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2000 Conference on Designing Augmented Reality Environments, DARE 2000

作者： Mynatt, Elizabeth D. Everyday Computing Lab Graphics Visualization and Usability Center Georgia Institute of Technology AtlantaGA30332-0280 United States

No abstract available.

ISBN: (纸本)9781450373265

No abstract available.

关键词： augmented reality visualization ubiquitous computing audio ligh-weight interaction awareness

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A Methodology for Integrating Population Health Surveys Using Spatial Statistics and visualizations for Cross-Sectional Analysis

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SN Computer Science 2023年第3期4卷 224页

作者： Ravindra, Harshitha Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab E-Health Research Center IIIT Bangalore 26/C Electronics City Hosur Road Karnataka Bangalore 560100 India

Large-scale population surveys are beneficial in gathering information on the performance indicators of public well-being, including health and socio-economic standing. However, conducting national population surveys for low and middle-income countries (LMIC) with high population density comes at a high economic cost. To conduct surveys at low-cost and efficiently, multiple surveys with different, but focused, goals are implemented through various organizations in a decentralized manner. Some of the surveys tend to overlap in outcomes with spatial, temporal or both scopes. Mining data jointly from surveys with significant overlap gives new insights while preserving their autonomy. We propose a three-step workflow for integrating surveys using spatial analytic workflow supported by visualizations. We implement the workflow on a case study using two recent population health surveys in India to study malnutrition in children under-five. Our case study focuses on finding hotspots and coldspots for malnutrition, specifically undernutrition, by integrating the outcomes of both surveys. Malnutrition in children under-five is a pertinent global public health problem that is widely prevalent in India. Our work shows that such an integrated analysis is beneficial alongside independent analyses of such existing national surveys to find new insights into national health indicators. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Anemia Hellinger distance Large-scale surveys Micronutrient deficiency Multi-source data fusion Spatial analysis Spatial autocorrelation Stunting Underweight visualization Wasting

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Comparing Community Detection Methods in Brain Functional Connectivity Networks 4th

Comparing Community Detection Methods in Brain Functional Co...

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4th International Conference on Computational Intelligence, Cyber Security, and Computational Models, ICC3 2019

作者： Vangimalla, Reddy Rani Sreevalsan-Nair, Jaya Graphics-Visualization-Computing Lab E-Health Research Center International Institute of Information Technology - Bangalore 26/C Electronics City Bangalore560100 India

ISBN: (纸本)9789811596995

Brain functional networks are essential for understanding the functional connectome. computing the temporal dependencies between the regions of brain activities from the functional magnetic resonance imaging (fMRI) gives us the functional connectivity between the regions. The pairwise connectivities in matrix form correspond to the functional network (fNet), also referred to as a functional connectivity network (FCN). We start with analyzing a correlation matrix, which is an adjacency matrix of the FCN. In this work, we perform a case study of comparison of different analytical approaches in finding node-communities of the brain network. We use five different methods of community detection, out of which two methods are implemented on the network after filtering out the edges with weight below a predetermined threshold. We additionally compute and observe the following characteristics of the outcomes: (i) modularity of the communities, (ii) symmetrical node-partition between the left and right hemispheres of the brain, i.e., hemispheric symmetry, and (iii) hierarchical modular organization. Our contribution is in identifying an appropriate test bed for comparison of outcomes of approaches using different semantics, such as network science, information theory, multivariate analysis, and data mining. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Composition of Geospatial visualizations for Scale-aware Views of Multiple Outcome Variables in Population Surveys

Composition of Geospatial Visualizations for Scale-aware Vie...

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International Conference on Information Visualisation (IV)

作者： Harshitha Ravindra Jaya Sreevalsan-Nair Graphics-Visualization-Computing Lab IIIT Bangalore Bangalore India

ISBN: (纸本)9781665490085

Population survey data is important for understanding the status of well-being along any dimensions, i.e., social, economic, political, health, etc. This data generates spatial point patterns which can be explored and analyzed using visualization. Given the spatial aspect of the data, there is a requirement of using cartographic maps, which are mostly limited to visualizing a single variable in most cases. Here, it is also important that the choice of visualizations also enable scale-aware analysis when zooming in and out of the maps, since the data is from the smaller political units and can be aggregated to larger political units. Thus, we explore the different visual compositions which use mathematical operators and the composite layouts for visualizing multiple outcome variables in survey data. The mathematical operators allow the use of univariate and bivariate data modeling and representation, and composite layouts of interest are juxtaposition and superimposed views. We demonstrate the inferences from visualizations using a case study on malnutrition in children under five in India. Our work shows that a visual composition of binary relationships represented in a visualization and a juxtaposed layout of such pairwise variables is effective in making inferences from the multivariate spatial point patterns in population data.

关键词： Heating systems visualization Sociology Layout Data visualization Spatial databases Mathematical models

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Message from the Program Co-Chairs

Message from the Program Co-Chairs

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Proceedings - graphics Interface 2003

作者： Moller, Torsten Ware, Colin Graphics Usability/Visualisation Lab School of Computing Science Simon Fraser University Canada Data Visualization Research Lab Center for Coastal and Ocean Mapping University of New Hampshire United States

No abstract available

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CAP-DSDN: Node Co-association Prediction in Communities in Dynamic Sparse Directed Networks and a Case Study of Migration Flow 14

CAP-DSDN: Node Co-association Prediction in Communities in D...

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14th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2022

作者： Sreevalsan-Nair, Jaya Jakher, Astha Graphics-Visualization-Computing Lab. International Institute of Information Technology Bangalore Bangalore India Department of Humanities and Social Sciences IIT Kharagpur West Bengal Kharagpur721302 India

ISBN: (纸本)9789897586149

Predicting the community structure in the time series, or snapshots, of a real-world graph in the future, is a pertinent challenge. This is motivated by the study of migration flow networks. The dataset is characterized by edge sparsity due to the inconsistent availability of data. Thus, we generalize the problem to predicting community structure in a dynamic sparse directed network (DSDN). We introduce a novel application of co-association which is a pairwise relationship between the nodes belonging to the same community. We thus propose a three-step algorithm, CAP-DSDN, for co-association prediction (CAP) in such a network. Given the absence of benchmark data or ground truth, we use an ensemble of community detection (CD) algorithms and evaluation metrics widely used for directed networks. We then define a metric based on entropy rate as a threshold to filter the network for determining a significant and data-complete subnetwork. We propose the use of autoregressive models for predicting the co-association relationship given in its matrix format. We demonstrate the effectiveness of our proposed method in a case study of international refugee migration during 2000–18. Our results show that our method works effectively for migration flow networks for short-term prediction and when the data is complete across all snapshots. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Forecasting

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Influence of Aleatoric Uncertainty on Semantic Classification of Airborne Lidar Point Clouds: A Case Study with Random Forest Classifier Using Multiscale Features

Influence of Aleatoric Uncertainty on Semantic Classificatio...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jaya Sreevalsan-Nair Pragyan Mohapatra Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Karnataka India

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

For semantic classification of LiDAR point clouds, the features derived from the local geometric descriptors are routinely used as features in (supervised) learning algorithms. In this study, our goal is to determine if the aleatoric uncertainty in the input to a supervised semantic classifier influences the outcomes. We consider two sources of such uncertainty - one from the computation of multiscale local geometric descriptors and the other, from class ambiguities at object boundaries. We perform ensembles of experiments to measure the significance of these uncertainties in the semantic classification of airborne LiDAR point clouds, when using random forest classifier. Our case study shows that the presence of aleatoric uncertainty improves the classification outcomes.

关键词： Uncertainty Semantics Three-dimensional displays Laser radar Training Probabilistic logic Measurement

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Rethinking visualization: A High-Level Taxonomy

Rethinking Visualization: A High-Level Taxonomy

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IEEE Symposium on Information visualization (INFOVIS)

作者： M. Tory T. Moller Graphics Usability and Visualization Lab School of Computing Science Simon Fraser University Canada

We present the novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the user's conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relates and overlap, and that may inspire research ideas in hybrid visualization areas

关键词： Taxonomy Data visualization Algorithm design and analysis graphics Usability Humans Displays Timing Information theory Application software

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Requirements and architecture of an information visualization tool 2nd

Requirements and architecture of an information visualizatio...

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Proceedings of the 1995 IEEE visualization Workshop

作者： Mukherjea, Sougata Foley, James D. C and C Research Lab NEC USA San Jose 95134 CA United States Graphics Visualization and Usability Center College of Computing Georgia Institute of Technology Atlanta 30332 Ga United States

ISBN: (纸本)3540622217

This paper talks about the requirements for an effective information visualization tool. The architecture of Navigational View Builder, a tool to develop visualizations of the information space of hypermedia systems is also described. We then show how the tool satisfies the requirements of an information visualization tool. To conclude the paper we discuss some of the required features of the underlying database for producing effective visualizations. © Springer-Verlag Berlin Heidelberg 1996.

关键词： visualization

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Scaling Up Study Area Size in Flood Susceptibility Mapping

Scaling Up Study Area Size in Flood Susceptibility Mapping

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Aswathi Mundayatt Jaya Sreevalsan-Nair Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Flood susceptibility mapping (FSM) is crucial for preparedness and risk mitigation of floods, and machine learning (ML)-based FSM methods have been well-studied. However, due to computational constraints, geographical variations, and differential data availability, FSM is often confined to smaller regions, much less than 1,000 km 2 . To address the computational constraints of FSM generation in data-rich regions, we propose a data-parallel approach using PySpark with two levels of spatial partitioning, where they address the computational load and geographical variations, respectively. In our case study of the state of Kerala, India, we propose grid lattice partitioning and the Ecologically Sensitive Zone (ESZ) map for the two levels of spatial partitioning, respectively. The latter is used to train random forest classifier models. We investigate the influence of contiguity in training grids on FSM outcomes within different ESZs. Our quantitative and qualitative comparative analysis of observations demonstrate the importance of contiguous training grids, especially in capturing finer details in FSM. Our implementation demonstrates the efficiency of our approach in the study area of Kerala spanning 38,863 km 2 .

关键词： Training Accuracy Computational modeling Biological system modeling Scalability Lattices Computational efficiency

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