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Matthew Forshaw
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2020 – today
- 2024
- [c46]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 - [c45]Tony Clear
, Åsa Cajander
A Plan for a Joint Study into the Impacts of AI on Professional Competencies of IT Professionals and Implications for Computing Students. ITiCSE (2) 2024 - [i15]Fulong Yao, Wanqing Zhao, Matthew Forshaw, Yang Song:
A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning. CoRR abs/2403.01013 (2024) - [i14]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) - [i13]Fulong Yao, Wanqing Zhao, Matthew Forshaw, Yang Song:
A New Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction. CoRR abs/2407.08010 (2024) - 2023
- [j10]Osman Akbulut
Visualizing ordered bivariate data on node-link diagrams. Vis. Informatics 7(3): 22-36 (2023) - [c44]Mohamad Elhadi Abushofa
FEGR: Feature Enhanced Graph Representation Method for Graph Classification. ASONAM 2023: 371-378 - [c43]Stuart Jamieson
On Improving Streaming System Autoscaler Behaviour using Windowing and Weighting Methods. DEBS 2023: 68-79 - [c42]Paul Omoregbee
A State-Size Inclusive Approach to Optimizing Stream Processing Applications. EPEW 2023: 325-339 - [c41]Mehmet Cengiz
Predicting the Performance of a Computing System with Deep Networks. ICPE 2023: 91-98 - [e4]Matthew Forshaw, Katja Gilly, William J. Knottenbelt, Nigel Thomas
Practical Applications of Stochastic Modelling - 11th International Workshop, PASM 2022, Alicante, Spain, September 23, 2022, Revised Selected Papers. Communications in Computer and Information Science 1786, Springer 2023, ISBN 978-3-031-44052-6 [contents] - [i12]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) - 2022
- [c40]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 - [c39]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 - [c38]Stuart Jamieson
Measuring Streaming System Robustness Using Non-parametric Goodness-of-Fit Tests. EPEW 2022: 3-18 - [c37]Andrew Stephen McGough
Analysis of Reinforcement Learning for Determining Task Replication in Workflows. EPEW 2022: 117-132 - [c36]Paul Omoregbee
Performability Requirements in Making a Rescaling Decision for Streaming Applications. EPEW 2022: 133-147 - [c35]Antreas Kasiotis
Towards Energy-Aware Management of Shared Printers. PASM 2022: 93-104 - [i11]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) - [i10]David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Long-term Reproducibility for Neural Architecture Search. CoRR abs/2207.04821 (2022) - [i9]Andrew Stephen McGough, Matthew Forshaw:
Analysis of Reinforcement Learning for determining task replication in workflows. CoRR abs/2209.13531 (2022) - 2021
- [j9]Osama Nasser Alrajeh, Matthew Forshaw, Nigel Thomas:
Using Virtual Machine live migration in trace-driven energy-aware simulation of high-throughput computing systems. Sustain. Comput. Informatics Syst. 29(Part): 100468 (2021) - [j8]Alexander J. M. Kell
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) - [c34]Cahyadi, Matthew Forshaw:
Hard Disk Failure Prediction on Highly Imbalanced Data using LSTM Network. IEEE BigData 2021: 3985-3991 - [i8]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) - [i7]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) - 2020
- [c33]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 - [c32]Alexander J. M. Kell
Long-term electricity market agent based model validation using genetic algorithm based optimization. e-Energy 2020: 1-13 - [c31]Alexander J. M. Kell
Optimizing carbon tax for decentralized electricity markets using an agent-based model. e-Energy 2020: 454-460 - [e3]Matthew Forshaw, Marco Gribaudo, William J. Knottenbelt, Nigel Thomas:
Tenth International Workshop on the Practical Application of Stochastic Modelling, PASM 2019, Milan, Italy, November 2019. Electronic Notes in Theoretical Computer Science 353, Elsevier 2020 [contents] - [i6]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) - [i5]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) - [i4]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)
2010 – 2019
- 2019
- [c30]Alexander J. M. Kell
Modelling Carbon Tax in the UK Electricity Market using an Agent-Based Model. e-Energy 2019: 425-427 - [c29]Alexander J. M. Kell
ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning. e-Energy 2019: 556-565 - [c28]Alexander J. M. Kell
Optimising energy and overhead for large parameter space simulations. IGSC 2019: 1-8 - [c27]Andharini D. Cahyani, Lindsay F. Marshall, Matthew Forshaw:
Students' Perception on Data Sources from Outside Virtual Learning Environment for Learning Analytics. ICETC 2019: 165-170 - [c26]Matthew Forshaw, Marco Gribaudo, William J. Knottenbelt, Nigel Thomas:
Preface. PASM 2019: 1-3 - [i3]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Optimising energy and overhead for large parameter space simulations. CoRR abs/1910.02516 (2019) - [i2]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) - 2018
- [j7]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) - [c25]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 - [c24]Alexander J. M. Kell
Segmenting Residential Smart Meter Data for Short-Term Load Forecasting. e-Energy 2018: 91-96 - [c23]A. Stephen McGough, Matthew Forshaw, John Brennan
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. IGSC 2018: 1-8 - [c22]Vasiliki Kalavri, John Liagouris, Moritz Hoffmann, Desislava C. Dimitrova, Matthew Forshaw, Timothy Roscoe:
Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows. OSDI 2018: 783-798 - [c21]A. Stephen McGough
Evaluation of Energy Consumption of Replicated Tasks in a Volunteer Computing Environment. ICPE Companion 2018: 85-90 - [e2]Matthew Forshaw, William J. Knottenbelt, Nigel Thomas, Katinka Wolter:
Proceedings of the Ninth International Workshop on the Practical Application of Stochastic Modelling, PASM 2017, Berlin, Germany, September 9, 2017. Electronic Notes in Theoretical Computer Science 337, Elsevier 2018 [contents] - [i1]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) - 2017
- [j6]Kiavash Satvat, Matthew Forshaw
Erratum to "On the Privacy of Private Browsing - A Forensic Approach" [JISA 19/1(2014), 88-100]. J. Inf. Secur. Appl. 33: 66 (2017) - [c20]Osama Nasser Alrajeh, Matthew Forshaw
Machine Learning Models for Predicting Timely Virtual Machine Live Migration. EPEW 2017: 169-183 - [c19]Saleh Mohamed, Matthew Forshaw
Automatic Generation of Distributed Run-Time Infrastructure for Internet of Things. ICSA Workshops 2017: 100-107 - [c18]Aad P. A. van Moorsel, Matthew Forshaw
Experience Report: How to Design Web-Based Competitions for Legal Proceedings: Lessons from a Court Case. ISSRE 2017: 240-249 - [c17]James Allen
Towards an Extensible and Scalable Energy Harvesting Wireless Sensor Network Simulation Framework. ICPE Companion 2017: 39-42 - [c16]A. Stephen McGough
Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems. ICPE Companion 2017: 55-60 - [c15]Saleh Mohamed, Matthew Forshaw
Performance and Dependability Evaluation of Distributed Event-based Systems: A Dynamic Code-injection Approach. ICPE 2017: 349-352 - [c14]Matthew Forshaw
Preface. PASM 2017: 1-3 - [c13]Adam Cattermole, Matthew Forshaw
An Automated Approach to Cloud Performance Benchmarking. UKPEW 2017: 23-39 - [e1]Nigel Thomas, Matthew Forshaw:
Analytical and Stochastic Modelling Techniques and Applications - 24th International Conference, ASMTA 2017, Newcastle-upon-Tyne, UK, July 10-11, 2017, Proceedings. Lecture Notes in Computer Science 10378, Springer 2017, ISBN 978-3-319-61427-4 [contents] - 2016
- [j5]Matthew Forshaw
HTC-Sim: a trace-driven simulation framework for energy consumption in high-throughput computing systems. Concurr. Comput. Pract. Exp. 28(12): 3260-3290 (2016) - [c12]Oonagh McGee, Matthew Forshaw
Out of the Comfort Zone: Embedding Entrepreneurship in a Cohort of Computer Science Doctoral Students. ITiCSE 2016: 83-88 - [c11]Matthew Forshaw
Meeting Graduate Employability Needs through Open-source Collaboration with Industry. SIGCSE 2016: 516-521 - 2015
- [c10]A. Stephen McGough, Matthew Forshaw
Energy-Aware Simulation of Workflow Execution in High Throughput Computing Systems. DS-RT 2015: 25-32 - [c9]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
- [j4]Andrew Stephen McGough, Matthew Forshaw
Comparison of a cost-effective virtual cloud cluster with an existing campus cluster. Future Gener. Comput. Syst. 41: 65-78 (2014) - [j3]Kiavash Satvat, Matthew Forshaw
On the privacy of private browsing - A forensic approach. J. Inf. Secur. Appl. 19(1): 88-100 (2014) - [j2]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) - [c8]Matthew Forshaw
Trace-Driven Simulation for Energy Consumption in High Throughput Computing Systems. DS-RT 2014: 27-34 - [c7]Matthew Forshaw, Andrew Stephen McGough, Nigel Thomas:
On Energy-efficient Checkpointing in High-throughput Cycle-stealing Distributed Systems. SMARTGREENS 2014: 262-267 - [c6]Jeremy T. Bradley, Matthew Forshaw
Time-inhomogeneous Population Models of a Cycle-Stealing Distributed System. UKPEW 2014: 5-17 - [c5]Matthew Forshaw
Energy-efficient Checkpointing in High-throughput Cycle-stealing Distributed Systems. PASM 2014: 65-90 - [c4]Thai Ha Nguyen, Matthew Forshaw
Operating Policies for Energy Efficient Dynamic Server Allocation. UKPEW 2014: 159-177 - 2013
- [j1]Andrew Stephen McGough
Analysis of power-saving techniques over a large multi-use cluster with variable workload. Concurr. Comput. Pract. Exp. 25(18): 2501-2522 (2013) - [c3]Kiavash Satvat, Matthew Forshaw
On the Privacy of Private Browsing - A Forensic Approach. DPM/SETOP 2013: 380-389 - 2012
- [c2]Andrew Stephen McGough, Matthew Forshaw
Reducing the Number of Miscreant Tasks Executions in a Multi-use Cluster. CGC 2012: 296-303 - [c1]Matthew Forshaw
A Novel Approach to Energy Efficient Content Distribution with BitTorrent. EPEW/UKPEW 2012: 188-196
Coauthor Index
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last updated on 2024-10-07 21:16 CEST by the dblp team
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