26 research outputs found

    Pro-Resume: The Infographic Resume Builder

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    Scoring an interview is a challenge for any job seeker these days, thus having a unique and well-organized resume is crucial to grab a recruiter’s attention. Online resume builders such as ResumeNow and VisualizeMe have been created to help users build resumes; however, their templates are lacking in quantity, customizability, and in some instances, even legibility. Thus, our team set out to create an infographic online resume builder, a web application that allows its users to build, organize, and beautify their resumes to aid them in their job search. Our system allows for easy integration with their LinkedIn profiles so that their work history can be easily duplicated without typing everything out. There is also a large scope of infographic template options that users can choose from and, most importantly, users will have the ability to further customize their content and organization by using the system’s editing mode

    Saliency Prediction in the Data Visualization Design Process

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Data-driven modelling of perceptual properties of 3D shapes

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    The recent surge in 3D content generation has led to the evolution of difficult to search, organise and re-use massive online 3D visual content libraries. We explore crowdsourcing and machine learning techniques to help alleviate these difficulties by focusing on the visual perceptual properties of 3D shapes. We study “style similarity” and “aesthetics” as two fundamental perceptual properties of 3D shapes and build data-driven models. We rely on crowdsourcing platforms to collect large number of human judgements on style matching and aesthetics of 3D shapes. The judgement data collected directly from humans is used to learn metrics of style matching and aesthetics. Our style similarity measure can be used to compute style distance between a pair of input 3D shapes. In contrast to previous work, we incorporate colour and texture in addition to geometric features to build a colour and texture aware style similarity metric. We also experiment with learning objective and personalised style metrics 3D shapes. The application prototypes we build demonstrate the use of style based search and scene composition. Further, our style distance metric is built iteratively to consume lesser amount of human style judgement data compared to previous methods. We study the problem of building a data-driven model of 3D shape aesthetics in two steps. We first focus on designing a study to crowdsource human aesthetics judgement data. We then formulate a deep learning based strategy to learn a measure of 3D shape aesthetics from collected data. The results of the study in first step helped us choose an appropriate shape representation i.e. voxels as an input to deep neural networks for learning a measure of visual aesthetics. In the same crowdsourcing study, we experiment with the use of polygonal, volumetric, and point based shape representations to create shape stimuli to collect and compare human shape aesthetics judgements. On analysis of the collected data we found that that humans can reliably distinguish more aesthetic shape in a pair even from coarser shape representations such as voxels. This observation implies that detailed shape representations are not needed to compare aesthetics in pairs. The aesthetic value of a 3D shape has traditionally been explored in terms of specific visual features (or handcrafted features) such as curvature and symmetry. For example, more symmetric and curved shapes are considered aesthetic compared to less curved and symmetric shapes. We call such properties as pre-existing notion (or rules) of aesthetics. In order to develop a measure of perceptual aesthetics of 3D shapes which is independent of any pre-existing notion or shape features, we train deep neural networks directly on human aesthetics judgement data. We demonstrate the usefulness of the learned measure by designing applications to rank a collection of shapes based on their aesthetics scores and interactively build scenes using shapes with high aesthetics scores. The overarching goal of this thesis is to demonstrate the use of machine learning and crowdsourcing approaches to build data-driven models of visual perceptual properties of 3D shapes for applications in search, organisation, scene composition, and visualisation of 3D shape data present in ever increasing online 3D shape content libraries. We believe that our exploration of perceptual properties of 3D shapes will motivate further research by looking into other important perceptual properties related to our vision system and will also fuel development of techniques to automatically enhance such properties of a given 3D shape

    Visual analytics and artificial intelligence for marketing

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    In today’s online environments, such as social media platforms and e-commerce websites, consumers are overloaded with information and firms are competing for their attention. Most of the data on these platforms comes in the form of text, images, or other unstructured data sources. It is important to understand which information on company websites and social media platforms are enticing and/or likeable by consumers. The impact of online visual content, in particular, remains largely unknown. Finding the drivers behind likes and clicks can help (1) understand how consumers interact with the information that is presented to them and (2) leverage this knowledge to improve marketing content. The main goal of this dissertation is to learn more about why consumers like and click on visual content online. To reach this goal visual analytics are used for automatic extraction of relevant information from visual content. This information can then be related, at scale, to consumer and their decisions

    Cartographic modelling for automated map generation

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    A Temporal Approach to Defining Place Types based on User-Contributed Geosocial Content

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    Place is one of the foundational concepts on which the field of Geography has been built. Traditionally, GIScience research into place has been approached from a spatial perspective. While space is an integral feature of place, it represents only a single dimension (or a combination of three dimensions to be exact), in the complex, multidimensional concept that is place. Though existing research has shown that both spatial and thematic dimensions are valuable, time has historically been under-utilized in its ability to describe and define places and their types. The recent availability and access to user-generated geosocial content has allowed for a much deeper investigation of the temporal dimension of place. Multi-resolution temporal signatures are constructed based on these data permitting both place instances and place types to be compared through a robust set of (dis)similarity measures. The primary contribution of this work lies in demonstrating how places are defined through a better understanding of temporal user behavior. Furthermore, the results of this research present the argument that the temporal dimension is the most indicative placial dimension for classifying places by type

    Applying the Free-Energy Principle to Complex Adaptive Systems

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    The free energy principle is a mathematical theory of the behaviour of self-organising systems that originally gained prominence as a unified model of the brain. Since then, the theory has been applied to a plethora of biological phenomena, extending from single-celled and multicellular organisms through to niche construction and human culture, and even the emergence of life itself. The free energy principle tells us that perception and action operate synergistically to minimize an organism’s exposure to surprising biological states, which are more likely to lead to decay. A key corollary of this hypothesis is active inference—the idea that all behavior involves the selective sampling of sensory data so that we experience what we expect to (in order to avoid surprises). Simply put, we act upon the world to fulfill our expectations. It is now widely recognized that the implications of the free energy principle for our understanding of the human mind and behavior are far-reaching and profound. To date, however, its capacity to extend beyond our brain—to more generally explain living and other complex adaptive systems—has only just begun to be explored. The aim of this collection is to showcase the breadth of the free energy principle as a unified theory of complex adaptive systems—conscious, social, living, or not
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