What triggers problem recognition? An exploration on young Australian male problematic online gamers

Help-seeking is a complex decision-making process that first begins with problem recognition. However, little is understood about the conceptualisation of the helpseeking process and the triggers of problem recognition. This research proposes the use of the Critical Incident Technique (CIT) to examine and classify incidents that serve as key triggers of problem recognition among young Australian male problematic online gamers. The research provides a classification of five different types of triggers that will aid social marketers into developing effective early detection, prevention and treatment focused social marketing interventions.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Performance enhancement: A whole of person approach to first year dance students’ transition into tertiary training

The transition into university presents very particular challenges for students. The First Year Experience (FYE) is a transitional liminal phase, fraught with uncertainty, ripe with potential. The complexity inherent in this initial phase of tertiary education is well documented and continues to be interrogated. Providing timely and effective support and interventions for potentially at-risk first year students as they transition into tertiary study is a key priority for universities across the globe (Gale et al., 2015). This article outlines the evolution of an established and highly successful Transitional Training Program (TTP) for first year tertiary dance students, with particular reference to the 2015 iteration of the program. TTP design embraces three dimensions: physical training in transition, learning in transition, and teaching for transition, with an emphasis on developing and encouraging a mindset that enables information to be transferred into alternative settings for practice and learning throughout life. The aim of the 2015 TTP was to drive substantial change in first year Dance students’ satisfaction, connectedness, and overall performance within the Bachelor of Fine Arts (BFA) Dance course, through the development and delivery of innovative curriculum and pedagogical practices that promote the successful transition of dance students into their first year of university. The program targeted first year BFA Dance students through the integration of specific career guidance; performance psychology; academic skills support; practical dance skills support; and specialized curricula and pedagogy.

Robust iris recognition using decision fusion and degradation modelling

This thesis investigates the use of fusion techniques and mathematical modelling to increase the robustness of iris recognition systems against iris image quality degradation, pupil size changes and partial occlusion. The proposed techniques improve recognition accuracy and enhance security. They can be further developed for better iris recognition in less constrained environments that do not require user cooperation. A framework to analyse the consistency of different regions of the iris is also developed. This can be applied to improve recognition systems using partial iris images, and cancelable biometric signatures or biometric based cryptography for privacy protection.

Conceptualising recognition of prior learning processes in the age of open learning

This chapter interrogates what recognition of prior learning (RPL) can and does mean in the higher education sector—a sector in the grip of the widening participation agenda and an open access age. The chapter discusses how open learning is making inroads into recognition processes and examines two studies in open learning recognition. A case study relating to e-portfolio-style RPL for entry into a Graduate Certificate in Policy and Governance at a metropolitan university in Queensland is described. In the first instance, candidates who do not possess a relevant Bachelor degree need to demonstrate skills in governmental policy work in order to be eligible to gain entry to a Graduate Certificate (at Australian Qualifications Framework Level 8) (Australian Qualifications Framework Council, 2013, p. 53). The chapter acknowledges the benefits and limitations of recognition in open learning and those of more traditional RPL, anticipating future developments in both (or their convergence).

Repeatable Condition-Invariant Visual Odometry for Sequence-Based Place Recognition

This paper describes a vision-only system for place recognition in environments that are tra- versed at different times of day, when chang- ing conditions drastically affect visual appear- ance, and at different speeds, where places aren’t visited at a consistent linear rate. The ma- jor contribution is the removal of wheel-based odometry from the previously presented algo- rithm (SMART), allowing the technique to op- erate on any camera-based device; in our case a mobile phone. While we show that the di- rect application of visual odometry to our night- time datasets does not achieve a level of perfor- mance typically needed, the VO requirements of SMART are orthogonal to typical usage: firstly only the magnitude of the velocity is required, and secondly the calculated velocity signal only needs to be repeatable in any one part of the environment over day and night cycles, but not necessarily globally consistent. Our results show that the smoothing effect of motion constraints is highly beneficial for achieving a locally consis- tent, lighting-independent velocity estimate. We also show that the advantage of our patch-based technique used previously for frame recogni- tion, surprisingly, does not transfer to VO, where SIFT demonstrates equally good performance. Nevertheless, we present the SMART system us- ing only vision, which performs sequence-base place recognition in extreme low-light condi- tions where standard 6-DOF VO fails and that improves place recognition performance over odometry-less benchmarks, approaching that of wheel odometry.

Enhancing human action recognition with region proposals

Deep convolutional network models have dominated recent work in human action recognition as well as image classification. However, these methods are often unduly influenced by the image background, learning and exploiting the presence of cues in typical computer vision datasets. For unbiased robotics applications, the degree of variation and novelty in action backgrounds is far greater than in computer vision datasets. To address this challenge, we propose an “action region proposal” method that, informed by optical flow, extracts image regions likely to contain actions for input into the network both during training and testing. In a range of experiments, we demonstrate that manually segmenting the background is not enough; but through active action region proposals during training and testing, state-of-the-art or better performance can be achieved on individual spatial and temporal video components. Finally, we show by focusing attention through action region proposals, we can further improve upon the existing state-of-the-art in spatio-temporally fused action recognition performance.

Personality in sport and exercise psychology: Integrating a whole person perspective

This paper draws on contemporary views in personality psychology as a means for understanding people participating in sport and physical activity. Specifically, we focus on McAdams’ integrative framework [McAdams (2013). The psychological self as actor, agent, and author. Perspectives on Psychological Science, 8, 272–295; McAdams & Pals (2006). A new big five: Fundamental principles for an integrative science of personality. American Psychologist, 61, 204–217] and suggest this framework as potentially generative in the field of sport and exercise psychology. McAdams indicates that people can be defined through three layers of understanding, incorporating (a) dispositional traits, (b) characteristic adaptations, and (c) narrative identities. Together these layers provide a vision of the whole person – a perspective of personality rarely adopted by the sport and exercise community. The aim of this paper is to introduce scholars and practitioners to the potential benefits of embracing this whole person outlook, and to discuss the opportunities and challenges McAdams’ framework may have for advancing scholarship in sport and exercise psychology.

The con-stitutional re-cognition (s)cam-pain: The campaign for the hidden recognition of first nations peoples' racial inferiority

The constitutional recognition campaign has received party-wide support and its efforts have been promoted by Prime Minister Tony Abbott as being something that would ‘complete our Constitution.’ The broader rhetoric surrounding this campaign suggests that it will result in a just, albeit delayed, recognition of indigenous peoples in the Australian legal system. However, beneath the surface of this seemingly benevolent gesture, is a reaffirmation of the colonial subordination and erasure of the several hundred original nations’ peoples and ways of being.

Art changing the world: one person at a time

Dr Michael Whelan from Autism Queensland talks about a mentoring program supporting young people to develop creative industries skills… Following the extreme social stresses of high school, a lot of young people on the autism spectrum retreat to their bedrooms and computers to hibernate for extended periods of time. Online gaming communities and digital media hubs often provide a more accessible forum for young adults on the autism spectrum to establish and maintain social connections. A recent study suggests that school leavers on the autism spectrum in Queensland spend an average of 9.5 hours per day (68 hours per week) engaged in solitary technology-based activities. While this astonishing figure has its foundations in the sobering fact that most of these young people have limited social networks and experience significant anxiety and depression, it also serves to illustrate the extraordinary skill sets that these extended hours of technological engagement can facilitate.

Recognition and enforcement in cross-border insolvency law: A proposal for judicial gap-filling

This Article analyzes the recognition and enforcement of cross-border insolvency judgments from the United States, United Kingdom, and Australia to determine whether the UNCITRAL Model Law’s goal of modified universalism is currently being practiced, and subjects the Model Law to analysis through the lens of international relations theories to elaborate a way forward. We posit that courts could use the express language of the Model Law text to confer recognition and enforcement of foreign insolvency judgments. The adoption of our proposal will reduce costs, maximize recovery for creditors, and ensure predictability for all parties.

Within-person relationships between daily individual and job characteristics and daily manifestations of career adaptability

Previous research showed that daily manifestations of career adaptability fluctuate within individuals over short periods of time, and predict important daily job and career outcomes. Using a quantitative daily diary study design (N = 156 employees; 591 daily entries), the author investigated daily job characteristics (i.e., daily job demands, daily job autonomy, and daily supervisory career mentoring) and daily individual characteristics (i.e., daily Big Five personality characteristics, daily core self-evaluations, and daily temporal focus) as within-person predictors of daily career adaptability and its four dimensions (concern, control, curiosity, and confidence). Results showed that daily job demands, daily job autonomy, daily conscientiousness, daily openness to experience, as well as daily past and future temporal focus positively predicted daily career adaptability. Differential results emerged for the four career adaptability dimensions. Implications for future research on within-person variability in career adaptability are discussed.

Age, Occupational Strain, and Well-Being: A Person-Environment Fit Perspective

We develop a conceptual model, based on person-environment fit theory, which explains how employee age affects occupational strain and well-being. We begin by explaining how age directly affects different dimensions of objective and subjective P-E fit. Next, we illustrate how age can moderate the relationship between objective P-E fit and subjective P-E fit. Third, we discuss how age can moderate the relationships between P-E fit, on one hand, and occupational strain and well-being on the other. Fourth, we explain how age can impact occupational strain and well-being directly independent of P-E fit. The chapter concludes with implications for future research and practice.

Robust automatic face clustering in news video

Clustering identities in a video is a useful task to aid in video search, annotation and retrieval, and cast identification. However, reliably clustering faces across multiple videos is challenging task due to variations in the appearance of the faces, as videos are captured in an uncontrolled environment. A person's appearance may vary due to session variations including: lighting and background changes, occlusions, changes in expression and make up. In this paper we propose the novel Local Total Variability Modelling (Local TVM) approach to cluster faces across a news video corpus; and incorporate this into a novel two stage video clustering system. We first cluster faces within a single video using colour, spatial and temporal cues; after which we use face track modelling and hierarchical agglomerative clustering to cluster faces across the entire corpus. We compare different face recognition approaches within this framework. Experiments on a news video database show that the Local TVM technique is able effectively model the session variation observed in the data, resulting in improved clustering performance, with much greater computational efficiency than other methods.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.