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检索条件"任意字段=Conference on Visual Data Exploration and Analysis"

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ECGLens: Interactive visual exploration of Large Scale ECG data for Arrhythmia Detection

ECGLens: Interactive Visual Exploration of Large Scale ECG D...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Xu, Ke Guo, Shunan Cao, Nan Gotz, David Xu, Aiwen Qu, Huamin Yao, Zhenjie Chen, Yixin Hong Kong Univ Sci & Technol Hong Kong Peoples R China East China Normal Univ IDVX Lab Shanghai Peoples R China Tongji Univ IDVX Lab Shanghai Peoples R China Univ North Carolina Chapel Hill Chapel Hill NC USA NYUSH Shanghai Peoples R China Beijing Univ Technol Beijing Peoples R China Washington Univ St Louis MO 14263 USA

ISBN: (纸本)9781450356206

The Electrocardiogram (ECG) is commonly used to detect arrhythmias. Traditionally, a single ECG observation is used for diagnosis, making it difficult to detect irregular arrhythmias. Recent technology developments, however, have made it cost-effective to collect large amounts of raw ECG data over time. This promises to improve diagnosis accuracy, but the large data volume presents new challenges for cardiologists. This paper introduces ECGLens, an interactive system for arrhythmia detection and analysis using large-scale ECG data. Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network, an outlier detection algorithm, and a set of rich interaction techniques. We also introduce A-glyph, a novel glyph designed to improve the readability and comparison of ECG signals. We report results from a comprehensive user study showing that A-glyph improves the efficiency in arrhythmia detection, and demonstrate the effectiveness of ECGLens in arrhythmia detection through two expert interviews.

关键词： visualization Health - Clinical Interaction Design visual Design Artifact or System

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CNVis: A web-based visual analytics tool for exploring conference navigator data

CNVis: A web-based visual analytics tool for exploring confe...

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visualization and data analysis 2018, VDA 2018

作者： Bailey, Samuel M. Wei, Justin A. Wang, Chaoli Parra, Denis Brusilovsky, Peter University of Notre Dame Notre DameIN United States University of North Texas DentonTX United States Pontificia Universidad Cat Olica de Chile Santiago Chile University of Pittsburgh PittsburghPA United States

In this paper, we present CNVis, a web-based visual analytics tool for exploring data from multiple related academic conferences, mainly consisting of the papers presented at the conferences and participants who bookmark these papers. Our goal is to investigate the bookmarking relationships within a single conference and interpret various conference relationships and trends via effective visualization, comparison, and recommendation. This is achieved through the design and development of three coordinated views (the bookmark, topic, and keyword views) for user interaction and exploration. We demonstrate the effectiveness of CNVis using real-world data from three related conferences over a period of five years, followed by an ad-hoc expert evaluation of the tool. Finally, we discuss the extension of this work and the generalizability of CNVis for other applications. © 2018, Society for Imaging Science and Technology.

关键词： visualization

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visual exploration and analysis of time series earthquake data 17

Visual exploration and analysis of time series earthquake da...

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2017 Practice and Experience in Advanced Research Computing, PEARC 2017

作者： Chourasia, A. Richards-Dinger, K.B. Dieterich, J.H. Cui, Y. 9500 Gilman Dr San DiegoCA92093 United States 900 University Ave. RiversideCA92521 United States

ISBN: (纸本)9781450352727

Earthquake hazard estimation requires systematic investigation of past records as well as fundamental processes that cause the quake. However, detailed long-Term records of earthquakes at all scales (magnitude, space and time) are not available. Hence a synthetic method based on first principals could be employed to generate such records to bridge this critical gap of missing data. RSQSim is such a simulator that generates seismic event catalogs for several thousand years at various scales. This synthetic catalog contains rich detail about the earthquake events and associated properties. Exploring this data is of vital importance to validate the simulator as well as to identify features of interest such as quake time histories, conduct analyses such as calculating mean recurrence interval of events on each fault section. This work1 describes and demonstrates a prototype web based visual tool that enables domain scientists and students explore this rich dataset, as well as discusses refinement and streamlining of data management and analysis that is less error prone and scalable. © 2017 Copyright is held by the owner/author(s).

关键词： visualization

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YourdataStories: Transparency and Corruption Fighting Through data Interlinking and visual exploration 4th

YourDataStories: Transparency and Corruption Fighting Throug...

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4th International conference on Internet Science (INSCI)

作者： Petasis, Georgios Triantafillou, Anna Karstens, Eric Natl Ctr Sci Res Demokritos Inst Informat & Telecommun POB 60228 Athens 15310 Greece Athens Technol Ctr Innovat Lab 10 Rizariou St Athens 15233 Greece European Journalism Ctr Oranjepl 106 NL-6224 KV Maastricht Netherlands

ISBN: (纸本)9783319775470;9783319775463

While there are several existing infrastructures for data analytics, mainly oriented towards the analysis of tabular data, the integration of analytics with linked data is still a challenging task. There are several reasons for this lack of interoperability between linked data and analytics, including representation issues and inability to exploit the full potential of linked data. In this paper we present "YourdataStories", an infrastructure that tries to minimise the gap between linked data (where RDF is dominant) and Web-based analytics platforms (where JSON is dominant), through the use of technologies like JSON-LD, Hydra, and a set of reusable visualisation components, which do not depend on a specific semantic model and can adapt to almost any semantic model. The presented infrastructure has been used to create a set of visualisation applications, ranging from Web visual applications (such as facet search, dashboards, and "wizard-like" interfaces allowing various kind of stakeholders to find, understand and consume the data), to mobile and social media applications that inform citizens and gather their feedback. Having as a central component the model analyser, which retrieves and analyses a semantic model (based on OWL and SKOS) from a triple store, the resulting infrastructure and applications allow users to "drill-down" on the available data and extract subsets of interest, which can be analysed through pre-configured and user-driven custom visualisations and dashboards.

关键词： Linked data

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Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs

Interpreting Distortions in Dimensionality Reduction by Supe...

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International conference on visualisation, VIS

作者： Benoît Colange Laurent Vuillon Sylvain Lespinats Denys Dutykh INES Univ. Grenoble Alpes Le Bourget du Lac France CNRS Univ. Grenoble Alpes Univ. Savoie Mont Blanc Chambéry France

ISBN: (纸本)9781728149424

To perform visual data exploration, many dimensionality reduction methods have been developed. These tools allow data analysts to represent multidimensional data in a 2D or 3D space, while preserving as much relevant information as possible. Yet, they cannot preserve all structures simultaneously and they induce some unavoidable distortions. Hence, many criteria have been introduced to evaluate a map's overall quality, mostly based on the preservation of neighbourhoods. Such global indicators are currently used to compare several maps, which helps to choose the most appropriate mapping method and its hyperparameters. However, those aggregated indicators tend to hide the local repartition of distortions. Thereby, they need to be supplemented by local evaluation to ensure correct interpretation of *** this paper, we describe a new method, called MING, for "Map Interpretation using Neighbourhood Graphs". It offers a graphical interpretation of pairs of map quality indicators, as well as local evaluation of the distortions. This is done by displaying on the map the nearest neighbours graphs computed in the data space and in the embedding. Shared and unshared edges exhibit reliable and unreliable neighbourhood information conveyed by the mapping. By this mean, analysts may determine whether proximity (or remoteness) of points on the map faithfully represents similarity (or dissimilarity) of original data, within the meaning of a chosen map quality criteria. We apply this approach to two pairs of widespread indicators: precision/recall and trustworthiness/continuity, chosen for their wide use in the community, which will allow an easy handling by users.

关键词： Distortion Reliability Image color analysis data visualization visualization Dimensionality reduction Three-dimensional displays

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Subspace clustering algorithms for mineral mapping 24

Subspace clustering algorithms for mineral mapping

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conference on Image and Signal Processing for Remote Sensing XXIV

作者： Khodadadzadeh, Mahdi Contreras, Cecilia Tusa, Laura Gloaguen, Richard Helmholtz Inst Freiberg Resource Technol Helmholtz Zentrum Dresden Rossendorf Freiburg Germany

ISBN: (纸本)9781510621626

The application of drill core hyperspectral data in exploration campaigns is receiving great interest to obtain a general overview of a mineral deposit. However, the main approach to the investigation of such data is by visual interpretation, which is subjective and time consuming. To address this issue, recently, the use of machine learning techniques is proposed for the analysis of this data. For drill core samples that for which only very little prior knowledge is often available, applying classification algorithms which are supervised learning methods is very challenging. In this paper, we suggest to use clustering (unsupervised) methods for mineral mapping, which are similar to classification but no predefined class labels are needed. To handle mapping of the very highly mixed pixels in drill core hyperspectral data, we propose to use advance subspace clustering methods, in which pixels are assumed to lie in a union of low-dimensional subspaces. We conduct a comparative study and evaluate the performance of two well-known subspace clustering methods, i.e., sparse subspace clustering (SSC) and low rank representation (LRR). For the experiments, we acquired VNIR-SWIR hyperspectral data and applied scanning electron microscopy based Mineral Liberation analysis (MLA) for two drill core samples. MLA is a high resolution imaging technique that allows detailed mineral charactrisition. We use the high-resolution MLA image as a reference to analyse the clustering results. Qualitative analysis of the obtained clustering maps indicate that the subspace clustering methods can accurately map the available minerals in the drill core hyperspectral data, especially in comparison to the traditional k-means clustering method.

关键词： Mineral mapping drill core hyperspectral data subspace clustering remote sensing

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ISS 2018 - Companion Proceedings of the 2018 ACM International conference on Interactive Surfaces and Spaces

ISS 2018 - Companion Proceedings of the 2018 ACM Internation...

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13th ACM International conference on Interactive Surfaces and Spaces, ISS 2018

ISBN: (纸本)9781450360104

The proceedings contain 12 papers. The topics discussed include: interweaving physical artifacts with visualization on digital media in museums;investigating the impact of data transfer user interfaces on communication grounding in cross- device environments;somewhat strange feeling of touching, lifting, and swinging in mixed-reality space - psychophysical analysis of haptic illusion caused by visual superimposition;design and implementation of the augmented volleyball court;aerial 3D imaging by retroreflective mirror array;metrics-based evaluation of graphical user interface aesthetics: the segmentation problem;design preferred aesthetic user interface with eye movement and electroencephalography data;encourage self-exploration through an interactive Chinese scroll painting design;and an entropy-based approach for computing the aesthetics of interfaces.

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Anomaly Detection and Target Prioritization in Planetary Imagery via the Automated Global Feature Analyzer™ (AGFA™): Progress Towards a Driver for Autonomous C⁴ISR Missions

Anomaly Detection and Target Prioritization in Planetary Ima...

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conference on Micro- and Nanotechnology Sensors, Systems, and Applications X

作者： Fink, Wolfgang Brooks, Alexander J. -W. Tarbell, Mark A. Univ Arizona Coll Engn Visual & Autonomous Explorat Syst Res Lab 1230 E Speedway Blvd Tucson AZ 85721 USA

ISBN: (数字)9781510617902

ISBN: (纸本)9781510617902

The Automated Global Feature Analyzer (TM) (AGFA (TM)) is a generically applicable automated sensor-data-fusion, feature extraction, feature vector clustering, anomaly detection, and target prioritization framework. AGFA (TM) operates in the respective feature space delivered by the sensor(s). In this paper we provide an overview of the inner workings of AGFA (TM) and apply AGFA (TM) to planetary imagery, representative of past, current, and future planetary missions, to demonstrate its automated and objective (i.e., unbiased) anomaly detection and target prioritization (i.e., region-of interest delineation) capabilities. Imaged operational areas are locally processed via a cascade of image segmentation, visual and geometric feature extraction, agglomerative clustering, and principal components analysis. Resulting clusters are labeled based on relative size and location in feature space. Anomalous regions may be considered immediate targets for follow-up in-situ investigation by local robotic agents, which can be directed via autonomous telecommanding, e.g., as part of a Tier-Scalable Reconnaissance mission architecture. These capabilities will be essential for driving fully autonomous (CISR)-I-4 missions of the future, since the speed of light prohibits "real time" Earth-controlled conduct of planetary exploration beyond the Moon.

关键词： Autonomous decision making sensor-data-fusion framework objective anomaly detection target prioritization agglomerative clustering principal components analysis autonomous (CISR)-I-4 systems multi-tiered robotic exploration architectures

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GPU-assisted scatterplots for millions of call events 36

GPU-assisted scatterplots for millions of call events

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36th Annual conference on Computer Graphics and visual Computing, CGVC 2018

作者： Rees, Dylan Roberts, Richard C. Laramee, Robert S. Brookes, Paul D’Cruze, Tony Smith, Gary A. Swansea University United Kingdom QPC Ltd United Kingdom

ISBN: (纸本)9783038680710

With four percent of the working population employed in call centers in both the United States and the UK, the contact center industry represents a sizable proportion of modern industrial landscapes. As with most modern industries, data collection is de rigueur, producing gigabytes of call records that require analysis. The scatterplot is a well established and understood form of data visualization dating back to the 17th century. In this paper we present an application for visualizing large call centre data sets using hardware-accelerated scatterplots. The application utilizes a commodity graphics card to enable visualization of a month’s worth of data, enabling fast filtering of multiple attributes. Filtering is implemented using the Open Computing Language (OpenCL), providing significant performance improvement over traditional methods. We demonstrate the value of our application for exploration and analysis of millions of call events from a real-world industry partner. Domain expert feedback from our industrial partners is reported. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： data visualization

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Evolutionary interactive analysis of MRI Gastric images using a multiobjective cooperative-coevolution scheme 36

Evolutionary interactive analysis of MRI Gastric images usin...

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36th Annual conference on Computer Graphics and visual Computing, CGVC 2018

作者： Al-Maliki, S.F. Lutton, É. Boué, F. Vidal, F.P. Bangor University United Kingdom Basrh University Iraq INRA-AgroParisTech France LLB CEA CNRS France

ISBN: (纸本)9783038680710

In this study, we combine computer vision and visualisation/data exploration to analyse magnetic resonance imaging (MRI) data and detect garden peas inside the stomach. It is a preliminary objective of a larger project that aims to understand the kinetics of gastric emptying. We propose to perform the image analysis task as a multi-objective optimisation. A set of 7 equally important objectives are proposed to characterise peas. We rely on a cooperation co-evolution algorithm called ‘Fly Algorithm’ implemented using NSGA-II. The Fly Algorithm is a specific case of the ‘Parisian Approach’ where the solution of an optimisation problem is represented as a set of individuals (e.g. the whole population) instead of a single individual (the best one) as in typical evolutionary algorithms (EAs). NSGA-II is a popular EA used to solve multi-objective optimisation problems. The output of the optimisation is a succession of datasets that progressively approximate the Pareto front, which needs to be understood and explored by the end-user. Using interactive Information visualisation (InfoVis) and clustering techniques, peas are then semi-automatically segmented. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： Multiobjective optimization

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