707 - [c51]Stephen Bonner, Amir Atapour Abarghouei, Philip T. G. Jackson, John Brennan, Ibad Kureshi, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara:
Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions. IEEE BigData 2019: 5336-5345 - [c50]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Modelling Carbon Tax in the UK Electricity Market using an Agent-Based Model. e-Energy 2019: 425-427 - [c49]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning. e-Energy 2019: 556-565 - [c48]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Optimising energy and overhead for large parameter space simulations. IGSC 2019: 1-8 - [i14]Fady Medhat, Mahnaz Mohammadi, Sardar F. Jaf, Chris G. Willcocks, Toby P. Breckon, Peter Matthews, Andrew Stephen McGough, Georgios Theodoropoulos, Boguslaw Obara:
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text. CoRR abs/1904.12387 (2019) - [i13]Cameron Trotter, Georgia Atkinson, Matthew Sharpe, A. Stephen McGough, Nick Wright, Per Berggren:
The Northumberland Dolphin Dataset: A Multimedia Individual Cetacean Dataset for Fine-Grained Categorisation. CoRR abs/1908.02669 (2019) - [i12]Amir Atapour Abarghouei, Stephen Bonner, Andrew Stephen McGough:
A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation. CoRR abs/1908.06750 (2019) - [i11]Stephen Bonner, Amir Atapour Abarghouei, Philip T. G. Jackson, John Brennan, Ibad Kureshi, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara:
Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions. CoRR abs/1908.08402 (2019) - [i10]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Optimising energy and overhead for large parameter space simulations. CoRR abs/1910.02516 (2019) - [i9]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning. CoRR abs/1911.01203 (2019) - [i8]Amir Atapour Abarghouei, Stephen Bonner, Andrew Stephen McGough:
Volenti non fit injuria: Ransomware and its Victims. CoRR abs/1911.08364 (2019) - 2018
- [j16]A. Stephen McGough, Matthew Forshaw:
Introduction to special issue on Energy-Aware Simulation and Modelling (ENERGY-SIM). Sustain. Comput. Informatics Syst. 18: 135-136 (2018) - [c47]Amit Gajbhiye, Sardar F. Jaf, Noura Al Moubayed, Steven Bradley, A. Stephen McGough:
CAM: A Combined Attention Model for Natural Language Inference. IEEE BigData 2018: 1009-1014 - [c46]Fady Medhat, Mahnaz Mohammadi, Sardar F. Jaf, Chris G. Willcocks, Toby P. Breckon, Peter Matthews, Andrew Stephen McGough, Georgios Theodoropoulos, Boguslaw Obara:
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text. IEEE BigData 2018: 2986-2994 - [c45]Stephen Bonner, John Brennan, Ibad Kureshi, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara:
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning. IEEE BigData 2018: 3737-3746 - [c44]Daniel Justus, John Brennan, Stephen Bonner, Andrew Stephen McGough:
Predicting the Computational Cost of Deep Learning Models. IEEE BigData 2018: 3873-3882 - [c43]Osama Nasser Alrajeh, Matthew Forshaw, Andrew Stephen McGough, Nigel Thomas:
Simulation of Virtual Machine Live Migration in High Throughput Computing Environments. DS-RT 2018: 47-54 - [c42]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Segmenting Residential Smart Meter Data for Short-Term Load Forecasting. e-Energy 2018: 91-96 - [c41]A. Stephen McGough, Matthew Forshaw, John Brennan, Noura Al Moubayed, Stephen Bonner:
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. IGSC 2018: 1-8 - [c40]Amit Gajbhiye, Sardar F. Jaf, Noura Al Moubayed, A. Stephen McGough, Steven Bradley:
An Exploration of Dropout with RNNs for Natural Language Inference. ICANN (3) 2018: 157-167 - [c39]Zakhriya Alhassan, A. Stephen McGough, Riyad Alshammari, Tahani Daghstani, David Budgen, Noura Al Moubayed:
Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data Using Deep Learning Models. ICANN (3) 2018: 468-478 - [c38]Thomas Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle:
Black-Box Variational Inference for Stochastic Differential Equations. ICML 2018: 4420-4429 - [c37]Zakhriya Alhassan, David Budgen, Riyad Alshammari, Tahani Daghstani, A. Stephen McGough, Noura Al Moubayed:
Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data. ICMLA 2018: 541-546 - [c36]A. Stephen McGough, Matthew Forshaw:
Evaluation of Energy Consumption of Replicated Tasks in a Volunteer Computing Environment. ICPE Companion 2018: 85-90 - [i7]Stephen Bonner, Ibad Kureshi, John Brennan, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara:
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study. CoRR abs/1806.07464 (2018) - [i6]Amit Gajbhiye, Sardar F. Jaf, Noura Al Moubayed, A. Stephen McGough, Steven Bradley:
An Exploration of Dropout with RNNs for Natural Language Inference. CoRR abs/1810.08606 (2018) - [i5]A. Stephen McGough, Matthew Forshaw, John Brennan, Noura Al Moubayed, Stephen Bonner:
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. CoRR abs/1810.08675 (2018) - [i4]Tom Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle:
Black-Box Autoregressive Density Estimation for State-Space Models. CoRR abs/1811.08337 (2018) - [i3]Stephen Bonner, John Brennan, Ibad Kureshi, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara:
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning. CoRR abs/1811.08366 (2018) - [i2]Daniel Justus, John Brennan, Stephen Bonner, Andrew Stephen McGough:
Predicting the Computational Cost of Deep Learning Models. CoRR abs/1811.11880 (2018) - 2017
- [c35]Stephen Bonner, John Brennan, Ibad Kureshi, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara:
Evaluating the quality of graph embeddings via topological feature reconstruction. IEEE BigData 2017: 2691-2700 - [c34]Noura Al Moubayed, David Wall, A. Stephen McGough:
Identifying Changes in the Cybersecurity Threat Landscape Using the LDA-Web Topic Modelling Data Search Engine. HCI (22) 2017: 287-295 - [c33]Noura Al Moubayed, Bashar Awwad Shiekh Hasan, Andrew Stephen McGough:
Enhanced detection of movement onset in EEG through deep oversampling. IJCNN 2017: 71-78 - [c32]A. Stephen McGough, Noura Al Moubayed, Matthew Forshaw:
Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems. ICPE Companion 2017: 55-60 - 2016
- [j15]Matthew Forshaw, A. Stephen McGough, Nigel Thomas:
HTC-Sim: a trace-driven simulation framework for energy consumption in high-throughput computing systems. Concurr. Comput. Pract. Exp. 28(12): 3260-3290 (2016) - [c31]Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough:
Deep topology classification: A new approach for massive graph classification. IEEE BigData 2016: 3290-3297 - [c30]Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough:
GFP-X: A parallel approach to massive graph comparison using spark. IEEE BigData 2016: 3298-3307 - [c29]Noura Al Moubayed, Toby P. Breckon, Peter Matthews, A. Stephen McGough:
SMS Spam Filtering Using Probabilistic Topic Modelling and Stacked Denoising Autoencoder. ICANN (2) 2016: 423-430 - [i1]Noura Al Moubayed, Toby P. Breckon, Peter Matthews, A. Stephen McGough:
SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder. CoRR abs/1606.05554 (2016) - 2015
- [j14]Andrew Stephen McGough, Budi Arief, Carl Gamble, David Wall, John Brennan, John S. Fitzgerald, Aad P. A. van Moorsel, Sujeewa Alwis, Georgios Theodoropoulos, Ed Ruck-Keene:
Ben-ware: Identifying Anomalous Human Behaviour in Heterogeneous Systems Using Beneficial Intelligent Software. J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. 6(4): 3-46 (2015) - [c28]Stephen Bonner, Andrew Stephen McGough, Ibad Kureshi, John Brennan, Georgios Theodoropoulos, Laura Moss, David Corsar, Grigoris Antoniou:
Data quality assessment and anomaly detection via map/reduce and linked data: A case study in the medical domain. IEEE BigData 2015: 737-746 - [c27]Andrew Stephen McGough, David Wall, John Brennan, Georgios Theodoropoulos, Ed Ruck-Keene, Budi Arief, Carl Gamble, John S. Fitzgerald, Aad P. A. van Moorsel, Sujeewa Alwis:
Insider Threats: Identifying Anomalous Human Behaviour in Heterogeneous Systems Using Beneficial Intelligent Software (Ben-ware). MIST@CCS 2015: 1-12 - [c26]A. Stephen McGough, Matthew Forshaw:
Energy-Aware Simulation of Workflow Execution in High Throughput Computing Systems. DS-RT 2015: 25-32 - [c25]Matthew Forshaw, A. Stephen McGough:
Flipping the priority: effects of prioritising HTC jobs on energy consumption in a multi-use cluster. SimuTools 2015: 357-364 - 2014
- [j13]Andrew Stephen McGough, Matthew Forshaw, Clive Gerrard, Stuart Wheater, Ben Allen, Paul Robinson:
Comparison of a cost-effective virtual cloud cluster with an existing campus cluster. Future Gener. Comput. Syst. 41: 65-78 (2014) - [j12]A. Stephen McGough, Matthew Forshaw:
Reduction of wasted energy in a volunteer computing system through Reinforcement Learning. Sustain. Comput. Informatics Syst. 4(4): 262-275 (2014) - [c24]Matthew Forshaw, Nigel Thomas, A. Stephen McGough:
Trace-Driven Simulation for Energy Consumption in High Throughput Computing Systems. DS-RT 2014: 27-34 - [c23]Andrew Stephen McGough, Isi Mitrani:
Optimal Hiring of Cloud Servers. EPEW 2014: 1-15 - [c22]Matthew Forshaw, Andrew Stephen McGough, Nigel Thomas:
On Energy-efficient Checkpointing in High-throughput Cycle-stealing Distributed Systems. SMARTGREENS 2014: 262-267 - [c21]Matthew Forshaw, Andrew Stephen McGough, Nigel Thomas:
Energy-efficient Checkpointing in High-throughput Cycle-stealing Distributed Systems. PASM 2014: 65-90 - 2013
- [j11]Andrew Stephen McGough, Matthew Forshaw, Clive Gerrard, Paul Robinson, Stuart Wheater:
Analysis of power-saving techniques over a large multi-use cluster with variable workload. Concurr. Comput. Pract. Exp. 25(18): 2501-2522 (2013) - [j10]Vassilis Glenis, Andrew Stephen McGough, Vedrana Kutija, Chris G. Kilsby, Simon Woodman:
Flood modelling for cities using Cloud computing. J. Cloud Comput. 2: 7 (2013) - 2012
- [c20]Andrew Stephen McGough, Matthew Forshaw, Clive Gerrard, Stuart Wheater:
Reducing the Number of Miscreant Tasks Executions in a Multi-use Cluster. CGC 2012: 296-303 - [c19]A. Stephen McGough, S. Liang, M. Rapoportas, R. Grey, G. Kumar Vinod, D. Maddy, A. Trueman, John Wainwright:
Massively parallel landscape-evolution modelling using general purpose graphical processing units. HiPC 2012: 1-10 - 2011
- [c18]Andrew Stephen McGough, Clive Gerrard, Jonathan Noble, Paul Robinson, Stuart Wheater:
Analysis of Power-Saving Techniques over a Large Multi-use Cluster. DASC 2011: 364-371 - [c17]Andrew Stephen McGough:
Developing a Cost-Effective Virtual Cluster on the Cloud. GECON 2011: 185-197 - 2010
- [c16]Dave Colling, Andrew Stephen McGough, Tiejun Ma, Vesso Novov, Jazz Mack Smith, David Wallom, Xin Xiong:
Adding Standards Based Job Submission to a Commodity Grid Broker. CIT 2010: 1530-1535 - [c15]Andrew Stephen McGough, Clive Gerrard, Paul Haldane, Dave Sharples, Daniel Swan, Paul Robinson, Sindre Hamlander, Stuart Wheater:
Intelligent Power Management Over Large Clusters. GreenCom/CPSCom 2010: 88-95
2000 – 2009
- 2008
- [j9]Ali Afzal, Andrew Stephen McGough, John Darlington:
Capacity planning and scheduling in Grid computing environments. Future Gener. Comput. Syst. 24(5): 404-414 (2008) - [j8]Andrew Stephen McGough, William Lee, Shikta Das:
A standards based approach to enabling legacy applications on the Grid. Future Gener. Comput. Syst. 24(7): 731-743 (2008) - [c14]Dave Colling, Andrew Stephen McGough, Jazz Mack Smith, Vesso Novov, Tiejun Ma, David Wallom, Xin Xiong:
Adding Standards Based Job Submission to a Commodity Grid Broker. eScience 2008: 380-381 - 2007
- [j7]Andrew Stephen McGough, Asif Akram, Li Guo, Marko Krznaric, Luke Dickens, Dave Colling, Janusz Martyniak, Roger S. Powell, Paul Kyberd, Chenxi Huang, Constantinos Kotsokalis, Panayotis Tsanakas:
GRIDCC: A Real-time Grid workflow system with QoS. Sci. Program. 15(4): 213-234 (2007) - [c13]Li Guo, Andrew Stephen McGough, Asif Akram, Dave Colling, Janusz Martyniak, Marko Krznaric:
Enabling QoS for Service-Oriented Workflow on GRID. CIT 2007: 1077-1082 - [c12]Andrew Stephen McGough, Asif Akram, Li Guo, Marko Krznaric, Luke Dickens, Dave Colling, Janusz Martyniak, Roger S. Powell, Paul Kyberd, Constantinos Kotsokalis:
GRIDCC: real-time workflow system. WORKS@HPDC 2007: 3-12 - [p1]A. Stephen McGough, William Lee, Jeremy Cohen, Eleftheria Katsiri, John Darlington:
ICENI. Workflows for e-Science, Scientific Workflows for Grids 2007: 395-415 - 2006
- [j6]Yash Patel, Andrew Stephen McGough, John Darlington:
A Profitable Broker in a Volatile Utility Grid. Int. Trans. Syst. Sci. Appl. 2(2): 167-176 (2006) - [c11]Ali Afzal, John Darlington, Andrew Stephen McGough:
Capacity Planning and Stochastic Scheduling in Large-Scale Grids. e-Science 2006: 86 - [c10]Ali Afzal, John Darlington, Andrew Stephen McGough:
Stochastic Workflow Scheduling with QoS Guarantees in Grid Computing Environments. GCC 2006: 185-194 - [c9]Ali Afzal, John Darlington, Andrew Stephen McGough:
QoS-Constrained Stochastic Workflow Scheduling in Enterprise and Scientific Grids. GRID 2006: 1-8 - [c8]Yash Patel, Andrew Stephen McGough, John Darlington:
QoS Support For Workflows In A Volatile Grid. GRID 2006: 64-71 - [c7]Dave Colling, Luke William Dickens, Tiziana Ferrari, Y. Hassoun, Constantinos Kotsokalis, Marko Krznaric, Janusz Martyniak, Andrew Stephen McGough, Elisabetta Ronchieri:
Adding Instruments and Workflow Support to Existing Grid Architectures. International Conference on Computational Science (3) 2006: 956-963 - [c6]Andrew Stephen McGough, William Lee, John Darlington:
Workflow Deployment in ICENI II. International Conference on Computational Science (3) 2006: 964-971 - 2005
- [j5]Andrew Stephen McGough, Ali Afzal, John Darlington, Nathalie Furmento, Anthony Edward Mayer, Laurie Robert Young:
Making the Grid Predictable through Reservations and Performance Modelling. Comput. J. 48(3): 358-368 (2005) - [j4]Andrew Stephen McGough, Jeremy Cohen, John Darlington, Eleftheria Katsiri, William Lee, Sofia Panagiotidi, Yash Patel:
An End-to-end Workflow Pipeline for Large-scale Grid Computing. J. Grid Comput. 3(3-4): 259-281 (2005) - 2002
- [j3]Nathalie Furmento, Anthony Edward Mayer, A. Stephen McGough, Steven J. Newhouse, Tony Field, John Darlington:
ICENI: Optimisation of component applications within a Grid environment. Parallel Comput. 28(12): 1753-1772 (2002) - [j2]Andrew Stephen McGough, Isi Mitrani:
Efficient parallel simulation of a sliding window protocol. Perform. Evaluation 48(1/4): 237-246 (2002) - [c5]Anthony Edward Mayer, A. Stephen McGough, Murtaza Gulamali, Laurie Robert Young, Jim Stanton, Steven J. Newhouse, John Darlington:
Meaning and Behaviour in Grid Oriented Components. GRID 2002: 100-111 - 2001
- [c4]Nathalie Furmento, Anthony Edward Mayer, A. Stephen McGough, Steven J. Newhouse, John Darlington:
A Component Framework for HPC Applications. Euro-Par 2001: 540-548 - [c3]Nathalie Furmento, Anthony Edward Mayer, A. Stephen McGough, Steven J. Newhouse, Tony Field, John Darlington:
An Integrated Grid Environment for Component Applications. GRID 2001: 26-37 - [c2]Nathalie Furmento, Anthony Edward Mayer, A. Stephen McGough, Steven J. Newhouse, Tony Field, John Darlington:
Optimisation of component-based applications within a grid environment. SC 2001: 30 - 2000
- [b1]Andrew Stephen McGough:
Parallel simulations using recurrence relations and relaxation. Newcastle University, Newcastle upon Tyne, UK, 2000 - [j1]Andrew Stephen McGough, Isi Mitrani:
Parallel simulation of ATM switches using relaxation. Perform. Evaluation 41(2-3): 149-164 (2000) - [c1]Andrew Stephen McGough, Isi Mitrani:
Efficient distributed simulation of a communication switch with bursty sources and losses. PADS 2000: 85-92
Coauthor Index
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