Red shoes : obsessive and contagious narratives

The fairytale motif of the red shoes continues to fascinate many contemporary authors and artists. What inspires these obsessive and contagious narratives? The raw, unfettered femaleness of the red shoes, reminiscent of the pre-oedipal realm, suggests a means of escaping confining patriarchal conceptions of woman, desire and creativity.

Daylight for sustainable development of historic sites

Consideration of daylight is one of the contextual ingredients for maintaining a sustainable identity when intervening in a heritage site. Appropriate consideration of daylighting ensures not only visual and thermal comfort in the urban setting, but also contributes to the preservation of the place visual identity. The historic district of el Darb al-Asfar is undergoing a rehabilitation project that raises questions about the new ‘sparking’ look of the place. The ongoing cultural debate in old Cairo on the extent of success of the rehabilitation efforts in preserving the place identity identified a gap in literature regarding the ingredients that constitute identity in heritage settings. The paper argues that the project using new finishing materials of facades has led to changes in daylight levels in the space and hence the visual perception of the place itself. This paper aims to assess the impact of such intervention on the visual perception and the space identity. A digital model is built utilising a combination of photogrammetric and 3D digital technique. The TOWNSCOPE simulation package is used to trace the performance of direct, diffused, and reflected components of daylight within the original and recently resorted scenes. The output of the simulation exercise has been validated by empirical data. The paper concludes by suggesting a set of measures for achieving an appropriate daylight performance in heritage sites Eastern Mediterranean climatic conditions.

Improving prediction interval quality : a genetic algorithm-based method applied to neural networks

The delta technique has been proposed in literature for constructing
prediction intervals for targets estimated by neural networks. Quality of constructed prediction intervals using this technique highly depends on neural network characteristics. Unfortunately, literature is void of information about how these dependences can be managed in order to optimize prediction intervals. This study attempts to optimize length and coverage probability of prediction intervals through modifying structure and parameters of the underlying neural networks. In an evolutionary optimization, genetic algorithm is applied for finding the optimal values of network size and training hyper-parameters. The applicability and efficiency of the proposed optimization technique is examined and demonstrated using a real case study. It is shown that application of the proposed optimization technique significantly improves quality of constructed prediction intervals in term of length and coverage probability.

A wordless horizon

This research identifies and explores the thematic and technical innovations of a number of politically-engaged poets as a means to illustrate important aspects of the poet's own creative work. Fundamental to the exegesis is the development of a contemporary poetic founded on the problem of 'intersectionist oppression'

Fictional femininity

This thesis examines gender-political representations of middle-aged, middle-class, white women in a sample of new millennial middlebrow novels. The findings demonstrate how popular contemporary fiction traffics a set of feminine norms that are complexly inflected by both feminism and the apolitical ethos of feminism.

Looking after well siblings of adolescent girls with anorexia : an important parental role

Background Research has suggested that well siblings of children with chronic and life-threatening illnesses are at risk for negative outcomes and that parents’ responses to the illnesses can influence the adaptation of well siblings. Yet, parents’ efforts to look after well siblings in the context of illness are rarely considered in literature about sibling adaptation. The importance of attending to the needs of well siblings was a major theme to emerge from a qualitative analysis of the experiences of parents of adolescent girls with anorexia nervosa.

Methods In-depth interviews were conducted with 24 parents of adolescent girls with anorexia and analysed using grounded theory method.

Results The data indicated that parents viewed caring for well siblings in the context of anorexia as an important role and responsibility. Parents reported making conscious and active efforts to look after well siblings by: maintaining normality; compensating for changes to routines; protecting siblings; providing emotional support; and managing the consequences.

Conclusions This paper provides a picture of the actions parents take to help well siblings adapt to anorexia in the family. Further research is needed to develop and expand this understanding to families experiencing a wide range of chronic and life-threatening illnesses. The findings underline the importance of clinical attention and further research into the critical parental role of caring for well siblings.

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.