DevLan/vispubs huggingface dataset:
Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making
Authors: Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, Matthew Kay 0001
Published at CHI, 2018

Everyday predictive systems typically present point predictions, making it hard for people to account for uncertainty when making decisions. Evaluations of uncertainty displays for transit prediction have assessed people's ability to extract probabilities, but not the quality of their decisions. In a controlled, incentivized experiment, we had subjects decide when to catch a bus using displays with textual uncertainty, uncertainty visualizations, or no-uncertainty (control). Frequency-based visualizations previously shown to allow people to better extract probabilities (quantile dotplots) yielded better decisions. Decisions with quantile dotplots with 50 outcomes were(1) better on average, having expected payoffs 97% of optimal(95% CI: [95%,98%]), 5 percentage points more than control (95% CI: [2,8]); and (2) more consistent, having within-subject standard deviation of 3 percentage points (95% CI:[2,4]), 4 percentage points less than control (95% CI: [2,6]).Cumulative distribution function plots performed nearly as well, and both outperformed textual uncertainty, which was sensitive to the probability interval communicated. We discuss implications for real time transit predictions and possible generalization to other domains.
Uncertainty Visualization Influences how Humans Aggregate Discrepant Information
Authors: Miriam Greis, Aditi Joshi, Ken Singer, Albrecht Schmidt 0001, Tonja Machulla
Published at CHI, 2018

The number of sensors in our surroundings that provide the same information steadily increases. Since sensing is prone to errors, sensors may disagree. For example, a GPS-based tracker on the phone and a sensor on the bike wheel may provide discrepant estimates on traveled distance. This poses a user dilemma, namely how to reconcile the conflicting information into one estimate. We investigated whether visualizing the uncertainty associated with sensor measurements improves the quality of users' inference. We tested four visualizations with increasingly detailed representation of uncertainty. Our study repeatedly presented two sensor measurements with varying degrees of inconsistency to participants who indicated their best guess of the "true" value. We found that uncertainty information improves users' estimates, especially if sensors differ largely in their associated variability. Improvements were larger for information-rich visualizations. Based on our findings, we provide an interactive tool to select the optimal visualization for displaying conflicting information.
Understanding Older Users' Acceptance of Wearable Interfaces for Sensor-based Fall Risk Assessment
Authors: Alan Yusheng Wu, Cosmin Munteanu
Published at CHI, 2018

Algorithms processing data from wearable sensors promise to more accurately predict risks of falling -- a significant concern for older adults. Substantial engineering work is dedicated to increasing the prediction accuracy of these algorithms; yet fewer efforts are dedicated to better engaging users through interactive visualizations in decision-making using these data. We present an investigation of the acceptance of a sensor-based fall risk assessment wearable device. A participatory design was employed to develop a mobile interface providing visualizations of sensor data and algorithmic assessments of fall risks. We then investigated the acceptance of this interface and its potential to motivate behavioural changes through a field deployment, which suggested that the interface and its belt-mounted wearable sensors are perceived as usable. We also found that providing contextual information for fall risk estimation combined with relevant practical fall prevention instructions may facilitate the acceptance of such technologies, potentially leading to behaviour change.
Using Animation to Alleviate Overdraw in Multiclass Scatterplot Matrices
Authors: Helen Chen, Sophie Engle, Alark Joshi, Eric D. Ragan, Beste F. Yuksel, Lane Harrison
Published at CHI, 2018

The scatterplot matrix (SPLOM) is a commonly used technique for visualizing multiclass multivariate data. However, multiclass SPLOMs have issues with overdraw (overlapping points), and most existing techniques for alleviating overdraw focus on individual scatterplots with a single class. This paper explores whether animation using flickering points is an effective way to alleviate overdraw in these multiclass SPLOMs. In a user study with 69 participants, we found that users not only performed better at identifying dense regions using animated SPLOMs, but also found them easier to interpret and preferred them to static SPLOMs. These results open up new directions for future work on alleviating overdraw for multiclass SPLOMs, and provide insights for applying animation to alleviate overdraw in other settings.
Visualizing API Usage Examples at Scale
Authors: Elena L. Glassman, Tianyi Zhang 0001, Björn Hartmann, Miryung Kim
Published at CHI, 2018

Using existing APIs properly is a key challenge in programming, given that libraries and APIs are increasing in number and complexity. Programmers often search for online code examples in Q&A forums and read tutorials and blog posts to learn how to use a given API. However, there are often a massive number of related code examples and it is difficult for a user to understand the commonalities and variances among them, while being able to drill down to concrete details. We introduce an interactive visualization for exploring a large collection of code examples mined from open-source repositories at scale. This visualization summarizes hundreds of code examples in one synthetic code skeleton with statistical distributions for canonicalized statements and structures enclosing an API call. We implemented this interactive visualization for a set of Java APIs and found that, in a lab study, it helped users (1) answer significantly more API usage questions correctly and comprehensively and (2) explore how other programmers have used an unfamiliar API.
vrSocial: Toward Immersive Therapeutic VR Systems for Children with Autism
Authors: LouAnne E. Boyd, Saumya Gupta, Sagar B. Vikmani, Carlos M. Gutierrez, Junxiang Yang, Erik Linstead, Gillian R. Hayes
Published at CHI, 2018

Social communication frequently includes nuanced nonverbal communication cues, including eye contact, gestures, facial expressions, body language, and tone of voice. This type of communication is central to face-to-face interaction, but can be challenging for children and adults with autism. Innovative technologies can provide support by augmenting human-delivered cuing and automated prompting. Specifically, immersive virtual reality (VR) offers an option to generalize social skill interventions by concretizing nonverbal information in real-time social interactions. In this work, we explore the design and evaluation of three nonverbal communication applications in immersive VR. The results of this work indicate that delivering real-time visualizations of proximity, speaker volume, and duration of one's speech is feasible in immersive VR and effective for real-time support for proximity regulation for children with autism. We conclude with design considerations for therapeutic VR systems.
What Moves Players?: Visual Data Exploration of Twitter and Gameplay Data
Authors: Christian Drescher, Guenter Wallner, Simone Kriglstein, Rafet Sifa, Anders Drachen, Margit Pohl
Published at CHI, 2018

In recent years, microblogging platforms have not only become an important communication channel for the game industry to generate and uphold audience interest but also a rich resource for gauging player opinion. In this paper we use data gathered from Twitter to examine which topics matter to players and to identify influential members of a game's community. By triangulating in-game data with Twitter activity we explore how tweets can provide contextual information for understanding fluctuations in in-game activity. To facilitate analysis of the data we introduce a visual data exploration tool and use it to analyze tweets related to the game Destiny. In total, we collected over one million tweets from about 250,000 users over a 14-month period and gameplay data from roughly 3,500 players over a six-month period.
What's the Difference?: Evaluating Variations of Multi-Series Bar Charts for Visual Comparison Tasks
Authors: Arjun Srinivasan, Matthew Brehmer, Bongshin Lee, Steven Mark Drucker
Published at CHI, 2018

An increasingly common approach to data analysis involves using information dashboards to visually compare changing data. However, layout constraints coupled with varying levels of visualization literacy among dashboard users make facilitating visual comparison in dashboards a challenging task. In this paper, we evaluate variants of bar charts, one of the most prevalent class of charts used in dashboards. We report an online experiment (N = 74) conducted to evaluate four alternative designs: 1) grouped bar chart, 2) grouped bar chart with difference overlays, 3) bar chart with difference overlays, and 4) difference bar chart. Results show that charts with difference overlays facilitate a wider range of comparison tasks while performing comparably to charts without them on individual tasks. Finally, we discuss the implications of our findings, with a focus on supporting visual comparison in dashboards.
When David Meets Goliath: Combining Smartwatches with a Large Vertical Display for Visual Data Exploration
Authors: Tom Horak, Sriram Karthik Badam, Niklas Elmqvist, Raimund Dachselt
Published at CHI, 2018

We explore the combination of smartwatches and a large interactive display to support visual data analysis. These two extremes of interactive surfaces are increasingly popular, but feature different characteristics-display and input modalities, personal/public use, performance, and portability. In this paper, we first identify possible roles for both devices and the interplay between them through an example scenario. We then propose a conceptual framework to enable analysts to explore data items, track interaction histories, and alter visualization configurations through mechanisms using both devices in combination. We validate an implementation of our framework through a formative evaluation and a user study. The results show that this device combination, compared to just a large display, allows users to develop complex insights more fluidly by leveraging the roles of the two devices. Finally, we report on the interaction patterns and interplay between the devices for visual exploration as observed during our study.
A Utility-Aware Visual Approach for Anonymizing Multi-Attribute Tabular Data
Authors: Xumeng Wang, Jia-Kai Chou, Wei Chen 0001, Huihua Guan, Wenlong Chen, Tianyi Lao, Kwan-Liu Ma
Published at VAST, 2017

Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.
A Visual Analytics System for Optimizing Communications in Massively Parallel Applications
Authors: Takanori Fujiwara, Preeti Malakar, Khairi Reda, Venkatram Vishwanath, Michael E. Papka, Kwan-Liu Ma
Published at VAST, 2017

Current and future supercomputers have tens of thousands of compute nodes interconnected with high-dimensional networks and complex network topologies for improved performance. Application developers are required to write scalable parallel programs in order to achieve high throughput on these machines. Application performance is largely determined by efficient inter-process communication. A common way to analyze and optimize performance is through profiling parallel codes to identify communication bottlenecks. However, understanding gigabytes of profiled at a is not a trivial task. In this paper, we present a visual analytics system for identifying the scalability bottlenecks and improving the communication efficiency of massively parallel applications. Visualization methods used in this system are designed to comprehend large-scale and varied communication patterns on thousands of nodes in complex networks such as the 5D torus and the dragonfly. We also present efficient rerouting and remapping algorithms that can be coupled with our interactive visual analytics design for performance optimization. We demonstrate the utility of our system with several case studies using three benchmark applications on two leading supercomputers. The mapping suggestion from our system led to 38% improvement in hop-bytes for Mini AMR application on 4,096 MPI processes.
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
Authors: Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanagphongs, Enrico Bertini
Published at VAST, 2017

Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages “instance-level explanations”, measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The workflow is derived from a long-term collaboration with a group of machine learning and healthcare professionals who used our method to make sense of machine learning models they developed. The case study from this collaboration demonstrates that the proposed workflow helps experts derive useful knowledge about the model and the phenomena it describes, thus experts can generate useful hypotheses on how a model can be improved.
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
Authors: Minsuk Kahng, Pierre Y. Andrews, Aditya Kalro, Polo Chau
Published at VAST, 2017

While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance-and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models.
Analyzing the Training Processes of Deep Generative Models
Authors: Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu 0001, Shixia Liu
Published at VAST, 2017

Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better understanding and diagnosing the training process of a DGM. To help experts understand the overall training process, we first extract a large amount of time series data that represents training dynamics (e.g., activation changes over time). A blue-noise polyline sampling scheme is then introduced to select time series samples, which can both preserve outliers and reduce visual clutter. To further investigate the root cause of a failed training process, we propose a credit assignment algorithm that indicates how other neurons contribute to the output of the neuron causing the training failure. Two case studies are conducted with machine learning experts to demonstrate how our approach helps understand and diagnose the training processes of DGMs. We also show how our approach can be directly used to analyze other types of deep models, such as CNNs.
Applying Pragmatics Principles for Interaction with Visual Analytics
Authors: Enamul Hoque, Vidya Setlur, Melanie Tory, Isaac Dykeman
Published at VAST, 2017

Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than other interaction paradigms. We explore how principles from language pragmatics can be applied to the flow of visual analytical conversations, using natural language as an input modality. We evaluate the effectiveness of pragmatics support in our system Evizeon, and present design considerations for conversation interfaces to visual analytics tools.
Beyond Tasks: An Activity Typology for Visual Analytics
Authors: Darren Edge, Nathalie Henry Riche, Jonathan Larson, Christopher M. White
Published at VAST, 2017

As Visual Analytics (VA) research grows and diversifies to encompass new systems, techniques, and use contexts, gaining a holistic view of analytic practices is becoming ever more challenging. However, such a view is essential for researchers and practitioners seeking to develop systems for broad audiences that span multiple domains. In this paper, we interpret VA research through the lens of Activity Theory (AT) - a framework for modelling human activities that has been influential in the field of Human-Computer Interaction. We first provide an overview of Activity Theory, showing its potential for thinking beyond tasks, representations, and interactions to the broader systems of activity in which interactive tools are embedded and used. Next, we describe how Activity Theory can be used as an organizing framework in the construction of activity typologies, building and expanding upon the tradition of abstract task taxonomies in the field of Information Visualization. We then apply the resulting process to create an activity typology for Visual Analytics, synthesizing a wide range of systems and activity concepts from the literature. Finally, we use this typology as the foundation of an activity-centered design process, highlighting both tensions and opportunities in the design space of VA systems.
BiDots: Visual Exploration of Weighted Biclusters
Authors: Jian Zhao 0010, Maoyuan Sun, Francine Chen 0001, Patrick Chiu
Published at VAST, 2017

Discovering and analyzing biclusters, i.e., two sets of related entities with close relationships, is a critical task in many real-world applications, such as exploring entity co-occurrences in intelligence analysis, and studying gene expression in bio-informatics. While the output of biclustering techniques can offer some initial low-level insights, visual approaches are required on top of that due to the algorithmic output complexity. This paper proposes a visualization technique, called BiDots, that allows analysts to interactively explore biclusters over multiple domains. BiDots overcomes several limitations of existing bicluster visualizations by encoding biclusters in a more compact and cluster-driven manner. A set of handy interactions is incorporated to support flexible analysis of biclustering results. More importantly, BiDots addresses the cases of weighted biclusters, which has been underexploited in the literature. The design of BiDots is grounded by a set of analytical tasks derived from previous work. We demonstrate its usefulness and effectiveness for exploring computed biclusters with an investigative document analysis task, in which suspicious people and activities are identified from a text corpus.
Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis
Authors: Manuel Stein, Halldór Janetzko, Andreas Lamprecht, Thorsten Breitkreutz, Philipp Zimmermann, Bastian Goldlücke, Tobias Schreck, Gennady L. Andrienko, Michael Grossniklaus, Daniel A. Keim
Published at VAST, 2017

Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.