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VEHICLE: Validation and exploration of the Hierarchical Integration of Conflict Event data

VEHICLE: Validation and Exploration of the Hierarchical Inte...

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Eurographics / IEEE VGTC conference on visualizaation

作者： Mayer, B. Lawonn, K. Donnay, K. Preim, B. Meuschke, M. Univ Magdeburg Dept Simulat & Graph Magdeburg Germany Univ Jena Dept Comp Sci Jena Germany Univ Zurich Dept Polit Sci Zurich Switzerland

The exploration of large-scale conflicts, as well as their causes and effects, is an important aspect of socio-political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi-automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user-defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developed VEHICLE, a web-based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map-based depictions as well as advanced interaction techniques. We showed the usefulness of VEHICLE in two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.

关键词： Integration

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Adaptive visualizations for enhanced data understanding and interpretation 21

Adaptive visualizations for enhanced data understanding and ...

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29th ACM conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Amyrotos, Christos UCLan Cyprus Pyla Cyprus

ISBN: (纸本)9781450383660

In a data driven economy where data volume and dimensions are explosively increasing, businesses rely on business intelligence and analytics (BI&A) platforms for analysing their data and coming to beneficial decisions. With the ever-growing generation of data, the process of data analysis is becoming more complicated for the business users, as the exploration of more demanding use cases increases. While the existing BI&A platforms provide myriads of data visualizations that support data exploration, none of those account for the user's individual differences, needs or requirements, and thus may hinder the user's understanding of visual data and consequently their decision-making processes. This work embarks on an interdisciplinary endeavour to introduce a human-centred adaptive data visualizations framework in the context of business, as the core of an adaptive data analytics platform, that aims to enhance the business user's decision making by increasing her understanding of data. The framework is built using a multi-dimensional human-centred user model that goes beyond traditional user characteristics and accounts for cognitive factors, domain expertise and experience and factors related to the business context i.e., data, visualizations and tasks;a data visualization engine that will recommend to the unique-user the best-fit data visualizations based on the abovementioned user model;and an intelligent data analytics component that enhances the efficiency and effectiveness of the data exploration process by leveraging user interactions during the explorations to further inform the user model on the user's expertise and experience. © 2021 Owner/Author.

关键词： data visualization

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Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species

Few-shot Keypoint Detection with Uncertainty Learning for Un...

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

作者： Lu, Changsheng Koniusz, Piotr Australian Natl Univ Canberra ACT Australia CSIRO Data61 Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current non-rigid object keypoint detectors perform well on a chosen kind of species and body parts, and require a large amount of labelled keypoints for training. Moreover, their heatmaps, tailored to specific body parts, cannot recognize novel keypoints (keypoints not labelled for training) on unseen species. We raise an interesting yet challenging question: how to detect both base (annotated for training) and novel keypoints for unseen species given a few annotated samples? Thus, we propose a versatile Few-shot Keypoint Detection (FSKD) pipeline, which can detect a varying number of keypoints of different kinds. Our FSKD provides the uncertainty estimation of predicted keypoints. Specifically, FSKD involves main and auxiliary keypoint representation learning, similarity learning, and keypoint localization with uncertainty modeling to tackle the localization noise. Moreover, we model the uncertainty across groups of keypoints by multivariate Gaussian distribution to exploit implicit correlations between neighboring keypoints. We show the effectiveness of our FSKD on (i) novel keypoint detection for unseen species, (ii) few-shot Fine-Grained visual Recognition (FGVR) and (iii) Semantic Alignment (SA) downstream tasks. For FGVR, detected keypoints improve the classification accuracy. For SA, we showcase a novel thin-plate-spline warping that uses estimated keypoint uncertainty under imperfect keypoint corespondences.

关键词： Training Location awareness Representation learning Computer vision visualization Uncertainty Statistical analysis

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Mondrian Abstraction and Language Model Embeddings for Differential Pathway analysis

Mondrian Abstraction and Language Model Embeddings for Diffe...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Fuad Al Abir Jake Y. Chen Department of Computer Science University of Alabama at Birmingham Birmingham AL USA Systems Pharmacology AI Research Center (SPARC) School of Medicine University of Alabama at Birmingham Birmingham AL USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this study, we introduce the Mondrian Map, an innovative visualization tool inspired by Piet Mondrian’s abstract art, to address the complexities inherent in visualizing biological networks. The Mondrian Map enables clear and meaningful representations of biological pathways by converting intricate biological data into a structured and intuitive format, facilitating a deeper understanding of molecular dynamics. Each pathway is represented by a square whose size corresponds to fold change, with color indicating the direction of regulation (up or down) and statistical significance. The spatial arrangement of pathways is derived from language model embeddings, preserving neighborhood relationships and enabling the identification of clusters of related pathways. Additionally, colored lines highlight potential crosstalk between pathways, with distinctions between short- and long-range functional interactions. In a glioblastoma multiforme (GBM) case study, the Mondrian Map effectively revealed distinct pathway patterns across patient profiles at different stages of disease progression. By surpassing traditional enriched pathway tables and presenting pathways in a relative context without causing visual information overload, this work highlights the tool’s potential to enhance downstream bioinformatics analyses by providing a more comprehensive and visually accessible overview of pathway interactions, offering new avenues for therapeutic exploration and personalized medicine.

关键词： Analytical models visualization Image color analysis Biological system modeling Precision medicine data visualization data models Regulation Bioinformatics Diseases

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Gesturemap: Supporting visual analytics and quantitative analysis of motion elicitation data by learning 2d embeddings 21

Gesturemap: Supporting visual analytics and quantitative ana...

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2021 CHI conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Dang, Hai Buschek, Daniel Research Group Hci + Ai Department of Computer Science University of Bayreuth Bayreuth Germany

ISBN: (纸本)9781450380966

This paper presents GestureMap, a visual analytics tool for gesture elicitation which directly visualises the space of gestures. Concretely, a Variational Autoencoder embeds gestures recorded as 3D skeletons on an interactive 2D map. GestureMap further integrates three computational capabilities to connect exploration to quantitative measures: Leveraging DTW Barycenter Averaging (DBA), we compute average gestures to 1) represent gesture groups at a glance;2) compute a new consensus measure (variance around average gesture);and 3) cluster gestures with *** evaluate GestureMap and its concepts with eight experts and an in-depth analysis of published data. Our findings show how GestureMap facilitates exploring large datasets and helps researchers to gain a visual understanding of elicited gesture spaces. It further opens new directions, such as comparing elicitations across *** discuss implications for elicitation studies and research, and opportunities to extend our approach to additional tasks in gesture elicitation. © 2021 ACM.

关键词： Large dataset

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Multimodal Misinformation Mitigation using Convolutional and Knowledge Transfer Learning Approach

Multimodal Misinformation Mitigation using Convolutional and...

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Sentiment analysis and Deep Learning (ICSADL), International conference on

作者： Nachiket Rathod Prabhakar Ramteke Deaprtment of Computer Science & Engineering H.V.P.M 'S College of Engineering & Technology Amravati Amravati Mahrashtra India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

In the era of information proliferation and digital media, the battle against the spread of misinformation has evolved into a critical challenge. This work presents an inclusive exploration of the pinnacle in misinformation mitigation, employing a multimodal method that integrates textual and visual data. Advanced techniques in knowledge transfer learning are used to develop and evaluate models for Misinformation detection based on textual content, images, and their fusion. DistilFND, a distillation of diverse models specialized in different modalities, is a focal point of proposed research. The paper involves a systematic analysis of training and testing these models on a shared dataset, enabling a comparison of their performance and the provision of insightful evaluations. Cohen's Kappa scores and ROC-AUC scores are also computed to gauge the models' efficacy. This extensive study marks a significant advance in misinformation mitigation, with knowledge transfer learning and multimodal fusion serving as pivotal elements of progress. By means of a sequence of detailed experiments and evaluation metrics, this work enhances to an improved understanding of how knowledge transfer learning, multimodal fusion, and pinnacle models can boost the correctness of Misinformation detection. The results and insights in this work aid the ongoing efforts to combat the detrimental impacts of misinformation in the digital sphere.

关键词： Training visualization Sentiment analysis Systematics Prevention and mitigation Computational modeling Network architecture

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visual analysis of player interactions in soccer games

Visual analysis of player interactions in soccer games

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2021 Workshops of the EDBT/ICDT Joint conference, EDBT/ICDT-WS 2021

作者： Ivkovic-Kihic, Ivona Seebacher, Daniel Stein, Manuel Schreck, Tobias Preiner, Reinhold Graz University of Technology Austria University of Sarajevo Bosnia and Herzegovina University of Konstanz Germany Subsequent GmbH Konstanz Germany

Recently, visualization of sport data in general, and soccer data in particular, has received much research attention. visual sport data exploration helps to understand behavior and performance of athletes and teams, identify possible influence factors, and changes over time, among other important tasks. In soccer match data, much of the play is determined by direct interactions between players spatially close to each other, competing for influence. We introduce a novel visual analytics system for exploring pairwise player interactions using a trajectory-based data representation in a highly interactive multiple view approach. Our notion of player interaction is based on proximity of pairs of players, and respective motion patterns represented as trajectories. Our approach segments player interactions from soccer match data, as the basis for linked analytical views. A matrix view allows to explore interaction frequencies between players, group of player roles, and assess overall game dominance between teams. An appropriately defined interaction glyph allows to compare interactions based on player motion, ball possession, and pitch position. We further investigate the design of a descriptor encoding the geometric configuration of interaction trajectory pairs, enabling common analytical tasks like clustering or searching for similar interactions. We demonstrate the applicability of our approach by use cases on real soccer match data, detailing the analytical tasks supported by our system. © 2021 Copyright for this paper by its author(s).

关键词： Trajectories

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visual Linking of Feature Values in Immersive Graph visualization Environment 21

Visual Linking of Feature Values in Immersive Graph Visualiz...

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14th International Symposium on visual Information Communication and Interaction, VINCI 2021

作者： Sassa, Hinako Cordeil, Maxime Yoshida, Mitsuo Itoh, Takayuki Ochanomizu University Tokyo Japan Monash University Melbourne Australia Toyohashi University of Technology Toyohashi Japan

ISBN: (纸本)9781450386470

There are a variety of graphs where multidimensional feature values are assigned to the nodes. visualization of such datasets is not an easy task since they are complex and often huge. Immersive Analytics is a powerful approach to support the interactive exploration of such large and complex data. Many recent studies on graph visualization have applied immersive analytics frameworks;however, there have been few studies on immersive analytics for visualization of multidimensional attributes associated with the input graphs. This paper presents a new immersive analytics system that supports the interactive exploration of multidimensional feature values assigned to the nodes of input graphs. The presented system displays "label-axes"corresponding to the dimensions of feature values, and "label-edges"that connect label-axes and corresponding to the nodes. The system supports visual linking operations which controls the display of edges that connect a label-axis and nodes of the graph. This paper introduces visualization examples with a graph dataset of Twitter users and reviews by experts on graph data analysis. © 2021 ACM.

关键词： Virtual reality

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Virtual Sample Generation Of Hyperspectral Mineral data

Virtual Sample Generation Of Hyperspectral Mineral Data

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International conference on

作者： Palla Parasuram Yadav Amba Shetty B. S. Raghavendra A. V. Narasimhadhan National Institute of Technology Karnataka Surathkal India

Identification of earth surface features or useful resources even at inaccessible locations for humans made possible with the help of spatial-spectral information provided by remote sensing data especially hyperspectral images. At present deep learning (DL) based models have become a best choice to address the issues and provide better solutions in many fields including remote sensing data analysis. Due to very subtle differences exhibited by mineral signatures and also lack of sufficient training samples, geo-science and remote sensing research community has not much explored the DL techniques in analyzing mineral data. Therefore, in this paper, a virtual sample generation technique using vector rotation is proposed to increase the mineral data for training DL models. The proposed virtual sample generation technique is explored on a spectral library of 5 minerals that is formed from Cuprite scene, a benchmark mineral data set. The quality of the mineral samples generated are assessed using visual inspection as well as a relative spectral discriminating power of target minerals with respect to non-target or remaining minerals. Results show that mineral samples generated by the proposed virtual sample generation technique are not only qualitative in nature but also helpful or encouraging in exploration of DL in mineral data analysis.

关键词： Training Earth visualization data analysis Inspection data models Libraries

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DRIFT: A visual analytic tool for scientific literature exploration based on textual and image content 34

DRIFT: A visual analytic tool for scientific literature expl...

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34th SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI)

作者： Pocco, Ximena Poco, Jorge Viana, Matheus de Paula, Rogerio Nonato, Luis G. Gomez-Nieto, Erick Univ Catolica San Pablo Dept Comp Sci Arequipa Peru Getulio Vargas Fdn Sch Appl Math Rio De Janeiro Brazil IBM Res Sao Paulo Brazil Univ Sao Paulo ICMC Sao Carlos Brazil

ISBN: (纸本)9781665423540

Exploring digital libraries of scientific articles is an essential task for any research community. The typical approach is to query the articles' data based on keywords and manually inspect the resulting list of documents to identify which papers are of interest. Besides being time-consuming, such a manual inspection is quite limited, as it can hardly provide an overview of articles with similar topics or subjects. Moreover, accomplishing queries based on content other than keywords is rarely doable, impairing finding documents with similar images. In this paper, we propose a visual analytic methodology for exploring and analyzing scientific document collections that consider the content of scientific documents, including images. The proposed approach relies on a combination of Content-Based Image Retrieval (CBIR) and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Additionally, we enable visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We show the effectiveness of our methodology through two case studies and a user evaluation, which attest to the usefulness of the proposed framework in exploring scientific document collections.

关键词： visual analytics Semantics Image retrieval Manuals Inspection Libraries Task analysis

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