497 research outputs found

    The interplay between methodologies, tasks and visualisation formats in the study of visual expertise

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    The author examines the  methodological contributions of the ten unique reviews developed in this special issue on the methodologies for studying visual expertise. Opportunities, research results and lessons, offered by each methodology are analyzed according to four dimensions criteria: (i) The different levels of visual and cognitive processes targeted in the different methods; (ii) The effect of the task gaols and  task design on the outcomes; (iii) The potential effect of the format (dynamic versus static) of the visual material and the interactive features provided in previous studies (iv) The potential modulating effect of learners individual differences. Concluding comments are developed  about the limitations of each method but also about the challenge and promises of designing and using combined and synchronized methods

    Metric for attractor overlap

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    We present the first general metric for attractor overlap (MAO) facilitating an unsupervised comparison of flow data sets. The starting point is two or more attractors, i.e., ensembles of states representing different operating conditions. The proposed metric generalizes the standard Hilbert-space distance between two snapshots to snapshot ensembles of two attractors. A reduced-order analysis for big data and many attractors is enabled by coarse-graining the snapshots into representative clusters with corresponding centroids and population probabilities. For a large number of attractors, MAO is augmented by proximity maps for the snapshots, the centroids, and the attractors, giving scientifically interpretable visual access to the closeness of the states. The coherent structures belonging to the overlap and disjoint states between these attractors are distilled by few representative centroids. We employ MAO for two quite different actuated flow configurations: (1) a two-dimensional wake of the fluidic pinball with vortices in a narrow frequency range and (2) three-dimensional wall turbulence with broadband frequency spectrum manipulated by spanwise traveling transversal surface waves. MAO compares and classifies these actuated flows in agreement with physical intuition. For instance, the first feature coordinate of the attractor proximity map correlates with drag for the fluidic pinball and for the turbulent boundary layer. MAO has a large spectrum of potential applications ranging from a quantitative comparison between numerical simulations and experimental particle-image velocimetry data to the analysis of simulations representing a myriad of different operating conditions.Comment: 33 pages, 20 figure

    sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

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    We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case study illustrates that even with relatively few sMolBoxes, it is possible to express complex analyses tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.Comment: 10 pages, 9 figures, IEEE VIS, TVC

    sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

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    We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.acceptedVersio

    Animation of a process for identifying and merging raster polygon areas

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    Collaborative geographic visualization

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    Dissertação apresentada na Faculdade de CiĂȘncias e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil GestĂŁo e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment

    Quadruple Helix Engagement: Reviews on Shariah Fintech Based SMEs Digitalization Readiness

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    The development of Sharia Financial Technology (Fintech) after Covid-19 has experienced significant fluctuations in affecting the country’s economy. The importance of the role of Financial Transactions in the digitalization readiness of SMEs makes a considerable contribution to the use of Financial Technology as an effort to maintain the sustainability of SMEs. Identification of the involvement of the quadruple Helix (government, investors, academics, and communities) in maintaining the financial stability of SMEs through Sharia Fintech emerged as new challenges and opportunities in improving the economy after Covid 19 in Indonesia. Therefore, a systematic literature review investigation regarding how to utilize Sharia fintech products that are adjusted to the degree of digitization is required to help SMEs’ sustainability as well as explore the role of the quadruple Helix in pursuing this success. We identified 110 papers published on Sharia Fintech for SMEs, SMEs Digitalization Readiness, and The Role of Quadruple Helix between 2003-2021 with 87 specifications from Scopus journals and 23 from proceedings conferences. The analysis was performed using Atlas.ti 9 Software Packages on the above topic by limiting the discussion to inclusion and exclusion criteria. The literature review found a lack of study about the evaluation of Quadruple Helix Engagement for Sharia Fintech Based SMEs Digitalization Readiness. Thus, it needs an enhancement of a new model of Sharia fintech quadruple helix recommendation focuses on the SMEs digital readiness assessment as an attempt to increase the utilization of proper Sharia fintech products for SMEs

    Designing AI Experiences: Boundary Representations, Collaborative Processes, and Data Tools

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    Artificial Intelligence (AI) has transformed our everyday interactions with technology through automation, intelligence augmentation, and human-machine partnership. Nevertheless, we regularly encounter undesirable and often frustrating experiences due to AI. A fundamental challenge is that existing software practices for coordinating system and experience designs fall short when creating AI for diverse human needs, i.e., ``human-centered AI'' or HAI. ``AI-first'' development workflows allow engineers to first develop the AI components, and then user experience (UX) designers create end-user experiences around the AI's capabilities. Consequently, engineers encounter end-user blindness when making critical decisions about AI training data needs, implementation logic, behavior, and evaluation. In the conventional ``UX-first'' process, UX designers lack the needed technical understanding of AI capabilities (technological blindness) that limits their ability to shape system design from the ground up. Human-AI design guidelines have been offered to help but neither describe nor prescribe ways to bridge the gaps in needed expertise in creating HAI. In this dissertation, I investigate collaboration approaches between designers and engineers to operationalize the vision for HAI as technology inspired by human intelligence that augments human abilities while addressing societal needs. In a series of studies combining technical HCI research with qualitative studies of AI production in practice, I contribute (1) an approach to software development that blurs rigid design-engineering boundaries, (2) a process model for co-designing AI experiences, and (3) new methods and tools to empower designers by making AI accessible to UX designers. Key findings from interviews with industry practitioners include the need for ``leaky'' abstractions shared between UX and AI designers. Because modular development and separation of concerns fail with HAI design, leaky abstractions afford collaboration across expertise boundaries and support human-centered design solutions through vertical prototyping and constant evaluation. Further, by observing how designers and engineers collaborate on HAI design in an in-lab study, I highlight the role of design `probes' with user data to establish common ground between AI system and UX design specifications, providing a critical tool for shaping HAI design. Finally, I offer two design methods and tool implementations --- Data-Assisted Affinity Diagramming and Model Informed Prototyping --- for incorporating end-user data into HAI design. HAI is necessarily a multidisciplinary endeavor, and human data (in multiple forms) is the backbone of AI systems. My dissertation contributions inform how stakeholders with differing expertise can collaboratively design AI experiences by reducing friction across expertise boundaries and maintaining agency within team roles. The data-driven methods and tools I created provide direct support for software teams to tackle the novel challenges of designing with data. Finally, this dissertation offers guidance for imagining future design tools for human-centered systems that are accessible to diverse stakeholders.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169917/1/harihars_1.pd
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