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International conference on visualisation, VIS

作者： Benoît Colange Laurent Vuillon Sylvain Lespinats Denys Dutykh INES Univ. Grenoble Alpes Le Bourget du Lac France CNRS Univ. Grenoble Alpes Univ. Savoie Mont Blanc Chambéry France

ISBN: (纸本)9781728149424

To perform visual data exploration, many dimensionality reduction methods have been developed. These tools allow data analysts to represent multidimensional data in a 2D or 3D space, while preserving as much relevant information as possible. Yet, they cannot preserve all structures simultaneously and they induce some unavoidable distortions. Hence, many criteria have been introduced to evaluate a map's overall quality, mostly based on the preservation of neighbourhoods. Such global indicators are currently used to compare several maps, which helps to choose the most appropriate mapping method and its hyperparameters. However, those aggregated indicators tend to hide the local repartition of distortions. Thereby, they need to be supplemented by local evaluation to ensure correct interpretation of *** this paper, we describe a new method, called MING, for "Map Interpretation using Neighbourhood Graphs". It offers a graphical interpretation of pairs of map quality indicators, as well as local evaluation of the distortions. This is done by displaying on the map the nearest neighbours graphs computed in the data space and in the embedding. Shared and unshared edges exhibit reliable and unreliable neighbourhood information conveyed by the mapping. By this mean, analysts may determine whether proximity (or remoteness) of points on the map faithfully represents similarity (or dissimilarity) of original data, within the meaning of a chosen map quality criteria. We apply this approach to two pairs of widespread indicators: precision/recall and trustworthiness/continuity, chosen for their wide use in the community, which will allow an easy handling by users.

关键词： Distortion Reliability Image color analysis data visualization visualization Dimensionality reduction Three-dimensional displays

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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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Subspace clustering algorithms for mineral mapping 24

Subspace clustering algorithms for mineral mapping

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conference on Image and Signal Processing for Remote Sensing XXIV

作者： Khodadadzadeh, Mahdi Contreras, Cecilia Tusa, Laura Gloaguen, Richard Helmholtz Inst Freiberg Resource Technol Helmholtz Zentrum Dresden Rossendorf Freiburg Germany

ISBN: (纸本)9781510621626

The application of drill core hyperspectral data in exploration campaigns is receiving great interest to obtain a general overview of a mineral deposit. However, the main approach to the investigation of such data is by visual interpretation, which is subjective and time consuming. To address this issue, recently, the use of machine learning techniques is proposed for the analysis of this data. For drill core samples that for which only very little prior knowledge is often available, applying classification algorithms which are supervised learning methods is very challenging. In this paper, we suggest to use clustering (unsupervised) methods for mineral mapping, which are similar to classification but no predefined class labels are needed. To handle mapping of the very highly mixed pixels in drill core hyperspectral data, we propose to use advance subspace clustering methods, in which pixels are assumed to lie in a union of low-dimensional subspaces. We conduct a comparative study and evaluate the performance of two well-known subspace clustering methods, i.e., sparse subspace clustering (SSC) and low rank representation (LRR). For the experiments, we acquired VNIR-SWIR hyperspectral data and applied scanning electron microscopy based Mineral Liberation analysis (MLA) for two drill core samples. MLA is a high resolution imaging technique that allows detailed mineral charactrisition. We use the high-resolution MLA image as a reference to analyse the clustering results. Qualitative analysis of the obtained clustering maps indicate that the subspace clustering methods can accurately map the available minerals in the drill core hyperspectral data, especially in comparison to the traditional k-means clustering method.

关键词： Mineral mapping drill core hyperspectral data subspace clustering remote sensing

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ISS 2018 - Companion Proceedings of the 2018 ACM International conference on Interactive Surfaces and Spaces

ISS 2018 - Companion Proceedings of the 2018 ACM Internation...

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13th ACM International conference on Interactive Surfaces and Spaces, ISS 2018

ISBN: (纸本)9781450360104

The proceedings contain 12 papers. The topics discussed include: interweaving physical artifacts with visualization on digital media in museums;investigating the impact of data transfer user interfaces on communication grounding in cross- device environments;somewhat strange feeling of touching, lifting, and swinging in mixed-reality space - psychophysical analysis of haptic illusion caused by visual superimposition;design and implementation of the augmented volleyball court;aerial 3D imaging by retroreflective mirror array;metrics-based evaluation of graphical user interface aesthetics: the segmentation problem;design preferred aesthetic user interface with eye movement and electroencephalography data;encourage self-exploration through an interactive Chinese scroll painting design;and an entropy-based approach for computing the aesthetics of interfaces.

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Anomaly Detection and Target Prioritization in Planetary Imagery via the Automated Global Feature Analyzer™ (AGFA™): Progress Towards a Driver for Autonomous C⁴ISR Missions

Anomaly Detection and Target Prioritization in Planetary Ima...

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conference on Micro- and Nanotechnology Sensors, Systems, and Applications X

作者： Fink, Wolfgang Brooks, Alexander J. -W. Tarbell, Mark A. Univ Arizona Coll Engn Visual & Autonomous Explorat Syst Res Lab 1230 E Speedway Blvd Tucson AZ 85721 USA

ISBN: (数字)9781510617902

ISBN: (纸本)9781510617902

The Automated Global Feature Analyzer (TM) (AGFA (TM)) is a generically applicable automated sensor-data-fusion, feature extraction, feature vector clustering, anomaly detection, and target prioritization framework. AGFA (TM) operates in the respective feature space delivered by the sensor(s). In this paper we provide an overview of the inner workings of AGFA (TM) and apply AGFA (TM) to planetary imagery, representative of past, current, and future planetary missions, to demonstrate its automated and objective (i.e., unbiased) anomaly detection and target prioritization (i.e., region-of interest delineation) capabilities. Imaged operational areas are locally processed via a cascade of image segmentation, visual and geometric feature extraction, agglomerative clustering, and principal components analysis. Resulting clusters are labeled based on relative size and location in feature space. Anomalous regions may be considered immediate targets for follow-up in-situ investigation by local robotic agents, which can be directed via autonomous telecommanding, e.g., as part of a Tier-Scalable Reconnaissance mission architecture. These capabilities will be essential for driving fully autonomous (CISR)-I-4 missions of the future, since the speed of light prohibits "real time" Earth-controlled conduct of planetary exploration beyond the Moon.

关键词： Autonomous decision making sensor-data-fusion framework objective anomaly detection target prioritization agglomerative clustering principal components analysis autonomous (CISR)-I-4 systems multi-tiered robotic exploration architectures

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GPU-assisted scatterplots for millions of call events 36

GPU-assisted scatterplots for millions of call events

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36th Annual conference on Computer Graphics and visual Computing, CGVC 2018

作者： Rees, Dylan Roberts, Richard C. Laramee, Robert S. Brookes, Paul D’Cruze, Tony Smith, Gary A. Swansea University United Kingdom QPC Ltd United Kingdom

ISBN: (纸本)9783038680710

With four percent of the working population employed in call centers in both the United States and the UK, the contact center industry represents a sizable proportion of modern industrial landscapes. As with most modern industries, data collection is de rigueur, producing gigabytes of call records that require analysis. The scatterplot is a well established and understood form of data visualization dating back to the 17th century. In this paper we present an application for visualizing large call centre data sets using hardware-accelerated scatterplots. The application utilizes a commodity graphics card to enable visualization of a month’s worth of data, enabling fast filtering of multiple attributes. Filtering is implemented using the Open Computing Language (OpenCL), providing significant performance improvement over traditional methods. We demonstrate the value of our application for exploration and analysis of millions of call events from a real-world industry partner. Domain expert feedback from our industrial partners is reported. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： data visualization

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Evolutionary interactive analysis of MRI Gastric images using a multiobjective cooperative-coevolution scheme 36

Evolutionary interactive analysis of MRI Gastric images usin...

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36th Annual conference on Computer Graphics and visual Computing, CGVC 2018

作者： Al-Maliki, S.F. Lutton, É. Boué, F. Vidal, F.P. Bangor University United Kingdom Basrh University Iraq INRA-AgroParisTech France LLB CEA CNRS France

ISBN: (纸本)9783038680710

In this study, we combine computer vision and visualisation/data exploration to analyse magnetic resonance imaging (MRI) data and detect garden peas inside the stomach. It is a preliminary objective of a larger project that aims to understand the kinetics of gastric emptying. We propose to perform the image analysis task as a multi-objective optimisation. A set of 7 equally important objectives are proposed to characterise peas. We rely on a cooperation co-evolution algorithm called ‘Fly Algorithm’ implemented using NSGA-II. The Fly Algorithm is a specific case of the ‘Parisian Approach’ where the solution of an optimisation problem is represented as a set of individuals (e.g. the whole population) instead of a single individual (the best one) as in typical evolutionary algorithms (EAs). NSGA-II is a popular EA used to solve multi-objective optimisation problems. The output of the optimisation is a succession of datasets that progressively approximate the Pareto front, which needs to be understood and explored by the end-user. Using interactive Information visualisation (InfoVis) and clustering techniques, peas are then semi-automatically segmented. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： Multiobjective optimization

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Association analysis for visual exploration of Multivariate Scientific data Sets

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2016年第1期22卷 955-964页

作者： Liu, Xiaotong Shen, Han-Wei Ohio State Univ Columbus OH 43210 USA

The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets. as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the data's multi-faceted properties. In this paper, we present a novel association analysis method that guides visual exploration of scalar-level associations in the multivariate context. We model the directional interactions between scalars of different variables as information flows based on association rules. We introduce the concepts of informativeness and uniqueness to describe how information flows between scalars of different variables and how they are associated with each other in the multivariate domain. Based on scalar-level associations represented by a probabilistic association graph, we propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to evaluate the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with confident associations in the multivariate spatial domain, and provide guidelines for visual exploration using our framework. We demonstrate the effectiveness and usefulness of our approach through case studies using three representative multivariate scientific data sets.

关键词： Multivariate data association analysis visual exploration multiple views

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Interactive exploration of musical space with parametric t-SNE 15

Interactive exploration of musical space with parametric t-S...

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15th Sound and Music Computing conference, SMC 2018

作者： Lionello, Matteo Pietrogrande, Luca Purwins, Hendrik Abou-Zleikha, Mohamed Audio Analysis Lab Aalborg University Copenhagen Denmark University of Padova Italy Moodagent Denmark Entrepreneur First United Kingdom

ISBN: (纸本)9789963697304

This paper presents a user interface for the exploration of music libraries based on parametric t-SNE. Each song in the music library is represented as a stack of 34-dimensional vectors containing features related to genres, emotions and other musical characteristics. Parametric t-SNE is used to construct a model that extracts a pair of coordinates from these features for each song, preserving similarity relations between songs in the high dimensional-feature space and their projection in a two-dimensional space. The twodimensional output of the model will be used for projecting and rendering a song catalogue onto a plane. We have investigated different models, which have been obtained by using different structures of hidden layers, pre-training technique, features selection, and data pre-processing. These results are an extension of a previous project published by Moodagent Company, which show that the clustering adaptation of genres and emotions, that is obtained by using parametric t-SNE, is by far more accurate than the previous methods based on PCA. Finally, our study proposes a visual representation of the resulting model. The model has been used to build a music-space of 20000 songs, visually rendered for browser interaction. This provides the user with a certain degree of freedom to explore the music-space by changing the highlighted features and it offers an immersive experience for music exploration and playlist generation. © Proceedings of the 15th Sound and Music Computing conference: Sonic Crossings, SMC 2018. All rights reserved.

关键词： Music

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AFEL - Analytics for Everyday Learning 18

AFEL - Analytics for Everyday Learning

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27th World Wide Web (WWW) conference

作者： d'Aquin, Mathieu Kowald, Dominik Fessl, Angela Lex, Elisabeth Thalmann, Stefan NUI Galway Insight Ctr Data Analyt Galway Ireland Know Ctr GmbH Graz Graz Austria

ISBN: (纸本)9781450356404

The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments;2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration;and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported;4) evaluate all the former items in real world large scale applications and platforms.

关键词： AFEL Learning Analytics Everyday Learning H2020

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