Although concurrency is generally perceived to be a `hard' subject, it can in fact be very simple --- provided that the underlying model is simple. The occam-pi parallel processing language provides such a simple yet powerful concurrency model that is based on CSP and the pi-calculus. This thesis presents pony, the occam-pi Network Environment. occam-pi and pony provide a new, unified, concurrency model that bridges inter- and intra-processor concurrency. This enables the development of distributed applications in a transparent, dynamic and highly scalable way. The author specified the layout of the pony system as presented in this thesis, and carried out about 90% of the implementation. This thesis is structured into three main parts, as well as an introduction and an appendix. In the introduction, the need for a unified concurrency model is examined in detail. Thereupon, the pony environment is presented as a solution that provides such a unified model. The first part of this thesis is concerned with the usage of the pony environment for the development of distributed applications. It presents the interface between pony and the user-level code, as well as pony's configuration and a sample application. The second part presents the design and implementation of the pony environment. It explains the internal structure of pony, the implementation of pony's components and public processes, and the integration of pony in the KRoC compiler. The third part evaluates pony's performance and contains the final conclusions. It presents a number of performance tests and concludes with a discussion of the work presented in this thesis, along with an outline of possible future research.

This thesis presents a novel data analysis technique inspired by the natural immune system. Immunological metaphors were extracted, simplified and applied to create an effective data analysis technique. This thesis builds on foundations of previous work, extracts salient features of the immune system and creates a principled and effective data analysis technique. Throughout this thesis, a methodical and principled approach was adopted. Previous work, along with background immunology was extensively surveyed. Problems with previous research were identified and principles from immunology were extracted to create the initial AIS for data analysis. The AIS, through the process of cloning and mutation, built up a network of B cells that were a diverse representation of data being analysed. This network was visualised via a specially developed tool. This allows the user to interact with the network and use the system for exploratory data analysis. Experiments were performed on two different data sets, a simple simulated data set and the Fisher Iris data set. Good results were obtained by the AIS on both sets, with the AIS being able to identify clusters known to exist within them. Extensive investigation into the algorithm's behaviour was undertaken and the way in which algorithm parameters effected performance and results was also examined. Despite initial success from the original AIS, problems were identified with the algorithm and the second stage of research was undertaken. This resulted in the resource limited artificial immune system (RLAIS) which created a stable network of objects that did not deteriorate or loose patterns once discovered. Periods of stable network size were observed with perturbations of the network size. This thesis presents a successful application of immune system metaphors to create a novel data analysis technique. Furthermore, the RLAIS goes a long way toward making AIS a viable contender for effective data analysis and further research is identified for study.

No description available.

Context-awareness enables applications and services to better fulfil the needs of users by adapting to their situation and their preferences. However, the use of contextual information is complicated by privacy concerns. A subjects context is personal and needs to be regarded as sensitive. Hence, contextual information must only be used with the consensus of the subject and according to their privacy preferences. This thesis examines the development of privacy-friendly context-aware systems. In particular the focus is on (A) improving the overall level of privacy, (B) evaluating access control mechanisms, (C) providing development support, and (D) offering protection to third-party infrastructures. The hypothesis investigated is whether these objectives can be achieved through the use of a privacy enhancing infrastructure. As part of the investigation two conceptual models are presented describing the assumptions made about context and privacy. Also presented is a decentralised privacy enhancing infrastructure developed and implemented to determine the validity of the hypothesis. Along with the infrastructure mechanisms for privacy protection including authentication, access control, and anonymity are discussed. A general data format for context communication in the infrastructure is also presented. Finally the thesis presents the findings uncovered during the investigation and evaluation of the hypothesis. This includes a qualitative analysis of whether the privacy enhancing infrastructure meets the key objectives, a user survey examining the performance of two candidate access control mechanism, a performance measure of the infrastructure when run with resource constrained devices, and a comparison with the approaches taken in related work.

Novelty detection, the identification of data that is unusual or different in some way, is relevant in a wide number of real-world scenarios, ranging from identifying unusual weather conditions to detecting evidence of damage in mechanical systems. However, utilising novelty detection approaches in a particular scenario presents significant challenges to the non-expert user. They must first select an appropriate approach from the novelty detection literature for their scenario. Then, suitable values must be determined for any parameters of the chosen approach. These challenges are at best time consuming and at worst prohibitively difficult for the user. Worse still, if no suitable approach can be found from the literature, then the user is left with the impossible task of designing a novelty detector themselves. In order to make novelty detection more accessible, an approach is required which does not pose the above challenges. This thesis presents such an approach, which aims to automatically construct novelty detectors for specific applications. The approach combines a neural network model, recently proposed to explain a phenomenon observed in the neural pathways of the retina, with an evolutionary algorithm that is capable of simultaneously evolving the structure and weights of a neural network in order to optimise its performance in a particular task. The proposed approach was evaluated over a number of very different novelty detection tasks. It was found that, in each task, the approach successfully evolved novelty detectors which outperformed a number of existing techniques from the literature. A number of drawbacks with the approach were also identified, and suggestions were given on ways in which these may potentially be overcome.

Reduced Ordered Binary Decision Diagrams (ROBDDs) provide a dense and memory efficient representation of Boolean functions. When ROBDDs are applied in logic synthesis, the problem arises of detecting both classical and generalised symmetries. State-of-the-art in symmetry detection is represented by Mishchenko's algorithm. Mishchenko showed how to detect symmetries in ROBDDs without the need for checking equivalence of all co-factor pairs. This work resulted in a practical algorithm for detecting all classical symmetries in an ROBDD in O(|G|³) set operations where |G| is the number of nodes in the ROBDD. Mishchenko and his colleagues subsequently extended the algorithm to find generalised symmetries. The extended algorithm retains the same asymptotic complexity for each type of generalised symmetry. Both the classical and generalised symmetry detection algorithms are monolithic in the sense that they only return a meaningful answer when they are left to run to completion. In this thesis we present efficient anytime algorithms for detecting both classical and generalised symmetries, that output pairs of symmetric variables until a prescribed time bound is exceeded. These anytime algorithms are complete in that given sufficient time they are guaranteed to find all symmetric pairs. Theoretically these algorithms reside in O(n³+n|G|+|G|³) and O(n³+n²|G|+|G|³) respectively, where n is the number of variables, so that in practice the advantage of anytime generality is not gained at the expense of efficiency. In fact, the anytime approach requires only very modest data structure support and offers unique opportunities for optimisation so the resulting algorithms are very efficient. The thesis continues by considering another class of anytime algorithms for ROBDDs that is motivated by the dearth of work on approximating ROBDDs. The need for approximation arises because many ROBDD operations result in an ROBDD whose size is quadratic in the size of the inputs. Furthermore, if ROBDDs are used in abstract interpretation, the running time of the analysis is related not only to the complexity of the individual ROBDD operations but also the number of operations applied. The number of operations is, in turn, constrained by the number of times a Boolean function can be weakened before stability is achieved. This thesis proposes a widening that can be used to both constrain the size of an ROBDD and also ensure that the number of times that it is weakened is bounded by some given constant. The widening can be used to either systematically approximate an ROBDD from above (i.e. derive a weaker function) or below (i.e. infer a stronger function). The thesis also considers how randomised techniques may be deployed to improve the speed of computing an approximation by avoiding potentially expensive ROBDD manipulation.

The study of attention aims to understand how the visual system focuses its resources on salient targets presented amongst competing distractors. In a continuously changing environment, temporal attention must pick out targets presented in between spatially coincident distractors that are offset in time. Cognitive theories have proposed that this task is mediated by a temporal spotlight of attention. This thesis combines evidence from behaviour and electrophysiology (EEG) with theoretical insights from neural network modelling to investigate the interplay between this spotlight and conscious perception. The experiments described in this thesis investigate the electrophysiology of temporal visual perception using the Rapid Serial Visual Presentation (RSVP) paradigm. Building upon behavioural research, we use EEG to investigate the influence of target discriminability, the Attentional Blink (AB) and feature integration on the temporal dynamics of visual perception. These findings characterise the influence of pre-attentional processes on attentional deployment, and the subsequent influence of this deployment on perception and behaviour. In addition, they provide the basis for a complementary computational elucidation. The theoretical component of this thesis is based on the ST2 neural network model. The notion of Transient Attentional Enhancement (TAE) embodied therein is the computational equivalent of the temporal spotlight. Its function is evaluated within the ST2 model and in relation to other modelling approaches. In addition, human ERP (Event-Related Potential) data from the experiments are compared with the models equivalent activation traces, termed Virtual ERPs. This combination of theory and experiment broadens our understanding of temporal visual perception, and in conjunction, highlights the role of neural modelling in informing EEG research.

The introduction of Electronic Medical Records (EMR) within healthcare organizations has the main goal of integrating heterogeneous patient information that is usually scattered over different locations. However, there are some barriers that impede the effective integration of EMR within the healthcare practice (e.g., educational, time/costs, security). A focus in improving access control definition and implementation is fundamental to define proper system workflow and access. The main objectives of this research are: to involve end users in the definition of access control rules; to determine which access control rules are important to those users; to define an access control model that can model these rules; and to implement and evaluate this model. Technical, methodological and legislative reviews were conducted on access control both in general and the healthcare domain. Grounded theory was used together with mixed methods to gather users experiences and needs regarding access control. Focus groups (main qualitative method) followed by structured questionnaires (secondary quantitative method) were applied to the healthcare professionals whilst structured telephone interviews were applied to the patients. A list of access control rules together with the new Break-The-Glass (BTG) RBAC model were developed. A prototype together with a pilot case study was implemented in order to test and evaluate the new model. A research process was developed during this work that allows translating access control procedures in healthcare, from legislation to practice, in a systematic and objective way. With access controls closer to the healthcare practice, educational, time/costs and security barriers of EMR integration can be minimized. This is achieved by: reducing the time needed to learn, use and alter the system; allowing unanticipated or emergency situations to be tackled in a controlled manner (BTG) and reducing unauthorized and non-justified accesses. All this helps to achieve a faster and safer patient treatment.

Concurrent programming supports multiple threads of concurrent execution. It is a suitable paradigm for interaction with the outside world, where many inputs (e.g. network packets, key-presses, mouse-clicks) may arrive at any time and need to be dealt with at the same time. Concurrent execution also potentially allows for parallel speed-up; multicore machines are now the standard for new PCs, but the quest to take full advantage of the available parallelism continues. One approach to concurrent programming is process-oriented programming, which uses message-passing and is based on Hoare and Roscoe's Communicating Sequential Processes (CSP). CSP is a process calculus centred on concurrent processes that communicate with each other via synchronous channels. This is used as a formal underpinning for process-oriented programming, either directly (proving behaviours of programs with a CSP model checker) or indirectly (providing sound design principles and patterns). Process-oriented programming has previously been provided in programming languages such as occam-π and libraries for other mainstream languages such as Java and C++. However, as yet process-oriented programming has failed to gain much traction via these implementations; occam-π is rooted in the much older language occam and thus lacks powerful data structures (among other features) which are standard in most modern languages - while languages such as Java and C++, where data is mutable and easily shared, can be an awkward fit to process-oriented programming. Haskell is a functional programming language that is notable for its purity, type-classes, monads and lazy evaluation: modern features that provide interesting and powerful ways to program, including good support for imperative programming. Like other functional languages, it eliminates mutable data - which immediately removes a whole class of problems in concurrent programming. This thesis contends that CSP and process-oriented programming fit well with Haskell. The thesis details the creation of a CSP library for Haskell (Communicating Haskell Processes: CHP) - which features powerful support for process composition - and its further augmentation with capabilities such as support for a new concurrency primitive (conjunction), tracing and the ability to generate formal models of CHP programs. This allows programmers to build concurrent message-passing systems with a strong formal underpinning in a modern programming language, with more features and less complications than process-oriented libraries for other languages.

No description available.