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

共 1765 条记录，以下是641-650 订阅

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How augmented reality affects collaborative learning of physics: A qualitative analysis 13

How augmented reality affects collaborative learning of phys...

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13th International conference on Computer Supported Collaborative Learning - A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, CSCL 2019

作者： Unahalekhaka, Apittha Radu, Iulian Schneider, Bertrand Harvard University Graduate School of Education United States

ISBN: (纸本)9781732467231

Augmented reality (AR) is a powerful visualization tool to support learning of scientific concepts across learners of various ages. AR can make information otherwise invisible visible in the physical world in real-time. In this study, we are looking at a subset of data from a larger study (N=120), in which participant pairs interacted with an augmented sound producing speaker. We explored the learning behaviors in eight pairs of learners (N=16) who participated in an unstructured physics activity under two conditions: With or without AR. Comparing behaviors between the two experimental conditions, we found that AR affected learning in four different ways: Participants in the AR condition (1) learned more about visual concepts (ex: Magnetic field structures) but learned less about nonvisual content (ex: Relationship between electricity and physical movement);(2) stopped exploring the system faster than NonAR participants;(3) used less aids in exploration and teaching;and (4) spent less time in teaching their collaborators. We discuss implications of those results for designing collaborative learning activities with augmented reality. © ISLS

关键词： Augmented reality

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26th International conference on Neural Information Processing, ICONIP 2019

26th International Conference on Neural Information Processi...

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26th International conference on Neural Information Processing, ICONIP 2019

ISBN: (纸本)9783030368012

The proceedings contain 111 papers. The special focus in this conference is on Neural Information Processing. The topics include: Dense Image Captioning Based on Precise Feature Extraction;improve Image Captioning by Self-attention;dual-Path Recurrent Network for Image Super-Resolution;attention-Based Image Captioning Using DenseNet Features;High-Performance Light Field Reconstruction with Channel-wise and SAI-wise Attention;image Generation Framework for Unbalanced License Plate data Set;cross-Domain Scene Text Detection via Pixel and Image-Level Adaptation;modified Adaptive Implicit Shape Model for Object Detection;an Expert Validation Framework for Improving the Quality of Crowdsourced Clustering;a Label Embedding Method for Multi-label Classification via Exploiting Local Label Correlations;discriminant Feature Learning with Self-attention for Person Re-identification;the Inadequacy of Entropy-Based Ransomware Detection;attention Network for Product Characteristics Prediction Based on Reviews;semi-supervised Regularized Coplanar Discriminant analysis;multi-object Tracking with Conditional Random Field;intra-Modality Feature Interaction Using Self-attention for visual Question Answering;reinforcement Learning with Attention that Works: A Self-Supervised Approach;budget Cost Reduction for Label Collection with Confusability Based exploration;prediction of Refactoring-Prone Classes Using Ensemble Learning;analysis on Dropout Regularization;on a Convergence Property of a Geometrical Algorithm for Statistical Manifolds;SCS: Style and Content Supervision Network for Character Recognition with Unseen Font Style;anomaly Detection Based on the Global-Local Anomaly Score for Trajectory data;patch Selection Denoiser: An Effective Approach Defending Against One-Pixel Attacks;demographic Prediction from Purchase data Based on Knowledge-Aware Embedding;addressing Reward Engineering for Deep Reinforcement Learning on Multi-stage Task;High-Speed Synchronization of Pulse-Coupled

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When David Meets Goliath: Combining Smartwatches with a Large Vertical Display for visual data exploration

When David Meets Goliath: Combining Smartwatches with a Larg...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Horak, Tom Badam, Sriram Karthik Elmqvist, Niklas Dachselt, Raimund Tech Univ Dresden Interact Media Lab Dresden Germany Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781450356206

We explore the combination of smartwatches and a large interactive display to support visual data analysis. These two extremes of interactive surfaces are increasingly popular, but feature different characteristics-display and input modalities, personal/public use, performance, and portability. In this paper, we first identify possible roles for both devices and the interplay between them through an example scenario. We then propose a conceptual framework to enable analysts to explore data items, track interaction histories, and alter visualization configurations through mechanisms using both devices in combination. We validate an implementation of our framework through a formative evaluation and a user study. The results show that this device combination, compared to just a large display, allows users to develop complex insights more fluidly by leveraging the roles of the two devices. Finally, we report on the interaction patterns and interplay between the devices for visual exploration as observed during our study.

关键词： Cross-device interaction visual analysis data exploration multi-display environment large display smartwatch

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SMARTexplore: Simplifying High-Dimensional data analysis through a Table-Based visual Analytics Approach 13

SMARTexplore: Simplifying High-Dimensional Data Analysis thr...

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13th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Blumenschein, Michael Behrisch, Michael Schmid, Stefanie Butscher, Simon Wahl, Deborah R. Villinger, Karoline Renner, Britta Reiterer, Harald Keim, Daniel A. Univ Konstanz Constance Germany Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781538668610

We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst's trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing high-dimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.

关键词： High-dimensional data visual exploration pattern-driven analysis tabular visualization subspace aggregation

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Notable Site Recognition using Deep Learning on Mobile and Crowd-sourced Imagery

Notable Site Recognition using Deep Learning on Mobile and C...

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International conference on Mobile data Management

作者： Jimin Tan Anastasios Noulas Diego Sáez Rossano Schifanella Center for Data Science New York University New York NY Wikimedia Research Wikimedia Foundation Barcelona Spain ISI Foundation University of Turin Turin Italy

ISBN: (数字)9781728146638

ISBN: (纸本)9781728146645

Being able to automatically recognize notable sites in the physical world using artificial intelligence embedded in mobile devices can pave the way to new forms of urban exploration and open novel channels of interactivity between residents, travellers and cities. Although the development of outdoor recognition systems has been a topic of interest for a while, most works have been limited in geographic coverage due to lack of high quality image data that can be used for training site recognition engines. As a result, prior systems usually lack generality and operate on a limited scope of pre-elected sites. In this work we design a mobile system that can automatically recognise sites of interest and project relevant information to a user that navigates the city. We build a collection of notable sites using Wikipedia and then exploit online services such as Google Images and Flickr to collect large collections of crowd-sourced imagery describing those sites. These images are then used to train minimal deep learning architectures that can be effectively deployed to dedicated applications on mobile devices. By conducting an evaluation and performing a series of online and real-world experiments, we recognise a number of key challenges in deploying site recognition system and highlight the importance of incorporating mobile contextual information to facilitate the visual recognition task. The similarity in the feature maps of objects that undergo identification, the presence of noise in crowd-sourced imagery and arbitrary user induced inputs are among the factors the impede correct classification for deep learning models. We show how curating the training data through the application of a class-specific image de-noising method and the incorporation of information such as user location, orientation and attention patterns can allow for significant improvement in classification accuracy and the election of an end-to-end system that can effectively be used to recognise sites

关键词： Encyclopedias Electronic publishing Internet Task analysis Training Urban areas

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A Synthetic visual analysis System for Time-Varying Network

A Synthetic Visual Analysis System for Time-Varying Network

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IEEE International conference on Computer and Communication Engineering Technology (CCET)

作者： Huang, Jian Zhang, Xiushan Naval Univ Engn Dept Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781538674376

In recent years, with the rapid development of computer science and information technology, the world is filled and propelled of time-varying and multivariate data. The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visual analysis method for the time-varying network is inadequate. Efficient methods for visual clutter reduction, network structure exploration and network behavior detection are needed. In this paper, we propose two methods: A triangular based algorithm for time-varying behavior presentation and an improved brush PCP aims to assist the visual analysis task in time-varying network pattern detection. A synthetic visual analysis system is designed and implemented on the basis of these two novel methods. Our system is capable of visually analyzing vast amounts of network data. To better describe and demonstrate the usefulness and performance of our system, we utilize the ChinaVis2015 Challenge dataset as a case study.

关键词： component visual analysis time-varying multivariate data network security threat pattern recognition

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Demonstrating VisTiles: visual data exploration Using Mobile Devices 18

Demonstrating VisTiles: Visual Data Exploration Using Mobile...

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14th International conference on Advanced visual Interfaces (AVI)

作者： Langner, Ricardo Horak, Tom Dachselt, Raimund Tech Univ Dresden Interact Media Lab Dresden Germany

ISBN: (纸本)9781450356169

We demonstrate the prototype of the conceptual VisTiles framework. VisTiles allows exploring multivariate data sets by using multiple coordinated views that are distributed across a set of mobile devices. This setup allows users to benefit from dynamic and user-defined interface arrangements and to easily initiate co-located data exploration sessions. The current web-based prototype runs on commodity devices and is able to determine the spatial device arrangement by either a cross-device pinch gesture or an external tracking system. Multiple data sets are provided that can be explored by different visualizations (e.g., scatterplots, parallel coordinate plots, stream graphs). With this demonstration, we showcase the general concepts of VisTiles and discuss ideas for enhancements as well the potential for application cases beyond data analysis.

关键词： visualization

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EVALUATION OF VISION-BASED LOCALIZATION AND MAPPING TECHNIQUES IN A SUBSEA METROLOGY SCENARIO

EVALUATION OF VISION-BASED LOCALIZATION AND MAPPING TECHNIQU...

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conference on Underwater 3D Recording and Modelling - A Tool for Modern Applications and CH Recording

作者： Menna, F. Torresani, A. Nocerino, E. Nawaf, M. M. Seinturier, J. Remondino, F. Drap, P. Chemisky, B. COMEX SA Innovat Dept COMEX 36 Bd OceanCS 80143 F-13275 Marseille France Bruno Kessler Fdn FBK 3D Opt Metrol 3DOM Unit Trento Italy Aix Marseille Univ CNRS ENSAM Univ ToulonLIS UMR 7020 Domaine Univ St JeromeBatiment Polytech F-13397 Marseille France Swiss Fed Inst Technol Inst Theoret Phys CH-8093 Zurich Switzerland

Metrology is fundamental in all the applications that require to qualify, verify and validate measured data according to standards or, in other words, to assess their compliance with predefined tolerances. At sea, metrology is commonly associated with the process of measuring underwater structures, mainly pipeline elements widely used in offshore industry. Subsea operations are very expensive;optimizing time and money resources are the core factors driving innovation in the subsea metrology industry. In this study, the authors investigate the use of state-of-art vision-based algorithms, i.e. ORB-SLAM2 and visual Odometry, as a navigation tool to assist and control a Remotely Operated Vehicle (ROV) while performing subsea metrology operations. In particular, the manuscript will focus on methods for assessing the accuracy of both trajectory and tie points provided by the tested approaches and evaluating whether the preliminary real time reconstruction meets the tolerances defined in typical subsea metrology scenarios.

关键词： Underwater photogrammetry SLAM visual odometry subsea metrology ROV deep underwater exploration deformation analysis accuracy evaluation

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14th International Symposium on visual Computing, ISVC 2019

14th International Symposium on Visual Computing, ISVC 2019

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14th International Symposium on visual Computing, ISVC 2019

ISBN: (纸本)9783030337193

The proceedings contain 100 papers. The special focus in this conference is on visual Computing. The topics include: Adaptive Attention Model for Lidar Instance Segmentation;view Dependent Surface Material Recognition;3D visual Object Detection from Monocular Images;skin Identification Using Deep Convolutional Neural Network;resolution-Independent Meshes of Superpixels;automatic Video Colorization Using 3D Conditional Generative Adversarial Networks;improving visual Reasoning with Attention Alignment;multi-camera Temporal Grouping for Play/Break Event Detection in Soccer Games;Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM;DeepGRU: Deep Gesture Recognition Utility;augmented Curiosity: Depth and Optical Flow Prediction for Efficient exploration;information visualization for Highlighting Conflicts in Educational Timetabling Problems;contourNet: Salient Local Contour Identification for Blob Detection in Plasma Fusion Simulation data;mutual Information-Based Texture Spectral Similarity Criterion;accurate Computation of Interval Volume Measures for Improving Histograms;Ant-SNE: Tracking Community Evolution via Animated t-SNE;Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images;Angio-AI: Cerebral Perfusion Angiography with Machine Learning;conformal Welding for Brain-Intelligence analysis;Learning Graph Cut Class Prototypes for Thigh CT Tissue Identification;delineation of Road Networks Using Deep Residual Neural Networks and Iterative Hough Transform;automatic Estimation of Arterial Input Function in Digital Subtraction Angiography;one-Shot-Learning for visual Lip-Based Biometric Authentication;age Group and Gender Classification of Unconstrained Faces;efficient 3D Face Recognition in Uncontrolled Environment;Pupil Center Localization Using SOMA and CNN;Real-Time Face Features Localization with Recurrent Refined Dense CNN Architectures;estimation of the Distance Between Fingertips Using Silhouette and Texture Information of Dorsa

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What Moves Players? visual data exploration of Twitter and Gameplay data 18

What Moves Players? Visual Data Exploration of Twitter and G...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Drescher, Christian Wallner, Guenter Kriglstein, Simone Sifa, Rafet Drachen, Anders Pohl, Margit TU Wien Vienna Austria Fraunhofer IAIS St Augustin Germany Univ York DC Labs York N Yorkshire England

ISBN: (纸本)9781450356206

In recent years, microblogging platforms have not only become an important communication channel for the game industry to generate and uphold audience interest but also a rich resource for gauging player opinion. In this paper we use data gathered from Twitter to examine which topics matter to players and to identify influential members of a game's community. By triangulating in-game data with Twitter activity we explore how tweets can provide contextual information for understanding fluctuations in in-game activity. To facilitate analysis of the data we introduce a visual data exploration tool and use it to analyze tweets related to the game Destiny. In total, we collected over one million tweets from about 250,000 users over a 14-month period and gameplay data from roughly 3,500 players over a six-month period.

关键词： Twitter Gameplay data Online Community Destiny

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