In recent years the software industry has paid significant attention to customising software products by means of outsourcing and Agile development practices. When these areas overlap there are benefits and challenges. This study investigates the customisation process for packaged software products in projects involving multiple clients, and the communication of their requirements across distributed boundaries. A literature review identified the challenges involved and a framework for challenges of customising software products across distributed boundaries (FCCSD) is proposed to address them using onsite development practices. Local decision making and local development are considered as a means of reducing difficulties in communicating the customisation requirements of multiple clients across distributed boundaries through a new model. This model for communicating customisation requirements in the distributed domain, termed (CCRD), has two scenarios, one using decision making at the distributed client’s location, and the second enhancing onsite development of certain requirements in order to reduce delays and misunderstandings between the clients and the teams involved. A preliminary study was conducted to confirm the FCCSD. This employed a questionnaire survey of 19 highly experienced participants. The CCRD model was evaluated in three stages using an industrial case study of a company with 18 distributed clients. The first was a contextual inquiry to create a baseline model for a real world case. The second involved the simulation of the CCRD model using a discrete-event simulation approach, traced by the baseline model using real data. Finally, the findings of previous experiments were validated through a study conducted by means of semi-structured interviews with seven experts at this same company. The key contributions of this study are as follows: First is the proposed framework (FCCSD)that addresses a number of challenges facing customisation across distributed boundaries from literature. Second, this study highlights issues of the communication and negotiation of clients’ customisation requirements across distributed boundaries, and their implications. Therefore, this study demonstrates the statistically significant impact of making decisions and negotiating clients’ requirements, as well as conducting certain development practices, on their premises to limit the implications of communication challenges over distributed boundaries, such as delays in making decisions, a long duration of development and the entire customisation process, and misunderstandings about clients’ requirements.

This thesis focuses on the phenomena of neuromodulation — these are a set of diffuse chemical pathways that modify the properties of neurons and act in concert with the more traditional pathways mediated by synapses (neurotransmission). There is a growing opinion within neuroscience that such processes constitute a radical challenge to the centrality of neurotransmission in our understanding of the nervous system. This thesis is an attempt to understand how the idea of neuromodulation should impact on the canonical ideas of information processing in the nervous system. The first goal of this thesis has been to systematise the ideas immanent in neuromodulation such that they are amenable to investigation through both simulation and analytical techniques. Specifically, the physiological properties of neuromodulation are distinct from those traditionally associated with neurotransmission. Hence, a first contribution has been to develop a principled but minimal mechanistic description of neuromodulation. Furthermore, neuromodulators are thought to underpin a distinct set of functional roles. Hence, a second contribution has been to define these in terms of a set of dynami- cal motifs. Subsequently the major goal of thesis has been to investigate the relationship between the mechanistic properties of neuromodulation and their dynamical motifs in order to understand whether the physiological properties of neuromodulation predispose them toward their functional roles? This thesis uses both simulation and analytical techniques to explore this question. The most significant progress, however, is made through the application of dynamical systems analysis. These results demonstrate that there is a strong relationship between the mechanistic and dynamical abstractions of neuromodulation developed in this thesis. In particular they suggest that in contrast to neurotransmission, neuromodulatory pathways are predisposed toward bifurcating a system’s dynamics. Consequently, this thesis argues that a true canonical picture of the dynamics of the nervous system requires an appreciation of the interplay between the properties of neurotransmission and the properties immanent in the idea of neuromodulation.

Achieving intended learning outcomes (ILOs) in education is an ongoing topic within distance learning and educational communities. The term "ILOs" has been introduced to indicate what learners will be able to do by the end of the course of study. Developing the ILO structure, in which the subject matter and their relationships are integrated with the capabilities to be learned, is a challenge to instructional designers. In this research, the ILO diagram – a novel conceptual model of intended learning outcomes – is proposed to support not only instructional designers in designing and developing courses of study, but also learners and instructors in performing the courses' learning and teaching activities. The research covers three objectives. First, in order to pioneer courses of study which should consider all stakeholders in education, the research aims primarily to reconcile constructivist and instructivist theories in order to propose an equivalent architecture, using ILOs to support learning and teaching. Second, more significantly, the research aims to contribute a novel conceptual model of ILOs (called an ILO diagram) using a diagrammatic technique. In the ILO diagram, ILO nodes are represented as the two-dimensional classification of a performance/content matrix based on the component display theory proposed by Merrill. The ILO relationships have formulated the hierarchical structure using the cognitive hierarchy comprising six levels adopted the Bloom's taxonomy of the cognitive domain. Moreover, three types of the principal relationship, two types of the composite relationship, and three relationship constraints are proposed. Finally, the third objective of the research is to experimentally ascertain how the structured ILOs format conceptualised through the proposed ILO diagram can contribute to both teaching and learning. Furthermore, the three experimental studies were conducted to explore whether providing the well-defined structure of ILOs, conceptualised through the ILO diagram, can facilitate teaching and learning. In the first experiment, the main aim was to investigate the instructors’ satisfaction with using the ILO diagram in teaching. The results revealed that the proposed ILO diagram met the instructors’ satisfactions with higher ratings for perceived usefulness, perceived ease of use, and attitude towards representing ILOs than the plain-text document. The second experiment was to investigate whether using the ILO diagram to facilitate learning can support learners to indicate the learning paths. The results revealed that the mean completeness of all learning paths was statistically significantly higher with the structured ILOs (ILO diagram), showing that the learners benefited from the ILO diagram in performing their self-regulated learning. Finally, the last experiment was to investigate how well the learners understand the conceptual representation of the ILO diagram. The results of the experiment revealed that the average mean of understandability for the conceptual representation of the ILO diagram was higher than for both the sentential and tabular representations. These findings indicate that the ILO diagram provides more understandability than the sentential and tabular representational styles of ILOs.

In broad terms, Recommender Systems use machine learning techniques to process his- torical data about their user’s interests, encoded in user profiles. Once the algorithms used have been trained on user profiles, their output is used to compile a ranked list of all resources available for recommendation, based on each profile. Collaborative Filtering is the most widespread method of carrying this out, building on the intuition that similar people will be interested in the same things. The point of failure in this approach lies in that similarity can only be assessed between users that have expressed their preferences on a common set of resources. This requirement prohibits the sharing of preference data across different systems, and causes additional problems when new resources for recommendation become available, or when new users subscribe to the system. I propose that the difficulty can be overcome by identifying and exploiting semantic re- lationships between the resources available for recommendation themselves. Moreover, systems that are able to assess the strength of the relationship between any two resources can provide recommendations from multiple domains. For example, music recommen- dations can be made based on a person’s film taste if strong semantic relationships can be identified between certain films and the music he/she listens to. As such the contributions made by this dissertation can be summarised in the following: 1. Facilitating the comparison of heterogeneous resources The use of Wikipedia is proposed for this purpose, under the assumption that hyper–links between articles in Wikipedia convey latent semantic relationships between the concepts they describe. Thus, a methodology for projecting domain resources onto Wikipedia has been developed. The assumption is then validated by showing evidence that the projections are successful in retaining similarity between domain resources, in three independent domains. 2. Enabling the provision of recommendations from multiple domains The aforementioned projections encode the links present in Wikipedia articles that are found to correspond to domain resources, and can be viewed collectively as a graph. In addition, the Internet is populated with social networks of people who express their preferences on a given set of resources in the form of ratings. Members of such communities are included as nodes in the graph and ratings regarding domain resources represented as edges. A reversible Markov chain model was implemented to describe the probabilities associated with the traversal of edges in the integrated graph. Nodes that represent resources and other concepts the user is known to be interested in are then identified in the graph. Using these nodes as a starting point, the resource nodes most likely to be reached after an arbitrarily large number of edge traversals are considered the most relevant to the user and are recommended. Experimental results show that the framework is successful in predicting user preferences in domains different to those of the input.

Our reliance on the correct functioning of embedded systems is growing rapidly. Such systems are used in a wide range of applications such as airbag control systems, mobile phones, and high-end television sets. These systems are becoming more and more complex and require multi-core processors with scalable shared memory to meet the increasing computational power demands. The reliability of the embedded (distributed) software is thus a key issue in the system development. In this thesis we describe and evaluate an approach to reason accurately and effectively about large embedded software using bounded model checking (BMC) based on Satisfiability Modulo Theories (SMT) techniques. We present three major novel contributions. First, we extend the encodings from previous SMT-based bounded model checkers to provide more accurate support for variables of finite bit width, bit-vector operations, arrays, structures, unions and pointers and thus making our approach suitable to reason about embedded software. We then provide new encodings into existing SMT theories and we show that our translations from ANSI-C programs to SMT formulas are as precise as bit-accurate procedures based on Boolean Satisfiability. Second, we develop three related approaches for model checking multi-threaded software in embedded systems. In the lazy approach, we generate all possible interleavings and call the SMT solver on each of them individually, until we either find a bug, or have systematically explored all interleavings. In the schedule recording approach, we encode all possible interleavings into one single formula and then exploit the high speed of the SMT solvers. In the underapproximation and widening approach, we reduce the state space by abstracting the number of interleavings from the proofs of unsatisfiability generated by the SMT solvers. Finally, we describe and evaluate an approach to integrate our SMT-based BMC into the software engineering process by making the verification process incremental. In particular, our approach looks at the modifications suffered by the software system since its last verification, and submits them to a partly static and dynamic verification process, which is thus guided by a set of test cases for coverage. Experiments show that our SMT-based BMC can analyze larger problems and reduce the verification time compared to state-of-the-art techniques that use BMC, iterative context-bounding or counterexample-guided abstraction refinement

Resource Description Framework (RDF) is a flexible, increasingly popular data model that allows for simple representation of arbitrarily structured information. This flexibility allows it to act as an effective underlying data model for the growing Semantic Web. Unfortunately, it remains a challenge to store and query RDF data in a performant manner, with existing stores struggling to meet the needs of demanding applications: particularly low latency, human-interactive systems. This is a result of fundamental properties of RDF data: RDF's small statement size tends to engender large joins with a lot of random I/O, and its limited structure impedes the generation of compact, relevant statistics for query optimisation. This thesis posits that the problem of performant RDF storage can be effectively mitigated using in-memory storage, thanks to RAM's extremely high throughput and rapid random I/O relative to disk. RAM is rapidly reducing in cost, and is finally reaching the stage where it is becoming a practical medium for the storage of substantial databases, particularly given the relatively small size at which RDF datasets become challenging for disk-backed systems. In-memory storage brings with it its own challenges. The relatively high cost of RAM necessitates a very compact representation, and the changing relationship between memory and CPU (particularly increasing RAM access latency) benefits designs that are aware of that relationship. This thesis presents an investigation into creating CPU-friendly data structures, along with a deep study of the common characteristics of popular RDF datasets. Together, these are used to inform the creation of a new data structure called the Adaptive Hierarchical RDF Index (AHRI), an in-memory, RDF-specific structure that outperforms traditional storage mechanisms in nearly every respect. AHRI is validated with a comprehensive evaluation against other commonly used in-memory data structures, along with a real world test against a memory-backed store, and a fast disk-based store allowed to cache its data in RAM. The results show that AHRI outperforms these systems with regards to both space consumption and read/write behaviour. The document subsequently describes future work that should provide substantial further improvements, making the use of RAM for RDF storage even more compelling.

This document charts a series of investigations into some basic questions concerning the relationship between life and the physical theories of thermodynamics. While equilibrium thermodynamics represents a foundational component of modern physics, methods for non equilibrium systems have yet to reach the same level of maturity. The first part of this thesis aims to establish the validity of a burgeoning theory of non-equilibrium thermodynamics known as the Maximum Entropy Production Principle (MEPP), in the context of heat transfer by convective fluid motion between heated boundaries. Applying the MEPP to systems with both fixed and negative feedback boundary conditions revealed that in fact, the steady state of convective fluids cannot be accurately predicted from an assumption of maximum entropy production alone. Rather the subtleties of the boundary conditions and the physical properties of the fluid must be properly accounted for. It is thus proposed that the MEPP should not, as has sometimes been suggested, be treated as a universally applicable law of nature. The second part of this thesis investigates the pattern-forming and transport properties of reactive fluid systems. It is found that under thermal driving forces, closed systems utilise the physical processes of reaction and advection to augment their heat transport abilities. Furthermore, the addition of thermal kinetics and fluid flow to the Gray-Scott reaction diffusion system, reveals a new range of phenomena including positive feedback, self-inhibition, competition and symbiosis. Such behaviour can readily be viewed from an ecological, rather than purely physico-chemical, perspective.

Design optimisation of electromagnetic and electromechanical devices is usually aided by numerical simulations, such as the finite element method, which often carry high computational costs, especially if three-dimensional transient modelling is required. Thus in addition to the task of finding the global optimum, while avoiding local minima traps, there is the additional requirement of achieving the final solution efficiently with as few objective function evaluations as possible. With this in mind several surrogate modelling techniques have been developed to replace, under controlled environment, the computationally expensive accurate field modelling by fast approximate substitutes. This thesis looks at a particular technique known as kriging, which in other applications has been demonstrated to provide accurate representations, even of complicated responses, based on a limited set of observations whilst providing an error estimate of the predictions and hence increasing the confidence in the answer. In the iterative optimisation process the critical issue is where to position the next point for evaluation to find a sensible compromise between conflicting goals to explore the search space thoroughly but at the same time exploit information already available. This thesis proposes several novel algorithms based on reinforcement learning theory using the concept of rewards for balancing exploration and exploitation automatically and adaptively. The performance of these algorithms has been assessed comprehensively using carefully selected test functions and real engineering problems (taken from TEAM workshops) and compared with the results published in literature. The kriging approach has generally been found to outperform significantly other available methods. One of the practical limitations, however, was found to be large-scale multi-dimensional or multi-objective tasks because of the need to create special correlation matrices for the kriging predictions to work. Several techniques have been developed and implemented to alleviate such problems and control the memory space occupied by such matrices. Finally, in practical design problems, the issue of robustness of the design has to be considered – related to manufacturing tolerances, material variability, etc – which requires the designer not only to find the theoretical optimum but also assess its quality (sensitivity) within specified uncertainties of variables. Several strategies for evaluation of design robustness assisted by kriging modelling have been developed and implemented in combination with commercial electromagnetic design software.

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