Computing and Mathematical Sciences

 

This community houses research from the Faculty of Computing and Mathematical Sciences at the University of Waikato.

Sub-communities within Computing and Mathematical Sciences

Collections in Computing and Mathematical Sciences

  • Habitat provision is a major driver of native bird communities in restored urban forests

    Elliot Noe, Elizabeth; Innes, John G.; Barnes, Andrew D.; Joshi, Chaitanya; Clarkson, Bruce D. (John Wiley & Sons Ltd, 2022)
    Urbanization, and the drastic loss of habitat it entails, poses a major threat to global avian biodiversity. Ecological restoration of urban forests is therefore increasingly vital for native bird conservation, but control ...
  • On the latency impact of remote peering

    Mazzola, Fabricio; Marcos, Pedro; Castro, Ignacio; Luckie, Matthew; Barcellos, Marinho (Springer, 2022)
    Internet Exchange Points (IXPs) play an essential role in the Internet, providing a fabric for thousands of Autonomous Systems (ASes) to interconnect. Initially designed to keep local traffic local, IXPs now interconnect ...
  • Predicting glucose level with an adapted branch predictor

    Koutny, Tomas; Mayo, Michael (Elsevier BV, 2022)
    Background and objective Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal ...
  • Experiments in cross-domain few-shot learning for image classification

    Wang, Hongyu; Gouk, Henry; Fraser, Huon; Frank, Eibe; Pfahringer, Bernhard; Mayo, Michael; Holmes, Geoffrey (Informa UK Limited, 2022)
    Cross-domain few-shot learning has many practical applications. This paper attempts to shed light on suitable configurations of feature exactors and ‘shallow’ classifiers in this machine learning setting. We apply ResNet-based ...
  • Methods for eliciting informative prior distributions: A critical review

    Falconer, Julia R.; Frank, Eibe; Polaschek, Devon L. L.; Joshi, Chaitanya (INFORMS, 2022)
    Eliciting informative prior distributions for Bayesian inference can often be complex and challenging. Although popular methods rely on asking experts probability-based questions to quantify uncertainty, these methods are ...
  • Energy digital twin technology for industrial energy management: Classification, challenges and future

    Yu, Wei; Patros, Panos; Young, Brent; Klinac, Elsa; Walmsley, Timothy Gordon (Elsevier BV, 2022)
    Digitalisation of the process and energy industries through energy digital twin technology promises step-improvements in energy management and optimisation, better servicing and maintenance, energy-efficient design and ...
  • GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles

    Mitchell, Rory; Frank, Eibe; Holmes, Geoffrey (PeerJ, 2022)
    SHapley Additive exPlanation (SHAP) values (Lundberg & Lee, 2017) provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values (Shapley, 1953). While exact calculation of ...
  • Interaction modelling for IoT

    Turner, Jessica; Bowen, Judy; van Zandwijk, Nikki (IEEE, 2021)
    Informal design artefacts allow end-users and nonexperts to contribute to software design ideas and development. In contrast, software engineering techniques such as modeldriven development support experts in ensuring ...
  • Wearable technology for hazardous remote environments: Smart shirt and Rugged IoT network for forestry worker health

    Hinze, Annika; Bowen, Judy; König, Jemma Lynette (Elsevier BV, 2022)
    This paper introduces the architecture and details of our wearable IoT solution for workplace health and safety in rugged outdoor environments. We focus on the specific requirements defined by the New Zealand forestry ...
  • Sampling permutations for Shapley value estimation

    Mitchell, Rory; Cooper, Joshua; Frank, Eibe; Holmes, Geoffrey (Microtome Publishing, 2022)
    Game-theoretic attribution techniques based on Shapley values are used to interpret blackbox machine learning models, but their exact calculation is generally NP-hard, requiring approximation methods for non-trivial models. ...
  • Enhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislation

    Goltz, Nachshon (Sean); Mayo, Michael (Full Court Press, 2018)
    As regulatory compliance (or compliance governance) becomes ever more challenging, attempts to engage IT solutions and especially artificial intelligence (AI) are on the rise. This paper suggest that regulatory compliance ...
  • Perceptual improvements for Super-Resolution of Satellite Imagery

    Bull, Dianel; Lim, Nick Jin Sean; Frank, Eibe (IEEE, 2021)
    Super-resolution of satellite imagery poses unique challenges. We propose a hybrid method comprising two existing deep network super-resolution approaches, namely a feedforward network called DeepSUM, and ESRGAN, a GAN-based ...
  • Using Coq to Enforce the Checks-Effects-Interactions Pattern in DeepSEA Smart Contracts

    Britten, Daniel; Sjöberg, Vilhelm; Reeves, Steve (2021)
    Using the DeepSEA system for smart contract proofs, this paper investigates how to use the Coq theorem prover to enforce that smart contracts follow the Checks-Effects-Interactions Pattern. This pattern is widely understood ...
  • The 'DEEP' Landing Error Scoring System

    Hébert-Losier, Kim; Hanzlíková, Ivana; Zheng, Chen; Streeter, Lee; Mayo, Michael (MDPI, 2020)
    The Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence that ...
  • Interpretable deep learning for surgical tool management

    Rodrigues, Mark; Mayo, Michael; Patros, Panos (Springer, 2021)
    This paper presents a novel convolutional neural network framework for multi-level classification of surgical tools. Our classifications are obtained from multiple levels of the model, and high accuracy is obtained by ...
  • Studying and exploiting the relationship between model accuracy and explanation quality

    Jia, Yunzhe; Frank, Eibe; Pfahringer, Bernhard; Bifet, Albert; Lim, Nick Jin Sean (Springer, 2021)
    Many explanation methods have been proposed to reveal insights about the internal procedures of black-box models like deep neural networks. Although these methods are able to generate explanations for individual predictions, ...
  • AI augmented approach to identify shared ideas from large format public consultation

    Weng, Min-Hsien; Wu, Shaoqun; Dyer, Mark (MDPI AG, 2021)
    Public data, contributed by citizens, stakeholders and other potentially affected parties, are becoming increasingly used to collect the shared ideas of a wider community. Having collected large quantities of text data ...
  • Barriers to diabetes self-management in a subset of New Zealand adults with Type 2 diabetes and poor glycaemic control

    Chepulis, Lynne Merran; Morison, Brittany; Cassim, Shemana; Norman, Kimberley; Keenan, Rawiri; Paul, Ryan G.; Lawrenson, Ross (Hindawi Ltd, 2021)
    Background. Despite the fact that there is an increasingly effective armoury of medications to treat diabetes, many people continue to have substantially elevated blood glucose levels. The purpose of this study was to ...
  • Convergence of public participation, participatory design and NLP to co-develop circular economy

    Dyer, Mark; Wu, Shaoqun; Weng, Min-Hsien (Springer Science and Business Media LLC, 2021)
    The concept of a circular economy is at a crossroads. To date, it has been largely driven by top-down national or trans-national legislation such as EU Circular Economy Package or Chinese Circular Economy Promotion. Bottom-up ...
  • Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

    Yap, Moi Hoon; Hachiuma, Ryo; Alavi, Azadeh; Brüngel, Raphael; Cassidy, Bill; Goyal, Manu; Zhu, Hongtao; Rückert, Johannes; Olshansky, Moshe; Huang, Xiao; Saito, Hideo; Hassanpour, Saeed; Friedrich, Christoph M.; Ascher, David B.; Song, Anping; Kajita, Hiroki; Gillespie, David; Reeves, Neil D.; Pappachan, Joseph M.; O'Shea, Claire; Frank, Eibe (Elsevier BV, 2021)
    There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art ...

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