Designing Web 2.0 ePortfolios for music postgraduate study : A practice-led enquiry

This article examines the design of ePortfolios for music postgraduate students utilizing a practice-led design iterative research process. It is suggested that the availability of Web 2.0 technologies such as blogs and social network software potentially provide creative artist with an opportunity to engage in a dialogue about art with artefacts of the artist products and processes present in that discussion. The design process applied Software Development as Research (SoDaR) methodology to simultaneously develop design and pedagogy. The approach to designing ePortfolio systems applied four theoretical protocols to examine the use of digitized artefacts to enable a dynamic and inclusive dialogue around representations of the students work. A negative case analysis identified a disjuncture between university access and control policy, and the relative openness of Web2.0 systems outside the institution that led to the design of an integrated model of ePortfolio.

A qualitative study of small-scale artist run 'Indie' music business in Brisbane

This study is an examination of three small-scale artist run music businesses based in Brisbane. The researcher embedded himself within these three environments over the space of three years, using participant observation and content analysis to establish the key motivations, theories, and ideas which drove these businesses. As a researcher participant the author also drew on his own experiences to interrogate those investigated by other researchers in the field, with the underlying key theories influenced by Pierre Bourdieu's writings on Small-Scale production. This study provides a fascinating insight into Brisbane music culture, in particular the independent music scenes.

Quantitative fit analysis of orthopedic bone plates : methods, criteria and approach

Studies on quantitative fit analysis of precontoured fracture fixation plates emerged within the last few years and therefore, there is a wide research gap in this area. Quantitative fit assessment facilitates the measure of the gap between a fracture fixation plate and the underlying bone, and specifies the required plate fit criteria. For clinically meaningful fit assessment outcome, it is necessary to establish the appropriate criteria and parameter. The present paper studies this subject and recommends using multiple fit criteria and the maximum distance between the plate and underlying bone as fit parameter for clinically relevant outcome. We also propose the development of a software tool for automatic plate positioning and fit assessment for the purpose of implant design validation and optimization in an effort to provide better fitting implant that can assist proper fracture healing. The fundamental specifications of the software are discussed.

The design and development of music eScape: A new iPhone mood management music App

The physical, emotional, educational and social developmental challenges of adolescence can be associated with high levels of emotional vulnerability. Thus, the development of effective emotion-regulation strategies is crucial during this time period. Young people commonly use music to identify, express and regulate their emotions. Modern mobile technology provides an engaging, easily accessible means of assisting young people through music. A systematic contextual review identified 20 iPhone applications addressing emotions through music and two independent raters, using the Mobile App Rating Scale (MARS), evaluated the quality of the apps. Their characteristics, key features and overall quality will be presented. Three participatory design workshops (N=13, 6 males, 7 females; age 15-25) were conducted to explore young people’s use of music to enhance wellbeing. Young people were also asked to trial existing mood and music apps and to conceptualise their ultimate mood targeting music application. A thematic analysis of the participatory design workshops content identified the following music affect-regulation strategies: relationship building, modifying cognitions, modifying emotions, and immersing in emotions. The application of the key learnings from the mobile app review and participatory design workshops and the design and development of the music eScape app were presented and implications for future research was discussed.

Analysis of ship roll gyrostabiliser control

The use of dedicated spinning wheels that generate gyroscopic forces for reducing the roll motion of ships was considered and tested over 100 years ago. These devices, known as gyrostabilisers, presented a remarkable performance, but they fell into disuse due to their relatively large size and, primarily, due to the inability of the control systems to maintain performance over an extended envelope of sea states and sailing conditions (speed and heading relative to the waves). To date, advances in materials, mechanical design, electrical drives, and computer control systems have resulted in a revitalized interest in gyro-stabilisers for ships. This paper revisits the modelling of the coupled vessel-gyrostabiliser and delves into the associated gyrostabiliser control design problem. It also describes design trade-offs and potential performance limitations. A simulation study based on a navy patrol vessel is presented.

Stochastic analysis of transit route segments' passenger load variation for capacity and quality of service assessment

This paper investigates stochastic analysis of transit segment hourly passenger load factor variation for transit capacity and quality of service (QoS) analysis using Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia. It compares stochastic analysis to traditional peak hour factor (PHF) analysis to gain further insight into variability of transit route segments’ passenger loading during a study hour. It demonstrates that hourly design load factor is a useful method of modeling a route segment’s capacity and QoS time history across the study weekday. This analysis method is readily adaptable to different passenger load standards by adjusting design percentile, reflecting either a more relaxed or more stringent condition. This paper also considers hourly coefficient of variation of load factor as a capacity and QoS assessment measure, in particular through its relationships with hourly average and design load factors. Smaller value reflects uniform passenger loading, which is generally indicative of well dispersed passenger boarding demands and good schedule maintenance. Conversely, higher value may be indicative of pulsed or uneven passenger boarding demands, poor schedule maintenance, and/or bus bunching. An assessment table based on hourly coefficient of variation of load factor is developed and applied to this case study. Inferences are drawn for a selection of study hours across the weekday studied.

Stochastic analysis of transit route segments' passenger load variation for capacity & quality of service assessment

This study uses weekday Automatic Fare Collection (AFC) data on a premium bus line in Brisbane, Australia •Stochastic analysis is compared to peak hour factor (PHF) analysis for insight into passenger loading variability •Hourly design load factor (e.g. 88th percentile) is found to be a useful method of modeling a segment’s passenger demand time-history across a study weekday, for capacity and QoS assessment •Hourly coefficient of variation of load factor is found to be a useful QoS and operational assessment measure, particularly through its relationship with hourly average load factor, and with design load factor •An assessment table based on hourly coefficient of variation of load factor is developed from the case study

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

Young people's uses of music for well-being

The period between 15 and 25 years is characterised by much personal change and is the peak age of onset of mental health problems. This prompts an interest in everyday strategies that young people might use to support their well-being. Music use is the preferred leisure activity among young people yet little is known about how music is linked to well-being in this population. This study aimed to develop and test a model of the relationships between young people’s use of music and their well-being, drawing on theories from the music psychology and clinical psychology fields. A qualitative analysis of transcripts from focus groups with 11 participants aged 15–25 years revealed four ways in which music listening links with well-being: relationship building, modifying emotions, modifying cognitions and emotional immersion. These linking variables were operationalised using questionnaire scores and tested on a new sample of 107 young people. Results of a multiple mediation analysis revealed that music listening was significantly related to all four linking variables, but not directly related to well-being as measured by the Mental Health Continuum. Nevertheless, the four linking variables indirectly mediated the effect of music listening on social wellbeing. The findings are consistent with earlier research on the role of music in emotion regulation and social connection although there are clearly other factors involved in determining young peoples’ well-being. These findings will help inform music-based interventions for young people.

Music in the prevention and treatment of substance misuse

Issues Research shows that young people at risk of developing a substance use disorder often use substances to deal with problems, particularly relationship problems and emotional problems. Music listening is a widely available and engaging activity that may help young people address these problem areas. This study was part of a larger project to develop a phone app for young people in which they use music for emotional wellbeing. Approach Three focus groups with young people aged 15–25 years were conducted and the transcripts were analysed by three of the authors using a thematic analysis procedure (Braun & Clarke, 2006). Key Findings: Young people used music in four main ways to achieve wellbeing: relationship building through sharing music; cre- ating an ambience using music; using music to experience an emotion more fully; and using music to modify an emotion. Several mecha- nisms by which music achieved these functions were identified. Par- ticipants also articulated specific times when they would not use music and why. Discussion and Conclusions The information from these focus groups provides many avenues for the development of the app and for understanding how music listening helps young people to achieve wellbeing. These ideas can readily be used with young people at risk of developing substance use problems as it gives them an engaging and low cost alternative for managing their emotions and building relationships.

Maintaining our rage: Inside Australia's longest-running music video program

This research presents an insider's account of rage, Australia's longest-running music video program. The research's significance is that there has been scarce scholarly analysis of this idiosyncratic ABC program, despite its longevity and uniqueness. The thesis takes a reflective and reflexive narrative journey across rage's decades, presenting the accounts of the program makers, aided by the perspective of an embedded researcher, the program's former Series Producer. This work addresses the rage research gap and contributes to the scholarly discussion on music video and its contexts, the ABC, public service broadcasting, creative labour, and the cultural sense-making of television producers.

Automatic population HARDI white matter tract clustering by label fusion of multiple tract atlases

Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults. © 2012 Springer-Verlag.

Integration over song classification replicates: song variant analysis in the hihi

Human expert analyses are commonly used in bioacoustic studies and can potentially limit the reproducibility of these results. In this paper, a machine learning method is presented to statistically classify avian vocalizations. Automated approaches were applied to isolate bird songs from long field recordings, assess song similarities, and classify songs into distinct variants. Because no positive controls were available to assess the true classification of variants, multiple replicates of automatic classification of song variants were analyzed to investigate clustering uncertainty. The automatic classifications were more similar to the expert classifications than expected by chance. Application of these methods demonstrated the presence of discrete song variants in an island population of the New Zealand hihi (Notiomystis cincta). The geographic patterns of song variation were then revealed by integrating over classification replicates. Because this automated approach considers variation in song variant classification, it reduces potential human bias and facilitates the reproducibility of the results.

Identifying vertebral end plate lesions (schmorl's nodes) using a semi-automatic method

INTRODUCTION There is a large range in the reported prevalence of end plate lesions (EPLs), sometimes referred to as Schmorl's nodes in the general population (3.8-76%). One possible reason for this large range is the differences in definitions used by authors. Previous research has suggested that EPLs may potentially be a primary disturbance of growth plates that leads to the onset of scoliosis. The aim of this study was to develop a technique to measure the size, prevalence and location of EPLs on Computed Tomography (CT) images of scoliosis patients in a consistent manner. METHODS A detection algorithm was developed and applied to measure EPLs for five adolescent females with idiopathic scoliosis (average age 15.1 years, average major Cobb 60°). In this algorithm, the EPL definition was based on the lesion depth, the distance from the edge of the vertebral body and the gradient of the lesion edge. Existing low-dose, CT scans of the patients' spines were segmented semi-automatically to extract 3D vertebral endplate morphology. Manual sectioning of any attachments between posterior elements of adjacent vertebrae and, if necessary, endplates was carried out before the automatic algorithm was used to determine the presence and position of EPLs. RESULTS EPLs were identified in 15 of the 170 (8.8%) endplates analysed with an average depth of 3.1mm. 73% of the EPLs were seen in the lumbar spines (11/15). A sensitivity study demonstrated that the algorithm was most sensitive to changes in the minimum gradient required at the lesion edge. CONCLUSION An imaging analysis technique for consistent measurement of the prevalence, location and size of EPLs on CT images has been developed. Although the technique was tested on scoliosis patients, it can be used to analyse other populations without observer errors in EPL definitions.