Whilst there has been a substantial amount of research into the uncanny valley, defining research that contextualises a character as they would normally be viewed remains an unexplored area. Often previous research focused solely on realistic render styles giving characters an unfair basis that tended towards the realistic, thus facilitating only one mode of animation style: realism. Furthermore, characters were not contextualized because researchers often used footage from previous productions. These characters also differed in quality as various artists worked on different productions. This research considers characterisation as three key components, the aesthetic, the behaviour and the contextualisation. Attempts were made to develop a greater understanding of how these components contribute to the appeal of a character within the field of 3D computer animation. Research consisted of two experiments. Both experiments were conducted using an online survey method. The first experiment used five different characters ranging from realistic to abstract. Each character displayed three different behaviours and the characters were contextualized within a six panel narrative. Data obtained from the first experiment was used to refine the second experiment. A further experiment was conducted to further define how combinations of different behaviours and the context containing a character affected the subject’s perception. The second experiment used three different character types and the characters were contextualized within a video stimulus. Findings from the first experiment indicated a strong relationship between character type and context. Interest with the various characters changed depending on adaptions to either the behaviour of the said character or the contextualisation. Certain character types based on appearance where better suited to different contexts than others. An abstract character was more likely to be perceived positively by the subject in a surprising context stipulated by the behaviour of the character and form of the narrative sequence. Other characters such as one based around an inanimate object found a greater positive reception with the subjects under sad contextual constraints rather than happy or surprise. The first experiment took into account various independent variables obtained from the subject and aimed to draw parallels if found between these variables and the subjects perception of a given character be it positive or negative. However, these variables namely gender, nationality and age had no effect on the subject’s perception. In the second experiment, it was found that in order for the realistic human character to be perceived more positively, the behaviour needed to match the context. When a mismatch occurred the subjects began to perceive the character more negatively. The cartoon character was however not affected by the mismatch of behaviour and context. The experiment was further expanded when two different character types were compared committing negative actions and having negative actions inflicted upon them and what effect it had on the subjects perception. It was found that a cartoon character committing a negative action was perceived positively whilst a human character committing the same act was perceived negatively. However, when a negative action was inflicted on these same characters, subjects were more concerned for the human character than the cartoon character. Results from both experiments confirm the idea that various characters are perceived very differently by the viewers and come with predefined notions within the viewer of how they should behave. What is expected of one character type is not acceptable for another character type. Cartoon characters can get away with bizarre behaviour. A real human character may have some sort of novel unusual behaviour, whilst a realistic CG human character is assessed on how realistically (normally) it behaves. This research expands upon previous research into this area by offering a greater understanding of character types and emphasising the importance of contextualisation.

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The way in which quality is modelled within an organisation has typically followed either a fixed-model or a tailorable approach. Fixed-model techniques suffer the disadvantage of inflexibility to local environments, since, the parameters of these models cannot be changed by users or designers to reflect their own views. The tailorable approaches tend to preclude cross-project comparisons. In addition, both techniques lack comprehensive guidelines for building quality into a software product, and lack the ability to resolve conflicts where individuals disagree about the model parameters. In this work, the construction of a new approach is described which overcomes these deficiencies. Since metrics and metric measurement is an important component of quality models, common metrics and measurement techniques are identified, before the construction and evaluation of the new quality modelling approach is presented. A common metric is software size, measurement of which can be performed by use of the Function Point Analysis (FPA) technique. The weighting and adjustment factors of the traditional FPA approach are simplified here, to produce a new estimation technique which can be used at early stages in the development lifecycle. The new model is validated against two project datasets, and the results show a good degree of accuracy when estimating the FPA count, although a lower performance is achieved when estimating actual effort. The major component of this thesis is the construction of the new quality modelling approach, that enables local requirements tailoring whilst providing the ability to perform cross-project comparisons. Unlike existing techniques, comprehensive conflict resolution mechanisms are incorporated, and it is shown that the approach can be used to measure different software entities, allowing direct comparisons between measurements and thus producing more consistent results. The implementation consists of the construction of a software tool supporting the new methodology, and use of both this tool and the technique on real projects at a large financial organisation. The validation of the approach is performed against a list of requirements for a good quality model, and from feedback both from use on the projects and from a questionnaire survey.

No description available.

Wireless is now common for access to multimedia services, with many different devices and choice of access technology. Access methods have become varied and with more types of services, which requires more consideration for coordinating existing protocols and quality of service (QoS). Increasingly, new wireless access technologies co-exist on the same devices, for example, smartphones already have third-generation cellular and WiFi. Devices with multiple links are described under the umbrella term ‘heterogeneous environments’. A trend towards heterogeneous wireless environments and varied types of media services requires that QoS and user satisfaction are prominent in next-generation networks. The problems in next-generation heterogeneous wireless environments include many levels of complexity; from link coexistence to user-centric policies and contexts. This thesis explores the issue of QoS in interface selection for devices with more than one wireless access link. A solution that provides link selection for QoS policy is investigated using analytical and simulation techniques. Different wireless networks have capabilities and limitations, determined by radio technology and network conditions. The research focused on an approach to improve QoS by leveraging the differences in wireless networks. However, it is complicated by issues such as: different protocols, physical device co-existence, mobility, and application QoS requirements. Following a review of artificial intelligence (AI) techniques, finite-state machines (FSMs) and fuzzy decision-making (FDM) are proposed as a solution approach. An agent-based prototype is used to combine FSMs and FDM for automating link selection, determined by user and QoS policy. Prototype evaluation was performed using sensitivity analysis for FDM, and discrete-event simulation for generating QoS metrics in wireless environments. The results are comparisons of FDM prototypes using different parameters; different agent prototypes were run with different QoS conditions for comparing points of handover between UMTS and WLAN networks for one service type. The research has shown an agent model can reduce the complexity for a user in wireless interface selection, while including QoS metrics and user preferences into the decision process. Core decision-making techniques in the design are relevant for emerging standardisation frameworks such as 802.21, and the next-generation of wireless networks to support heterogeneous access.

This work deals with the problems of performance evaluation and background modelling for the detection of moving objects in outdoor video surveillance datasets. Such datasets are typically affected by considerable background variations caused by global and partial illumination variations, gradual and sudden lighting condition changes, and non-stationary backgrounds. The large variation of backgrounds in typical outdoor video sequences requires highly adaptable and robust models able to represent the background at any time instance with sufficient accuracy. Furthermore, in real life applications it is often required to detect possible contaminations of the scene in real time or when new observations become available. A novel adaptive multi-modal algorithm for on-line background modelling is proposed. The proposed algorithm applies the principles of the Gaussian Mixture Model, previously used to model the grey-level (or colour) variations of individual pixels, to the modelling of illumination variations in image regions. The image observations are represented in the eigen-space, where the dimensionality of the data is significantly reduced using the method of the principal components analysis. The projections of image regions in the reduced eigen-space are clustered using K-means into clusters (or modes) of similar backgrounds and are modelled as multivariate Gaussian distributions. Such an approach allows the model to adapts to the changes in the dataset in a timely manner. This work proposed modifications to a previously published method for incremental update of the uni-modal eigne-models. The modifications are twofold. First, the incremental update is performed on the individual modes of the multi-modal model, and second, the mechanism for adding new dimensions is adapted to handle problems typical for outdoor video surveillance scenes with a wide range of illumination changes. Finally, a novel, objective, comparative, object-based methodology for performance evaluation of object detection is also developed. the proposed methodology is concerned with the evaluation of object detection in the context of the end-user defined quality of performance in complex video surveillance applications.

The activity of learning programming languages is a difficult and complex process. During this complicated procedure, many problems and difficulties might occur. A straightforward and clear approach, which can help to break down the numerous interacting processes into a series of simpler components, would appear useful. Therefore, the main aim of this research is to design and develop an appropriate method that can meet these criteria. The new method offers a new systematic approach for collecting, modelling and analysing data to discover difficulties within the activity of learning programming. Thus, to achieve these aims, the research work commenced with an investigation into the existing variety of frameworks and methodologies, which have been used in Information Technology (IT). The initial research showed that there are many suitable approaches that have been previously used in the IT field. However, most of these do not offer any clear pathway for collecting and analysing the data from beginning to end of the research process. To address these issues, Activity Theory (AT) has been chosen to be used as an initial framework for the study. AT has been selected due to the nature of the topic being examined. There are several communities involved in the process of learning programming, including students, lecturers, technicians and teaching assistants. AT allows for a holistic consideration of the multiple perspectives involved. In addition, the solid ontology of AT assists with the breakdown of complicated environments into simpler units. However, AT does not specify any particular research methodology that should be used. As a result, an appropriate approach has to be identified and coupled with AT in order to create a new systematic method. The following research methodologies are considered: Action Research (AR), Grounded Theory (GT) and Phenomenography (Ph). It is concluded that GT offers the best approach to complement the use of AT in the context of examining the activity of learning programming languages. Consequently, an initial method has been created by combining AT and GT, which has been used to collect and analyse test cases to investigate whether this combination is effective. After using this initial procedure, changes and improvements were made to create a revised method which has been used to collect and analyse a larger set of data. The results of this research, using three type of case studies of responses from the individual students, focus groups including staff, and observation of the activities in workshop sessions, demonstrated the benefits of the method developed. It was found out that this systematic approach facilitated the process of collecting and analysing the data. In turn, this enabled the discovery of contradictions within the activity of learning programming and the proposed of shifts to solve them. Although this method was tested on first-year students at Kingston University, it is potentially generic, allowing it to be considered for use in other similar domains.

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Web-based development is a relatively immature area of Software Engineering, producing often complex applications to many different types of end user and stakeholders. Web Engineering as a research area, was created to introduce processes that enable web based development to be repeatable and to avoid potential failure in the fast changing landscape that is the current ubiquitous Internet. A survey of existing perspectives from the literature highlights a number of points. Firstly, that web development has a number of subtle differences to Software Engineering and that many web development methods are not used. Further, that there has been little work done on what should be in a web development method. A full survey of 50 web development methods finds that they do not give enough detail to be used in their entirety; they are difficult for a non-computer scientist to understand in the techniques they use and most do not cover the lifecycle, particularly in the area of requirements, implementation and testing. This thesis introduces a requirements framework for novice web developers. It is created following an in-depth case study carried out over two years that investigates the use of web development methods by novice developers. The study finds that web development methods are not easy to understand, there is a lack of explanation as to how to use the techniques within the method and the language used is too complex. A high level method is derived with an iterative process and with the requirements phase in the form of a framework; it addresses the problems that are discussed and provides excellent support for a novice web developer in the requirements phase of the lifecycle. An evaluation of the method using a group of novice developers who reflect on the method and a group who use it for development finds that the method is both easy to understand and use.

There is a continuing drive for better, more robust generalisation performance from classification systems, and prediction systems in general. Ensemble methods, or the combining of multiple classifiers, have become an accepted and successful tool for doing this, though the reasons for success are not always entirely understood. In this thesis, we review the multiple classifier literature and consider the properties an ensemble of classifiers - or collection of subsets - should have in order to be combined successfully. We find that the framework of Stochastic Discrimination provides a well-defined account of these properties, which are shown to be strongly encouraged in a number of the most popular/successful methods in the literature via differing algorithmic devices. This uncovers some interesting and basic links between these methods, and aids understanding of their success and operation in terms of a kernel induced on the training data, with form particularly well suited to classification. One property that is desirable in both the SD framework and in a regression context, the ambiguity decomposition of the error, is de-correlation of individuals. This motivates the introduction of the Negative Correlation Learning method, in which neural networks are trained in parallel in a way designed to encourage de-correlation of the individual networks. The training is controlled by a parameter λ governing the extent to which correlations are penalised. Theoretical analysis of the dynamics of training results in an exact expression for the interval in which we can choose λ while ensuring stability of the training, and a value λ∗ for which the training has some interesting optimality properties. These values depend only on the size N of the ensemble. Decision level combination methods often result in a difficult to interpret model, and NCL is no exception. However in some applications, there is a need for understandable decisions and interpretable models. In response to this, we depart from the standard decision level combination paradigm to introduce a number of model level combination methods. As decision trees are one of the most interpretable model structures used in classification, we chose to combine structure from multiple individual trees to build a single combined model. We show that extremely compact, well performing models can be built in this way. In particular, a generalisation of bottom-up pruning to a multiple-tree context produces good results in this regard. Finally, we develop a classification system for a real-world churn prediction problem, illustrating some of the concepts introduced in the thesis, and a number of more practical considerations which are of importance when developing a prediction system for a specific problem.