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21st Eurographics/IEEE VGT conference on visualization, EuroVis 2019

作者： Micallef, L. Schulz, H.-J. Angelini, M. Aupetit, M. Chang, R. Kohlhammer, J. Perer, A. Santucci, G. Aarhus University Denmark Sapienza University of Rome Italy Qatar Computing Research Institute HBKU Qatar Tufts University United States Fraunhofer IGD Darmstadt Germany Carnegie Mellon University United States

ISBN: (纸本)9783038680901

The amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation without waiting for the computation to complete. However, there has been limited consideration on how these techniques impact the user. Based on discussions from a Dagstuhl seminar held in October 2018, this paper characterizes PVA users by their common roles, their main tasks, and their distinct focus of analysis. It further discusses cognitive biases that play a particular role in PVA. This work will help PVA visualization designers in devising systems that are tailored for their specific target users and their characteristics. © 2019 The Author(s)

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SOM Flow: Guided Exploratory Cluster analysis with Self-Organizing Maps and Analytic Provenance

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 120-130页

作者： Sacha, Dominik Kraus, Matthias Bernard, Juergen Behrisch, Michael Schreck, Tobias Asano, Yuki Keim, Daniel A. Univ Konstanz Constance Germany Tech Univ Darmstadt Darmstadt Germany Graz Univ Technol Graz Austria Univ Tubingen Tubingen Germany

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

关键词： visual Analytics Interaction visual Cluster analysis Quality Metrics Guidance Self -Organizing Maps Time Series

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River-water Quality exploration 13

River-water Quality Exploration

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13th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Beham, Michael Splechtna, Rainer Gracanin, Denis Ginina, Elena Matkovic, Kresimir VRVis Res Ctr Vienna Austria Virginia Tech Blacksburg VA USA

ISBN: (纸本)9781538668610

We describe our analysis of VAST Challenge 2018 Mini-Challenge 2 data set using a collection of visualization tools. We used the tools for better user interaction and to introduce new views in support of visual analytics. We answer some of the challenge questions and plan further research on data exploration and analysis based on the newly introduced data model, interaction, and views.

关键词： Human-centered computing visualization visualization Techniques

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Prediction of Weather Radar Images via a Deep LSTM for Nowcasting

Prediction of Weather Radar Images via a Deep LSTM for Nowca...

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International Joint conference on Neural Networks (IJCNN)

作者： Guang Yao Zongxuan Liu Xufeng Guo Chaoshi Wei Xinfeng Li Zhihao Chen Em-Data AI Research Shanghai China China Eastern Region Air Traffic Management Bureau(ECATMB) Shanghai China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Weather radar images provide critical information for mesoscale weather nowcasting which plays significant roles in a range of fields including civil aviation and navigation. Differed from traditional radar exploration methods, this paper presents a novel prediction model based on a deep recurrent neural network (DeepRNN). The approach converts the task of nowcasting to a task of image series prediction. We first design a new loss function that pays more attention to the changes of images in the input sequence. In the mean while, an image discriminator is incorporated into the model to improve the visual quality of predicted images. Furthermore, optical flow is explored to preserve the the motion information. The prediction results are evaluated based on widely used statistic scores. The experimental results show that the proposed model leads to significant improvement in tasks of 2 hours forecasting of radar echo.

关键词： Predictive models Radar imaging Task analysis Optical flow Meteorology

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MTDES: Multi-dimensional Temporal data exploration System;Strong Support for Exploratory analysis Award in VAST 2018, Mini-Challenge 2 13

<i>MTDES</i>: Multi-dimensional Temporal Data Exploration Sy...

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13th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Pham, Vung V. Dang, Tommy Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9781538668610

This work proposes a visual analytic solution which is well-designed to provide investigative functions with fluent interactions to analyze multi-dimensional temporal data. The solution allows users to view different dimensions of the data at different levels of details with a well-designed mixture of different visualizations and smooth interactions. At the general/overview level, various aggregation strategies are used to reduce data to be visualized, and different sorting procedures are used to cluster correlated data together to help discover patterns. Detail views are provided to explore and confirm/reject the identified patterns. Interaction and smooth transition between views are implemented to enable natural actions while performing analysis tasks. This work also presents the result of applying the solution to the VAST 2018 - Mini-Challenge (MC) 2 dataset, which led to the Strong Support for Exploratory analysis award for the challenge.

关键词： Multi-dimensional, temporal data analysis-Heat-map-Line-graph-Interaction Design VAST Challenge 2018-Mini-Challenge 2-Award: Strong Support for Exploratory analysis

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Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

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visual COMPUTER 2018年第9期34卷 1225-1241页

作者： Jentner, Wolfgang Sacha, Dominik Stoffel, Florian Ellis, Geoffrey Zhang, Leishi Keim, Daniel A. Middlesex Univ Data Intens Syst London England Univ Konstanz Univ Str 10 D-78464 Constance Germany Univ Konstanz Data Anal & Visualizat Grp Univ Str 10 D-78464 Constance Germany

A fundamental task in criminal intelligence analysis is to analyze the similarity of crime cases, called comparative case analysis (CCA), to identify common crime patterns and to reason about unsolved crimes. Typically, the data are complex and high dimensional and the use of complex analytical processes would be appropriate. State-of-the-art CCA tools lack flexibility in interactive data exploration and fall short of computational transparency in terms of revealing alternative methods and results. In this paper, we report on the design of the Concept Explorer, a flexible, transparent and interactive CCA system. During this design process, we observed that most criminal analysts are not able to understand the underlying complex technical processes, which decrease the users' trust in the results and hence a reluctance to use the tool. Our CCA solution implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed visual analytics workflow iteratively supports the interpretation of the results of clustering with the respective feature relations, the development of alternative models, as well as cluster verification. The visualizations offer an understandable and usable way for the analyst to provide feedback to the system and to observe the impact of their interactions. Expert feedback confirmed that our user-centered design decisions made this computational complexity less scary to criminal analysts.

关键词： Crime intelligence analysis visual analytics Clustering System design Human-computer interaction Sequential pattern mining Text analysis Dimensionality reduction

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analysis on the performance of VIO according to Trajectory Planning of UAV

Analysis on the performance of VIO according to Trajectory P...

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International conference on Control, Automation and Systems ( ICCAS)

作者： EungChang Mason Lee Hyun Myung School of Electrical Engineering KI-AI KI-R Korea Advanced Institute of Science and Technology Daejeon Korea

ISBN: (数字)9788993215205

ISBN: (纸本)9781728185620

Recently, Unmanned Aerial Vehicles (UAVs) have been widely researched to accomplish tasks such as structural inspection, drone racing, and exploration of an unknown environment. To make the most of UAV and correctly estimate its pose with inexpensive and light-weight sensors such as Inertial Measurement Unit (IMU) and cameras, visual Inertial Odometry (VIO) has been intensively researched. There were a few analyses on the performance of VIO methods where only estimated pose error and utilization of the computing resources were considered. However, the performance of them according to trajectory planning was not treated. Trajectory planning can be one of the main reasons for the drift of localization from VIO since the sensors acquire the data at their sampling rates. In this paper, we analyze the performance of VIO on various trajectories that have different orders of polynomials for the desired position over time and hence different velocity, acceleration, and the first, second derivative of acceleration, using simulator which reflects the real dynamics of UAV. As a result, the higher the order of polynomial for trajectory, the better performance of VIO can be observed.

关键词： Trajectory Acceleration Cameras visualization Trajectory planning State estimation Pose estimation

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25th International conference on MultiMedia Modeling, MMM 2019

25th International Conference on MultiMedia Modeling, MMM 20...

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25th International conference on MultiMedia Modeling, MMM 2019

ISBN: (纸本)9783030057152

The proceedings contain 119 papers. The special focus in this conference is on MultiMedia Modeling. The topics include: From Movement to Events: Improving Soccer Match Annotations;multimodal Video Annotation for Retrieval and Discovery of Newsworthy Video in a News Verification Scenario;integration of exploration and Search: A Case Study of the M 3 Model;face Swapping for Solving Collateral Privacy Issues in Multimedia Analytics;exploring the Impact of Training data Bias on Automatic Generation of Video Captions;fashion Police: Towards Semantic Indexing of Clothing Information in Surveillance data;CNN-Based Non-contact Detection of Food Level in Bottles from RGB Images;personalized Recommendation of Photography Based on Deep Learning;two-Level Attention with Multi-task Learning for Facial Emotion Estimation;SCOD: Dynamical Spatial Constraints for Object Detection;user Interaction for visual Lifelog Retrieval in a Virtual Environment;query-by-Dancing: A Dance Music Retrieval System Based on Body-Motion Similarity;joint visual-Textual Sentiment analysis Based on Cross-Modality Attention Mechanism;deep Hashing with Triplet Labels and Unification Binary Code Selection for Fast Image Retrieval;incremental Training for Face Recognition;Character Prediction in TV Series via a Semantic Projection Network;a Test Collection for Interactive Lifelog Retrieval;SEPHLA: Challenges and Opportunities Within Environment - Personal Health Archives;Athens Urban Soundscape (ATHUS): A dataset for Urban Soundscape Quality Recognition;v3C – A Research Video Collection;STMP: Spatial Temporal Multi-level Proposal Network for Activity Detection;image Aesthetics Assessment Using Fully Convolutional Neural Networks;detecting Tampered Videos with Multimedia Forensics and Deep Learning;improving Robustness of Image Tampering Detection for Compression.

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25th International conference on MultiMedia Modeling, MMM 2019

25th International Conference on MultiMedia Modeling, MMM 20...

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25th International conference on MultiMedia Modeling, MMM 2019

ISBN: (纸本)9783030057091

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A visual Interaction Framework for Dimensionality Reduction Based data exploration

A Visual Interaction Framework for Dimensionality Reduction ...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Cavallo, Marco Demiralp, Cagatay IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9781450356206

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data, We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.

关键词： Dimensionality reduction interaction bidirectional binding visual embedding forward projection backward projection PCA autoencoder deep learning proline feasibility map exploratory data analysis what-if analysis Praxis

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