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International conference on Information visualisation (IV)

作者： Imran Chowdhury Abdul Moeid Enamul Hoque Muhammad Ashad Kabir Md. Sabir Hossain Mohammad Mainul Islam Chittagong University of Engineering & Technology Chittagong Bangladesh York University Toronto Canada Charles Sturt University NSW Australia Verizon Media Sunnyvale CA USA

ISBN: (数字)9781728191348

ISBN: (纸本)9781728191355

Typically, people perform visual data analysis using mouse and touch interactions. While such interactions are often easy to use, they can be inadequate for users to express complex information and may require many steps to complete a task. Recently natural language interaction has emerged as a promising technique for supporting exploration with visualization, as the user can express a complex analytical question more easily. In this paper, we investigate how to synergistically combine language and mouse-based direct manipulations so that weakness of one modality can be complemented by the other. To this end, we have developed a novel system, named Multimodal Interactions System for visual analysis (MIVA), that allows user to provide input using both natural language (e.g., through speech) and direct manipulation (e.g., through mouse or touch) and presents the answer accordingly. To answer the current question in the context of past interactions, the system incorporates previous utterances and direct manipulations made by the user within a finite-state model. We tested the applicability of MIVA on several dashboards including a COVID-19 dashboard that visualizes coronavirus cases around the globe. Our demonstration provides initial indication that the MIVA system enhances the flow of visual analysis by enabling fluid, iterative exploration and refinement of data in a dashboard with multiple-coordinated views.

关键词： COVID-19 visualization Fluids Natural languages data visualization Mice Task analysis

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visual Analytics for Analyzing Technological Trends from Text 23

Visual Analytics for Analyzing Technological Trends from Tex...

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23rd International conference on the Information visualisation (IV) - Incorporating Biomedical visualization, Learning Analytics and Geometric Modelling and Imaging

作者： Nazemi, Kawa Burkhardt, Dirk Darmstadt Univ Appl Sci Human Comp Interact & Visual Analyt Darmstadt Germany

ISBN: (纸本)9781728128382

The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends.

关键词： visual Analytics information visualization trend analytics emerging trend identification visual business analytics

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Vehicle number plate image analysis using GANs & YOLO

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AIP conference Proceedings 2024年第1期3101卷

作者： M. Laxmikanth Mohammed Ahemed Mohiuddin G. Shivaprasad J. Rajkumar D. Maharaj K. Swapna Rani N. V. Ganapathi Raju Meera Sharma Dept. of Computer Science and Engineering KG Reddy College of Engineering Moinabad Telangana India GRIET Bachupally HyderabadTelangana India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun 248007 India

Even though most about current methods considering identifying license plates (LPs) are now much more accurate, they can only be used in ideal situations where training data is accurately annotated among specific scenarios. By changing over Low Resolution (LR) pictures into High-Resolution (HR) pictures, ill-disposed Super-Resolution (SR) strategies are incorporated among single-stage character division & acknowledgment towards improve LP. A start towards finish profound learning system in view about generative adversarial networks (GAN) certain can deliver authenticity in super-goal pictures might be summarized as critical commitment about exploration The SRGAN can handle LPs among small 72 x 72 photos. Study examines SRGAN’s performance across a number about datasets from a variety about perspectives, including optical character recognition (OCR), visual analysis, PSNR, SSIM, & more.

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Point-placement techniques and Temporal Self-similarity Maps for visual analysis of Surveillance Videos 23

Point-placement techniques and Temporal Self-similarity Maps...

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23rd International conference on the Information visualisation (IV) - Incorporating Biomedical visualization, Learning Analytics and Geometric Modelling and Imaging

作者： Mendes, Gilson Paiva, Jose Gustavo S. Schwartz, William Robson Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781728128382

The manual analysis of surveillance videos is unfeasible due to the excessive amount of data, the associated subjectivity, or the eventual presence of distracting noise. Automatic summarization approaches provide little/no user interaction, limiting his/her comprehension regarding the involved phenomena. visual analytics techniques represent a potential tool for such analysis, providing video representations that clearly communicate their content, potentially revealing patterns that may represent events of interest. In this paper, we present a methodology for visual analysis of surveillance video that combines point-placement techniques and Temporal Self-similarity Maps (TSSMs) to reveal the events occurrence structure and to enhance the comprehension of their temporal properties. Experiments in several surveillance scenarios demonstrate that our proposed methodology provides an effective events summarization and the exploration of both the structure of each event and the relationship among them, allowing the security agent to filter/explore those that represent potential alert situations.

关键词： Surveillance video Information visualization Multidimensional visualization Events Detection

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Software Engineering Projects analysis using Interactive Multimodal Graph Explorer - IMiGEr 10

Software Engineering Projects Analysis using Interactive Mul...

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14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 10th International conference on Information visualization Theory and Applications (IVAPP)

作者： Holy, Lukas Picha, Petr Lipka, Richard Brada, Premek Univ West Bohemia NTIS New Technol Informat Soc European Ctr Excellence Fac Appl Sci Univ 8 Plzen Czech Republic

ISBN: (纸本)9789897583544

This paper describes a visualization technique designed to help work with complex diagrams containing multiple types of nodes and edges, by using a combination of visual clutter reduction and graph exploration techniques. We show its application, including preliminary evaluation, on software engineering projects data gathered from various tools and repositories used for software development. An online tool implementing the technique and plans for its extension by a connected view of time perspective data are briefly presented.

关键词： Graph visualization visual Clutter Large Graphs Software Engineering

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visual analytics for exploring air quality data in an AI-enhanced IoT environment 19

Visual analytics for exploring air quality data in an AI-enh...

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Proceedings of the 11th International conference on Management of Digital EcoSystems

作者： Ilias Kalamaras Ioannis Xygonakis Konstantinos Glykos Sigmund Akselsen Arne Munch-Ellingsen Hai Thanh Nguyen Andreas Jacobsen Lepperod Kerstin Bach Konstantinos Votis Dimitrios Tzovaras Information Technologies Institute - Centre for Research and Technology Hellas Telenor Group Telenor Group & Norwegian University of Science and Technology Norwegian University of Science and Technology

ISBN: (纸本)9781450362382

visual analytics have an important role in the exploration and analysis of large amounts of data in IoT applications. data visualizations can provide overviews of different aspects of data and user interaction can assist exploration. Recent advances in machine learning and Artificial Intelligence have provided methods that can be used in conjunction with visual analytics to enhance user perception. However, AI methods are often used as "black boxes", making them difficult for end-users to trust. In this paper, a novel visual analytics platform is presented, targeting two goals: a) an architecture for the creation of custom interactive visual analytics dashboards using well-defined components linked to each other, and b) the inclusion of components specifically for making AI methods more explainable. The proposed architecture and components are being used in the context of the AI4IoT pilot within the AI4EU project, which targets air quality monitoring through AI and visualization.

关键词： visual analytics reactive workflows IoT explainable AI

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Optimizing Exploratory Workflows for Embedded Platform Trace analysis and Its Application to Mobile Devices 21st

Optimizing Exploratory Workflows for Embedded Platform Trace...

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21st International conference on Human-Computer Interaction (HCII)

作者： Sue, Jonathan Ah Brand, Peter Falk, Joachim Hasholzner, Ralph Teich, Juergen Intel Deutschland GmbH Munich Germany Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (纸本)9783030300333;9783030300326

As 5G wireless communication technology is currently deployed, an increasing amount of data is available from mobile devices out in the field. Exploiting this data, also called system traces, recent investigations show the potential to improve the wireless modem design and performance using datacentric approaches. Such data-centric workflows are exploratory and iterative by nature. For instance, time pattern identification is performed by domain experts to derive assumptions on potential optimizations and these assumptions are continuously refined during multiple iterations of data collection, visualization and exploration. In this context, we propose three optimizations to increase the exploration speed in iterative data-centric workflows. First, we present a methodology based on persistent memoization in order to minimize the data processing duration when additional event sequences need to be extracted from a trace. We show that up to 84.5% of the event extraction time can be spared for a typical modem trace data set. Secondly, we present a novel entropy-based data interaction technique for visual exploration of event sequences and finally, a similarity measure to perform subsequence matching in order to assist the user when identifying frequent time patterns in a trace.

关键词： Exploratory workflow Iterative feature extraction Workflow optimization Time-oriented data visualization visual interaction Zoom plus Slant Event sequence similarity measure Categorical event sequence

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Modelling of SDG's Achievement in East Java Using Bi-responses Nonparametric Regression with Mixed Estimator Spline Truncated and Kernel 15

Modelling of SDG's Achievement in East Java Using Bi-respons...

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15th International Symposium on Geometric Function Theory and Applications, GFTA 2019

作者： Dewanti, Paramita Nyoman Budiantara, I. Rumiati, Agnes Tuti Department of Statistics Sepuluh Nopember Institute of Technology Surabaya Indonesia

The Government of Indonesia has grouped 17 objectives of SDG's into 4 pillars, among others: social pillars, economic pillars, the pillars of environmental development and the pillars of legal development and Governance. Some strategic indicators are used to know the achievement of SDG'S especially achievement in social and economic pillars such as the Human Development Index (HDI) and Gini Ratio. There are several variables affecting then. A method used to model the effects of its variables is regression analysis. The results of the initial exploration through the scatter plot shows a pattern that changes and disconnects at a certain point for a variable IPM with a variable that is allegedly influencing it. With a scatter plot, it can be found that between the Gini ratio variables with some of its predictive variables, does not have a specific pattern. Bi-responses nonparametric regression is an analysis that appropriates with that case. Bi-responses nonparametric regression analysis is a type of nonparametric regression analysis used when the pattern between two response variables and some of the predictor variables is unknown or if there is no prior information regarding the pattern of them. Most of researchers use bi-responses nonparametric regression approached by spline truncated, kernel and etc. Most of the research, use kernel and spline truncated because of their advantages such as: have a good capability to develop model from unknown pattern data and have a good visual representation. The first aim of this paper is obtaining the estimator of mixed estimator spline truncated and kernel in bi-responses nonparametric regression with weighted least square. The second aim is gaining a best model to explain the relationship indicators of SDG's achievement with some variables affecting them. To obtain the best model, it is necessary to select the optimal of knots point and bandwidth parameters using a smallest Generalized Cross Validation (GCV). © Published under

关键词： Regression analysis

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ConceptExplorer: visual analysis of Concept Drifts in Multi-source Time-series data

ConceptExplorer: Visual Analysis of Concept Drifts in Multi-...

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IEEE Symposium on visual Analytics Science and Technology (VAST)

作者： Xumeng Wang Wei Chen Jiazhi Xia Zexian Chen Dongshi Xu Xiangyang Wu Mingliang Xu Tobias Schreck State Key Lab of CAD&CG Zhejiang University School of Computer Science and Engineering Central South University College of Information Engineering China Jiliang University Institute of Graphics and Image Hangzhou Dianzi University Zhengzhou University Graz University of Technology

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

关键词： Predictive models data visualization data models Adaptation models Indexes Analytical models visual analytics

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Avoiding Drill-down Fallacies with VisPilot: Assisted exploration of data Subsets 24

Avoiding Drill-down Fallacies with VisPilot: Assisted Explor...

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24th ACM International conference on Intelligent User Interfaces (IUI)

作者： Lee, Doris Jung-Lin Dev, Himel Hu, Huizi Elmeleegy, Hazem Parameswaran, Aditya Univ Illinois Urbana IL 61801 USA Google Inc Redwood City CA USA

ISBN: (纸本)9781450362726

As datasets continue to growin size and complexity, exploring multidimensional datasets remain challenging for analysts. A common operation during this exploration is drill-down-understanding the behavior of data subsets by progressively adding filters. While widely used, in the absence of careful attention towards confounding factors, drill-downs could lead to inductive fallacies. Specifically, an analyst may end up being "deceived" into thinking that a deviation in trend is attributable to a local change, when in fact it is a more general phenomenon;we term this the drill-down fallacy. One way to avoid falling prey to drill-down fallacies is to exhaustively explore all potential drill-down paths, which quickly becomes infeasible on complex datasets with many attributes. We present VisPilot, an accelerated visual data exploration tool that guides analysts through the key insights in a dataset, while avoiding drill-down fallacies. Our user study results show that VisPilot helps analysts discover interesting visualizations, understand attribute importance, and predict unseen visualizations better than other multidimensional data analysis baselines.

关键词： exploratory data analysis visualization recommendation drill-down data analysis

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