Distributed Caching in Wireless Cellular Networks Incorporating Parallel Processing (2018)
Journal Article
Fan. (2018). Distributed Caching in Wireless Cellular Networks Incorporating Parallel Processing. IEEE Internet Computing, 52 -61. https://doi.org/10.1109/MIC.2018.112101645

Distributed caching is a promising technique for reducing redundant data traffic and user content access delay in telecommunications systems. This article explores caching technologies with a focus on the processing of content requests in today’s hie... Read More about Distributed Caching in Wireless Cellular Networks Incorporating Parallel Processing.

Enhanced Collision Avoidance for Distributed LTE Vehicle to Vehicle Broadcast Communications (2018)
Journal Article
Fan. (2018). Enhanced Collision Avoidance for Distributed LTE Vehicle to Vehicle Broadcast Communications. IEEE Communications Letters, 630 - 633. https://doi.org/10.1109/LCOMM.2018.2791399

In this letter, we investigate the distributed autonomous resource selection for LTE vehicle to vehicle (V2V) broadcast. The effectiveness of collision avoidance and location based resource allocation enhancements is examined. It is found that collis... Read More about Enhanced Collision Avoidance for Distributed LTE Vehicle to Vehicle Broadcast Communications.

An Ambient Intelligence System for Assisted Living (2017)
Book Chapter
Ortolani. (2017). An Ambient Intelligence System for Assisted Living. In 2017 AEIT International Annual Conference, Cagliari, Italy, 20-22 Sep (1 -6). https://doi.org/10.23919/AEIT.2017.8240559

Nowadays, the population's average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, w... Read More about An Ambient Intelligence System for Assisted Living.

Turning Models Inside Out (2017)
Journal Article
(2017). Turning Models Inside Out. CEUR Workshop Proceedings,

We present an approach for change-based (as opposed to state-based) model persistence that can facilitate highperformance incremental model processing (e.g. validation, transformation) by minimising the cost of change identification when models evolv... Read More about Turning Models Inside Out.

Effect sizes and their variance for AB/BA crossover design studies (2017)
Journal Article
(2017). Effect sizes and their variance for AB/BA crossover design studies. Empirical Software Engineering, 1982 - 2017. https://doi.org/10.1007/s10664-017-9574-5

Vegas et al. IEEE Trans Softw Eng 42(2):120:135 (2016) raised concerns about the use of AB/BA crossover designs in empirical software engineering studies. This paper addresses issues related to calculating standardized effect sizes and their variance... Read More about Effect sizes and their variance for AB/BA crossover design studies.

Towards an Accurate Identification of Pyloric Neuron Activity with VSDi (2017)
Presentation / Conference Contribution
dos Santos, F., Andras, P., & Lam, K. (2017, September). Towards an Accurate Identification of Pyloric Neuron Activity with VSDi. Presented at 26th International Conference on Artificial Neural Networks, Alghero, Sardinia, Italy

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.

The contribution that empirical studies performed in industry make to the findings of systematic reviews: A tertiary study (2017)
Journal Article
(2017). The contribution that empirical studies performed in industry make to the findings of systematic reviews: A tertiary study. Information and Software Technology, 234-244. https://doi.org/10.1016/j.infsof.2017.10.012

Context
Systematic reviews can provide useful knowledge for software engineering practice, by aggregating and synthesising empirical studies related to a specific topic.

Objective
We sought to assess how far the findings of systematic reviews ad... Read More about The contribution that empirical studies performed in industry make to the findings of systematic reviews: A tertiary study.

The contributions the empirical studies performed in industry make to the findings of systematic reviews: a tertiary study (2017)
Journal Article
(2017). The contributions the empirical studies performed in industry make to the findings of systematic reviews: a tertiary study. Information and Software Technology, https://doi.org/10.1016/j.infsof.2017.10.012

Context:
Systematic reviews can provide useful knowledge for software engineering practice, by aggregating and synthesising empirical studies related to a specific topic.
Objective:
We sought to assess how far the findings of systematic reviews ad... Read More about The contributions the empirical studies performed in industry make to the findings of systematic reviews: a tertiary study.

A Context-Aware System for Ambient Assisted Living (2017)
Book Chapter
Ortolani. (2017). A Context-Aware System for Ambient Assisted Living. In Ubiquitous Computing and Ambient Intelligence. UCAmI 2017 (426 - 438). https://doi.org/10.1007/978-3-319-67585-5_44

De Paola A. et al. (2017) A Context-Aware System for Ambient Assisted Living. In: Ochoa S., Singh P., Bravo J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science, vol 10586. Springer, Cham

Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc (2017)
Journal Article
Al-Bander, B., Al-Nuaimy, W., Williams, B. M., & Zheng, Y. (2018). Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc. Biomedical Signal Processing and Control, 40, 91 - 101. https://doi.org/10.1016/j.bspc.2017.09.008

I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization (2017)
Presentation / Conference Contribution
Molino, A., Mandal, B., Jie, L., Lim, J.-H., Subbaraju, V., & Chandrasekhar, V. I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization

In this paper we describe our approach for the ImageCLEF-lifelog summarization task. A total of ten runs were submitted, which used only visual features, only metadata information, or both. In the first step, a set of relevant frames are drawn from t... Read More about I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization.

Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer (2017)
Presentation / Conference Contribution
Butcher, J. B., Rutter, A. V., Wootton, A. J., Day, C. R., & Sulé-Suso, J. (2017, September). Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer. Presented at 17th Annual UK Workshop on Computational Intelligence, Cardiff, Wales, UK

Lung cancer is a widespread disease and it is well understood that systematic, non-invasive and early detection of this progressive and life-threatening disorder is of vital importance for patient outcomes. In this work we present a convergence of fa... Read More about Artificial Neural Network Analysis of Volatile Organic Compounds for the detection of lung cancer.

Lexicase Selection Outperforms Previous Strategies for Incremental Evolution of Virtual Creature Controllers (2017)
Presentation / Conference
(2017, September). Lexicase Selection Outperforms Previous Strategies for Incremental Evolution of Virtual Creature Controllers. Presented at ECAL 2017: 14th European Conference on Artificial Life, Lyon

Evolving robust behaviors for robots has proven to be a challenging problem. Determining how to optimize behavior for a specific instance, while also realizing behaviors that generalize to variations on the problem often requires highly customized al... Read More about Lexicase Selection Outperforms Previous Strategies for Incremental Evolution of Virtual Creature Controllers.

Spontaneous mutation rate is a plastic trait associated with population density across domains of life. (2017)
Journal Article
Aston, E., McBain, A., Knight, C., Krašovec, R., Richards, H., Hatcher, C., Faulkner, K., Belavkin, R., Channon, A., & Gifford, D. (2017). Spontaneous mutation rate is a plastic trait associated with population density across domains of life. PLoS Biology, e2002731 - ?. https://doi.org/10.1371/journal.pbio.2002731

Rates of random, spontaneous mutation can vary plastically, dependent upon the environment. Such plasticity affects evolutionary trajectories and may be adaptive. We recently identified an inverse plastic association between mutation rate and populat... Read More about Spontaneous mutation rate is a plastic trait associated with population density across domains of life..

Standard Enabled Model Generator for Genetic Circuit Design (2017)
Presentation / Conference
Myers, C., Wipat, A., Misirli, G., Nguyen, T., & McLaughlin, J. (2017, August). Standard Enabled Model Generator for Genetic Circuit Design. Presented at 9th International Workshop on Bio-Design Automation, Pittsburgh

A Virtual 3D Cuneiform Tablet Reconstruction Interaction (2017)
Presentation / Conference Contribution
Hanes, L., Lewis, A., Nash, D., Gehlken, E., Hernandez-Munoz, L., Ch’ng, E., Woolley, S. I., & Collins, T. (2018, July). A Virtual 3D Cuneiform Tablet Reconstruction Interaction. Presented at 32nd Human Computer Interaction Conference, Belfast, Northern Ireland

A Collaborative Artefact Reconstruction Environment (2017)
Presentation / Conference Contribution
Woolley, S. I., Ch’ng, E., Hernandez-Munoz, L., Gehlken, E., Collins, T., Nash, D., Lewis, A., & Hanes, L. (2017, July). A Collaborative Artefact Reconstruction Environment. Presented at 31st International BCS Human Computer Interaction Conference (HCI)

A novel collaborative artefact reconstruction environment design is presented that is informed by experimental task observation and participatory design. The motivation for the design was to enable collaborative human and computer effort in the recon... Read More about A Collaborative Artefact Reconstruction Environment.

Using supervised machine learning algorithms to detect suspicious URLs in online social networks (2017)
Presentation / Conference Contribution
Al-Janabi, M., de Quincey, E., & Andras, P. (2017, August). Using supervised machine learning algorithms to detect suspicious URLs in online social networks. Presented at International Symposium on Foundations of Open Source Intelligence and Security Informatics, Sydney

Reporting Statistical Validity and Model Complexity in Machine Learning based Computational Studies (2017)
Book Chapter
(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 (EASE'17) (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.

A standard-enabled workflow for synthetic biology. (2017)
Journal Article
Mısırlı, G., Nguyen, T., Oberortner, E., Samineni, M., Wipat, A., Zhang, M., Zundel, Z., Madsen, C., McLaughlin, J., Myers, C., Beal, J., Gorochowski, T., & Kuwahara, H. (2017). A standard-enabled workflow for synthetic biology. Transactions, 793 - 803. https://doi.org/10.1042/BST20160347

A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select... Read More about A standard-enabled workflow for synthetic biology..