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The rapidly increasing reach of computation into our everyday public settings presents new and significant challenges for the design of interfaces. One key feature of these settings is the increased presence of third parties to interaction, watching or passing-by as conduct with an interface takes place. This thesis assumes a performative perspective on interaction in public, presenting a framework derived from four empirical studies of interaction in a diverse series of public places---museums and galleries, city streets and funfairs---as well as observations on a variety of computer science, art and sociological literatures. As these settings are explored, a number of basic framework concepts are built up: * The first study chapter presents a deployment of an interactive exhibit within an artistic installation, introducing a basic division of roles and the ways in which visitors may be seen as `audience to manipulations of interactive devices by `participants . It also examines how visitors in an audience role may transition to active participant and vice versa. * The second study chapter describes a storytelling event that employed a torch-based interface. This chapter makes a distinction between non-professional and professional members of settings, contrasting the role of `actor with that of participants. * The third study chapter examines a series of scientific and artistic performance events that broadcast live telemetry data from a fairground ride to a watching audience. The study expands the roles introduced in previous chapters through making a further distinction between `behind-the-scenes ---in which `orchestrators operate---and `centre-stage settings---in which actors present the rider s experience to the audience. * The final study chapter presents a performance art game conducted on city streets, in which participants follow a series of often ambiguous clues in order to lead them to their goal. This chapter introduces a further `front-of-house setting, the notion of a circumscribing performance `frame in which the various roles are situated, and the additional role of the `bystander as part of this. These observations are brought together into a design framework which analyses other literature to complement the earlier studies. This framework seeks to provide a new perspective on and language for human-computer interaction (HCI), introducing a series of sensitising concepts, constraints and strategies for design that may be employed in order to approach the various challenges presented by interaction in public settings.

The purpose of this thesis was to develop a conceptual framework that shows the relationship between aesthetics, performance, and preference in computer interface design. To investigate this relationship, the thesis focused on investigating the effect of layout aesthetics on visual search performance and preference. This thesis begins with a literature review of related work followed by the rationale for conducting this research, in particular, defining what it meant by visual aesthetics in the context of interface design. Chapter 4 focused on investigating the effect of layout aesthetics on performance and preference. The results show that response time performance and preference increased with increasing aesthetic level. Preference and performance were found to be highly correlated. Chapter 5 focused on investigating users’ layout preference when they were not involved with a performance-based task. The results showed, surprisingly, that preference was highest with a “moderate” level of layout aesthetics and lowest with “high” and “low” levels of aesthetics. Chapter 6 focused on investigating visual effort by measuring eye movement pattern during task performance. The results showed that visual effort increased with a decreasing level of aesthetics. Chapter 7 extended the experiment in Chapter 4 using more “ecologically valid” stimuli. The results essentially replicated the results produced in Chapter 4. Chapter 8 focused on investigating the relationship between so-called “classical” aesthetics and background “expressive” aesthetics. The results showed that task performance using classical aesthetics was highest with high and low levels of aesthetics and worst with medium levels of aesthetics. Performance with expressive aesthetics increased with decreasing aesthetic levels. This thesis concludes with a conceptual framework for aesthetic design to help interface designers design interfaces that look aesthetically pleasing while at the same time supporting good task performance.

This thesis presents an investigation into the integration of information extracted from co-aligned range and intensity images to achieve pose invariant object recognition. Local feature matching is a fundamental technique in image analysis that underpins many computer vision-based applications; the approach comprises identifying a collection of interest points in an image, characterising the local image region surrounding the interest point by means of a descriptor, and matching these descriptors between example images. Such local feature descriptors are formed from a measure of the local image statistics in the region surrounding the interest point. The interest point locations and the means of measuring local image statistics should be chosen such that resultant descriptor remains stable across a range of common image transformations. Recently the availability of low cost, high quality range imaging devices has motivated an interest in local feature extraction from range images. It has been widely assumed in the vision community that the range imaging domain has properties which remain quasi-invariant through a wide range of changes in illumination and pose. Accordingly, it has been suggested that local feature extraction in the range domain should allow the calculation of local feature descriptors that are potentially more robust than those calculated from the intensity imaging domain alone. However, range images represent differing characteristics from those represented within intensity images which are frequently used, independently from range images, to create robust local features. Therefore, this work attempts to establish the best means of combining information from these two imaging modalities to further increase the reliability of matching local features. Local feature extraction comprises a series of processes applied to an image location such that a collection of repeatable descriptors can be established. By using co-aligned range and intensity images this work investigates the choice of modality and method for each step in the extraction process as an approach to optimising the resulting descriptor. Additionally, multimodal features are formed by combining information from both domains in a single stage in the extraction process. To further improve the quality of feature descriptors, a calculation of the surface normals and a use of the 3D structure from the range image are applied to correct the 3D appearance of a local sample patch, thereby increasing the similarity between observations. The matching performance of local features is evaluated using an experimental setup comprising a turntable and stereo pair of cameras. This experimental setup is used to create a database of intensity and range images for 5 objects imaged at 72 calibrated viewpoints, creating a database of 360 object observations. The use of a calibrated turntable in combination with the 3D object surface coordiantes, supplied by the range image allow location correspondences between object observations to be established; and therefore descriptor matches to be labelled as either true positive or false positive. Applying this methodology to the formulated local features show that two approaches demonstrate state-of-the-art performance, with a ~40% increase in area under ROC curve at a False Positive Rate of 10% when compared with standard SIFT. These approaches are range affine corrected intensity SIFT and element corrected surface gradients SIFT. Furthermore,this work uses the 3D structure encoded in the range image to organise collections of interest points from a series of observations into a collection of canonical views in a new model local feature. The canonical views for a interest point are stored in a view compartmentalised structure which allows the appearance of a local interest point to be characterised across the view sphere. Each canonical view is assigned a confidence measure based on the 3D pose of the interest point at observation, this confidence measure is then used to match similar canonical views of model and query interest points thereby achieving a pose invariant interest point description. This approach does not produce a statistically significant performance increase. However, does contribute a validated methodology for combining multiple descriptors with differing confidence weightings into a single keypoint.

The computing community is making significant efforts towards the development of automatic approaches for the analysis of social interactions. The way people interact depends on the context, but there is one aspect that all social interactions seem to have in common: humans behave according to roles. Therefore, recognizing the roles of participants is an essential step towards understanding social interactions and the construction of socially aware computer. This thesis addresses the problem of automatically recognizing roles of participants in multi-party recordings. The objective is to assign to each participant a role. All the proposed approaches use a similar strategy. They all start by segmenting the audio into turns. Those turns are used as basic analysis units. The next step is to extract features accounting for the organization of turns. The more sophisticated approaches extend the features extracted with features from either the prosody or the semantic. Finally, the mapping of people or turns to roles is done using statistical models. The goal of this thesis is to gain a better understanding of role recognition and we will investigate three aspects that can influence the performance of the system: We investigate the impact of modelling the dependency between the roles. We investigate the contribution of different modalities for the effectiveness of role recognition approach. We investigate the effectiveness of the approach for different scenarios. Three models are proposed and tested on three different corpora totalizing more than 90 hours of audio. The first contribution of this thesis is to investigate the combination of turn-taking features and semantic information for role recognition, improving the accuracy of role recognition from a baseline of 46.4% to 67.9% on the AMI meeting corpus. The second contribution is to use features extracted from the prosody to assign roles. The performance of this model is 89.7% on broadcast news and 87.0% on talk-shows. Finally, the third contribution is the development of a model robust to change in the social setting. This model achieved an accuracy of 86.7% on a database composed of a mixture of broadcast news and talk-shows.

Security breaches occur due to system vulnerabilities with numerous reasons including; erro- neous design (human errors), management or implementation errors. Vulnerabilities are the weaknesses that allow an attacker to violate the integrity of a system. To address this, system administrators and security professionals typically employ tools to determine the existence of vulerabilities. Security breaches can be dealt with through reactive or proactive methods. Reactive approaches are passive, in which when a breach occurs, site administrators respond to provide damage control, tracking down how the attacker got in, resolving the vulnerability and fixing the system. On the other hand, proactive approaches preemptively discover and fix vulnerabilities in their systems and networks before attacks can occur. For many research and business areas, organizations need to collaborate with peers by sharing their resources (storage servers, clusters, databases etc). This is often achieved through formation of Virtual Organisations (VO). For successful operation of such endeavors, security is a key issue and system configuration is vital. A faulty or incomplete configuration of a given site can cause hinderances to their normal operation and indeed be a threat to the whole VO. Management of such infrastructures is complex since they should ideally address the overall configuration and management of a dynamic set of VO-specific resources across multiple sites, as well as configuration and management of the underlying infrastructure upon which the VO exists - referred to in this thesis as the fabric. This thesis investigates the feasibility of using a proactive approach towards detecting vulner- abilities across VO resources. First, it investigates whether vulnerability assessment tools can preemptively help in detecting fabric level weaknesses. Then it explores how the combination of advanced authorisation infrastructures with configuration management tools can allow distributed site administrators to address the challenges associated with vulnerabilities. The primary contribution of this work is a novel approach for vulnerability management which addresses the specific challenges facing VO-wide security and incorporation of fabric man- agement security considerations.