DevLan/vispubs huggingface dataset:
Dziban: Balancing Agency & Automation in Visualization Design via Anchored Recommendations
Authors: Halden Lin, Dominik Moritz, Jeffrey Heer
Published at CHI, 2020

Visualization recommender systems attempt to automate design decisions spanning choices of selected data, transformations, and visual encodings. However, across invocations such recommenders may lack the context of prior results, producing unstable outputs that override earlier design choices. To better balance automated suggestions with user intent, we contribute Dziban, a visualization API that supports both ambiguous specification and a novel anchoring mechanism for conveying desired context. Dziban uses the Draco knowledge base to automatically complete partial specifications and suggest appropriate visualizations. In addition, it extends Draco with chart similarity logic, enabling recommendations that also remain perceptually similar to a provided "anchor" chart. Existing APIs for exploratory visualization, such as ggplot2 and Vega-Lite, require fully specified chart definitions. In contrast, Dziban provides a more concise and flexible authoring experience through automated design, while preserving predictability and control through anchored recommendations.
Ecology Meets Computer Science: Designing Tools to Reconcile People, Data, and Practices
Authors: Kellie Vella, Jessica L. Oliver, Tshering Dema, Margot Brereton, Paul Roe
Published at CHI, 2020

Ecoacoustics draws together computer scientists and ecologists to achieve an understanding of ecosystems and wildlife using acoustic recordings of the environment. Computer scientists are challenged to manage increasingly large datasets while developing analytic and visualisation tools. Ecologists struggle to find and use tools that answer highly heterogeneous research questions. These two fields are naturally drawn together at the tool interface, however, less attention has been paid to how their practices influence tool design and use. We interviewed and collected email correspondence from four computer scientists and eight ecologists to learn how their practices indicate opportunities for reconciling difference through design. We found that different temporal rhythms, relationships to data, and data-driven questions demand tool configuration, data integration, and standardisation. This research outlines interfacing opportunities for new ecological research utilising large acoustic datasets, and also contributes to evolving HCI approaches in areas making use of big data and human-in-the-loop processes.
Embodied Axes: Tangible, Actuated Interaction for 3D Augmented Reality Data Spaces
Authors: Maxime Cordeil, Benjamin Bach, Andrew Cunningham, Bastian Montoya, Ross T. Smith, Bruce H. Thomas, Tim Dwyer
Published at CHI, 2020

We present Embodied Axes, a controller which supports selection operations for 3D imagery and data visualisations in Augmented Reality. The device is an embodied representation of a 3D data space -- each of its three orthogonal arms corresponds to a data axis or domain specific frame of reference. Each axis is composed of a pair of tangible, actuated range sliders for precise data selection, and rotary encoding knobs for additional parameter tuning or menu navigation. The motor actuated sliders support alignment to positions of significant values within the data, or coordination with other input: e.g., mid-air gestures in the data space, touch gestures on the surface below the data, or another Embodied Axes device supporting multi-user scenarios. We conducted expert enquiries in medical imaging which provided formative feedback on domain tasks and refinements to the design. Additionally, a controlled user study was performed and found that the Embodied Axes was overall more accurate than conventional tracked controllers for selection tasks.
Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing Approach
Authors: Carolina Nobre, Dylan Wootton, Lane Harrison, Alexander Lex
Published at CHI, 2020

Visualizing multivariate networks is challenging because of the trade-offs necessary for effectively encoding network topology and encoding the attributes associated with nodes and edges. A large number of multivariate network visualization techniques exist, yet there is little empirical guidance on their respective strengths and weaknesses. In this paper, we describe a crowdsourced experiment, comparing node-link diagrams with on-node encoding and adjacency matrices with juxtaposed tables. We find that node-link diagrams are best suited for tasks that require close integration between the network topology and a few attributes. Adjacency matrices perform well for tasks related to clusters and when many attributes need to be considered. We also reflect on our method of using validated designs for empirically evaluating complex, interactive visualizations in a crowdsourced setting. We highlight the importance of training, compensation, and provenance tracking.
Evaluating the Effect of Timeline Shape on Visualization Task Performance
Authors: Sara Di Bartolomeo, Aditeya Pandey, Aristotelis Leventidis, David Saffo, Uzma Haque Syeda, Elín Carstensdóttir, Magy Seif El-Nasr, Michelle A. Borkin, Cody Dunne
Published at CHI, 2020

Timelines are commonly represented on a horizontal line, which is not necessarily the most effective way to visualize temporal event sequences. However, few experiments have evaluated how timeline shape influences task performance. We present the design and results of a controlled experiment run on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape affects task completion time, correctness, and user preference. We tested 12 combinations of 4 shapes --- horizontal line, vertical line, circle, and spiral  and 3 data types  recurrent, non-recurrent, and mixed event sequences. We found good evidence that timeline shape meaningfully affects user task completion time but not correctness and that users have a strong shape preference. Building on our results, we present design guidelines for creating effective timeline visualizations based on user task and data types. A free copy of this paper, the evaluation stimuli and data, and code are available https://osf.io/qr5yu/
Evaluation of a Financial Portfolio Visualization using Computer Displays and Mixed Reality Devices with Domain Experts
Authors: Kay Schröder, Batoul Ajdadilish, Alexander P. Henkel, André Calero Valdez
Published at CHI, 2020

With the advent of mixed reality devices such as the Microsoft HoloLens, developers have been faced with the challenge to utilize the third dimension in information visualization effectively. Research on stereoscopic devices has shown that three-dimensional representation can improve accuracy in specific tasks (e.g., network visualization). Yet, so far the field has remained mute on the underlying mechanism. Our study systematically investigates the differences in user perception between a regular monitor and a mixed reality device. In a real-life within-subject experiment in the field with twenty-eight investment bankers, we assessed subjective and objective task performance with two- and three-dimensional systems, respectively. We tested accuracy with regard to position, size, and color using single and combined tasks. Our results do not show a significant difference in accuracy between mixed-reality and standard 2D monitor visualizations.
FDHelper: Assist Unsupervised Fraud Detection Experts with Interactive Feature Selection and Evaluation
Authors: Jiao Sun, Yin Li, Charley Chen, Jihae Lee, Xin Liu, Zhongping Zhang, Ling Huang, Lei Shi 0002, Wei Xu 0005
Published at CHI, 2020

Online fraud is the well-known dark side of the modern Internet. Unsupervised fraud detection algorithms are widely used to address this problem. However, selecting features, adjusting hyperparameters, evaluating the algorithms, and eliminating false positives all require human expert involvement. In this work, we design and implement an end-to-end interactive visualization system, FDHelper, based on the deep understanding of the mechanism of the black market and fraud detection algorithms. We identify a workflow based on experience from both fraud detection algorithm experts and domain experts. Using a multi-granularity three-layer visualization map embedding an entropy-based distance metric ColDis, analysts can interactively select different feature sets, refine fraud detection algorithms, tune parameters and evaluate the detection result in near real-time. We demonstrate the effectiveness and significance of FDHelper through two case studies with state-of-the-art fraud detection algorithms, interviews with domain experts and algorithm experts, and a user study with eight first-time end users.
From Data to Insights: A Layered Storytelling Approach for Multimodal Learning Analytics
Authors: Roberto Martínez Maldonado, Vanessa Echeverría, Gloria Fernández-Nieto, Simon Buckingham Shum
Published at CHI, 2020

Significant progress to integrate and analyse multimodal data has been carried out in the last years. Yet, little research has tackled the challenge of visualising and supporting the sensemaking of multimodal data to inform teaching and learning. It is naïve to expect that simply by rendering multiple data streams visually, a teacher or learner will be able to make sense of them. This paper introduces an approach to unravel the complexity of multimodal data by organising it into meaningful layers that explain critical insights to teachers and students. The approach is illustrated through the design of two data storytelling prototypes in the context of nursing simulation. Two authentic studies with educators and students identified the potential of the approach to create learning analytics interfaces that communicate insights on team performance, as well as concerns in terms of accountability and automated insights discovery.
Genie in the Bottle: Anthropomorphized Perceptions of Conversational Agents
Authors: Anastasia Kuzminykh, Jenny Sun, Nivetha Govindaraju, Jeff Avery, Edward Lank
Published at CHI, 2020

This paper presents a qualitative multi-phase study seeking to identify patterns in users' anthropomorphized perceptions of conversational agents. Through a comparative analysis of behavioral perceptions and visual conceptions of three agents - Alexa, Google Assistant, and Siri - we first show that the perceptions of an agent's character are structured according to five categories: approachability, sentiment toward a user, professionalism, intelligence, and individuality. We then explore visualizations of the agents' appearance and discuss the specifics assigned to each agent. Finally, we analyze associative explanations for these perceptions. We demonstrate that the anthropomorphized behavioral and visual perceptions of agents yield structural consistency and discuss how these perceptions are linked with each other and system features.
GoTree: A Grammar of Tree Visualizations
Authors: Guozheng Li 0002, Min Tian, Qinmei Xu 0001, Michael J. McGuffin, Xiaoru Yuan
Published at CHI, 2020

We present GoTree, a declarative grammar allowing users to instantiate tree visualizations by specifying three aspects: visual elements, layout, and coordinate system. Within the set of all possible tree visualization techniques, we identify a subset of techniques that are both "unit-decomposable" and "axis-decomposable" (terms we define). For tree visualizations within this subset, GoTree gives the user flexible and fine-grained control over the parameters of the techniques, supporting both explicit and implicit tree visualizations. We developed Tree Illustrator, an interactive authoring tool based on GoTree grammar. Tree Illustrator allows users to create a considerable number of tree visualizations, including not only existing techniques but also undiscovered and hybrid visualizations. We demonstrate the expressiveness and generative power of GoTree with a gallery of examples and conduct a qualitative study to validate the usability of Tree Illustrator.
Heatmaps, Shadows, Bubbles, Rays: Comparing Mid-Air Pen Position Visualizations in Handheld AR
Authors: Philipp Wacker, Adrian Wagner, Simon Voelker, Jan O. Borchers
Published at CHI, 2020

In Handheld Augmented Reality, users look at AR scenes through the smartphone held in their hand. In this setting, having a mid-air pointing device like a pen in the other hand greatly expands the interaction possibilities. For example, it lets users create 3D sketches and models while on the go. However, perceptual issues in Handheld AR make it difficult to judge the distance of a virtual object, making it hard to align a pen to it. To address this, we designed and compared different visualizations of the pen's position in its virtual environment, measuring pointing precision, task time, activation patterns, and subjective ratings of helpfulness, confidence, and comprehensibility of each visualization. While all visualizations resulted in only minor differences in precision and task time, subjective ratings of perceived helpfulness and confidence favor a 'heatmap' technique that colors the objects in the scene based on their distance to the pen.
How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results
Authors: Jake M. Hofman, Daniel G. Goldstein, Jessica Hullman
Published at CHI, 2020

When presenting visualizations of experimental results, scientists often choose to display either inferential uncertainty (e.g., uncertainty in the estimate of a population mean) or outcome uncertainty (e.g., variation of outcomes around that mean) about their estimates. How does this choice impact readers' beliefs about the size of treatment effects? We investigate this question in two experiments comparing 95% confidence intervals (means and standard errors) to 95% prediction intervals (means and standard deviations). The first experiment finds that participants are willing to pay more for and overestimate the effect of a treatment when shown confidence intervals relative to prediction intervals. The second experiment evaluates how alternative visualizations compare to standard visualizations for different effect sizes. We find that axis rescaling reduces error, but not as well as prediction intervals or animated hypothetical outcome plots (HOPs), and that depicting inferential uncertainty causes participants to underestimate variability in individual outcomes.
InChorus: Designing Consistent Multimodal Interactions for Data Visualization on Tablet Devices
Authors: Arjun Srinivasan, Bongshin Lee, Nathalie Henry Riche, Steven Mark Drucker, Ken Hinckley
Published at CHI, 2020

While tablet devices are a promising platform for data visualization, supporting consistent interactions across different types of visualizations on tablets remains an open challenge. In this paper, we present multimodal interactions that function consistently across different visualizations, supporting common operations during visual data analysis. By considering standard interface elements (e.g., axes, marks) and grounding our design in a set of core concepts including operations, parameters, targets, and instruments, we systematically develop interactions applicable to different visualization types. To exemplify how the proposed interactions collectively facilitate data exploration, we employ them in a tablet-based system, InChorus that supports pen, touch, and speech input. Based on a study with 12 participants performing replication and factchecking tasks with InChorus, we discuss how participants adapted to using multimodal input and highlight considerations for future multimodal visualization systems.
Interacting with Literary Style through Computational Tools
Authors: Sarah Sterman, Evey Huang, Vivian Liu, Eric Paulos
Published at CHI, 2020

Style is an important aspect of writing, shaping how audiences interpret and engage with literary works. However, for most people style is difficult to articulate precisely. While users frequently interact with computational word processing tools with well-defined metrics, such as spelling and grammar checkers, style is a significantly more nuanced concept. In this paper, we present a computational technique to help surface style in written text. We collect a dataset of crowdsourced human judgments of style, derive a model of style by training a neural net on this data, and present novel applications for visualizing and browsing style across broad bodies of literature, as well as an interactive text editor with real-time style feedback. We study these interactive style applications with users and discuss implications for enabling this novel approach to style.
Interaction Techniques for Visual Exploration Using Embedded Word-Scale Visualizations
Authors: Pascal Goffin, Tanja Blascheck, Petra Isenberg, Wesley Willett
Published at CHI, 2020

We describe a design space of view manipulation interactions for small data-driven contextual visualizations (word-scale visualizations). These interaction techniques support an active reading experience and engage readers through exploration of embedded visualizations whose placement and content connect them to specific terms in a document. A reader could, for example, use our proposed interaction techniques to explore word-scale visualizations of stock market trends for companies listed in a market overview article. When readers wish to engage more deeply with the data, they can collect, arrange, compare, and navigate the document using the embedded word-scale visualizations, permitting more visualization-centric analyses. We support our design space with a concrete implementation, illustrate it with examples from three application domains, and report results from two experiments. The experiments show how view manipulation interactions helped readers examine embedded visualizations more quickly and with less scrolling and yielded qualitative feedback on usability and future opportunities.
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning
Authors: Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
Published at CHI, 2020

Machine learning (ML) models are now routinely deployed in domains ranging from criminal justice to healthcare. With this newfound ubiquity, ML has moved beyond academia and grown into an engineering discipline. To that end, interpretability tools have been designed to help data scientists and machine learning practitioners better understand how ML models work. However, there has been little evaluation of the extent to which these tools achieve this goal. We study data scientists' use of two existing interpretability tools, the InterpretML implementation of GAMs and the SHAP Python package. We conduct a contextual inquiry (N=11) and a survey (N=197) of data scientists to observe how they use interpretability tools to uncover common issues that arise when building and evaluating ML models. Our results indicate that data scientists over-trust and misuse interpretability tools. Furthermore, few of our participants were able to accurately describe the visualizations output by these tools. We highlight qualitative themes for data scientists' mental models of interpretability tools. We conclude with implications for researchers and tool designers, and contextualize our findings in the social science literature.
Investigating Collaborative Exploration of Design Alternatives on a Wall-Sized Display
Authors: Yujiro Okuya, Olivier Gladin, Nicolas Ladevèze, Cédric Fleury, Patrick Bourdot
Published at CHI, 2020

Industrial design review is an iterative process which mainly relies on two steps involving many stakeholders: design discussion and CAD data adjustment. We investigate how a wall-sized display could be used to merge these two steps by allowing multidisciplinary collaborators to simultaneously generate and explore design alternatives. We designed ShapeCompare based on the feedback from a usability study. It enables multiple users to compute and distribute CAD data with touch interaction. To assess the benefit of the wall-sized display in such context, we ran a controlled experiment which aims to compare ShapeCompare with a visualization technique suitable for standard screens. The results show that pairs of participants performed a constraint solving task faster and used more deictic instructions with ShapeCompare. From these findings, we draw generic recommendations for collaborative exploration of alternatives.
MaraVis: Representation and Coordinated Intervention of Medical Encounters in Urban Marathon
Authors: Quan Li, Huanbin Lin, Xiguang Wei, Yangkun Huang, Lixin Fan, Jian Du, Xiaojuan Ma, Tianjian Chen
Published at CHI, 2020

There is an increased use of Internet-of-Things and wearable sensing devices in the urban marathon to ensure effective response to unforeseen medical needs. However, the massive amount of real-time, heterogeneous movement and psychological data of runners impose great challenges on prompt medical incident analysis and intervention. Conventional approaches compile such data into one dashboard visualization to facilitate rapid data absorption but fail to support joint decision-making and operations in medical encounters. In this paper, we present MaraVis, a real-time urban marathon visualization and coordinated intervention system. It first visually summarizes real-time marathon data to facilitate the detection and exploration of possible anomalous events. Then, it calculates an optimal camera route with an arrangement of shots to guide offline effort to catch these events in time with a smooth view transition. We conduct a within-subjects study with two baseline systems to assess the efficacy of MaraVis.
Move Your Body: Engaging Museum Visitors with Human-Data Interaction
Authors: Milka Trajkova, A'aeshah Alhakamy, Francesco Cafaro, Rashmi Mallappa, Sreekanth R. Kankara
Published at CHI, 2020

Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user ("mode type") next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.