The application of sentiment analysis as a method for the automatic categorisation of opinions in text has grown increasingly popular across a number of domains over the past few years. In particular, health services have started to consider sentiment analysis as a solution for the task of processing the ever-growing amount of feedback that is received in regards to patient care. However, the domain is relatively under-studied in regards to the application of the technology, and the effectiveness and performance of methods have not been substantially demonstrated. Beginning with a survey of sentiment analysis and an examination of the work undertaken so far in the clinical domain, this thesis examines the application of supervised machine learning models to the classification of sentiment in patient feedback. As a starting point, this requires a suitably annotated patient feedback dataset, for both analysis and experimentation. Following the construction and detailed analysis of such a resource, a series of machine learning experiments study the impact of different models, features and review types to the problem. These experiments examine the applicability of the selected methods and demonstrate that model and feature choice may not be a significant issue in sentiment classification, whereas the type of review that the models train and test across does affect the outcome of classification. Finally, by examining the role that responses play in the patient feedback process and developing the idea of incorporating the inter-document context provided by the response into the feedback classification process, a recalibration framework for the labelling of sentiment in ambiguous texts for patient feedback is developed. As this detailed analysis will demonstrate, while some problems in performance remain despite the development and implementation of the recalibration framework, sentiment analysis of patient feedback is indeed viable, and achieves a classification accuracy of 91.4% and F1 of 0.902 on the gathered data. Furthermore, the models and data can serve as a baseline to study the nature of patient feedback, and provide a unique opportunity for the development of sentiment analysis in the clinical domain.

The advent of modern railway signalling and train control technology allows the implementation of advanced real-time railway management. A number of researchers throughout the world have previously considered the problem of minimising the costs of train delays and have used various optimisation algorithms for differing scenarios. However, little work has been carried out to evaluate and compare the different approaches. Firstly, this thesis compares and contrasts a number of optimisation approaches that have been previously used and applies them to a series of common scenarios. It is found that simple disturbances (i.e. one train delayed) can be managed efficiently using straightforward approaches, such as first-come-first-served. For more complex scenarios, advanced methods are found to be more appropriate. For the scenarios considered in this comparison, ant colony optimisation performed well. Secondly, in order to improve the currently available algorithm so that it can more reliably find optimal or close to optimal results within a practical computation time, a new hybrid algorithm, based on ant colony optimisation, has been developed. In order to evaluate the new approach 100 randomly generated delay scenarios are tested, and a comparison is made between the results of the new algorithm and first-come-first-served, brute force and standard ant colony optimisation. It is shown that the hybrid algorithm has improved performance in terms of optimality and computation speed. Finally, a new multi-stage rescheduling approach for finding an optimal solution over multiple junctions is proposed. A case study is considered, and it is shown that the proposed approach performs well.

Achim Jung and Drew Moshier developed a Stone-type duality theory for bitopological spaces, amongst others, as a practical tool for solving a particular problem in the theory of stably compact spaces. By doing so they discovered that the duality of bitopological spaces and their algebraic counterparts, called d-frames, covers several of the known dualities. In this thesis we aim to take Jung's and Moshier's work as a starting point and fill in some of the missing aspects of the theory. In particular, we investigate basic categorical properties of d-frames, we give a Vietoris construction for d-frames which generalises the corresponding known Vietoris constructions for other categories, and we investigate the connection between bispaces and a paraconsistent logic and then develop a suitable (geometric) logic for d-frames.

Agent based computational economics (ACE), as a research field, has been using co-evolutionary algorithms for modelling the socio-economic learning and adaptation process of players within games that model socio-economic interactions. In addition, it has also been using these algorithms for optimising towards the game equilibria via socio-economic learning. However, the field has been diverging from evolutionary computation, specifically co-evolutionary algorithm design research. It is common practice in ACE to explain the process and outcomes of such co-evolutionary simulations in socio-economic terms. However, co-evolutionary algorithms are known to have unexpected dynamics that lead to unexpected outcomes. This has often lead to mis-interpretations of the process and outcomes in socio-economic terms, a case in point being the lack of a methodical use of the term bounded rationality. This mis-interpretation can be attributed to the lack of a proper consideration of the solution concept being implemented by the coevolutionary algorithm used for the simulation. We propose a holistic methodical framework for analysing and designing co-evolutionary simulations, such that mis-interpretations of socio-economic phenomena be methodically avoided, disabling the algorithm from being mis-interpreted in socio-economic terms, aimed at benefiting ACE as a research field. More specifically, we consider the methodical treatment of co-evolutionary algorithms, as enabled by the framework, such that mis-interpretations of bounded rationality be avoided when these algorithms are used to optimise towards equilibrium solutions in bargaining games. The framework can be broken down into two parts: • Analysing and refining co-evolution for ACE, using the notion behind co-evolutionary solution concepts from co-evolutionary algorithm design research: Challenging the value of the implicit assumption of bounded rationality within co-evolutionary simulations, which leads to it being mis-interpreted, we show that convergence to the equilibrium solutions can be achieved with boundedly rational agents by working on the elements of the implemented co-evolutionary solution concept, as opposed to previous studies where bounded rationality was seen as the cause for deviations from equilibrium. Analysis and refinements guided by the presence of top-down equilibrium solutions, allow for a top-down avoidance of misinterpretations of bounded rationality within simulations. • Analysing and refining co-evolution for ACE, using the notion behind reconciliation variables proposed in the thesis: Reasonably associating mis-interpreted socio-economic phenomena of interest with the elements of the implemented co-evolutionary solution concept, parametrising and quantifying the elements, we obtain our reconciliation variables. Systematically analysing the simulation for its relationship with the reconciliation variables or for its closeness to desired behaviour, using this parametrisation, is the suggested idea. Bounded rationality is taken as a reconciliation variable, reasonably associated with agent strategies, parametrised and quantified, and analysis of simulations with respect to this variable carried out. Analysis and refinements based on such an explicit expression of bounded rationality, as opposed to the erstwhile implicit assumption, allow for a bottom-up avoidance of mis-interpretations of bounded rationality within simulations. We thus remove the causes that lead to bounded rationality being mis-interpreted altogether using this framework. We see this framework as one next step in ACE socio-economic learning simulation research, which must not be overlooked.

This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and short vowels, the complex morphology, and the highly in infection process. Instead of classifying the detected NE phrases into small sets of classes, we target a broader range (i.e. 50 fine-grained classes 'hierarchal-based of two levels') to increase the depth of the semantic knowledge extracted. This has increased the number of classes, complicating the task, when compared with traditional (coarse-grained) NER, because of the increase in the number of semantic classes and the decrease in semantic differences between fine-grained classes. Our approach to developing fine-grained NER relies on two different supervised Machine Learning (ML) technologies (i.e. Maximum Entropy 'ME' and Conditional Random Fields 'CRF'), which require annotated training data in order to learn by extracting informative features. We develop a methodology which exploit the richness of Arabic Wikipedia (A W) in order to create a scalable fine-grained lexical resource and a corpus automatically. Moreover, two gold-standard created corpora from different genres were also developed to perform comparable evaluation. The thesis also developed a new approach to feature representation by relying on the dependency structure of the sentence to overcome the limitation of traditional window-based (i.e. n-gram) representation. Furthermore, by exploiting the richness of unannotated textual data to extract global informative features using word-level clustering technique was also achieved. Each contribution was evaluated via controlled experiment and reported using three commonly applied metrics, i.e. precision, recall and harmonic F-measure.

This thesis extends the concept of compactifications of topological spaces to a setting where spaces carry a partial order and maps are order-preserving. The main tool is a Stone-type duality between the category of d-frames, which was developed by Jung and Moshier, and bitopological spaces. We demonstrate that the same concept that underlies d-frames can be used to do recover short proofs of well-known facts in domain theory. In particular we treat the upper, lower and double powerdomain constructions in this way. The classification of order-preserving compactifications follows ideas of B. Banaschewski and M. Smyth. Unlike in the categories of spaces or locales, the lattice-theoretic notion of normality plays a central role in this work. It is shown that every compactification factors as a normalisation followed by the maximal compactification, the Stone-Cech compactification. Sample applications are the Fell compactification and a stably compact extension of algebraic domains.

Remote electronic voting has long been considered a panacea for many of the problems with existing, paper-based election mechanisms: assurance that one’s vote has been counted as cast; ability to vote without fear of coercion; fast and reliable tallying; improvement in voter turnout. Despite these promised improvements, take-up of remote electronic voting schemes has been very poor, particularly when considering country-wide general elections. In this thesis, we explore a new class of remote electronic voting protocols: specfically, those which fit with the United Kingdom’s requirement that it should be possible to link a ballot to a voter in the case of personation. We address the issue of revocable anonymity in electronic voting. Our contributions are threefold. We begin with the introduction of a new remote electronic voting protocol, providing revocable anonymity for any voter with access to an Internet-connected computer of their choice. We provide a formal analysis for the security properties of this protocol. Next, we are among the first to consider client-side security in remote electronic voting, providing a protocol which uses trusted computing to assure the voter and authorities of the state of the voter’s machine. Finally, we address revocable anonymity more generally: should a user have the right to know when their anonymity has been revoked? We provide a protocol which uses trusted computing to achieve this. Ultimately, the work in this thesis can be seen as a sound starting point for the deployment of remote electronic voting in the United Kingdom.

Children like to draw, but how easy is it for them to draw on a touch screen device? More specifically, how do children adapt the way that they draw to the device, to their own limitations and to the motivational context for action? Despite the fact that many children choose to draw on tablets there have been few studies of how they do so. Arguably tablets offer a more flexible drawing tool than paper and pencil but, on the other hand, there is some evidence that they also introduce some additional perceptual/motor difficulties for children. To answer this question, I conducted a series of laboratory experiments to examine how children aged between 4 to 11 years old adapt their drawing actions to their own motor variability and to extrinsic rewards. The thesis seeks a better understanding of the psychological process involved in drawing and drawing development in children given motivational factors. To this end it adopts a utility maximization approach to framing questions about drawing that derives its explanatory power from three components; ecology, utility and information processing mechanisms. The framework motivates theories that provide an explanatory and predictive account of children's adaptation of drawing strategies on a tablet, derived, in part, from the cognitive psychology of human movement control. The results indicate a developing sensitivity to utility and motor variability.

Game Semantics is an approach to denotational semantics that has been successful in providing accurate, fully abstract models for various programming languages. It has thereafter been applied, amongst other things, to model checking, access control analysis, information flow analysis, and recently, hardware synthesis. While the roots of modern Game Semantics are sequential, several game models of asynchronous concurrency have since been devised. However, synchronous concurrency has not been considered hitherto. This thesis studies synchronous concurrency in game-like models. The central idea is to investigate deriving such synchronous models from their asynchronous counterparts using round abstraction--a technique that allows aggregating a sequence of computational steps to form a larger, more abstract macro-step. We define round abstraction within a trace-semantic setting that generalises game semantic models. We note that, in general, round abstraction is not compositional. We then identify sufficient conditions to guarantee correct composition, thereby proposing a framework for round abstraction that is sound when applied to synchronous and asynchronous behaviours. We explore extensions of our synchronous model with causality, global clocks and determinism.

Rheumatoid arthritis is an autoimmune disease that affects approximately 1% of the population, where chronic inflammation of the synovial joints can lead to active destruction of cartilage and bone. New therapeutic targets are discovered by investigating genes or processes that exacerbate or ameliorate disease progression. Mouse models of inflammatory arthritis are commonly employed for this purpose, in conjunction with biomedical imaging techniques and suitable measures of disease severity. This thesis investigated the hypothesis that a statistical model of non-pathological bone shape variation could be used to quantify bone destruction present in micro-CT images. A framework for constructing statistical shape models of the hind paw was developed, based on articulated registration of a manually segmented reference image. Successful registration of the reference towards ten healthy hind paw samples was followed by statistical shape analysis. Mouse models of inflammatory arthritis were then investigated and compared by identifying bone abnormalities as deviations from the model statistics. Validation of the model against digital phantoms and clinical scores indicates that the method is largely successful in this effort. Application of the method in a novel study of macrophage-mediated inflammation shows promising results that are supportive of previous findings.