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IEEE Pacific visualization Symposium (IEEE PacificVis)

作者： Hajij, Mustafa Wang, Bei Scheidegger, Carlos Rosen, Paul Univ S Florida Tampa FL 33620 USA Univ Utah Salt Lake City UT 84112 USA Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9781538614242

Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data. In this paper, we propose a novel method using persistent homology to quantify structural changes in time-varying graphs. Specifically, we transform each instance of the time-varying graph into a metric space, extract topological features using persistent homology, and compare those features over time. We provide a visualization that assists in time-varying graph exploration and helps to identify patterns of behavior within the data. To validate our approach, we conduct several case studies on real-world datasets and show how our method can find cyclic patterns, deviations from those patterns, and one-time events in time-varying graphs. We also examine whether a persistence-based similarity measure satisfies a set of well-established, desirable properties for graph metrics.

关键词： Topological data analysis time-varying graph persistent homology graph visualization

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Situated visualization in Augmented Reality: Exploring Information Seeking Strategies

Situated Visualization in Augmented Reality: Exploring Infor...

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International IEEE conference on Signal-Image Technologies and Internet-Based System

作者： Giuseppe Caggianese Valerio Colonnese Luigi Gallo Institute for High Performance Computing and Networking National Research Council of Italy Naples Italy

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156873

In recent years augmented reality applications have been increasingly demonstrating the requirement for an interaction with information related to and directly shown in the surrounding environment. Situated information is visualized in its semantic and spatial context, building up an environment enhanced by an information level that dynamically adapts to the production of the information and to the actions of the user. The exploration and manipulation of this type of data through see-through augmented reality devices still represents a challenging task. The development of specific interaction strategies capable to mitigating the current limitations of augmented reality devices is essential. In this context, our contribution has been to design possible solutions to address some of these challenges allowing a dynamic interaction with situated information. Following the visual "information-seeking mantra" proposed by Shneiderman and introducing some "superpowers" for the users, in this work we present different strategies aimed at obtaining an overview and filtering, and acquiring details of a collection of situated data.

关键词： data visualization Augmented reality Semantics Task analysis visualization Layout Indexes

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A Framework for Sedimentological data Structure Extraction in Gold Mining Facies Delineation

A Framework for Sedimentological Data Structure Extraction i...

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Computer Science and data Engineering (CSDE), IEEE Asia-Pacific conference on

作者： Phumlani Nhlanganiso Khoza Scilinx Research School of Computer Science & Applied Mathematics University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

Quartz-pebble conglomerate (reef) horizons of the Witwatersrand Basin are the most important sources of gold (Au) in South Africa. Concentration of gold to economic ore-grades in the Witwatersrand-type gold deposits has been linked to sedimentary processes having operated as principal driving forces. Therefore, in Witwatersrand-type gold deposits, sedimentological data are often utilized to make qualitative assessment of in-situ gold-grade. This paper presents a framework that prescribes a normative and principled analysis of sedimentological data in order to quantify in-situ gold-grades. Its significance is the provision of a scalable process for extracting correlational structures, regardless of the attribute value-types. The framework applies network analysis, and a bespoke clustering algorithm to support visual analysis of spatial correlation between sedimentological data samples. A key result is the preservation of the semantic interpretation of the resulting cluster assignments via a topologically invariant transformation. This is achieved by applying non-uniform weighting to the attributes to emphasize the influence of non-numeric attributes in cluster assignment. Additionally, sedimentological attributes that are strongly correlated with gold-grade are identified by adopting total variance reduction from decision tree regression as a guiding principle. Using this approach, and data collected from a mining company located in the Witwatersrand Basin, spatial correlation structures defined over sedimentological data attributes are reported. As a byproduct, observations that lack spatial correlation with the rest of the data are identified. These isolated observations can inform quality assurance and quality control departments about potential irregularities in the reporting of sedimentological data within exploration and mining companies.

关键词： Machine learning data mining Gold Uncertainty Mineralization Estimation

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Interesting Recommendations Based on Hierarchical visualizations of Medical data 22nd

Interesting Recommendations Based on Hierarchical Visualizat...

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22nd Pacific-Asia conference on Knowledge Discovery and data Mining (PAKDD)

作者： Ibrahim, Ibrahim A. Almars, Abdulqader M. Pokharel, Suresh Zhao, Xin Li, Xue Univ Queensland Brisbane Qld Australia Minia Univ Al Minya Egypt

ISBN: (纸本)9783030045036;9783030045029

Due to the dramatic growth in Electronic Health Records (EHR), many opportunities are arising for discovering new medical knowledge. Those kinds of knowledge are useful for related stakeholders such as hospital planners, data analysts, doctors, insurance companies for better patients' management. However, many challenges need to be addressed while dealing with medical domain such as (1) how to measure the interestingness of information (2) how to visualize such interestingness (3) how to handle high dimensionality of medical data. To address these challenges, we present MedVIS, an interactive tool to visually explore the data space and finds interesting visualizations compared with another dataset. MedVIS structures visualizations into a hierarchical coherent tree to reveal interestingness of dimensions and other measures. We introduce a novel analysis work flow, and discuss various optimization mechanisms to effectively and efficiently explore the data space. Additionally, we discuss various approaches to mitigate the problem of high-dimensional medical data analysis and its visual exploration. In our experiments, we apply MedVIS to a real-world dataset and show promising visualization outcomes in terms of effectiveness and efficiency.

关键词： Big data visualizations visual analytics Knowledge discovery

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2018 IEEE Scientific visualization conference, SciVis 2018 - Proceedings

2018 IEEE Scientific Visualization Conference, SciVis 2018 -...

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2018 IEEE Scientific visualization conference, SciVis 2018

ISBN: (纸本)9781538668825

The proceedings contain 22 papers. The topics discussed include: cluster-based visualization for merger tree data: the challenge of missing expectations;interactive visual exploration and comparison on the effect of asteroid impacts;interactive visualization and analysis of high resolution HPC simulation data on a laptop with VTK;color interpolation for non-Euclidean color spaces;a lagrangian method for extracting eddy boundaries in the red sea and the Gulf of Aden;QuFlow: visualizing parameter flow in quantum circuits for understanding quantum computation;visualization of uncertainty for computationally intensive simulations using high fidelity emulators;and using cinematic effects to visualize the deep water impact data set.

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CASSL: Curriculum Accelerated Self-Supervised Learning

CASSL: Curriculum Accelerated Self-Supervised Learning

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IEEE International conference on Robotics and Automation (ICRA)

作者： Murali, Adithyavairavan Pinto, Lerrel Gandhi, Dhiraj Gupta, Abhinav Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781538630815

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for higher-dimensional control requires either scaling up the data collection efforts or using a clever sampling strategy for training. We present a novel approach Curriculum Accelerated Self-Supervised Learning (CASSL) to train policies that map visual information to high-level, higher-dimensional action spaces. CASSL orders the sampling of training data based on control dimensions: the learning and sampling are focused on few control parameters before other parameters. The right curriculum for learning is suggested by variance-based global sensitivity analysis of the control space. We apply our CASSL framework to learning how to grasp using an adaptive, underactuated multi-fingered gripper, a challenging system to control. Our experimental results indicate that CASSL provides significant improvement and generalization compared to baseline methods such as staged curriculum learning (8% increase) and complete end-to-end learning with random exploration (14% improvement) tested on a set of novel objects.

关键词： Aerospace electronics Grasping Training Task analysis Robots Sensitivity analysis"

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Surprise! Predicting Infant visual Attention in a Socially Assistive Robot Contingent Learning Paradigm

Surprise! Predicting Infant Visual Attention in a Socially A...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Lauren Klein Laurent Itti Beth A. Smith Marcelo Rosales Stefanos Nikolaidis Maja J. Matarić Interaction Lab University of Southern California Los Angeles CA USA iLab University of Southern California Infant Neuromotor Control Laboratory Division of Biokinesiology and Physical Therapy University of Southern California Interactive and Collaborative Autonomous Robotic Systems Lab University of Southern California

Early intervention to address developmental disability in infants has the potential to promote improved outcomes in neurodevelopmental structure and function [1]. Researchers are starting to explore Socially Assistive Robotics (SAR) as a tool for delivering early interventions that are synergistic with and enhance human-administered therapy. For SAR to be effective, the robot must be able to consistently attract the attention of the infant in order to engage the infant in a desired activity. This work presents the analysis of eye gaze tracking data from five 6-8 month old infants interacting with a Nao robot that kicked its leg as a contingent reward for infant leg movement. We evaluate a Bayesian model of low-level surprise on video data from the infants' head-mounted camera and on the timing of robot behaviors as a predictor of infant visual attention. The results demonstrate that over 67% of infant gaze locations were in areas the model evaluated to be more surprising than average. We also present an initial exploration using surprise to predict the extent to which the robot attracts infant visual attention during specific intervals in the study. This work is the first to validate the surprise model on infants; our results indicate the potential for using surprise to inform robot behaviors that attract infant attention during SAR interactions.

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COVIZ: A system for visual formation and exploration of patient cohorts 45th

COVIZ: A system for visual formation and exploration of pati...

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45th International conference on Very Large data Bases, VLDB 2019

作者： Pahins, Cícero A.L. Omidvar-Tehrani, Behrooz Amer-Yahia, Sihem Siroux, Valérie Pepin, Jean-Louis Borel, Jean-Christian Comba, Joao L.D. UFRGS Brazil Univ. Grenoble Alpes CNRS LIG France Institute for Advanced Biosciences and Inserm France Grenoble University Hospital France AGIR-à-Dom France

We demonstrate COVIZ, an interactive system to visually form and explore patient cohorts. COVIZ seamlessly integrates visual cohort formation and exploration, making it a single destination for hypothesis generation. COVIZ is easy to use by medical experts and offers many features: (1) It provides the ability to isolate patient demographics (e.g., their age group and location), health markers (e.g., their body mass index), and treatments (e.g., Ventilation for respiratory problems), and hence facilitates cohort formation;(2) It summarizes the evolution of treatments of a cohort into health trajectories, and lets medical experts explore those trajectories;(3) It guides them in examining different facets of a cohort and generating hypotheses for future analysis;(4) Finally, it provides the ability to compare the statistics and health trajectories of multiple cohorts at once. COVIZ relies on QDS, a novel data structure that encodes and indexes various data distributions to enable their efficient retrieval. Additionally, COVIZ visualizes air quality data in the regions where patients live to help with data interpretations. We demonstrate two key scenarios, ecological scenario and case cross-over scenario. A video demonstration of COVIZ is accessible via http://***/video-coviz. © 2019 VLDB Endowment.

关键词： Medical problems

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visually Exploring data Provenance and Quality of Open data

Visually Exploring Data Provenance and Quality of Open Data

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20th Eurographics/IEEE VGTC conference on visualization, EuroVis 2018

作者： Bors, C. Gschwandtner, T. Miksch, S. TU Wien Institute of Visual Computing and Human-Centered Technology Austria

ISBN: (纸本)9783038680659

While open data platforms are increasingly popular among end-users as well as data providers, there is a growing problem with inconsistent update frequencies and lack of quality in datasets. Efforts to monitor data quality are currently limited to checking meta-information and creating revisions to allow manual inspection of former datasets. We employ a visual Analytics framework for generating and visualizing data provenance from data quality to facilitate data analysis and help users to understand the impact of updates on the data. data quality metrics are utilized to quantify the development of data quality over time for open data projects. We combine quality metrics, data provenance, and data transformation information in an interactive exploration environment to expedite assessment and selection of appropriate open datasets. © 2018 The Author(s)

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GLOSSary: the GLobal Ocean 16S subunit web accessible resource

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BMC BIOINFORMATICS 2018年第SUPPL 15期19卷 131-143页

作者： Tangherlini, M. Miralto, M. Colantuono, C. Sangiovanni, M. Anno, A. Dell' Corinaldesi, C. Danovaro, R. Chiusano, M. L. Staz Zool Anton Dohrn I-80121 Naples Italy Polytech Univ Marche Dipartimento Sci Vita & Ambiente Via Brecce Bianche I-60131 Ancona Italy Polytech Univ Marche Dipartimento Sci & Ingn Materi Ambiente & Urbanis Via Brecce Bianche I-60131 Ancona Italy Univ Naples Federico II Dipartimento Agr Via Univ 100 I-80055 Portici Italy

BackgroundEnvironmental metagenomics is a challenging approach that is exponentially spreading in the scientific community to investigate taxonomic diversity and possible functions of the biological components. The massive amount of sequence data produced, often endowed with rich environmental metadata, needs suitable computational tools to fully explore the embedded information. Bioinformatics plays a key role in providing methodologies to manage, process and mine molecular data, integrated with environmental metagenomics collections. One such relevant example is represented by the Tara Ocean *** considered the Tara 16S miTAGs released by the consortium, representing raw sequences from a shotgun metagenomics approach with similarities to 16S rRNA genes. We generated assembled 16S rDNA sequences, which were classified according to their lengths, the possible presence of chimeric reads, the putative taxonomic affiliation. The dataset was included in GLOSSary (the GLobal Ocean 16S Subunit web accessible resource), a bioinformatics platform to organize environmental metagenomics data. The aims of this work were: i) to present alternative computational approaches to manage challenging metagenomics data;ii) to set up user friendly web-based platforms to allow the integration of environmental metagenomics sequences and of the associated metadata;iii) to implement an appropriate bioinformatics platform supporting the analysis of 16S rDNA sequences exploiting reference datasets, such as the SILVA database. We organized the data in a next-generation NoSQL schema-less database, allowing flexible organization of large amounts of data and supporting native geospatial queries. A web interface was developed to permit an interactive exploration and a visual geographical localization of the data, either raw miTAG reads or 16S contigs, from our processing pipeline. Information on unassembled sequences is also available. The taxonomic affiliations of contigs and miTAGs,

关键词： Tara Ocean project Marine metagenomics 16S rRNA Taxonomic analysis

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