Computing and Mathematical Sciences Papers


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

  • Concrete ephemeriality

    Simmons, Rowan; Soo, Chin-En Keith (2020)
    Concrete ephemerality is a project designed to capture something that was once transitory and seeks to create something concrete from it. With ever more tracking and aggregation of our digital lives, online identities that ...
  • A note on families over a p-adic disk, circa 2012

    Delbourgo, Daniel; Smith, Lachlan (2012)
    The following notes discuss how the local conditions H¹ₑ and H¹𝓰 of Bloch-Kato vary in an analytic family, culminating in the proof of a control theorem (Theorem 1.1).
  • On ensemble techniques for data stream regression

    Gomes, Heitor Murilo; Montiel, Jacob; Mastelini, Saulo Martiello; Pfahringer, Bernhard; Bifet, Albert (IEEE, 2020)
    An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were ...
  • Towards embedding data provenance in files

    Phua, Thye Way; Patros, Panos; Kumar, Vimal (IEEE, 2021)
    Data provenance (keeping track of who did what, where, when and how) boasts of various attractive use cases for distributed systems, such as intrusion detection, forensic analysis and secure information dependability. This ...
  • CS-ARF: Compressed adaptive random forests for evolving data stream classification

    Bahri, Maroua; Gomes, Heitor Murilo; Bifet, Albert; Maniu, Silviu (IEEE, 2020)
    Ensemble-based methods are one of the most often used methods in the classification task that have been adapted to the stream setting because of their high learning performance achievement. For instance, Adaptive Random ...
  • Orthogonal grid generation software

    Delbourgo, Daniel; Walker, Stephen (C.S.I.R.O. Division of Oceanography, 1992)
    Calculates transformations between physical and index space within a numerical grid
  • Classifier chains: A review and perspectives

    Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey; Frank, Eibe (AI Access Foundation, 2021)
    The family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems. This approach involves chaining together off-the-shelf binary classifiers in a directed structure, ...
  • Inferring trust using personality aspects extracted from texts

    Granatyr, Jones; Gomes, Heitor Murilo; DIas, João Miguel; Paiva, Ana Maria; Nunes, Maria Augusta Silveira Netto; Scalabrin, Edson Emílio; Spak, Fábio (IEEE, 2019)
    Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do ...
  • An empirical study of moment estimators for quantile approximation

    Mitchell, Rory; Frank, Eibe; Holmes, Geoffrey (Association for Computing Machinery (ACM), 2021)
    We empirically evaluate lightweight moment estimators for the single-pass quantile approximation problem, including maximum entropy methods and orthogonal series with Fourier, Cosine, Legendre, Chebyshev and Hermite basis ...
  • AS-Path Prepending: There is no rose without a thorn

    Marcos, Pedro; Prehn, Lars; Leal, Lucas; Dainotti, Alberto; Feldmann, Anja; Barcellos, Marinho (ACM, 2020)
    Inbound traffic engineering (ITE) - -the process of announcing routes to, e.g., maximize revenue or minimize congestion - -is an essential task for Autonomous Systems (ASes). AS Path Prepending (ASPP) is an easy to use and ...
  • Everyone everywhere: A distributed and embedded paradigm for usability

    Twidale, Michael B.; Nichols, David M.; Lueg, Christopher P. (Wiley, 2021)
    We present a new paradigm to address the persistence of difficulties that people have in accessing and using information. Our idea consists of two main aspects: engaging wider society with usability and distributing the ...
  • Metaheuristic optimization of insulin infusion protocols using historical data with validation using a patient simulator

    Wang, Hongyu; Chepulis, Lynne Merran; Paul, Ryan G.; Mayo, Michael (World Scientific, 2021)
    Metaheuristic search algorithms are used to develop new protocols for optimal intravenous insulin infusion rate recommendations in scenarios involving hospital in-patients with Type 1 Diabetes. Two metaheuristic search ...
  • Regularisation of neural networks by enforcing Lipschitz continuity

    Gouk, Henry; Frank, Eibe; Pfahringer, Bernhard; Cree, Michael J. (Springer, 2020)
    We investigate the effect of explicitly enforcing the Lipschitz continuity of neural networks with respect to their inputs. To this end, we provide a simple technique for computing an upper bound to the Lipschitz constant—for ...
  • ESCAPADE: Encryption-type-ransomeware: system call based pattern detection

    Chew, Christopher; Kumar, Vimal; Patros, Panos; Malik, Robi (Springer, 2020)
    Encryption-type ransomware has risen in prominence lately as the go-to malware for threat actors aiming to compromise Android devices. In this paper, we present a ransomware detection technique based on behaviours observed ...
  • Quantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learning

    Barker, Rocky D.; Barker, Shaun L.L.; Cracknell, Matthew J.; Stock, Elizabeth D.; Holmes, Geoffrey (Society of Economic Geologists, 2020)
    Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the ...
  • confstream: automated algorithm selection and configuration of stream clustering algorithms

    Carnein, Matthias; Trautmann, Heike; Bifet, Albert; Pfahringer, Bernhard (Springer, 2020)
    Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult ...
  • Mining attribute evolution rules in dynamic attributed graphs

    Fournier-Viger, Philippe; He, Ganghuan; Lin, Jerry Chun-Wei; Gomes, Heitor Murilo (Springer, 2020)
    A dynamic attributed graph is a graph that changes over time and where each vertex is described using multiple continuous attributes. Such graphs are found in numerous domains, e.g., social network analysis. Several studies ...
  • Transfer of pretrained model weights substantially improves semi-supervised image classification

    Sahito, Attaullah; Frank, Eibe; Pfahringer, Bernhard (Springer, 2020)
    Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled ...
  • A comparison of machine learning methods for cross-domain few-shot learning

    Wang, Hongyu; Gouk, Henry; Frank, Eibe; Pfahringer, Bernhard; Mayo, Michael (Springer, 2020)
    We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, ...
  • On the generation of compressible mirror-mode fluctuations in the inner heliosheath

    Fichtner, Horst; Kleimann, Jens; Yoon, Peter H.; Scherer, Klaus; Oughton, Sean; Engelbrecht, N. Eugene (IOP Publishing Ltd, 2020)
    Measurements made with the Voyager 1 spacecraft indicate that significant levels of compressive fluctuations exist in the inner heliosheath. Some studies have already been performed with respect to the mirror-mode instability ...

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