Scandalous texts : the anxieties of the literary

This thesis explores contemporary literary scandals in order to identify the cultural and literary anxieties revealed by controversial works. Examining how scandals emerge in relation to challenging representations of children, women, religion and authenticity, the thesis argues that literary controversies reveal concerns about the construction of identity, history and reality.

Optimizing the quality of bootstrap-based prediction intervals

The bootstrap method is one of the most widely used methods in literature for construction of confidence and prediction intervals. This paper proposes a new method for improving the quality of bootstrap-based prediction intervals. The core of the proposed method is a prediction interval-based cost function, which is used for training neural networks. A simulated annealing method is applied for minimization of the cost function and neural network parameter adjustment. The developed neural networks are then used for estimation of the target variance. Through experiments and simulations it is shown that the proposed method can be used to construct better quality bootstrap-based prediction intervals. The optimized prediction intervals have narrower widths with a greater coverage probability compared to traditional bootstrap-based prediction intervals.

Zero-day malware detection based on supervised learning algorithms of API call signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k−Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO – PolyKernel, SMO – Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.

Smartphone malware based on synchronisation vulnerabilities

Smartphones are mobile phones that offer processing power and features like personal computers (PC) with the aim of improving user productivity as they allow users to access and manipulate data over networks and Internet, through various mobile applications. However, with such anywhere and anytime functionality, new security threats and risks of sensitive and personal data are envisaged to evolve. With the emergence of open mobile platforms that enable mobile users to install applications on their own, it opens up new avenues for propagating malware among various mobile users very quickly. In particular, they become crossover targets of PC malware through the synchronization function between smartphones and computers. Literature lacks detailed analysis of smartphones malware and synchronization vulnerabilities. This paper addresses these gaps in literature, by first identifying the similarities and differences between smartphone malware and PC malware, and then by investigating how hackers exploit synchronization vulnerabilities to launch their attacks.

Oh yes, he is hot : female football fans and the sexual objectification of sportsmen’s bodies

The past decade has witnessed a growing focus on the study of women sports fans within the social sciences and related disciplines. Emerging from and responding to the historical marginalization of women in sport and the bias towards the male fan in literature on sports spectatorship, critical research on women spectators serves the valuable function of illuminating “women’s everyday experiences of being a sports fan” (Gosling 2007: 250). This chapter considers one aspect of women’s participation as followers of male sports, namely, the extent to which female fans partake in the sexual objectification of sportsmen. We aim to assess how looking at male athletes in sexually desiring ways impacts on the individual and collective construction of women’s gender and sports fan identities.

A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data

The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited amount of work is reported in literature on the subject of model fitting to actual EEG data. Here, we present a Bayesian approach for parameter estimation of the EEG model via a marginalized Markov Chain Monte Carlo (MCMC) approach.

Composite spectral shape descriptor

Effective spectral representations of shape region are proposed in literature. On the other hand, a significant spatial characteristic of shape region is shape contour because human beings discriminate shapes mainly by their contour features. It is proposed that a descriptor is obtained by complementing the spectral representation of shape region with the spectral representation of shape contour. Such a descriptor, obtained by explicitly combining two (or more) descriptors is termed composite descriptor. A composite descriptor is proposed; the effectiveness of the composite descriptor to represent shape region is evaluated.

Optimization of bypass graft using FSI numerical method

It is well documented in literature that the coronary artery bypass graft is normally fail after a short period of time, due to the development of plaque known as intimal hyperplasia within the graft. Various in vivo and in vitro studies have linked the development of intimal hyperplasia to the abnormal hemodynamics and compliance mismatch. Therefore, it is essential to fully understand the relationship between the hemodynamics inside the coronary artery bypass and its mechanical and geometrical characteristics under the correct physiological conditions. In this work, hemodynamic of the bypass graft is studied numerically. The effect of the host and graft diameters ratio, the angle of anastomosis and the graft configuration on the local flow patterns and the distribution of wall shear stress are examined. The pulsatile waveforms boundary conditions are adopted from in vivo measurement data to study the hemodynamics of composite grafts namely Consequence and Y grafting in terms temporal and spatial distributions of the blood flows. Moreover, various non-Newtonian and Newtonian models of blood have been carried out to examine the numerical simulation of blood flow in stenosis artery. The results are presented and discussed for various operating conditions.

The novel as social satire: 60 years later, the wind done gone and the limitations of fair use

The absence of the doctrine of fair use from Australian copyright law has been a bone of contention in Australia after the Australia-United States Free Trade Agreement (FTA). As the Australian government reformed the Copyright Act 1968 (Cth) in the aftermath of the FTA it eschewed the option of adopting fair use. Instead, Australia chose to incorporate a version of fair use into its existing fair dealing framework. Accordingly, the Copyright Amendment Act 2006 (Cth) inserted ss 41A and 103AA into the Copyright Act. These provisions provide that a fair dealing with a copyright protected work does not constitute an infringement if it is done for the purposes of parody or satire. These provisions codify part of the ratio of the United States Supreme Court in the seminal case of Campbell v Acuff Rose Music. However, the parameters of these new provisions are unexplored and the sparse nature of fair dealing jurisprudence means that the true meaning of the provisions is unclear. Moreover, two cases from the United States, SunTrust Bank v Houghton Mifflin and Salinger v Colting, underline just how important it is to have legal rules that protect literary ‘re-writes’. Both cases involved authors using an original novel to ‘write back’ to the original author and the broader culture. ‘Writing back’ or the ‘re-write’ has a firm basis in literature. It adds something invaluable to our culture. The key question is whether our legal landscape can allow it to flourish. This paper examines the interaction between fair use and literary re-writes.

Girls' school stories, 1749-1929

As part of the ongoing project of retrieving women writers from the margins of literary and cultural history, scholars of literature, history, and gender studies are increasingly exploring and interrogating girls’ print culture. School stories, in particular, are generating substantial scholarly interest because of their centrality to the history of girls’ reading, their engagement with cultural ideas about the education and socialization of girls, and their enduring popularity with book collectors. However, while serious scholars have begun to document the vast corpus of English-language girls’ school stories, few scholarly editions or facsimile editions of these novels and short stories are readily available.

A history of the lie of innocence: sons who become orphans

This thesis deals with a history of the lie of innocence, its inception in biblical representation and its development in literary representations from the eighteenth century to contemporary times. The aim is to disclose the way the lie functioned across time both in Christian societies and in the secular ones in their wake.

Network dynamics of mobile social networks

With the rapid development of smartphones and mobile Internet technology, we witness an overwhelming growth of mobile social networks (MSN), which is a type of social network, forming virtual communities among people with similar interests or commonalities. In MSNs, users play a crucial role for their development, deployment and success. Understanding the MSN user behavior therefore attracts interests of different entities - ISPs, service providers, and researchers. However, it is hard to gather a comprehensive real data set, little is known and even less has been published about MSN user activities. In this paper, we focus on analyzing MSN user behavior from the perspective of ISP network, which is seldom reported in literature. Based on the real data set collected from the mobile network gateway of a major mobile carrier who has more than five million subscribers, we present an in-depth user behavior analysis of four popular social networks. We study the MSN user behavior from six aspects: user requests, active online time, sessions, inter-session, the number of requests in a session, and inter-request. We found that power law and lognormal are two popular features of the studied objects, and exposed some interesting findings as well. We hope our work could be helpful for ISPs, MSN content providers, and researchers. © 2014 IEEE.

Actor-Network Theory (ANT) based visualisation of socio-technical facets of RFID technology translation: an Australian hospital scenario

In the early 2000s, Information Systems researchers in Australia had begun to emphasise socio-technical approaches in innovation adoption of technologies. The ‘essentialist' approaches to adoption (for example, Innovation Diffusion or TAM), suggest an essence is largely responsible for rate of adoption (Tatnall, 2011) or a new technology introduced may spark innovation. The socio-technical factors in implementing an innovation are largely flouted by researchers and hospitals. Innovation Translation is an approach that purports that any innovation needs to be customised and translated in to context before it can be adopted. Equally, Actor-Network Theory (ANT) is an approach that embraces the differences in technical and human factors and socio-professional aspects in a non-deterministic manner. The research reported in this paper is an attempt to combined the two approaches in an effective manner, to visualise the socio-technical factors in RFID technology adoption in an Australian hospital. This research investigation demonstrates RFID technology translation in an Australian hospital using a case approach (Yin, 2009). Data was collected using a process of focus groups and interviews, analysed with document analysis and concept mapping techniques. The data was then reconstructed in a ‘movie script' format, with Acts and Scenes funnelled to ANT informed abstraction at the end of each Act. The information visualisation at the end of each Act using ANT informed Lens reveal the re-negotiation and improvement of network relationships between the people (factors) involved including nurses, patient care orderlies, management staff and non-human participants such as equipment and technology. The paper augments the current gaps in literature regarding socio-technical approaches in technology adoption within Australian healthcare context, which is transitioning from non-integrated nearly technophobic hospitals in the last decade to a tech-savvy integrated era. More importantly, the ANT visualisation addresses one of the criticisms of ANT i.e. its insufficiency to explain relationship formations between participants and over changes of events in relationship networks (Greenhalgh & Stones, 2010).

Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems.

Linear approximation of Karnik-Mendel type reduction algorithm

Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact. Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low
computational complexities.