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9th KES International conference on Intelligent Decision Technologies (KES-IDT)

作者： Burch, Michael Univ Stuttgart VISUS D-70569 Stuttgart Germany

ISBN: (纸本)9783319594248;9783319594231

Eye tracking has become an increasingly important technology in many fields of research like marketing, psychology, human-computer interaction, and also in visualization. Understanding the eye movements of people while solving a given task can be of great support to improve a visual stimulus. The challenging problem with this kind of spatio-temporal data is the difficulty to provide a useful visualization that can provide an overview about the fixations with their durations and sequential order, the saccades with their orientations and lengths, but also the distances of several fixations in space. Traditional visualizations like gaze plots - showing the stimulus in its original form overplotted with the scan paths - typically produce vast amounts of visual clutter and make a visual exploration of the eye movement data a difficult task. In this paper we introduce the fixation distance plots that place the fixation sequences to a horizontal line of color coded and differently thick circles while showing additional saccadic information. Moreover, the user can apply distance thresholds that indicate if fixations are within a certain distance allowing to get an impression about the spatial stimulus information. We illustrate the usefulness of the approach by applying it to eye movement data from a formerly conducted eye tracking experiment investigating route finding tasks in public transport maps.

关键词： Eye movements

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visual exploration of Semantic Relationships in Neural Word Embeddings

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 553-562页

作者： Liu, Shusen Bremer, Peer-Timo Thiagarajan, Jayaraman J. Srikumar, Vivek Wang, Bei Livnat, Yarden Pascucci, Valerio Lawrence Livermore Natl Lab Lawrence CA 94550 USA Univ Utah SCI Inst Salt Lake City UT 84112 USA Univ Utah Sch Comp Salt Lake City UT 84112 USA

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.

关键词： Natural Language Processing Word Embedding High-Dimensional data

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visualization and analysis of Deep Water Asteroid Impacts

Visualization and Analysis of Deep Water Asteroid Impacts

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IEEE Scientific visualization conference (Scivis)

作者： Imahorn, Raphael Rojo, Irene Baeza Guenther, Tobias Swiss Fed Inst Technol Comp Graph Lab Zurich Switzerland

ISBN: (纸本)9781538668825

As evident by the Earth's history, severe asteroid impacts pose a significant threat to all species living on our planet. The severity of an impact is difficult to predict due to the vast amount of dependent parameters and thus the potential consequences on climate, plate tectonics and organisms are not yet fully understood. To improve our understanding of the threats imposed by asteroid impacts, scientists at Los Alamos National Laboratory conducted an ensemble simulation for varying impact scenarios into deep water oceans. The IEEE Scientific visualization Contest 2018 is dedicated to the visual analysis of these asteroid impact simulations. In this paper, we apply a number of recent state-of-the-art visualization techniques to visually compare and analyze the varying impact scenarios. We discuss both automatic approaches applicable to an in-situ analysis, as well as interactive approaches for a detailed exploration.

关键词： data visualization Terrestrial atmosphere visualization Earth Meteorology Monte Carlo methods

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Hi-D Maps: An Interactive visualization Technique for Multi-Dimensional Categorical data

Hi-D Maps: An Interactive Visualization Technique for Multi-...

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International conference on visualisation, VIS

作者： Radi Muhammad Reza Benjamin A. Watson North Carolina State University

ISBN: (纸本)9781728149424

In this paper, we present Hi-D maps, a novel method for the visualization of multi-dimensional categorical data. Our work addresses the scarcity of techniques for visualizing a large number of data-dimensions in an effective and space-efficient manner. We have mapped the full data-space onto a 2D regular polygonal region. The polygon is cut hierarchically with lines parallel to a user-controlled, ordered sequence of sides, each representing a dimension. We have used multiple visual cues such as orientation, thickness, color, countable glyphs, and text to depict cross-dimensional information. We have added interactivity and hierarchical browsing to facilitate flexible exploration of the display: small areas can be scrutinized for details. Thus, our method is also easily extendable to visualize hierarchical information. Our glyph animations add an engaging aesthetic during interaction. Like many visualizations, Hi-D maps become less effective when a large number of dimensions stresses perceptual limits, but Hi-D maps may add clarity before those limits are reached.

关键词： data visualization visualization Image color analysis Mice Two dimensional displays Animation Tools

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Toward a programmable analysis and visualization framework for interactive performance analytics 1

Toward a programmable analysis and visualization framework f...

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1st IEEE/ACM Workshop on Programming and Performance visualization Tools, ProTools 2019

作者： Islam, Tanzima Ayala, Alexis Jensen, Quentin Ibrahim, Khaled Department of Computer Science Texas State University United States Department of Computer Science Western Washington University United States Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9781728160269

Understanding the performance characteristics of applications in modern HPC environments is becoming more challenging due to the increase in the architectural and programming complexities. HPC software developers rely on sources such as hardware counters and event traces to infer performance problems while focusing on designing mitigation strategies. A large number of in-house tools exist in the community, which indicate replicated effort. This paper presents a customizable framework for analyzing performance measurements and visualizing through a web-based interactive dashboard for interactively exploring a large volume of hierarchical information. In this paper, we analyze three ECP applications as use cases and identify as well as optimize problematic resource utilization behaviors exposed by our visualizations. This framework is a step towards a unified platform for visual identification of performance scaling bottlenecks to ease the collaboration between application developers, performance analysts, and hardware vendors. © 2019 IEEE.

关键词： visualization

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Seeking New Ways to visually Represent Uncertainty in data: What We Can Learn from the Fine Arts

Seeking New Ways to Visually Represent Uncertainty in Data: ...

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IEEE-VIS-Arts-Program (VISAP) conference

作者： Hill, Aaron Churchouse, Clare Schober, Michael F. Parsons Sch Design New York NY 10011 USA New Sch Social Res New York NY 10011 USA

ISBN: (纸本)9781728128054

In data visualization, the representation of uncertainty and error estimation is often difficult to display effectively. Constraints on the number of dimensions that can be expressed visually as well as limitations of statistical graphing software often lead to data visualizations that inadvertently omit and/or poorly convey the uncertainty and vulnerability of the underlying data. This research is based on more than 400 works of fine art from museum collections and galleries across several countries, curated and analyzed for inspiration and information on potentially effective ways to visually communicate uncertainty, ambiguity, and vulnerability. We chose these artworks because we feel they have a unique ability to convey uncertainty using a range of approaches and techniques. This paper includes observations from the analysis, examples of compelling works of art from the research, and an exploration of ways these works might inform data visualization practice, specifically for the visual display of uncertainty.

关键词： I.2.3 [Artificial Intelligence]: Deduction and Theorem Proving-Uncertainty, fuzzy, and probabilistic reasoning H.5.2 [Information Interfaces and Presentation]: User Interfaces-Screen design

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visual Anomaly Detection in Event Sequence data

Visual Anomaly Detection in Event Sequence Data

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IEEE International conference on Big data

作者： Shunan Guo Zhuochen Jin Qing Chen David Gotz Hongyuan Zha Nan Cao Department of Software Engineering East China Normal University College of Design and Innovation Tongji University School of Information and Library Science University of North Carolina at Chapel Hill

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When applied to the analysis of event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE) to estimate underlying normal progressions for each given sequence represented as occurrence probabilities of events along the sequence progression. Events in violation of their occurrence probability are identified as abnormal. We also introduce a visualization system, EventThread3 (ET 3 , to support interactive exploration and interpretations of anomalies within the context of normal sequence progressions in the dataset through comprehensive one-to-many sequence comparison. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm and demonstrate the effectiveness of our system through a case study.

关键词： Anomaly detection data visualization Interactive systems Probability data security

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A Multi-Scale Correlative Approach for Crowd-Sourced Multi-Variate Spatiotemporal data 51

A Multi-Scale Correlative Approach for Crowd-Sourced Multi-V...

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51st Annual Hawaii International conference on System Sciences (HICSS)

作者： Gorko, Thomas Yau, Calvin Malik, Abish Harris, Matt Tee, Jun Xiang Maciejewski, Ross Qian, Cheryl Afzal, Shehzad Pijanowski, Bryan Ebert, David EPICS Healthcare Madison WI 53726 USA Purdue Univ W Lafayette IN 47907 USA Davista Technol W Lafayette IN USA Google Inc Mountain View CA USA Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9780998133119

With the increase in community-contributed data availability, citizens and analysts are interested in identifying patterns, trends and correlation within these datasets. Various levels of aggregation are often applied to interpret such large data schemes. Identifying the proper scales of aggregation is a non-trivial task in this exploratory data analysis process. In this paper, we present an integrated visual analytics environment that facilitates the exploration of multivariate categorical spatiotemporal data at multiple spatial scales of aggregation, focusing on citizen-contributed data. We propose a compact visual correlation representation by embedding various statistical measures across different spatial regions to enable users to explore correlations between multiple data categories across different spatial scales. The system provides several scale-sensitive spatial partitioning strategies to examine the sensitivity of correlations at varying spatial extents. To demonstrate the capabilities of our system, we provide several usage scenarios from various domains including citizen-contributed social media (soundscape ecology) data.

关键词： Crowdsourcing

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visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology 11

Visual Detection of Structural Changes in Time-Varying Graph...

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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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