Going beyond Visualization. Verbalization as Complementary Medium to Explain Machine Learning Models

In this position paper, we argue that a combination of visualization and verbalization techniques is beneficial for creating broad and versatile insights into the structure and decision-making processes of machine learning models. Explainability of machine learning models is emerging as an important area of research. Hence, insights into the inner workings of a trained model allow users and analysts, alike, to understand the models, develop justifications, and gain trust in the systems they inform. Explanations can be generated through different types of media, such as visualization and verbalization. Both are powerful tools that enable model interpretability. However, while their combination is arguably more powerful than each medium separately, they are currently applied and researched independently. To support our position that the combination of the two techniques is beneficial to explain machine learning models, we describe the design space of such a combination and discuss arising research questions, gaps, and opportunities

Exploranative Code Quality Documents

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.Comment: IEEE VIS VAST 201

Visualizing Information on Smartwatch Faces: A Review and Design Space

We present a systematic review and design space for visualizations on smartwatches and the context in which these visualizations are displayed--smartwatch faces. A smartwatch face is the main smartwatch screen that wearers see when checking the time. Smartwatch faces are small data dashboards that present a variety of data to wearers in a compact form. Yet, the usage context and form factor of smartwatch faces pose unique design challenges for visualization. In this paper, we present an in-depth review and analysis of visualization designs for popular premium smartwatch faces based on their design styles, amount and types of data, as well as visualization styles and encodings they included. From our analysis we derive a design space to provide an overview of the important considerations for new data displays for smartwatch faces and other small displays. Our design space can also serve as inspiration for design choices and grounding of empirical work on smartwatch visualization design. We end with a research agenda that points to open opportunities in this nascent research direction. Supplementary material is available at: https://osf.io/nwy2r/.Comment: 13 pages, appendi

EmphasisChecker: A Tool for Guiding Chart and Caption Emphasis

Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., news media, poll reports, government reports, academic articles, and Tableau Public), we find that captions often do not emphasize the same information in practice, which could limit how effectively readers take away the authors' intended messages. Motivated by the survey findings, we present EmphasisChecker, an interactive tool that highlights visually prominent chart features as well as the features emphasized by the caption text along with any mismatches in the emphasis. The tool implements a time-series prominent feature detector based on the Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies time references and data descriptions in the caption and matches them with chart data. This information enables authors to compare features emphasized by these two modalities, quickly see mismatches, and make necessary revisions. A user study confirms that our tool is both useful and easy to use when authoring charts and captions.Comment: IEEE VIS 202

Textension: Digitally Augmenting Document Spaces in Analog Texts

In this paper, we present a system that automatically adds visualizations and natural language processing applications to analog texts, using any web-based device with a camera. After taking a picture of a particular page or set of pages from a book or uploading an existing image, our system builds an interactive digital object that automatically inserts modular elements in a digital space. Leveraging the findings of previous studies, our framework augments the reading of analog texts with digital tools, making it possible to work with texts in both a digital and analog environment

Visual Analysis of Hyperproperties for Understanding Model Checking Results

Model checkers provide algorithms for proving that a mathematical model of a system satisfies a given specification. In case of a violation, a counterexample that shows the erroneous behavior is returned. Understanding these counterexamples is challenging, especially for hyperproperty specifications, i.e., specifications that relate multiple executions of a system to each other. We aim to facilitate the visual analysis of such counterexamples through our HYPERVIS tool, which provides interactive visualizations of the given model, specification, and counterexample. Within an iterative and interdisciplinary design process, we developed visualization solutions that can effectively communicate the core aspects of the model checking result. Specifically, we introduce graphical representations of binary values for improving pattern recognition, color encoding for better indicating related aspects, visually enhanced textual descriptions, as well as extensive cross-view highlighting mechanisms. Further, through an underlying causal analysis of the counterexample, we are also able to identify values that contributed to the violation and use this knowledge for both improved encoding and highlighting. Finally, the analyst can modify both the specification of the hyperproperty and the system directly within HYPERVIS and initiate the model checking of the new version. In combination, these features notably support the analyst in understanding the error leading to the counterexample as well as iterating the provided system and specification. We ran multiple case studies with HYPERVIS and tested it with domain experts in qualitative feedback sessions. The participants’ positive feedback confirms the considerable improvement over the manual, text-based status quo and the value of the tool for explaining hyperproperties

Micro Visualizations: Design and Analysis of Visualizations for Small Display Spaces

The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.Le sujet de cette habilitation est l'étude de très petites visualisations de données, les micro visualisations, dans des contextes d'affichage qui ne peuvent consacrer qu'un espace de rendu minimal aux représentations de données. Depuis plusieurs années, avec mes collaborateurs, j'étudie la perception humaine, l'interaction et l'analyse conduite avec des micro visualisations dans de multiples contextes.Dans ce document, je rassemble trois de mes axes de recherche liés aux micro visualisations~: les glyphes de données, où ma recherche s'est concentrée sur l'étude de la perception de micro visualisations dans un context \textit{small-multiple}, les \textit{word-scale visualizations}, où ma recherche s'est concentrée sur les petites visualisations intégrées dans les documents textuels, et les petites visualisations de données mobiles pour les montres connectées. Je considère ces types de petites visualisations sous le terme générique de ``micro visualisations.'' Les micro visualisations sont utiles dans de multiples contextes de visualisation et j'ai travaillé à une meilleure compréhension de la complexité des conceptions et utilisations des micro visualisations. Je définirai ici le terme de micro visualisation, je résumerai mes propres recherches et celles d'autres chercheurs, ainsi que les directives de conception, et j'esquisserai plusieurs espaces de conception pour différents types de micro visualisations, sur la base de certains des travaux auxquels j'ai participé depuis mon doctorat