Visual Informatics最新文献

By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters. Such nodes are typically found either at the interface between clusters (the undecided) or at their boundaries (the skeptics). Identifying these nodes is relevant in marketing applications like voter targeting, because the persons represented by such nodes are often more likely to be affected in marketing campaigns than nodes deeply within clusters. So far this identification task is not as well studied as other network analysis tasks like clustering, identifying central nodes, and detecting motifs. We approach this task by deriving novel geometric features from the network structure that naturally lend themselves to an interactive visual approach for identifying interface and boundary nodes.

Abundant tourism user-generated content (UGC) contains a wealth of cognitive and emotional information, providing valuable data for building destination images that depict tourists’ experiences and appraisal of the destinations during the tours. In particular, multiple destination images can assist tourism managers in exploring the commonalities and differences to investigate the elements of interest of tourists and improve the competitiveness of the destinations. However, existing methods usually focus on the image of a single destination, and they are not adequate to analyze and visualize UGC to extract valuable information and knowledge. Therefore, we discuss requirements with tourism experts and present MDIVis, a multi-level interactive visual analytics system that allows analysts to comprehend and analyze the cognitive themes and emotional experiences of multiple destination images for comparison. Specifically, we design a novel sentiment matrix view to summarize multiple destination images and improve two classic views to analyze the time-series pattern and compare the detailed information of images. Finally, we demonstrate the utility of MDIVis through three case studies with domain experts on real-world data, and the usability and effectiveness are confirmed through expert interviews.

Virtual marine scene authoring plays an important role in generating large-scale 3D scenes and it has a wide range of applications in computer animation and simulation. Existing marine scene authoring methods either produce periodic patterns or generate unnatural group distributions when tiling marine entities such as schools of fish and groups of reefs. To this end, we propose a new large-scale marine scene authoring method based on real examples in order to create more natural and realistic results. Our method first extracts the distribution of multiple marine entities from real images to create Octahedral Blocks, and then we use a modified Wang Cubes algorithm to quickly tile the 3D marine scene. As a result, our method is able to generate aperiodic tiling results with diverse distributions of density and orientation of entities. We validate the effectiveness of our method through intensive comparative experiments. User study results show that our method can generate satisfactory results which are in accord with human preferences.

Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or for personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.

Autopsy reports play a pivotal role in forensic science. Medical examiners (MEs) and diagnostic radiologists (DRs) cross-reference autopsy results in the form of autopsy reports, while judicial personnel derive legal documents from final autopsy reports. In our prior study, we presented a visual analysis system called the forensic autopsy system for e-court instruments (FORSETI) with an extended legal medicine markup language (x-LMML) that enables MEs and DRs to author and review e-autopsy reports. In this paper, we present our extended work to incorporate provenance infrastructure with authority management into FORSETI for forensic data accountability, which contains two features. The first is a novel provenance management mechanism that combines the forensic autopsy workflow management system (FAWfMS) and a version control system called lmmlgit for x-LMML files. This management mechanism allows much provenance data on e-autopsy reports and their documented autopsy processes to be individually parsed. The second is provenance-supported immersive analytics, which is intended to ensure that the DRs’ and MEs’ autopsy provenances can be viewed, listed, and analyzed so that a principal ME can author their own report through accountable autopsy referencing in an augmented reality setting. A fictitious case with a synthetic wounded body is used to demonstrate the effectiveness of the provenance-aware FORSETI system in terms of data accountability through the experience of experts in legal medicine.

Visualization literacy, the ability to interpret and comprehend visual designs, is recognized as an essential skill by the visualization community. We identify and investigate barriers to comprehending parallel coordinates plots (PCPs), one of the advanced graphical representations for the display of multivariate and high-dimensional data. We develop a parallel coordinates literacy test with diverse images generated using popular PCP software tools. The test improves PCP literacy and evaluates the user’s literacy skills. We introduce an interactive educational tool that assists the teaching and learning of parallel coordinates by offering a more active learning experience. Using this pedagogical tool, we aim to advance novice users’ parallel coordinates literacy skills. Based on the hypothesis that an interactive tool that links traditional Cartesian Coordinates with PCPs interactively will enhance PCP literacy further than static slides, we compare the learning experience using traditional slides with our novel software tool and investigate the efficiency of the educational software with an online, crowdsourced user-study. User-study results show that our pedagogical tool positively impacts a user’s PCP comprehension.

We present new guidelines for choosing hyperparameters for t-SNE and an evaluation comparing these guidelines to current ones. These guidelines include a proposed empirically optimum guideline derived from a t-SNE hyperparameter grid search over a large collection of data sets. We also introduce a new method to featurize data sets using graph-based metrics called scagnostics; we use these features to train a neural network that predicts optimal t-SNE hyperparameters for the respective data set. This neural network has the potential to simplify the use of t-SNE by removing guesswork about which hyperparameters will produce the best embedding. We evaluate and compare our neural network-derived and empirically optimum hyperparameters to several other t-SNE hyperparameter guidelines from the literature on 68 data sets. The hyperparameters predicted by our neural network yield embeddings with similar accuracy as the best current t-SNE guidelines. Using our empirically optimum hyperparameters is simpler than following previously published guidelines but yields more accurate embeddings, in some cases by a statistically significant margin. We find that the useful ranges for t-SNE hyperparameters are narrower and include smaller values than previously reported in the literature. Importantly, we also quantify the potential for future improvements in this area: using data from a grid search of t-SNE hyperparameters we find that an optimal selection method could improve embedding accuracy by up to two percentage points over the methods examined in this paper.

N-body numerical simulation is an important tool in astronomy. Scientists used this method to simulate the formation of structure of the universe, which is key to understanding how the universe formed. As research on this subject further develops, astronomers require a more precise method that enables expansion of the simulation and an increase in the number of simulation particles. However, retaining all temporal information is infeasible due to a lack of computer storage. In the circumstances, astronomers reserve temporal data at intervals, merging rough and baffling animations of universal evolution. In this study, we propose a deep-learning-assisted interpolation application to analyze the structure formation of the universe. First, we evaluate the feasibility of applying interpolation to generate an animation of the universal evolution through an experiment. Then, we demonstrate the superiority of deep convolutional neural network (DCNN) method by comparing its quality and performance with the actual results together with the results generated by other popular interpolation algorithms. In addition, we present PRSVis, an interactive visual analytics system that supports global volume rendering, local area magnification, and temporal animation generation. PRSVis allows users to visualize a global volume rendering, interactively select one cubic region from the rendering and intelligently produce a time-series animation of the high-resolution region using the deep-learning-assisted method. In summary, we propose an interactive visual system, integrated with the DCNN interpolation method that is validated through experiments, to help scientists easily understand the evolution of the particle region structure.

We present VCNet, a new deep learning approach for volume completion by synthesizing missing subvolumes. Our solution leverages a generative adversarial network (GAN) that learns to complete volumes using the adversarial and volumetric losses. The core design of VCNet features a dilated residual block and long-term connection. During training, VCNet first randomly masks basic subvolumes (e.g., cuboids, slices) from complete volumes and learns to recover them. Moreover, we design a two-stage algorithm for stabilizing and accelerating network optimization. Once trained, VCNet takes an incomplete volume as input and automatically identifies and fills in the missing subvolumes with high quality. We quantitatively and qualitatively test VCNet with volumetric data sets of various characteristics to demonstrate its effectiveness. We also compare VCNet against a diffusion-based solution and two GAN-based solutions.

Classification methods for binary (yes/no) tasks often produce a continuously valued score. Machine learning practitioners must perform model selection, calibration, discretization, performance assessment, tuning, and fairness assessment. Such tasks involve examining classifier results, typically using summary statistics and manual examination of details. In this paper, we provide an interactive visualization approach to support such continuously-valued classifier examination tasks. Our approach addresses the three phases of these tasks: calibration, operating point selection, and examination. We enhance standard views and introduce task-specific views so that they can be integrated into a multi-view coordination (MVC) system. We build on an existing comparison-based approach, extending it to continuous classifiers by treating the continuous values as trinary (positive, unsure, negative) even if the classifier will not ultimately use the 3-way classification. We provide use cases that demonstrate how our approach enables machine learning practitioners to accomplish key tasks.