This Creative Writing thesis argues for the need to rethink our understanding of climate change and focuses on the response of creative writers to this phenomenon, whilst also offering its own creative contribution. The critical component aims at articulating a post-climate change poetics. It reviews the mainstream literature in popular science writing, fiction and poetry from the point of view of a political frame-analysis of climate change, to demonstrate how a certain understanding of climate change maps onto conventions of literary genre. The thesis takes the view that many mainstream literary attempts to negotiate climate change are compromised by the teleological way in which they conceive of the phenomenon. As an alternative position, it draws on the work of climatologist Mike Hulme and physicist and cultural theorist Karen Barad to encourage participation in climate change as a condition for negotiating its meaning. Lewis Carroll’s Through the Looking Glass is proposed as a model for literary production informed by this poetics and as a model for the author’s own creative practice. The creative component of this thesis is an intra-generic text presenting the fictionalised narrative of a cycle expedition the author made from Salford to the Greek island of Ikaria in the summer of 2010. This substantial work aims to interrogate, imagine, and enquire into the epistemology of a post-climate change world.

With the rapid growth of the internet and digital media techniques over the last decade, multimedia data such as images, video and audio can easily be copied, altered and distributed over the internet without any loss in quality. Therefore, protection of ownership of multimedia data has become a very significant and challenging issue. Three novel image watermarking algorithms have been designed and implemented for copyright protection. The first proposed algorithm is based on embedding multiple watermarks in the blue channel of colour images to achieve more robustness against attacks. The second proposed algorithm aims to achieve better trade-offs between imperceptibility and robustness requirements of a digital watermarking system. It embeds a watermark in adaptive manner via classification of DCT blocks with three levels: smooth, edges and texture, implemented in the DCT domain by analyzing the values of AC coefficients. The third algorithm aims to achieve robustness against geometric attacks, which can desynchronize the location of the watermark and hence cause incorrect watermark detection. It uses geometrically invariant feature points and image normalization to overcome the problem of synchronization errors caused by geometric attacks. Experimental results show that the proposed algorithms are robust and outperform related techniques found in literature.

Critical discourses on contemporary digital cinema tend to be either overtly negative, framed within a rhetoric of loss or disenfranchisement, or unilaterally positive, celebrating the user agency and freedom digital technologies enable. Both these conceptual positions are unhelpful because they either focus on what contemporary digital cinema fails to do or what it should do, without examining more closely how it actually functions. What is needed is a third, neutral approach which takes both sides into consideration but is also aware of their limitations and weaknesses. This thesis takes as its impetus Giles Deleuze’s suggestion that, just like the cinemas of the movement-image and the time-image before it, contemporary digital cinema needs a basic will to art – a new aesthetic principle, a new function of the image, a new politics, a new representational potential distinct from those that have come before it. The aim of this thesis is therefore to establish this will to art and explore its ramifications for and manifestations in contemporary digital cinema. Taking into consideration a variety of filmic texts from the 1980s to the present day which prominently feature diegetically recorded footage, as well as amateur film-making practices from the home movies of the 1960s to the video clips now uploaded to online media sharing platforms, the increasing relevance of home media in the reception of contemporary digital cinema, and most crucially the process of convergence inherent in digital media, this thesis argues that the will to art of contemporary digital cinema is intimacy.

Classification is one of the primary tasks of data mining and aims to assign a class label to unseen examples by using a model learned from a training dataset. Most of the accepted classifiers are designed to minimize the error rate but in practice data mining involves costs such as the cost of getting the data, and cost of making an error. Hence the following question arises: Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem? It is well known to the machine learning community that there is no single algorithm that performs best for all domains. This observation motivates the need to develop an “algorithm selector” which is the work of automating the process of choosing between different algorithms given a specific domain of application. Thus, this research develops a new meta-learning system for recommending cost-sensitive classification methods. The system is based on the idea of applying machine learning to discover knowledge about the performance of different data mining algorithms. It includes components that repeatedly apply different classification methods on data sets and measuring their performance. The characteristics of the data sets, combined with the algorithm and the performance provide the training examples. A decision tree algorithm is applied on the training examples to induce the knowledge which can then be applied to recommend algorithms for new data sets, and then active learning is used to automate the ability to choose the most informative data set that should enter the learning process. This thesis makes contributions to both the fields of meta-learning, and cost sensitive learning in that it develops a new meta-learning approach for recommending cost-sensitive methods. Although, meta-learning is not new, the task of accelerating the learning process remains an open problem, and the thesis develops a novel active learning strategy based on clustering that gives the learner the ability to choose which data to learn from and accordingly, speed up the meta-learning process. Both the meta-learning system and use of active learning are implemented in the WEKA system and evaluated by applying them on different datasets and comparing the results with existing studies available in the literature. The results show that the meta-learning system developed produces better results than METAL, a well-known meta-learning system and that the use of clustering and active learning has a positive effect on accelerating the meta-learning process, where all tested datasets show a decrement of error rate prediction by 75 %.

This study looks into the intricacies of long-distance romantic relationships and the ways in which intimacy is created and nurtured with the help of technology and digital media platforms. It uses a methodological triangulation, combining quantitative content analysis with qualitative interviews, as tools to investigate the doings of couples who find themselves geographically challenged. The results point out to the fact that people adapt to these  circumstances and use technology as a third player of the relationship, creating and maintaining intimacy mainly through conversations and stepping away from the physical side of a romantic relationship.

The acoustic character of a space is often quantified using objective room acoustic parameters. The measurement of these parameters is difficult in occupied conditions and thus measurements are usually performed when the space is un-occupied. This is despite the knowledge that occupancy can impact significantly on the measured parameter value. Within this thesis new methods are developed by which naturalistic signals such as speech and music can be used to perform acoustic parameter measurement. Adoption of naturalistic signals enables passive measurement during orchestral performances and spoken announcements, thus facilitating easy in-situ measurement. Two methods are described within this work; (1) a method utilising artificial neural networks where a network is taught to recognise acoustic parameters from received, reverberated signals and (2) a method based on the maximum likelihood estimation of the decay curve of the room from which parameters are then calculated. (1) The development of the neural network method focuses on a new pre-processor for use with music signals. The pre-processor utilises a narrow band filter bank with centre frequencies chosen based on the equal temperament scale. The success of a machine learning method is linked to the quality of the training data and therefore realistic acoustic simulation algorithms were used to generate a large database of room impulse responses. Room models were defined with realistic randomly generated geometries and surface properties; these models were then used to predict the room impulse responses. (2) In the second approach, a statistical model of the decay of sound in a room was further developed. This model uses a maximum likelihood (ML) framework to yield a number of decay curve estimates from a received reverberant signal. The success of the method depends on a number of stages developed for the algorithm; (a) a pre-processor to select appropriate decay phases for estimation purposes, (b) a rigorous optimisation algorithm to ensure the correct maximum likelihood estimate is found and (c) a method to yield a single optimum decay curve estimate from which the parameters are calculated. The ANN and ML methods were tested using orchestral music and speech signals. The ANN method tended to perform well when estimating the early decay time (EDT), for speech and music signals the error was within the subjective difference limens. However, accuracy was reduced for the reverberation time (Rt) and other parameters. By contrast the ML method performed well for Rt with results for both speech and music within the difference limens for reasonable (<4s) reverberation time. In addition reasonable accuracy was found for EDT, Clarity (C80), Centre time (Ts) and Deutichkeit (D). The ML method is also capable of producing accurate estimates of the binaural parameters Early Lateral Energy Fraction (LEF) and the late lateral strength (LG). A number of real world measurements were carried out in concert halls where the ML accuracy was shown to be sufficient for most parameters. The ML method has the advantage over the ANN method due to its truly blind nature (the ANN method requires a period of learning and is therefore semi-blind). The ML method uses gaps of silence between notes or utterances, when these silence regions are not present the method does not produce an estimate. Accurate estimation requires a long recording (hours of music or many minutes of speech) to ensure that at least some silent regions are present. This thesis shows that, given a sufficiently long recording, accurate estimates of many acoustic parameters can be obtained directly from speech and music. Further extensions to the ML method detailed in this thesis combine the ML estimated decay curve with cepstral methods which detect the locations of early reflections. This improves the accuracy of many of the parameter estimates.

Social media is increasingly used for social protest, but does online participation advance the aims of social movements, or does it undermine efforts for social change? We explore this question in the present thesis by examining how the use of social media for collective action shapes, and is shaped by, the social psychological concerns of technology users. Adopting a diverse approach in terms of research questions and methodology, we examine how collective action is affected by: (1) features of the digital environment, (2) internet-enabled modes of participation, and (3) digitally-facilitated communities. Our findings demonstrate that group-level representations of the self and salient others are integral to the relationship between digital technology and collective action. Ultimately, we argue that digital technology can act as both a psychological bridge and barrier between disparate groups and issues; in this way it can both facilitate and undermine mobilisation efforts and broader aims for social change.

Thesis (M.A. (Journalism and Media Studies)) - Rhodes University, 2007 / "Thesis submitted in partial fulfilment for the degree of Master of Arts."

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [244]-265).

Thesis (doctoral)--Universität, Zürich, 2007. / Includes bibliographical references.