Executive coaching : investigating effects of leader-empowering behaviours and psychological empowerment

Executive coaching is a rapidly expanding approach to leadership development which has grown at a rate that warrants extensive examination of its effects (Wasylyshyn, 2003). This thesis has therefore examined both behavioural and psychological effects based on a nine month executive coaching intervention within a large not-for-profit organisation. The intervention was a part of a larger ongoing integrated organisational strategy to create an organisational coaching culture. In order to examine the effectiveness of the nine month executive coaching intervention two studies were conducted. A quantitative study used a pre and post questionnaire to examine leaders and their team members‘ responses before and after the coaching intervention. The research examined leader-empowering behaviours, psychological empowerment, job satisfaction and affective commitment. Significant results were demonstrated from leaders‘ self-reports on leader-empowering behaviours and their team members‘ self-reports revealed a significant flow on effect of psychological empowerment. The second part of the investigation involved a qualitative study which explored the developmental nature of psychological empowerment through executive coaching. The examination dissected psychological empowerment into its widely accepted four facets of meaning, impact, competency and self-determination and investigated, through semi-structured interviews, leaders‘ perspectives of the effect of executive coaching upon them (Spreitzer, 1992). It was discovered that a number of the common practices within executive coaching, including goal-setting, accountability and action-reflection, contributed to the production of outcomes that developed higher levels of psychological empowerment. Careful attention was also given to organisational context and its influence upon the outcomes.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Shape discrimination using invariants defined from higher-order spectra

An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles

A general procedure for the derivation of principal domains of higher-order spectra

A general procedure to determine the principal domain (i.e., nonredundant region of computation) of any higher-order spectrum is presented, using the bispectrum as an example. The procedure is then applied to derive the principal domain of the trispectrum of a real-valued, stationary time series. These results are easily extended to compute the principal domains of other higher-order spectra

Position, rotation, and scale invariant recognition of images using higher-order spectra

A new approach to recognition of images using invariant features based on higher-order spectra is presented. Higher-order spectra are translation invariant because translation produces linear phase shifts which cancel. Scale and amplification invariance are satisfied by the phase of the integral of a higher-order spectrum along a radial line in higher-order frequency space because the contour of integration maps onto itself and both the real and imaginary parts are affected equally by the transformation. Rotation invariance is introduced by deriving invariants from the Radon transform of the image and using the cyclic-shift invariance property of the discrete Fourier transform magnitude. Results on synthetic and actual images show isolated, compact clusters in feature space and high classification accuracies

Labyrinthine literacies : navigating cultural agency in the arrival and requiem for a beast

The OED reminds us as surely as Ovid that a labyrinth is a “structure consisting of a number of intercommunicating passages arranged in bewildering complexity, through which it is it difficult or impossible to find one’s way without guidance”. Both Shaun Tan’s The Arrival (2006) and Matt Ottley’s Requiem for a Beast: A Work for Image, Word and Music (2007) mark a kind of labyrinthine watershed in Australian children’s literature. Deploying complex, intercommunicating logics of story and literacy, these books make high demands of their reader but also offer guidance for the successful navigation of their stories; for their protagonists as surely as for readers. That the shared logic of navigation in each book is literacy as privileged form of meaning-making is not surprising in the sense that within “a culture deeply invested in myths of individualism and self-sufficiency, it is easy to see why literacy is glorified as an attribute of individual control and achievement” (Williams and Zenger 166). The extent to which these books might be read as exemplifying desired norms of contemporary Australian culture seems to be affirmed by the fact of Tan and Ottley winning the Australian “Picture Book of the Year” prize awarded by the Children’s Book Council of Australia in 2007 and 2008 respectively. However, taking its cue from Ottley’s explicit intertextual use of the myth of Theseus and from Tan’s visual rhetoric of lostness and displacement, this paper reads these texts’ engagement with tropes of “literacy” in order to consider the ways in which norms of gender and culture seemingly circulated within these texts might be undermined by constructions of “nation” itself as a labyrinth that can only partly be negotiated by a literate subject. In doing so, I argue that these picture books, to varying degrees, reveal a perpetuation of the “literacy myth” (Graff 12) as a discourse of safety and agency but simultaneously bear traces of Ariadne’s story, wherein literacy alone is insufficient for safe navigation of the labyrinth of culture.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

Numerical analysis for a variable-order nonlinear cable equation

In this paper, a variable-order nonlinear cable equation is considered. A numerical method with first-order temporal accuracy and fourth-order spatial accuracy is proposed. The convergence and stability of the numerical method are analyzed by Fourier analysis. We also propose an improved numerical method with second-order temporal accuracy and fourth-order spatial accuracy. Finally, the results of a numerical example support the theoretical analysis.

Drinking and driving in Vietnam : perceptions and risk

Road traffic injuries are a major global public health problem but continue to receive inadequate attention. Alcohol influences both risk and consequence of road traffic injury but the scale of the problem is not well understood in many countries. In Vietnam, economic development has brought a substantial increase in the number of registered motorcycles as well as alcohol consumption. Traffic injury is among the leading causes of death in Vietnam but there is little local information regarding alcohol related traffic injuries. The primary goal of this study is to explore the drinking and driving patterns of males and their perceptions towards drink-driving and to determine the relationship between alcohol consumption and road traffic injuries. Furthermore, this thesis aims to present the situation analysis for choosing priority actions to reduce drinking and driving in Vietnam. The study is a combination of two cross-sectional surveys and a pilot study. The pilot study, involving 224 traffic injured patients, was conducted to test the tools and the feasibility of approach methods. In the first survey, male patrons (n=464) were randomly selected at seven restaurants. Face-to-face interviews were conducted when patrons just arrived and breath tests were collected when they were about to leave the restaurant. In the second survey, male patients admitted to hospital following a traffic injury (n=480, of which 414 were motorcycle or bicycle riders) were interviewed and their blood alcohol concentration (BAC) measured by breathalyzer. The results show broadly similar patterns of drinking and driving among male patrons and male traffic injured patients with a high frequency of drinking and drink-driving reported among the majority of the two groups. A high proportion of male patrons were leaving restaurants with a BAC over the legal limit. Factors that significantly associate with the number of drinks and BAC were age, hazardous drinking, frequency of drink-driving in the past year, self-estimated number of drinks consumed to drive legally, perceived family’s disapproval of drink-driving, and perceived legal risk and physical risk. The proportion of patrons and patients with BAC above the legal limit of 0.05 were 86.7% and 60.4% respectively, which was much higher than found in previous studies. In addition, both groups had a high prevalence of BAC over 0.15g/100ml (39.7% of patrons and 45.6% patients), a level that can seriously affect driving capacity. Results from the case-crossover analysis for patients indicate a dose-response relationship between alcohol consumption and the risk of traffic injury. The risk of traffic injury increased when alcohol was consumed before driving and there was a more than 13 fold increase when six or more drinks were consumed. Regarding perceptions towards drinking and driving, findings corroborate the low awareness among males in Vietnam, with a majority of respondents holding a low knowledge of safe and legally permissible alcohol use, and a low perceived risk of drinking and driving. The results also indicate a huge gap in prevention skills in terms of planning ahead or using alternative transport to avoid drink-driving and a perception by patrons and patients of a low rate of disapproval of drink-driving from peers and family. Findings in this study have considerable implications for national policy, injury prevention, clinical practice, reporting systems, and for further research. The low rate of compliance with existing laws and a generally low perceived legal risk toward drink-driving in this study call for the strengthening of enforcement along with mass media campaigns and news coverage in order to decrease the widespread perception of impunity and thereby, to reduce the level of drink-driving. In addition, no significant difference was found in this study on risk of traffic injuries between car drivers and motorcycle drivers. The current inconsistency between legal BAC for drivers of motorcycles, compared to cars, thus needs addressing. Furthermore, as drinking was found to be very common, rather than solely targeting drink-driving, it is important to call for a more strategic and comprehensive approach to alcohol policy in Viet Nam. This study also has considerable implications for clinical practice in terms of screening and brief interventions. Our study suggests that the short form of the AUDIT (AUDIT-C) screening tool is appropriate for use in busy emergency departments. The high proportion of traffic injured patients with evidence of alcohol abuse or hazardous drinking suggests that brief interventions by alcohol and drug counselors in emergency departments are a sensible option to addressing this important problem. The significance of this study is in the combination of the systematic collection of breath test and use of case-crossover design to estimate the risk of traffic injuries after alcohol consumption. The results provide convincing evidence to policy makers, health authorities and the media to help raise community awareness and policy advocacy toward the drinkdriving problem in Vietnam. The findings suggest an urgent need for a multi-sectoral approach to curtail drink-driving in Vietnam, especially programs to raise community awareness and effective legal enforcement. Furthermore, serving as a situation analysis, the thesis should inform the formulation of interventions designed to curtail drinking and driving in Vietnam and other developing countries.

Order of instruction effects - do they make a difference when teaching senior chemistry with computer based visualizations?

This study investigated whether conceptual development is greater if students learning senior chemistry hear teacher explanations and other traditional teaching approaches first then see computer based visualizations or vice versa. Five Canadian chemistry classes, taught by three different teachers, studied the topics of Le Chatelier’s Principle and dynamic chemical equilibria using scientific visualizations with the explanation and visualizations in different orders. Conceptual development was measured using a 12 item test based on the Chemistry Concepts Inventory. Data was obtained about the students’ abilities, learning styles (auditory, visual or kinesthetic) and sex, and the relationships between these factors and conceptual development due to the teaching sequences were investigated. It was found that teaching sequence is not important in terms of students’ conceptual learning gains, across the whole cohort or for any of the three subgroups.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Non-redundant sequential association rule mining based on closed sequential patterns

In many applications, e.g., bioinformatics, web access traces, system utilisation logs, etc., the data is naturally in the form of sequences. People have taken great interest in analysing the sequential data and finding the inherent characteristics or relationships within the data. Sequential association rule mining is one of the possible methods used to analyse this data. As conventional sequential association rule mining very often generates a huge number of association rules, of which many are redundant, it is desirable to find a solution to get rid of those unnecessary association rules. Because of the complexity and temporal ordered characteristics of sequential data, current research on sequential association rule mining is limited. Although several sequential association rule prediction models using either sequence constraints or temporal constraints have been proposed, none of them considered the redundancy problem in rule mining. The main contribution of this research is to propose a non-redundant association rule mining method based on closed frequent sequences and minimal sequential generators. We also give a definition for the non-redundant sequential rules, which are sequential rules with minimal antecedents but maximal consequents. A new algorithm called CSGM (closed sequential and generator mining) for generating closed sequences and minimal sequential generators is also introduced. A further experiment has been done to compare the performance of generating non-redundant sequential rules and full sequential rules, meanwhile, performance evaluation of our CSGM and other closed sequential pattern mining or generator mining algorithms has also been conducted. We also use generated non-redundant sequential rules for query expansion in order to improve recommendations for infrequently purchased products.

Supplement : Efficient weak second-order stochastic Runge-Kutta methods for non-commutative Stratonvich stochastic differential equations

This paper gives a modification of a class of stochastic Runge–Kutta methods proposed in a paper by Komori (2007). The slight modification can reduce the computational costs of the methods significantly.