A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VISis needed. In this paper, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?" This survey reveals seven main processes where the employment of ML techniques can benefit visualizations:Data Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table

Design of an Air-Assisted Mechanical Seed-Metering Device for Millet (Setaria Italica) Based on Experiments and Simulation Analysis

In this study, an air-assisted mechanical seed-metering device for millet (Setariaitalica) was developed. The discrete element method (DEM) and response surface method (RSM) were used to research the influences of the side length, depth, and oblique angle of the shaped hole on the seeding performance (quality, multiples, and miss indices) of the seed-metering device, and the parameters of the shaped hole were optimized. Furthermore, after determining the size of the shaped hole, the influence of negative pressure on the quality index was studied under the condition of the higher rotational speed of the seed-sowing wheel. At the rotational speed of 20 r/min, the optimal values of the side length, depth, and oblique angle of the shaped hole were found to be 3.55 mm, 2.1 mm, and 109°, which resulted in a quality index of 94%. The optimal parameters were consistent with the simulated values and bench test values, with a relative deviation of 5.05%. Moreover, under the condition of a rotational speed of 40 r/min, the application of appropriate negative pressure to the seeds was found to promote seed entry into the shaped hole, thus significantly reducing the miss index and increasing the quality index. At the negative pressure of −90 Pa, the quality index was found to exceed 90%. These results provide a theoretical basis for future studies on a seed-metering device for millet (Setaria italica)

Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline

Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information, i.e., the representation, scale, layout, and orientation of the timeline, and 2) the local information, i.e., the location, category, and pixels of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques, i.e., Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.Comment: 10 pages, Automated Infographic Design, Deep Learning-based Approach, Timeline Infographics, Multi-task Mode

Anomalous phase transition of layered lepidocrocite titania nanosheets to anatase and rutile

In this study, phase transformations from lepidocrocite titania (L-TiO2) nanosheets to rutile (R-TiO2) and anatase (A-TiO2) have been systematically investigated as a function of the preparation conditions, such as pH and freeze-drying, and as a function of the temperature treatment. We have found that the transformation of (L-TiO2) into rutile takes place upon freeze-drying treatment. We report that temperature determined the final phase structure in the transition phase of the L-TiO2 nanosheets into TiO2 nanoparticles, while the pH determined the final morphology and particle size. On the basis of the experimental results, two different transition pathways of dissolution–recrystallization and topologically rolling transition have been proposed. Our results give a full map of phase transition and morphology evolution of L-TiO2 to R-TiO2/A-TiO2 that can provide guideline to new materials design, especially for photocatalysts

HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models

In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple hypotheses. The framework defines a controlled configuration for testing a number of hypotheses as to whether and how some extra information about a "concept" or "feature" may benefit or hinder a ML model. Because reasoning multiple hypotheses is not always straightforward, we provide HypoML as a visual analysis tool, with which, the multi-thread testing data is transformed to a visual representation for rapid observation of the conclusions and the logical flow between the testing data and hypotheses.We have applied HypoML to a number of hypothesized concepts, demonstrating the intuitive and explainable nature of the visual analysis.Comment: This article was submitted to EuroVis 2020 on 5 December 2020. It was not accepted. Because the reviews have not identified any technical problems that would undermine the novelty and validity of this work, we think that the article is ready to be released as an arXiv report. The EuroVis 2020 reviews and authors' short feedback can be found in the anc folde

Visual analysis of discrimination in machine learning

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination