DevLan/vispubs huggingface dataset:
Visual-Interactive Preprocessing of Multivariate Time Series Data
Authors: Jürgen Bernard, Marco Hutter 0002, Heiko Reinemuth, Hendrik Pfeifer, Christian Bors, Jörn Kohlhammer
Published at EuroVis, 2019

Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.
Visualization of Equivalence in 2D Bivariate Fields
Authors: Boyan Zheng, Bastian Rieck, Heike Leitte, Filip Sadlo
Published at EuroVis, 2019

In this paper, we show how the equivalence property leads to the novel concept of equivalent regions in mappings from ℝn to ℝn. We present a technique for obtaining these regions both in the domain and the codomain of such a mapping, and determine their correspondence. This enables effective investigation of variation equivalence within mappings, and between mappings in terms of comparative visualization. We implement our approach for n = 2, and demonstrate its utility using different examples.
Visualization Support for Developing a Matrix Calculus Algorithm: A Case Study
Authors: Joachim Giesen, Julien Klaus, Sören Laue, Ferdinand Schreck
Published at EuroVis, 2019

The development of custom interactive visualization tools for specific domains and applications has been made much simpler recently by a surge of visualization tools, libraries and frameworks. Most of these tools are developed for classical data science applications, where a user is supported in analyzing measured or simulated data. But recently, there has also been an increasing interest in visual support for understanding machine learning algorithms and frameworks, especially for deep learning. Many, if not most, of the visualization support for (deep) learning addresses the developer of the learning system and not the end user (data scientist). Here we show on a specific example, namely the development of a matrix calculus algorithm, that supporting visualizations can also greatly benefit the development of algorithms in classical domains like in our case computer algebra. The idea is similar to visually supporting the understanding of learning algorithms, namely provide the developer with an interactive, visual tool that provides insights into the workings and, importantly, also into the failures of the algorithm under development. Developing visualization support for matrix calculus development went similar as the development of more traditional visual support systems for data analysts. First, we had to acquaint ourselves with the problem, its language and challenges by talking to the core developer of the matrix calculus algorithm. Once we understood the challenge, it was fairly easy to develop visual support that streamlined the development of the matrix calculus algorithm significantly.
Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization
Authors: Jinho Choi, Sanghun Jung, Deok Gun Park 0001, Jaegul Choo, Niklas Elmqvist
Published at EuroVis, 2019

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.
A Bayesian Cognition Approach to Improve Data Visualization
Authors: Yea-Seul Kim, Logan A. Walls, Peter M. Krafft, Jessica Hullman
Published at CHI, 2019

People naturally bring their prior beliefs to bear on how they interpret the new information, yet few formal models exist for accounting for the influence of users' prior beliefs in interactions with data presentations like visualizations. We demonstrate a Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs and show how this model provides a guide for improving visualization evaluation. In a first study, we show how applying a Bayesian cognition model to a simple visualization scenario indicates that people's judgments are consistent with a hypothesis that they are doing approximate Bayesian inference. In a second study, we evaluate how sensitive our observations of Bayesian behavior are to different techniques for eliciting people subjective distributions, and to different datasets. We find that people don't behave consistently with Bayesian predictions for large sample size datasets, and this difference cannot be explained by elicitation technique. In a final study, we show how normative Bayesian inference can be used as an evaluation framework for visualizations, including of uncertainty.
A Comparison of Notification Techniques for Out-of-View Objects in Full-Coverage Displays
Authors: Julian Petford, Iain Carson, Miguel A. Nacenta, Carl Gutwin
Published at CHI, 2019

Full-coverage displays can place visual content anywhere on the interior surfaces of a room (e.g., a weather display near the coat stand). In these settings, digital artefacts can be located behind the user and out of their field of view - meaning that it can be difficult to notify the user when these artefacts need attention. Although much research has been carried out on notification, little is known about how best to direct people to the necessary location in room environments. We designed five diverse attention-guiding techniques for full-coverage display rooms, and evaluated them in a study where participants completed search tasks guided by the different techniques. Our study provides new results about notification in full-coverage displays: we showed benefits of persistent visualisations that could be followed all the way to the target and that indicate distance-to-target. Our findings provide useful information for improving the usability of interactive full-coverage environments.
A Lie Reveals the Truth: Quasimodes for Task-Aligned Data Presentation
Authors: Jacob Ritchie, Daniel Wigdor, Fanny Chevalier
Published at CHI, 2019

Designers are often discouraged from creating data visualizations that omit or distort information, because they can easily be misleading. However, the same representations that could be used to deceive can provide benefits when chosen to appropriately align with user tasks. We present an interaction technique, Perceptual Glimpses, which allows for the transparent presentation of so-called 'deceptive' views of information that are made temporary using quasimodes. When presented using Perceptual Glimpses, message-level exaggeration caused by a truncated axis on a bar chart was reduced under some conditions, but users require guidance to avoid errors, and view presentation order may affect trust. When Perceptual Glimpses was extended to display a range of views that might otherwise be deceptive or difficult to understand if shown out of context, users were able to understand and leverage these transformations to perform a range of low-level tasks. Design recommendations and examples suggest extensions of the technique.
A Rough Sketch of the Freehand Drawing Process: Blending the Line between Action and Artifact
Authors: Piyum Fernando, Jennifer Weiler, Stacey Kuznetsov
Published at CHI, 2019

Dynamic elements of the drawing process (e.g., order of compilation, speed, length, and pressure of strokes) are considered important because they can reveal the technique, process, and emotions of the artist. To explore how sensing, visualizing, and sharing these aspects of the creative process might shape art making and art viewing experiences, we designed a research probe which unobtrusively tracks and visualizes the movement and pressure of the artist's pencil on an easel. Using our probe, we conducted studies with artists and art viewers, which reveal digital and physical representations of creative process as a means of reflecting on a multitude of factors about the finished artwork, including technique, style, and the emotions of the artists. We conclude by discussing future directions for HCI systems that sense and visualize aspects of the creative process in digitally-mediated arts, as well as the social considerations of sharing and curating intimate process information.
A Wee Bit More Interaction: Designing and Evaluating an Overactive Bladder App
Authors: Ana-Maria Salai, Lynne Baillie
Published at CHI, 2019

Overactive Bladder (OAB) is a widespread condition, affecting 20% of the population. Even though it is a treatable condition, people often do not seek treatment. In this paper, we describe how we co-designed and evaluated with 30 stakeholders (9 medical professionals and 21 end-users) an OAB mobile health application that aims to increase adherence to self-managed treatment. Our results support previous research that visualizing progress, setting goals, receiving reminders and feedback increases use. We discovered that games could be used successfully as a distraction technique for urge suppression. Contrary to the current research direction, automatically calculated features could be a detriment to app interaction. Regarding evaluation, we found that designers may not want to rely only on questionnaires when assessing the success of a game and its emotional impact on users.
Aggregated Visualization of Playtesting Data
Authors: Günter Wallner, Nour Halabi, Pejman Mirza-Babaei
Published at CHI, 2019

Playtesting is a key component in the game development process aimed at improving the quality of games through the collection of gameplay data and identification of design issues. Visualization techniques are currently being employed to help integrate quantitative and qualitative data. Despite that, two existing challenges are to determine the level of detail to be presented to developers based on their needs and to effectively communicate the collected data so that informed design changes can be reached. In this paper, we first propose an aggregated visualization technique that makes use of clustering, territory tessellation, and trajectory aggregation to simultaneously display mixed playtesting data. Secondly, to assess the usefulness of our technique we evaluate it through interviews with professional game developers and compare it to a non-aggregated visualization. The results of this study also provide an important contribution towards identifying areas of improvement in the portrayal of gameplay data.
Analyzing Value Discovery in Design Decisions Through Ethicography
Authors: Shruthi Sai Chivukula, Colin M. Gray, Jason A. Brier
Published at CHI, 2019

HCI scholarship is increasingly concerned with the ethical impact of socio-technical systems. Current theoretically driven approaches that engage with ethics generally prescribe only abstract approaches by which designers might consider values in the design process. However, there is little guidance on methods that promote value discovery, which might lead to more specific examples of relevant values in specific design contexts. In this paper, we elaborate a method for value discovery, identifying how values impact the designer's decision making. We demonstrate the use of this method, called Ethicography, in describing value discovery and use throughout the design process. We present analysis of design activity by user experience (UX) design students in two lab protocol conditions, describing specific human values that designers considered for each task, and visualizing the interplay of these values. We identify opportunities for further research, using the Ethicograph method to illustrate value discovery and translation into design solutions.
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Authors: Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J. Smith, Kalyan Veeramachaneni, Huamin Qu
Published at CHI, 2019

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and in analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.
Bookly: An Interactive Everyday Artifact Showing the Time of Physically Accumulated Reading Activity
Authors: Somi Ju, Kyung-Ryong Lee, Subin Kim, Young-Woo Park
Published at CHI, 2019

We introduce Bookly, an interactive artifact that physically represents the accumulated time of users' reading activity through abstract volumetric changes. Bookly accumulates the time of actions (e.g., picking up and putting down books) that users performed for reading and provides a designated space for the ongoing book being read. The results of our 2-week in-field study with six participants showed that continuous exposure to volumetric changes representing the accumulated time of reading activities helped the users to understand their unsettled reading patterns. Bookly also motivated the users to improve their reading behavior by gradually making reading part of their schedules. Additionally, the definite distinction of the ongoing book improved its visual affordance and accessibility for the users to start reading books. Based on the findings, we confirmed the possibility of making intangible data physical for self-reflection to enhance changes in behaviors that are difficult to perform due to weak motivation.
Card Mapper: Enabling Data-Driven Reflections on Ideation Cards
Authors: Dimitrios Paris Darzentas, Raphael Velt, Richard Wetzel, Peter J. Craigon, Hanne Gesine Wagner, Lachlan D. Urquhart, Steve Benford
Published at CHI, 2019

We explore how usage data captured from ideation cards can enable reflection on design. We deployed a deck of ideation cards on a Masters level module over two years, developing the means to capture the students' designs into a digital repository. We created two visualisations to reveal the relative co-occurrences of the cards as concept space and the relative proximity of designs (through cards used in common) as design space. We used these to elicit reflections from the perspectives of students, teachers and card designers. Our findings inspire ideas for extending the data-driven use of ideation cards throughout the design process; informing the redesign of cards, the rules for using them and their live connection to supporting materials and enabling stakeholders to reflect and recognise challenges and opportunities. We also identified the need, and potential ways, to capture a richer design rationale, including annotations, discarded cards and varying card interpretations.
Color Builder: A Direct Manipulation Interface for Versatile Color Theme Authoring
Authors: Maria Shugrina, Wenjia Zhang, Fanny Chevalier, Sanja Fidler, Karan Singh
Published at CHI, 2019

Color themes or palettes are popular for sharing color combinations across many visual domains. We present a novel interface for creating color themes through direct manipulation of color swatches. Users can create and rearrange swatches, and combine them into smooth and step-based gradients and three-color blends -- all using a seamless touch or mouse input. Analysis of existing solutions reveals a fragmented color design workflow, where separate software is used for swatches, smooth and discrete gradients and for in-context color visualization. Our design unifies these tasks, while encouraging playful creative exploration. Adjusting a color using standard color pickers can break this interaction flow with mechanical slider manipulation. To keep interaction seamless, we additionally design an in situ color tweaking interface for freeform exploration of an entire color neighborhood. We evaluate our interface with a group of professional designers and students majoring in this field.
Communicating Uncertainty in Fertility Prognosis
Authors: Hanna Schneider, Julia Wayrauther, Mariam Hassib, Andreas Butz
Published at CHI, 2019

Communicating uncertainty has been shown to provide positive effects on user understanding and decision-making. Surprisingly however, most personal health tracking applications fail to disclose the accuracy of their measurements and predictions. In the case of fertility tracking applications (FTAs), inaccurate predictions have already led to numerous unwanted pregnancies and law suits. However, integrating uncertainty into FTAs is challenging: Prediction accuracy is hard to understand and communicate, and its effect on users' trust and behavior is not well understood. We created a prototype for uncertainty visualizations for FTAs and evaluated it in a four-week field study with real users and their own data (N=9). Our results uncover far-reaching effects of communicating uncertainty: For example, users interpreted prediction accuracy as a proxy for their cycle health and as a security indicator for contraception. Displaying predicted and detected fertile phases next to each other helped users to understand uncertainty without negative emotional effects.
Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations
Authors: In Kwon Choi, Taylor Childers, Nirmal Kumar Raveendranath, Swati Mishra 0006, Kyle Harris, Khairi Reda
Published at CHI, 2019

Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.
Connect-to-Connected Worlds: Piloting a Mobile, Data-Driven Reflection Tool for an Open-Ended Simulation at a Museum
Authors: Aditi Mallavarapu, Leilah Lyons, Stephen M. Uzzo, Wren Thompson, Rinat Levy-Cohen, Brian Slattery
Published at CHI, 2019

Immersive open-ended museum exhibits promote ludic engagement and can be a powerful draw for visitors, but these qualities may also make learning more challenging. We describe our efforts to help visitors engage more deeply with an interactive exhibit's content by giving them access to visualizations of data skimmed from their use of the exhibit. We report on the motivations and challenges in designing this reflective tool, which positions visitors as a "human in the loop" to understand and manage their engagement with the exhibit. We used an iterative design process and qualitative methods to explore how and if visitors could (1) access and (2) comprehend the data visualizations, (3) reflect on their prior engagement with the exhibit, (4)plan their future engagement with the exhibit, and (5) act on their plans. We further discuss the essential design challenges and the opportunities made possible for visitors through data-driven reflection tools.
Data is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania
Authors: Evan M. Peck, Sofia E. Ayuso, Omar El-Etr
Published at CHI, 2019

Many of the guidelines that inform how designers create data visualizations originate in studies that unintentionally exclude populations that are most likely to be among the 'data poor'. In this paper, we explore which factors may drive attention and trust in rural populations with diverse economic and educational backgrounds - a segment that is largely underrepresented in the data visualization literature. In 42 semi-structured interviews in rural Pennsylvania (USA), we find that a complex set of factors intermix to inform attitudes and perceptions about data visualization - including educational background, political affiliation, and personal experience. The data and materials for this research can be found at https://osf.io/uxwts/
DataSelfie: Empowering People to Design Personalized Visuals to Represent Their Data
Authors: Nam Wook Kim, Hyejin Im, Nathalie Henry Riche, Alicia Wang, Krzysztof Gajos, Hanspeter Pfister
Published at CHI, 2019

Many personal informatics systems allow people to collect and manage personal data and reflect more deeply about themselves. However, these tools rarely offer ways to customize how the data is visualized. In this work, we investigate the question of how to enable people to determine the representation of their data. We analyzed the Dear Data project to gain insights into the design elements of personal visualizations. We developed DataSelfie, a novel system that allows individuals to gather personal data and design custom visuals to represent the collected data. We conducted a user study to evaluate the usability of the system as well as its potential for individual and collaborative sensemaking of the data.