Inducing shades of meaning by matrix methods : a first step towards thematic analysis of opinion

This article explores two matrix methods to induce the ``shades of meaning" (SoM) of a word. A matrix representation of a word is computed from a corpus of traces based on the given word. Non-negative Matrix Factorisation (NMF) and Singular Value Decomposition (SVD) compute a set of vectors corresponding to a potential shade of meaning. The two methods were evaluated based on loss of conditional entropy with respect to two sets of manually tagged data. One set reflects concepts generally appearing in text, and the second set comprises words used for investigations into word sense disambiguation. Results show that for NMF consistently outperforms SVD for inducing both SoM of general concepts as well as word senses. The problem of inducing the shades of meaning of a word is more subtle than that of word sense induction and hence relevant to thematic analysis of opinion where nuances of opinion can arise.

Numerical methods for fractional partial differential equations with Riesz space fractional derivatives

In this paper, we consider the numerical solution of a fractional partial differential equation with Riesz space fractional derivatives (FPDE-RSFD) on a finite domain. Two types of FPDE-RSFD are considered: the Riesz fractional diffusion equation (RFDE) and the Riesz fractional advection–dispersion equation (RFADE). The RFDE is obtained from the standard diffusion equation by replacing the second-order space derivative with the Riesz fractional derivative of order αset membership, variant(1,2]. The RFADE is obtained from the standard advection–dispersion equation by replacing the first-order and second-order space derivatives with the Riesz fractional derivatives of order βset membership, variant(0,1) and of order αset membership, variant(1,2], respectively. Firstly, analytic solutions of both the RFDE and RFADE are derived. Secondly, three numerical methods are provided to deal with the Riesz space fractional derivatives, namely, the L1/L2-approximation method, the standard/shifted Grünwald method, and the matrix transform method (MTM). Thirdly, the RFDE and RFADE are transformed into a system of ordinary differential equations, which is then solved by the method of lines. Finally, numerical results are given, which demonstrate the effectiveness and convergence of the three numerical methods.

Intentional first year curriculum design as a means of facilitating student engagement: some exemplars

The importance of student engagement to higher education quality, making deep learning outcomes possible for students, and achieving student retention, is increasingly being understood. The issue of student engagement in the first year of tertiary study is of particular significance. This paper takes the position that the first year curriculum, and the pedagogical principles that inform its design, are critical influencers of student engagement in the first year learning environment. We use an analysis of case studies prepared for Kift’s ALTC Senior Fellowship to demonstrate ways in which student engagement in the first year of tertiary study can be successfully supported through intentional curriculum design that motivates students to learn, provides a positive learning climate, and encourages students to be active in their learning.

Numerical investigations of linear least squares methods for derivative estimation

The results of a numerical investigation into the errors for least squares estimates of function gradients are presented. The underlying algorithm is obtained by constructing a least squares problem using a truncated Taylor expansion. An error bound associated with this method contains in its numerator terms related to the Taylor series remainder, while its denominator contains the smallest singular value of the least squares matrix. Perhaps for this reason the error bounds are often found to be pessimistic by several orders of magnitude. The circumstance under which these poor estimates arise is elucidated and an empirical correction of the theoretical error bounds is conjectured and investigated numerically. This is followed by an indication of how the conjecture is supported by a rigorous argument.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Fundamental solution and discrete random walk model for time-space fractional diffusion equation

In this paper, we consider a time-space fractional diffusion equation of distributed order (TSFDEDO). The TSFDEDO is obtained from the standard advection-dispersion equation by replacing the first-order time derivative by the Caputo fractional derivative of order α∈(0,1], the first-order and second-order space derivatives by the Riesz fractional derivatives of orders β 1∈(0,1) and β 2∈(1,2], respectively. We derive the fundamental solution for the TSFDEDO with an initial condition (TSFDEDO-IC). The fundamental solution can be interpreted as a spatial probability density function evolving in time. We also investigate a discrete random walk model based on an explicit finite difference approximation for the TSFDEDO-IC.

Reconciling Nicci Lane : the 'unspeakable' significance of Australia's first indigenous porn star

This article will explore X-rated representations of Aboriginality in Australian-produced pornographic videos, particularly the image of Australia's first Indigenous porn-star, Nicci Lane. It investigates how pornographic narratives involving 'Aboriginal' characters or motifs are connected to broader embodiments of Aboriginality in popular culture. Drawing a parallel with Australian television drama and mainstream films, the article highlights how contemporary sexualized images of Aboriginal people are intimately tied to a politics of reconciliation. By surveying recent literature on pornography, which describe how certain pornographic narratives engage 'unspoken' community desires, my argument will discuss Nicci Lane's career as a unique development in the history of representations of Aboriginality. Through analysis of Lane’s Arigato Baby (1991), these ‘unspoken’ desires relate to showing Indigenous people in everyday sexual contexts, as romantic partners, friends and lovers. My argument will go on to suggest that, through Nicci Lane's performance and profile, the image of Australia’s first Indigenous porn-star offers the possibility for imagining new kinds of interracial intimacy within the Australian public sphere.

Crystallographic characterization of the first reported crystalline form of the potent hallucinogen (R)-2-amino-1-(8-bromobenzo[1,2-b;5,4-b']difuran-4-yl)propane or `bromodragonfly': the 1:1 anhydrous proton-transfer compound with 3,5-dinitrosalicylic acid

The 1:1 proton-transfer compound of the potent substituted amphetamine hallucinogen (R)-1-(8-bromobenzo[1,2-b; 4,5-b']difuran-4-yl)-2-aminopropane (common trivial name 'bromodragonfly') with 3,5-dinitrosalicylic acid, 1-(8-bromobenzo[1,2-b;4,5-b']difuran-4-yl)-2-mmoniopropane 2-carboxy-4,6-dinitrophenolate, C13H13BrNO2+ C7H3N2O7- forms hydrogen-bonded cation-anion chain substructures comprising undulating head-to-tail anion chains formed through C(8) carboxyl O-H...O(nitro) associations and incorporating the aminium groups of the cations. The intra-chain cation-anion hydrogen-bonding associations feature proximal cyclic R33(8) interactions involving both a N+-H...O(phenolate) and the carboxyl O--H...O(nitro)associations. Also present are aromatic pi-pi ring interactions [minimum ring centroid separation, 3.566(2)A; inter-plane dihedral angle, 5.13(1)deg]. A lateral hydrogen-bonding interaction between the third aminium proton and a carboxyl O acceptor link the chain substructures giving a two-dimensional sheet structure. This determination represents the first of any form of this compound and confirms that it has the (R) absolute configuration. The atypical crystal stability is attributed both to the hydrogen-bonded chain substructures provided by the anions, which accommodate the aminium proton-donor groups of the cations and give cross-linking, and to the presence of cation--anion aromatic ring pi-pi interactions.

An economic case for systematic student monitoring and intervention in the first year in higher education

Previous work has established the effectiveness of systematically monitoring first year higher education students and intervening with those identified as at-risk of attrition. This nuts-and-bolts paper establishes an economic case for a systematic monitoring and intervention program, identifying the visible costs and benefits of such a program at a major Australian university. The benefit of such a program is measured in savings to the institution which would otherwise be lost revenue, in the form of retained equivalent full-time student load (EFTSL). The session will present an economic model based on a number of assumptions. These assumptions are explored along with the applicability of the model to other institutions.

The development of a "Good Practice Guide" for first year curriculum development, revision and evaluation : a moving feast

Since 2002 QUT has sponsored a range of first year-focussed initiatives, most recently the Transitions In Project (TIP) which was designed to complement the First Year Experience Program and be a capacity building initiative. A primary focus of TIP was The First Year Curriculum Project: the review, development, implementation and evaluation of first year curriculum which has culminated in the development of a “Good Practice Guide” for the management of large first year units. First year curriculum initiates staff-student relationships and provides the scaffolding for the learning experience and engagement. Good practice in first year curriculum is within the control of the institution and can be redesigned and reviewed to improve outcomes. This session will provide a context for the First Year Curriculum Project and a concise overview of the suite of resources developed that have culminated in the Good Practice Guide.

Detection and prevention of distributed denial of services attacks by collaborative effort of software agents, first prototype implementation

Distributed Denial of Services DDoS, attacks has become one of the biggest threats for resources over Internet. Purpose of these attacks is to make servers deny from providing services to legitimate users. These attacks are also used for occupying media bandwidth. Currently intrusion detection systems can just detect the attacks but cannot prevent / track the location of intruders. Some schemes also prevent the attacks by simply discarding attack packets, which saves victim from attack, but still network bandwidth is wasted. In our opinion, DDoS requires a distributed solution to save wastage of resources. The paper, presents a system that helps us not only in detecting such attacks but also helps in tracing and blocking (to save the bandwidth as well) the multiple intruders using Intelligent Software Agents. The system gives dynamic response and can be integrated with the existing network defense systems without disturbing existing Internet model. We have implemented an agent based networking monitoring system in this regard.

Communicating with first year students ; so many channels but is anyone listening? A practice report

Communicating with first year students has become a far more complex prospect in the digital age. There is a lot of competition for limited attentional resources from media sources in almost endless channels. Getting important messages to students when there is so much competing information is a difficult prospect for academic and professional divisions of the university alike. Students’ preferences for these communication channels are not well understood and are constantly changing with the introduction of new technology. A first year group was surveyed about their use and preference for various sources of information. Students were generally positive about the use of social networking and other new online media but strongly preferred more established channels for official academic and administrative information. A discussion of the findings and recommendations follows.

Longing to belong : social inclusion and wellbeing among youth with refugee backgrounds in the first three years in Melbourne, Australia

For young people with refugee backgrounds, establishing a sense of belonging to their family and community, and to their country of resettlement is essential for wellbeing. This paper describes the psychosocial factors associated with subjective health and wellbeing outcomes among a cohort of 97 refugee youth (aged 11-19) during their first three years in Melbourne, Australia. The findings reported here are drawn from the Good Starts Study, a longitudinal investigation of settlement and wellbeing among refugee youth conducted between 2004 and 2008. The overall aim of Good Starts was to identify the psychosocial factors that assist youth with refugee backgrounds in making a good start in their new country. A particular focus was on key transitions: from pre-arrival to Australia, from the language school to mainstream school, and from mainstream school to higher education or to the workforce. Good Starts used a mix of both method and theory from anthropology and social epidemiology. Using standardized measures of wellbeing and generalised estimating equations to model the predictors of wellbeing over time, this paper reports that key factors strongly associated with wellbeing outcomes are those that can be described as indicators of belonging e the most important being subjective social status in the broader Australian community, perceived discrimination and bullying. We argue that settlement specific policies and programs can ultimately be effective if embedded within a broader socially inclusive society - one that offers real opportunities for youth with refugee backgrounds to flourish.