DEPLOYING, IMPROVING AND EVALUATING EDGE BUNDLING METHODS FOR VISUALIZING LARGE GRAPHS

A tremendous increase in the scale of graphs has been witnessed in a wide range of fields, which demands efficient and effective visualization techniques to assist users in better understandings of large graphs. Conventional node-link diagrams are often used to visualize graphs, whereas excessive edge crossings can easily incur severe visual clutter in the node-link diagram of a large graph. Edge bundling can effectively remedy visual clutter and reveal high-level graph structures. Although significant efforts have been devoted to developing edge bundling, three challenging problems remain. First, edge bundling techniques are often computationally expensive and are not easy to deploy for web-based applications. The state-of-the-art edge bundling methods often require special system supports and techniques such as high-end GPU acceleration for large graphs, which makes these methods less portable, especially for ubiquitous mobile devices. Second, the quantitative quality of edge bundling results is barely assessed in the literature. Currently, the comparison of edge bundling mainly focuses on computational performance and perceptual results. Third, although the family of edge bundling techniques has a rich set of bundling layout, there is a lack of a generic method to generate different styles of edge bundling. In this research, I aim to address these problems and have made the following contributions. First, I provide an efficient framework to deploy edge bundling for web-based platforms by exploiting standard graphics hardware functions and libraries. My framework can generate high-quality edge bundling results on web-based platforms, and achieve a speedup of 50X compared to the previous state-of-the-art edge bundling method on a graph with half of a million edges. Second, I propose a new moving least squares based approach to lower the algorithm complexity of edge bundling. In addition, my approach can generate better bundling results compared to other methods based on a quality metric. Third, I provide an information-theoretic metric to evaluate the edge bundling methods. I leverage information theory in this metric. With my information-theoretic metric, domain users can choose appropriate edge bundling methods with proper parameters for their applications. Last but not least, I present a deep learning framework for edge bundling visualizations. Through a training process that learns the results of a specific edge bundling method, my deep learning framework can infer the final layout of the edge bundling method. My deep learning framework is a generic framework that can generate the corresponding results of different edge bundling methods. Adviser: Hongfeng Y

Cruiser and PhoTable: Exploring Tabletop User Interface Software for Digital Photograph Sharing and Story Capture

Digital photography has not only changed the nature of photography and the photographic process, but also the manner in which we share photographs and tell stories about them. Some traditional methods, such as the family photo album or passing around piles of recently developed snapshots, are lost to us without requiring the digital photos to be printed. The current, purely digital, methods of sharing do not provide the same experience as printed photographs, and they do not provide effective face-to-face social interaction around photographs, as experienced during storytelling. Research has found that people are often dissatisfied with sharing photographs in digital form. The recent emergence of the tabletop interface as a viable multi-user direct-touch interactive large horizontal display has provided the hardware that has the potential to improve our collocated activities such as digital photograph sharing. However, while some software to communicate with various tabletop hardware technologies exists, software aspects of tabletop user interfaces are still at an early stage and require careful consideration in order to provide an effective, multi-user immersive interface that arbitrates the social interaction between users, without the necessary computer-human interaction interfering with the social dialogue. This thesis presents PhoTable, a social interface allowing people to effectively share, and tell stories about, recently taken, unsorted digital photographs around an interactive tabletop. In addition, the computer-arbitrated digital interaction allows PhoTable to capture the stories told, and associate them as audio metadata to the appropriate photographs. By leveraging the tabletop interface and providing a highly usable and natural interaction we can enable users to become immersed in their social interaction, telling stories about their photographs, and allow the computer interaction to occur as a side-effect of the social interaction. Correlating the computer interaction with the corresponding audio allows PhoTable to annotate an automatically created digital photo album with audible stories, which may then be archived. These stories remain useful for future sharing -- both collocated sharing and remote (e.g. via the Internet) -- and also provide a personal memento both of the event depicted in the photograph (e.g. as a reminder) and of the enjoyable photo sharing experience at the tabletop. To provide the necessary software to realise an interface such as PhoTable, this thesis explored the development of Cruiser: an efficient, extensible and reusable software framework for developing tabletop applications. Cruiser contributes a set of programming libraries and the necessary application framework to facilitate the rapid and highly flexible development of new tabletop applications. It uses a plugin architecture that encourages code reuse, stability and easy experimentation, and leverages the dedicated computer graphics hardware and multi-core processors of modern consumer-level systems to provide a responsive and immersive interactive tabletop user interface that is agnostic to the tabletop hardware and operating platform, using efficient, native cross-platform code. Cruiser's flexibility has allowed a variety of novel interactive tabletop applications to be explored by other researchers using the framework, in addition to PhoTable. To evaluate Cruiser and PhoTable, this thesis follows recommended practices for systems evaluation. The design rationale is framed within the above scenario and vision which we explore further, and the resulting design is critically analysed based on user studies, heuristic evaluation and a reflection on how it evolved over time. The effectiveness of Cruiser was evaluated in terms of its ability to realise PhoTable, use of it by others to explore many new tabletop applications, and an analysis of performance and resource usage. Usability, learnability and effectiveness of PhoTable was assessed on three levels: careful usability evaluations of elements of the interface; informal observations of usability when Cruiser was available to the public in several exhibitions and demonstrations; and a final evaluation of PhoTable in use for storytelling, where this had the side effect of creating a digital photo album, consisting of the photographs users interacted with on the table and associated audio annotations which PhoTable automatically extracted from the interaction. We conclude that our approach to design has resulted in an effective framework for creating new tabletop interfaces. The parallel goal of exploring the potential for tabletop interaction as a new way to share digital photographs was realised in PhoTable. It is able to support the envisaged goal of an effective interface for telling stories about one's photos. As a serendipitous side-effect, PhoTable was effective in the automatic capture of the stories about individual photographs for future reminiscence and sharing. This work provides foundations for future work in creating new ways to interact at a tabletop and to the ways to capture personal stories around digital photographs for sharing and long-term preservation

Cruiser and PhoTable: Exploring Tabletop User Interface Software for Digital Photograph Sharing and Story Capture

Digital photography has not only changed the nature of photography and the photographic process, but also the manner in which we share photographs and tell stories about them. Some traditional methods, such as the family photo album or passing around piles of recently developed snapshots, are lost to us without requiring the digital photos to be printed. The current, purely digital, methods of sharing do not provide the same experience as printed photographs, and they do not provide effective face-to-face social interaction around photographs, as experienced during storytelling. Research has found that people are often dissatisfied with sharing photographs in digital form. The recent emergence of the tabletop interface as a viable multi-user direct-touch interactive large horizontal display has provided the hardware that has the potential to improve our collocated activities such as digital photograph sharing. However, while some software to communicate with various tabletop hardware technologies exists, software aspects of tabletop user interfaces are still at an early stage and require careful consideration in order to provide an effective, multi-user immersive interface that arbitrates the social interaction between users, without the necessary computer-human interaction interfering with the social dialogue. This thesis presents PhoTable, a social interface allowing people to effectively share, and tell stories about, recently taken, unsorted digital photographs around an interactive tabletop. In addition, the computer-arbitrated digital interaction allows PhoTable to capture the stories told, and associate them as audio metadata to the appropriate photographs. By leveraging the tabletop interface and providing a highly usable and natural interaction we can enable users to become immersed in their social interaction, telling stories about their photographs, and allow the computer interaction to occur as a side-effect of the social interaction. Correlating the computer interaction with the corresponding audio allows PhoTable to annotate an automatically created digital photo album with audible stories, which may then be archived. These stories remain useful for future sharing -- both collocated sharing and remote (e.g. via the Internet) -- and also provide a personal memento both of the event depicted in the photograph (e.g. as a reminder) and of the enjoyable photo sharing experience at the tabletop. To provide the necessary software to realise an interface such as PhoTable, this thesis explored the development of Cruiser: an efficient, extensible and reusable software framework for developing tabletop applications. Cruiser contributes a set of programming libraries and the necessary application framework to facilitate the rapid and highly flexible development of new tabletop applications. It uses a plugin architecture that encourages code reuse, stability and easy experimentation, and leverages the dedicated computer graphics hardware and multi-core processors of modern consumer-level systems to provide a responsive and immersive interactive tabletop user interface that is agnostic to the tabletop hardware and operating platform, using efficient, native cross-platform code. Cruiser's flexibility has allowed a variety of novel interactive tabletop applications to be explored by other researchers using the framework, in addition to PhoTable. To evaluate Cruiser and PhoTable, this thesis follows recommended practices for systems evaluation. The design rationale is framed within the above scenario and vision which we explore further, and the resulting design is critically analysed based on user studies, heuristic evaluation and a reflection on how it evolved over time. The effectiveness of Cruiser was evaluated in terms of its ability to realise PhoTable, use of it by others to explore many new tabletop applications, and an analysis of performance and resource usage. Usability, learnability and effectiveness of PhoTable was assessed on three levels: careful usability evaluations of elements of the interface; informal observations of usability when Cruiser was available to the public in several exhibitions and demonstrations; and a final evaluation of PhoTable in use for storytelling, where this had the side effect of creating a digital photo album, consisting of the photographs users interacted with on the table and associated audio annotations which PhoTable automatically extracted from the interaction. We conclude that our approach to design has resulted in an effective framework for creating new tabletop interfaces. The parallel goal of exploring the potential for tabletop interaction as a new way to share digital photographs was realised in PhoTable. It is able to support the envisaged goal of an effective interface for telling stories about one's photos. As a serendipitous side-effect, PhoTable was effective in the automatic capture of the stories about individual photographs for future reminiscence and sharing. This work provides foundations for future work in creating new ways to interact at a tabletop and to the ways to capture personal stories around digital photographs for sharing and long-term preservation

Novel Texture-based Probabilistic Object Recognition and Tracking Techniques for Food Intake Analysis and Traffic Monitoring

More complex image understanding algorithms are increasingly practical in a host of emerging applications. Object tracking has value in surveillance and data farming; and object recognition has applications in surveillance, data management, and industrial automation. In this work we introduce an object recognition application in automated nutritional intake analysis and a tracking application intended for surveillance in low quality videos. Automated food recognition is useful for personal health applications as well as nutritional studies used to improve public health or inform lawmakers. We introduce a complete, end-to-end system for automated food intake measurement. Images taken by a digital camera are analyzed, plates and food are located, food type is determined by neural network, distance and angle of food is determined and 3D volume estimated, the results are cross referenced with a nutritional database, and before and after meal photos are compared to determine nutritional intake. We compare against contemporary systems and provide detailed experimental results of our system\u27s performance. Our tracking systems consider the problem of car and human tracking on potentially very low quality surveillance videos, from fixed camera or high flying \acrfull{uav}. Our agile framework switches among different simple trackers to find the most applicable tracker based on the object and video properties. Our MAPTrack is an evolution of the agile tracker that uses soft switching to optimize between multiple pertinent trackers, and tracks objects based on motion, appearance, and positional data. In both cases we provide comparisons against trackers intended for similar applications i.e., trackers that stress robustness in bad conditions, with competitive results

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Análise colaborativa de grandes conjuntos de séries temporais

The recent expansion of metrification on a daily basis has led to the production of massive quantities of data, and in many cases, these collected metrics are only useful for knowledge building when seen as a full sequence of data ordered by time, which constitutes a time series. To find and interpret meaningful behavioral patterns in time series, a multitude of analysis software tools have been developed. Many of the existing solutions use annotations to enable the curation of a knowledge base that is shared between a group of researchers over a network. However, these tools also lack appropriate mechanisms to handle a high number of concurrent requests and to properly store massive data sets and ontologies, as well as suitable representations for annotated data that are visually interpretable by humans and explorable by automated systems. The goal of the work presented in this dissertation is to iterate on existing time series analysis software and build a platform for the collaborative analysis of massive time series data sets, leveraging state-of-the-art technologies for querying, storing and displaying time series and annotations. A theoretical and domain-agnostic model was proposed to enable the implementation of a distributed, extensible, secure and high-performant architecture that handles various annotation proposals in simultaneous and avoids any data loss from overlapping contributions or unsanctioned changes. Analysts can share annotation projects with peers, restricting a set of collaborators to a smaller scope of analysis and to a limited catalog of annotation semantics. Annotations can express meaning not only over a segment of time, but also over a subset of the series that coexist in the same segment. A novel visual encoding for annotations is proposed, where annotations are rendered as arcs traced only over the affected series’ curves in order to reduce visual clutter. Moreover, the implementation of a full-stack prototype with a reactive web interface was described, directly following the proposed architectural and visualization model while applied to the HVAC domain. The performance of the prototype under different architectural approaches was benchmarked, and the interface was tested in its usability. Overall, the work described in this dissertation contributes with a more versatile, intuitive and scalable time series annotation platform that streamlines the knowledge-discovery workflow.A recente expansão de metrificação diária levou à produção de quantidades massivas de dados, e em muitos casos, estas métricas são úteis para a construção de conhecimento apenas quando vistas como uma sequência de dados ordenada por tempo, o que constitui uma série temporal. Para se encontrar padrões comportamentais significativos em séries temporais, uma grande variedade de software de análise foi desenvolvida. Muitas das soluções existentes utilizam anotações para permitir a curadoria de uma base de conhecimento que é compartilhada entre investigadores em rede. No entanto, estas ferramentas carecem de mecanismos apropriados para lidar com um elevado número de pedidos concorrentes e para armazenar conjuntos massivos de dados e ontologias, assim como também representações apropriadas para dados anotados que são visualmente interpretáveis por seres humanos e exploráveis por sistemas automatizados. O objetivo do trabalho apresentado nesta dissertação é iterar sobre o software de análise de séries temporais existente e construir uma plataforma para a análise colaborativa de grandes conjuntos de séries temporais, utilizando tecnologias estado-de-arte para pesquisar, armazenar e exibir séries temporais e anotações. Um modelo teórico e agnóstico quanto ao domínio foi proposto para permitir a implementação de uma arquitetura distribuída, extensível, segura e de alto desempenho que lida com várias propostas de anotação em simultâneo e evita quaisquer perdas de dados provenientes de contribuições sobrepostas ou alterações não-sancionadas. Os analistas podem compartilhar projetos de anotação com colegas, restringindo um conjunto de colaboradores a uma janela de análise mais pequena e a um catálogo limitado de semântica de anotação. As anotações podem exprimir significado não apenas sobre um intervalo de tempo, mas também sobre um subconjunto das séries que coexistem no mesmo intervalo. Uma nova codificação visual para anotações é proposta, onde as anotações são desenhadas como arcos traçados apenas sobre as curvas de séries afetadas de modo a reduzir o ruído visual. Para além disso, a implementação de um protótipo full-stack com uma interface reativa web foi descrita, seguindo diretamente o modelo de arquitetura e visualização proposto enquanto aplicado ao domínio AVAC. O desempenho do protótipo com diferentes decisões arquiteturais foi avaliado, e a interface foi testada quanto à sua usabilidade. Em geral, o trabalho descrito nesta dissertação contribui com uma abordagem mais versátil, intuitiva e escalável para uma plataforma de anotação sobre séries temporais que simplifica o fluxo de trabalho para a descoberta de conhecimento.Mestrado em Engenharia Informátic

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters