Malay-language stemmer

Stemming is the removal of affixes (prefixes and suffixes) in a word in order to generate its root word. The objectives of this research were to build a software stemmer that can stem any given Malay word, and to develop a standard stemming algorithm for the Malay language. The Malay language was chosen because a complete stemmer for this language is unavailable. Stemmers have a wide variety of applications, such as in information retrieval and machine translation. It is expected that when this system is fully developed, it will benefit users and customers tremendously

Neoliberalism, post-feminism, and gender equality: Why educational attainment does not translate into income equality for contemporary Australian women

Although arguably the birth of neo-liberalism and the atomisation of both individuals and ideas under New-Wave Capitalism not only allowed, but encouraged feminism to get on with its task of awarding equality to women, empirical evidence in sociological and public health data still suggest otherwise. The women’s movement, and those struggling to obtain equality for the genders across health, education, income, and political representation saw an interesting transition during neo-liberalism to the popular theoretical notion of ‘DIY feminism’ – or ‘postfeminism’ – where women could simply exercise individual agency to avail themselves to the educational and career opportunities so far awarded primarily to their male counterparts. However, in 2015, we see Capitalism in a post-GFC state; the shine of neoliberalism considerably dulled, and data from the Global Gender Gap Index (2015) showing that despite massive improvements in educational attainment for many women, these are not translating into income inequality, nor any tangible career success for women across a broad range of disciplines and industries. This presentation will discuss the failings and successes of neo-liberalism in getting us to this point, and the structural, social, and economic changes needed for these successes to be sustained for women in a meaningful and financially rewarding way

Underwater Fish Detection with Weak Multi-Domain Supervision

Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier. However, for the task of fish classification and/or fish detection, if a CNN was trained to detect or classify particular fish species in particular background habitats, the same CNN exhibits much lower accuracy when applied to new/unseen fish species and/or fish habitats. Therefore, in practice, the CNN needs to be continuously fine-tuned to improve its classification accuracy to handle new project-specific fish species or habitats. In this work we present a labelling-efficient method of training a CNN-based fish-detector (the Xception CNN was used as the base) on relatively small numbers (4,000) of project-domain underwater fish/no-fish images from 20 different habitats. Additionally, 17,000 of known negative (that is, missing fish) general-domain (VOC2012) above-water images were used. Two publicly available fish-domain datasets supplied additional 27,000 of above-water and underwater positive/fish images. By using this multi-domain collection of images, the trained Xception-based binary (fish/not-fish) classifier achieved 0.17% false-positives and 0.61% false-negatives on the project's 20,000 negative and 16,000 positive holdout test images, respectively. The area under the ROC curve (AUC) was 99.94%.Comment: Published in the 2019 International Joint Conference on Neural Networks (IJCNN-2019), Budapest, Hungary, July 14-19, 2019, https://www.ijcnn.org/ , https://ieeexplore.ieee.org/document/885190

Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning

Deep learning Convolutional Neural Network (CNN) models are powerful classification models but require a large amount of training data. In niche domains such as bird acoustics, it is expensive and difficult to obtain a large number of training samples. One method of classifying data with a limited number of training samples is to employ transfer learning. In this research, we evaluated the effectiveness of birdcall classification using transfer learning from a larger base dataset (2814 samples in 46 classes) to a smaller target dataset (351 samples in 10 classes) using the ResNet-50 CNN. We obtained 79% average validation accuracy on the target dataset in 5-fold cross-validation. The methodology of transfer learning from an ImageNet-trained CNN to a project-specific and a much smaller set of classes and images was extended to the domain of spectrogram images, where the base dataset effectively played the role of the ImageNet.Comment: Accepted for IEEE Digital Image Computing: Techniques and Applications, 2019 (DICTA 2019), 2-4 December 2019 in Perth, Australia, http://dicta2019.dictaconference.org/index.htm

Simulation of implant fitting in the femur bone

This research aims to simulate implant-fitting procedures in the human femoral anatomy. Three-dimensional models of the femur and metal femoral implants are used to build a custom-fitted implant model, with the aim of benefiting medical practitioners and patients

Reoptimisation strategies for dynamic vehicle routing problems with proximity-dependent nodes

Autonomous vehicles create new opportunities as well as new challenges to dynamic vehicle routing. The introduction of autonomous vehicles as information-collecting agents results in scenarios, where dynamic nodes are found by proximity. This paper presents a novel dynamic vehicle-routing problem variant with proximity-dependent nodes. Here, we introduced a novel variable, detectability, which determines whether a proximal dynamic node will be detected, based on the sight radius of the vehicle. The problem considered is motivated by autonomous weed-spraying vehicles in large agricultural operations. This work is generalisable to many other autonomous vehicle applications. The first step to crafting a solution approach for the problem is to decide when reoptimisation should be triggered. Two reoptimisation trigger strategies are considered—exogenous and endogenous. Computational experiments compared the strategies for both the classical dynamic vehicle routing problem as well as the introduced variant. Experiments used extensive standardised vehicle-routing problem benchmarks with varying degrees of dynamism and geographical node distributions. The results showed that for both the classical problem and the novel variant, an endogenous trigger strategy is better in most cases, while an exogenous trigger strategy is only suitable when both detectability and dynamism are low. Furthermore, the optimal level of detectability was shown to be dependent on the combination of trigger, degree of dynamism, and geographical node distribution, meaning practitioners may determine the required detectability based on the attributes of their specific problem

‘Women on a Wiki’: Social Constructivist Analysis of the Effectiveness of Online Collaborative Spaces for Reflective Learning in Women‘s Health Studies

Public health undergraduate students studying the unit Women\u27s Health undertook a teaching and learning exercise which required them to learn to create and use a wiki website for reflective learning purposes. A wiki is a group of web pages that allows users to add content, similar to a discussion forum or blog, while permitting others to edit and provide feedback. The Women\u27s Health wiki provided an online shared, collaborative and creative space wherein the students’ perceptions of women\u27s health issues could be discussed, reflected upon and debated. This chapter develops a social constructivist theoretical framework for analysing the content developed on the Women\u27s Health wiki by the students and provides a theoretical model for how the wiki worked to aid reflective and critical thinking, as well as developing technological and communicative skills among students, and discusses implications for their future use in a tertiary setting

Improving Network-Based Anomaly Detection in Smart Home Environment

The Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, the traffic patterns of an SH IoT device under attack often changes in the Home Area Network (HAN). Therefore, a Network-Based Intrusion Detection System (NIDS) logically becomes the forefront security solution for the SH. In this paper, we propose a novel method to assist classification machine learning algorithms generate an anomaly-based NIDS detection model, hence, detecting the abnormal SH IoT device network behaviour. Three network-based attacks were used to evaluate our NIDS solution in a simulated SH test-bed environment. The detection model generated by traditional and ensemble classification Mechanical Learning (ML) methods shows outstanding overall performance. The accuracy of all detection models is over 98.8%

Systematic evaluation of GoSoapBox in tertiary education: a student response system for improving learning experiences and outcomes

There is currently a wide range of research into the recent introduction of student response systems in higher education and tertiary settings (Banks 2006; Kay and Le Sange, 2009; Beatty and Gerace 2009; Lantz 2010; Sprague and Dahl 2009). However, most of this pedagogical literature has generated 'how to' approaches regarding the use of 'clickers', keypads, and similar response technologies. There are currently no systematic reviews on the effectiveness of 'GoSoapBox' — a more recent, and increasingly popular student response system — for its capacity to enhance critical thinking, and achieve sustained learning outcomes. With rapid developments in teaching and learning technologies across all undergraduate disciplines, there is a need to obtain comprehensive, evidence-based advice on these types of technologies, their uses, and overall efficacy. This paper addresses this current gap in knowledge. Our teaching team, in an undergraduate Sociology and Public Health unit at the Queensland University of Technology (QUT), introduced GoSoapBox as a mechanism for discussing controversial topics, such as sexuality, gender, economics, religion, and politics during lectures, and to take opinion polls on social and cultural issues affecting human health. We also used this new teaching technology to allow students to interact with each other during class — both on both social and academic topics — and to generate discussions and debates during lectures. The paper reports on a data-driven study into how this interactive online tool worked to improve engagement and the quality of academic work produced by students. This paper will firstly, cover the recent literature reviewing student response systems in tertiary settings. Secondly, it will outline the theoretical framework used to generate this pedagogical research. In keeping with the social and collaborative features of Web 2.0 technologies, Bandura's Social Learning Theory (SLT) will be applied here to investigate the effectiveness of GoSoapBox as an online tool for improving learning experiences and the quality of academic output by students. Bandura has emphasised the Internet as a tool for 'self-controlled learning' (Bandura 2001), as it provides the education sector with an opportunity to reconceptualise the relationship between learning and thinking (Glassman & Kang 2011). Thirdly, we describe the methods used to implement the use of GoSoapBox in our lectures and tutorials, and which aspects of the technology we drew on for learning purposes, as well as the methods for obtaining feedback from the students about the effectiveness or otherwise of this tool. Fourthly, we report cover findings from an examination of all student/staff activity on GoSoapBox as well as reports from students about the benefits and limitations of it as a learning aid. We then display a theoretical model that is produced via an iterative analytical process between SLT and our data analysis for use by academics and teachers across the undergraduate curriculum. The model has implications for all teachers considering the use of student response systems to improve the learning experiences of their students. Finally, we consider some of the negative aspects of GoSoapBox as a learning aid