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Exploring the Design of visualizations of Personal Online data Based on Users' Mental Models 27

Exploring the Design of Visualizations of Personal Online Da...

27th International Conference on Information Visualisation (IV) / 19th International Conference Computer Graphics, Imaging and visualization (CGiV)

作者： Dutz, Marija Starcevic, Natasa Reynolds, Steven Lamarr Kohlhammer, Joern Fraunhofer IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350341614

As data becomes more and more pervasive in our daily lives, supporting people in getting visual insights into their data is an important challenge to address. However, as data visualization literacy is still low, a gap between designers' mental model and users is not uncommon. To best pick up users where they are, we propose to include the data mental models of users into the design process. In this paper, we present our investigations in this direction by incorporating user sketches of their idea about their personal data stored at online services as a basis of our designs. We present our design study on personal data visualization interfaces resulting in a set of user sketches for three types of user groups and in three visualization interfaces. Finally, we reflect on our learnings and identify pitfalls to support other researchers in applying similar approaches.

关键词： information visualization privacy design study

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Web based-BigSync: A Grid Analytics Platform including Power System database Management 10

Web based-BigSync: A Grid Analytics Platform including Power...

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10th ieee International Conference on Power Systems, ICPS 2023

作者： Thoufeer, K.K. Kumar Jena, Manas Indian Institute of Technology Palakkad Department of Electrical Engineering Palakkad India

ISBN: (纸本)9798350318739

This paper discusses an offline engineering analytical software named BigSync (Big data Analytics for Synchrophasors) developed at the Indian Institute of Technology, Palakkad. The software solution enables the user to perform offline engineering analysis by leveraging various power system measurements that are stored in a relational database (RDBMS). The software package includes applications such as data visualization, statistical analysis, data de-noising and anomaly detection/cyberattack detection. This paper details the applicability of the developed software product in the energy management system (EMS) or control room of a modern power system. © 2023 ieee.

关键词： data visualization

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POSTS with NO RESPONSE: THE ISLAND of LONELINESS 11

POSTS with NO RESPONSE: THE ISLAND of LONELINESS

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11th ieee VIS Arts Program Conference (VISAP)

作者： Tang, Junxiu Sheng, Rui Wang, Yifang Shu, Xinhuan Chen, Xiaojiao Tang, Tan Wu, Yingcai Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Zhejiang Univ Sch Art & Archaeol Hangzhou Peoples R China Northwestern Univ Kellogg Sch Management Evanston IL USA

ISBN: (纸本)9798350330229

Loneliness and isolation are eternal emotions in human beings. Technological advancements create ample avenues, like social medias, for individuals to articulate themselves and record emotions. However, the sense of loneliness has never vanished, as their expressions are easily buried in the digital stream. We analyze tweets that express loneliness during holiday seasons but receive few responses. By superimposing digital charts on physical models, we visualize these lonely posts and generate the island of loneliness. We aim to reveal the complexities of human emotions in the digital age and reflect on the interconnections between technology, solitude, and social communication.

关键词： Social Media Loneliness Physical visualization data visualization data Art

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CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical data

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 756-766页

作者： Zhang, Wei Wong, Jason K. Wang, Xumeng Gong, Youcheng Zhu, Rongchen Liu, Kai Yan, Zihan Tan, Siwei Qu, Huamin Chen, Siming Chen, Wei State Key Lab of CAD&CG Zhejiang University China Laboratory of Art and Archaeology Image Zhejiang University Ministry of Education China Hong Kong University of Science and Technology Hong Kong TMCC CS Nankai University China Fudan University China

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation. © 2022 ieee.

关键词： visualization

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Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks 10

Towards Explaining Satellite Based Poverty Predictions with ...

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10th ieee International Conference on data science and Advanced Analytics, DSAA 2023

作者： Sarmadi, Hamid Rognvaldsson, Thorsteinn Carlsson, Nils Roger Ohlsson, Mattias Wahab, Ibrahim Hall, Ola Sweden Lund University Centre for Environmental and Climate Science Sweden Lund University Department of Human Geography Sweden

ISBN: (纸本)9798350345032

Deep convolutional neural networks (CNNs) have been shown to predict poverty and development indicators from satellite images with surprising accuracy. This paper presents a first attempt at analyzing the CNNs responses in detail and explaining the basis for the predictions. The CNN model, while trained on relatively low resolution day- and night-time satellite images, is able to outperform human subjects who look at high-resolution images in ranking the Wealth Index categories. Multiple explainability experiments performed on the model indicate the importance of the sizes of the objects, pixel colors in the image, and provide a visualization of the importance of different structures in input images. A visualization is also provided of type images that maximize the network prediction of Wealth Index, which provides clues on what the CNN prediction is based on. © 2023 ieee.

关键词： Forecasting

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An NN Framework for Target Number Detection in FMCW Radar Systems via Hankelization: A Low-Dimensional data Representation Perspective

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ieee ACCESS 2025年 13卷 58823-58834页

作者： Kim, Jung-Hwan Kim, Gyung-Eun Lee, Woong-Hee Dongguk Univ Seoul Div Elect & Elect Engn Seoul 04620 South Korea

In this paper, we introduce a novel neural network (NN)-based algorithm that significantly improves the target number detection in frequency modulated continuous wave (FMCW) radar systems. By integrating the mathematical processes of Hankelization and singular value extraction, we can perform input data manipulation for effective target number detection, resulting in constructing an efficient NN framework. This is based on the following mathematical properties: 1) A sequence obtained by uniform sampling of the superposition of K radio waves can be represented as a superposition of K geometric sequences;2) A Hankelized matrix formed by the superposition of K geometric sequences exhibits low-rank characteristics;and 3) In an FMCW radar system with K targets, if the received signal, which is represented as a matrix, is ideal, the vectors obtained by extracting this matrix in row, column, diagonal, and anti-diagonal patterns can all be modeled as a superposition of K geometric sequences. The proposed NN framework showcases remarkable improvements in accuracy and efficiency for target number detection, leveraging a small sized dataset and a compact NN design to achieve unprecedented performance levels. Numerical results validate the superiority of our method across various scenarios, establishing a new benchmark for low-dimensional data representation in radar systems.

关键词： Radar Estimation Accuracy Heuristic algorithms Radar detection Radar antennas Artificial neural networks Vectors Signal processing algorithms data visualization Hankelization singular value extraction neural networks radar signal processing

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visualization and Labeling of Terrestrial LiDAR data for Three-Dimensional Fuel Classification 19

Visualization and Labeling of Terrestrial LiDAR Data for Thr...

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19th ieee International Conference on e-science, e-science 2023

作者： Moreno, Ivannia Gomez Nealey, Isaac Roten, Daniel Nguyen, Mai H. Crawl, Daniel O'Laughlin, Kate Floca, Melissa Pokswinski, Scott Altintas, Ilkay Cetys University Campus Tijuana BC Tijuana Mexico University of California San Diego La Jolla CA United States New Mexico Consortium Santa FeNM United States

ISBN: (纸本)9798350322231

Wildland fire modeling tools can ingest high resolution 3D vegetation models as inputs. However, data used to build the surface fuels in these models is often at a 30-meter resolution, which does not necessarily provide sufficient detail for accurate modeling of fires. Terrestrial laser scans are increasingly being used to collect detailed vegetation data that could be integrated with new approaches to fuel and fire modeling, but manual segmentation of scans is not scalable beyond a small number of scans. There is a need to automatically segment these high resolution point clouds as they are collected in the field, such that they may be leveraged by fuel and fire models for wildland fire response and mitigation and other applied climate science. This paper summarizes our early work on a labeling, visualization and machine learning pipeline for detailed segmentation of fuels. Specific contributions are: (1) a labeling approach involving 3 dimensional segmentation of point clouds using a point cloud processing engine;(2) a visualization approach using a computer graphics engine;and (3) early results from a deep learning modeling approach for fuel segmentation by category (live and dead) and size class (1, 10, 100 and 1000 hour fuels). © 2023 ieee.

关键词： visualization

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Edge Devices Clustering for Federated Visual Classification: A Feature Norm Based Framework

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ieee TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 995-1010页

作者： Wei, Xiao-Xiang Huang, Hua Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

Federated learning is a privacy-preserving distributed learning paradigm where multiple devices collaboratively train a model, which is applicable to edge computing environments. However, the non-IID data distributed in multiple devices degrades the performance of the federated model due to severe weight divergence. This paper presents a clustered federated learning framework named cFedFN for visual classification tasks in order to reduce the degradation. Especially, this framework introduces the computation of feature norm vectors in the local training process and divides the devices into multiple groups by the similarities of the data distributions to reduce the weight divergences for better performance. As a result, this framework gains better performance on non-IID data without leakage of the private raw data. Experiments on various visual classification datasets demonstrate the superiority of this framework over the state-of-the-art clustered federated learning frameworks.

关键词： data models Feature extraction Computational modeling visualization Training Federated learning Adaptation models edge computing visual classification feature norm clients clustering

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UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第2期29卷 1559-1572页

作者： Espadoto, Mateus Appleby, Gabriel Suh, Ashley Cashman, Dylan Li, Mingwei Scheidegger, Carlos Anderson, Erik W. W. Chang, Remco Telea, Alexandru C. C. Univ Sao Paulo BR-05508070 Sao Paulo Brazil Tufts Univ Medford MA 02155 USA Novartis CH-4056 Basel Switzerland Univ Arizona Tucson AZ 85721 USA Univ Utrecht NL-3584 CS Utrecht Netherlands

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection - the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this article we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

关键词： Multidimensional data multidimensional projection inverse-projection back-projection

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Automatic Scatterplot Design Optimization for Clustering Identification

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第10期29卷 4312-4327页

作者： Quadri, Ghulam Jilani Nieves, Jennifer Adorno Wiernik, Brenton M. Rosen, Paul Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Univ S Florida Dept Psychol Tampa FL 33620 USA Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality for low-level tasks like clustering. Hence, constructing frameworks that consider both the perceptions of the visual encodings and the task being performed enables optimizing visualizations to maximize efficacy. In this article, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image. We validate our approach with user and case studies that show it efficiently provides high-quality scatterplot designs from a large parameter space.

关键词： Scatterplot overdraw clustering design optimization perception topological data analysis

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