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

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A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree 18

A visual analytics approach for exploration of high-dimensio...

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10th International conference on Computer Modeling and Simulation, ICCMS 2018

作者： Rodríguez, Roberto Alfonte, Reynaldo Cuadros, Ana Maria Urquiaga Universidad Nacional de San Agustín Arequipa Peru Zapana Universidad Nacional de San Agustín Arequipa Peru Valdivia Universidad la Salle Arequipa Peru

ISBN: (纸本)9781450363396

High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two wellknown similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively. © 2018 Association for Computing Machinery.

关键词： visualization

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visualising hidden spatiotemporal patterns at multiple levels of detail 22

Visualising hidden spatiotemporal patterns at multiple level...

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22nd International conference Information visualisation (iv) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Silva, Ricardo Almeida Pires, Joao Moura Datia, Nuno Santos, Maribel Yasmina Martins, Bruno Birra, Fernando Inst Politecn Lisboa ISEL Lisbon Portugal Inst Politecn Lisboa NOVA LINCS Lisbon Portugal Univ Nova Lisboa FCT NOVA LINCS Caparica Portugal Univ Minho ALGORITMI Res Ctr Guimaraes Portugal Univ Lisbon INESC ID Lisbon Portugal Univ Lisbon IST Lisbon Portugal

ISBN: (纸本)9781538672020

Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, enhancing the user's perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected. Modeling phenomena at different LoDs is needed, as there is no exclusive LoD at which data can be analyzed. Current practices work mainly on a single LoD, driven by the analysts perception, ignoring the fact that the identification of the suitable LoDs is a key issue for pointing relevant patterns. This paper presents a visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs patterns emerge or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets allowed the evaluation of VAST, which was able to suggest LoDs with different interesting spatiotemporal patterns and the type of expected patterns.

关键词： data visualisation spatiotemporal patterns multiple levels of detail visual analytics

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Making Convolutional Networks Recurrent for visual Sequence Learning 31

Making Convolutional Networks Recurrent for Visual Sequence ...

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31st IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang, Xiaodong Molchanov, Pavlo Kautz, Jan NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781538664209

Recurrent neural networks (RNNs) have emerged as a powerful model for a broad range of machine learning problems that involve sequential data. While an abundance of work exists to understand and improve RNNs in the context of language and audio signals such as language modeling and speech recognition, relatively little attention has been paid to analyze or modify RNNs for visual sequences, which by nature have distinct properties. In this paper, we aim to bridge this gap and present the first large-scale exploration of RNNs for visual sequence learning. In particular, with the intention of leveraging the strong generalization capacity of pre-trained convolutional neural networks (CNNs), we propose a novel and effective approach, Pre-RNN, to make pre-trained CNNs recurrent by transforming convolutional layers or fully connected layers into recurrent layers. We conduct extensive evaluations on three representative visual sequence learning tasks: sequential face alignment, dynamic hand gesture recognition, and action recognition. Our experiments reveal that PreRNN consistently outperforms the traditional RNNs and achieves stateof- the-art results on the three applications, suggesting that PreRNN is more suitable for visual sequence learning.

关键词： visualization Logic gates Recurrent neural networks Task analysis Convolution Speech recognition Face recognition

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cpmViz: A Web-Based visualization Tool for Uncertain Spatiotemporal data

cpmViz: A Web-Based Visualization Tool for Uncertain Spatiot...

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IEEE Symposium on visual Analytics Science and Technology (VAST)

作者： Fabian Nagel Giuliano Castiglia Gemza Ademaj Juri Buchmller Udo Schlegel Daniel A. Keim Data Analysis and Visualization Group University of Konstanz Germany

The goal of the VAST challenge 2019 Mini Challenge 2 was to visualize radioactive contaminations measured by mobile and static sensors and their changes over time, allowing city officials to determine the severity of the leakage at the city's nuclear power plant. We propose cpmViz, a web-based tool that allows for interactive data exploration of the sensor readings in both of the spatial and temporal dimensions. The tool consists out of three views that are connected via linking and scrolling. We visualize static sensor uncertainty by introducing Voronoi cells to illustrate how much space is covered by an individual measurement unit. For mobile sensors, we showcase their activity periods and introduce the concept of sensor streaks as periods of uninterrupted recordings as a temporal uncertainty measure. As for spatial uncertainty, we color individual districts based on the amount of data that was recorded inside the user's selected time window. Using our system, we were able to easily spot major events like the city's initial earthquake in the sensor readings. Certain southern districts are clearly visible as areas of concern that we consider in need of more static sensors. Furthermore, we were also able to identify static as well as moving contaminations.

关键词： Sensors Pollution measurement Contamination Uncertainty Urban areas data visualization Measurement uncertainty

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myTune: Webvisualization Technologies for Irish Traditional Music Archives 18

myTune: WebVisualization Technologies for Irish Traditional ...

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5th International conference on Digital Libraries for Musicology (DLfM)

作者： Timoney, Joe Lysaght, Tom Tshibasu, Corneille Maynooth Univ Dept Comp Sci Maynooth Kildare Ireland

ISBN: (纸本)9781450365222

Online digital music libraries have become important repositories for music for all enthusiasts of Irish traditional music. These libraries have certainly facilitated the discovery of new material and enabled extensive exploration of musical variations on well-known favorites. Current web interfaces to these repositories, offer a combination of text and notation-based information as well as providing audio and midi listening formats. However, these interfaces could take advantage of more recently available technologies offering graph-based, interactive, visual displays and more useful data analytic content. Drawing on the state-of the-art data visualization web libraries, the objective of this work is to present a prototype of a new digital library for Irish traditional music. The design and architecture is detailed using standard Unified Modelling Language (UML) notation. Evaluation is carried out by a usability survey to determine the effectiveness of the enhancements.

关键词： Digital Music Library Music visualization Musicology analysis

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visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Visual Analytics for Deep Embeddings of Large Scale Molecula...

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IEEE International conference on Big data

作者： Junghoon Chae Debsindhu Bhowmik Heng Ma Arvind Ramanathan Chad Steed Oak Ridge National Laboratory Oak Ridge USA Argonne National Laboratory Lemont USA

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

关键词： Biological system modeling Three-dimensional displays Analytical models data visualization Shape data models Proteins

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Adaptation and automation of a cancellation test for evaluation of exploratory visual behavior 2018

Adaptation and automation of a cancellation test for evaluat...

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17th Brazilian Symposium on Human Factors in Computing Systems, IHC 2018

作者： Cascaes, Renata Pinheiro, Kelly Lameira, Kalill Mota, Marcelle Pereira Sampaio Neto, Nelson Cruz Sarmanho, Ricardo Pereira, Antônio Universidade Federal do Pará BelémPará Brazil

ISBN: (纸本)9781450366014

Tests that evaluate the individual strategies of visual exploration may be useful for characterizing deviations from typical development, such as autism spectrum disorder and dyslexia. One subgroup of visual exploration tests, called cancellation tests, require the identification of specific targets surrounded by distractors. However, the lack of automated versions of these tests limit their availability to a larger audience. In the present paper, we present an automated version of a cancellation test: the car test. The test is designed to be used by specialists and to be user-friendly, optimizing its application and the analysis of collected data. We evaluated the car test in a group of 6-9 y.o. infants enrolled in a private school. The results suggest the car test is effective in providing information about visual exploration strategies of infants and is in agreement with the results of similar tests found in the literature. © 2018 Association for Computing Machinery.

关键词： Automation

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On non-routine places in urban human mobility 5

On non-routine places in urban human mobility

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5th IEEE International conference on data Science and Advanced Analytics (IEEE DSAA)

作者： Quadri, Christian Zignani, Matteo Gaito, Sabrina Rossi, Gian Paolo Univ Milan Dept Comp Sci Milan Italy

ISBN: (纸本)9781538650905

The dichotomy between two opposite propensities, exploration and exploitation, characterizes and drives many human behaviors, from decision making to social learning. Recently, this dichotomy has been found also in human mobility where people can be divided into two basic types: returners, those who are very regular in their daily mobility;and explorers, those who are inclined to break out of their daily mobility routine and explore new places. While the former attitude has been widely studied in literature and results in the well-known tendency to frequently visit a few locations (e.g. home and workplace), the latter trait remains an unexplored aspect and deserves further investigation. In this work we focus on the characterization of the places that an individual visits when he/she is driven by his/her propensity for exploration, i.e. non-routine places which are outside his/her usual daily mobility patterns. To this end, we mine an anonymized mobile phone dataset which integrates call, text and data activities of about one million subscribers in Milan, to detect and characterize the non-routine places. Moreover, we complement it with Foursquare venues along with their category to semantically characterize the reasons driving the choice of the places to explore. The analysis of the non-routine places and the mobility patterns during the exploration phase brings to light some interesting findings: i) to a greater or lesser extent, all individuals are explorers since they visit a significant number of non-routine places;ii) due to the exceptionality of a visit, non-routine places are farther from home and workplace than frequently visited places;iii) we are explorers in our leisure time;iv) we get to a non-routine place leaving our home, then we return home later;and v) in Milan, shopping, in particular at fashion and clothing stores, is the main interest behind the need to explore non-routine places.

关键词： urban mobility region of interest daily mobility patterns non-routine places urban functions mobile phone data human mobility

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TopoText: Context-Preserving Text data exploration Across Multiple Spatial Scales

TopoText: Context-Preserving Text Data Exploration Across Mu...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Zhang, Jiawei Surakitbanharn, Chittayong Elmqvist, Niklas Maciejewski, Ross Qian, Zhenyu Ebert, David S. Purdue Univ W Lafayette IN 47907 USA Univ Maryland Baltimore MD 21201 USA Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9781450356206

TopoText is a context-preserving technique for visualizing text data for multi-scale spatial aggregates to gain insight into spatial phenomena. Conventional exploration requires users to navigate across multiple scales but only presents the information related to the current scale. This limitation potentially adds more steps of interaction and cognitive overload to the users. TopoText renders multi-scale aggregates into a single visual display combining novel text-based encoding and layout methods that draw labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each individual scale, but also indicates the spatial coverage of the aggregates and their underlying hierarchical relationships. We validate TopoText with both a user study as well as several application examples.

关键词： Geospatial visualization text visualization typographic map context preservation multi-scale analysis

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Interaction Patterns of Spatial Navigation in VR Workspaces

Interaction Patterns of Spatial Navigation in VR Workspaces

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International conference on Cognitive Infocommunications (CogInfoCom)

作者： Anna sudár Ádám B. Csapó Multidisciplinary Doctoral School of Engineering Sciences Széchenyi István University Hungary

ISBN: (数字)9781728147932

ISBN: (纸本)9781728147949

During the discovery of any new environment - be it real or virtual - the first step is usually that of exploration. The goal exploration in this context is to find the location that provides the most informative, or otherwise useful viewpoint in the space. Depending on the complexity of the space and the task at hand, this can be a difficult challenge. Nevertheless, the finding of useful viewpoints can be facilitated, at least in virtual reality, by changing the layout of the space, as well as by changing other visual features to lower the cognitive load of navigation. In this paper, we present a set of experiments using the MaxWhere desktop virtual environment which suggest that besides the arrangement of a space, its key points of discovery can also influence the time it takes to accomplish a task. This conclusion is supported both by data on key points of navigation and on the activation mode that was utilized on the various smartboards laid out in 3D space. In this research, we hope to show how specific nodes can be found, based on a data-oriented approach, which can lead users to focus on the most important information and obtain the best overview of any virtual space in general.

关键词： Navigation Task analysis Virtual environments Three-dimensional displays Cognition Atmospheric measurements

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