DevLan/vispubs huggingface dataset:
MyPart: Personal, Portable, Accurate, Airborne Particle Counting
Authors: Rundong Tian, Christine Dierk, Christopher Myers, Eric Paulos
Published at CHI, 2016

In 2012, air pollution in both cities and rural areas was estimated to have caused 3.7 million premature deaths, 88% of those in at risk communities. The primary pollutant was small airborne particulate matter of 10 microns or less in diameter which led to the development of cardiovascular and respiratory diseases. In response, we developed MyPart, the first personal, portable, and accurate particle sensor under $50 capable of distinguishing and counting differently sized particles. We demonstrate how MyPart offers substantial enhancements over most existing air particle sensors by simultaneously improving accessibility, flexibility, portability, and accuracy. We describe the evolution and implementation of the sensor design, demonstrate its performance across twenty everyday urban environments versus a calibrated instrument, and conduct a preliminary user study to report on the overall user experience of MyPart. We also present a novel smart-phone visualization interface and a series of simple form factor adaptations of our design.
Personalized Compass: A Compact Visualization for Direction and Location
Authors: Daniel Miau, Steven Feiner
Published at CHI, 2016

Maps on mobile/wearable devices often make it difficult to determine the location of a point of interest (POI). For example, a POI may exist outside the map or on a background with no meaningful cues. To address this issue, we present Personalized Compass, a self-contained compact graphical location indicator. Personalized Compass uses personal a priori POIs to establish a reference frame, within which a POI in question can then be localized. Graphically, a personalized compass combines a multi-needle compass with an abstract overview map. We analyze the characteristics of Personalized Compass and the existing Wedge technique, and report on a user study comparing them. Personalized Compass performs better for four inference tasks, while Wedge is better for a locating task. Based on our analysis and study results, we suggest the two techniques are complementary and offer design recommendations.
Physikit: Data Engagement Through Physical Ambient Visualizations in the Home
Authors: Steven Houben, Connie Golsteijn, Sarah Gallacher, Rose Johnson, Saskia Bakker, Nicolai Marquardt, Licia Capra, Yvonne Rogers
Published at CHI, 2016

Internet of things (IoT) devices and sensor kits have the potential to democratize the access, use, and appropriation of data. Despite the increased availability of low cost sensors, most of the produced data is "black box" in nature: users often do not know how to access or interpret data. We propose a "human-data design" approach in which end-users are given tools to create, share, and use data through tangible and physical visualizations. This paper introduces Physikit, a system designed to allow users to explore and engage with environmental data through physical ambient visualizations. We report on the design and implementation of Physikit, and present a two-week field study which showed that participants got an increased sense of the meaning of data, embellished and appropriated the basic visualizations to make them blend into their homes, and used the visualizations as a probe for community engagement and social behavior.
Programming, Problem Solving, and Self-Awareness: Effects of Explicit Guidance
Authors: Dastyni Loksa, Amy J. Ko, Will Jernigan, Alannah Oleson, Christopher J. Mendez, Margaret M. Burnett
Published at CHI, 2016

More people are learning to code than ever, but most learning opportunities do not explicitly teach the problem solving skills necessary to succeed at open-ended programming problems. In this paper, we present a new approach to impart these skills, consisting of: 1) explicit instruction on programming problem solving, which frames coding as a process of translating mental representations of problems and solutions into source code, 2) a method of visualizing and monitoring progression through six problem solving stages, 3) explicit, on-demand prompts for learners to reflect on their strategies when seeking help from instructors, and 4) context-sensitive help embedded in a code editor that reinforces the problem solving instruction. We experimentally evaluated the effects of our intervention across two 2-week web development summer camps with 48 high school students, finding that the intervention increased productivity, independence, programming self-efficacy, metacognitive awareness, and growth mindset. We discuss the implications of these results on learning technologies and classroom instruction.
ResViz: Politics and Design Issues in Visualizing Academic Metrics
Authors: Chris Elsden, Sebastian Mellor, Patrick Olivier, Pete Wheldon, David S. Kirk, Rob Comber
Published at CHI, 2016

The use of data and metrics on a professional and personal level has led to considerable discourse around the performative power and politics of 'big data' and data visualization, with academia being no exception. We have developed a university system, ResViz, which publicly visualizes the externally funded research projects of academics, and their internal collaborations. We present an interview study that engages 20 key stakeholders, academics and administrators who are part of the pilot release for the first version of this system. In doing so, we describe and problematize our design space, considering the implications of making metrics visible and their social use within a large organization. Our findings cut across the way people communicate, review and manage performance with metrics. We raise seven design issues in this space -- practical considerations that expose the tensions in making metrics available for public contestation.
SnapToReality: Aligning Augmented Reality to the Real World
Authors: Benjamin Nuernberger, Eyal Ofek, Hrvoje Benko, Andrew D. Wilson
Published at CHI, 2016

Augmented Reality (AR) applications may require the precise alignment of virtual objects to the real world. We propose automatic alignment of virtual objects to physical constraints calculated from the real world in real time ("snapping to reality"). We demonstrate SnapToReality alignment techniques that allow users to position, rotate, and scale virtual content to dynamic, real world scenes. Our proof-of-concept prototype extracts 3D edge and planar surface constraints. We furthermore discuss the unique design challenges of snapping in AR, including the user's limited field of view, noise in constraint extraction, issues with changing the view in AR, visualizing constraints, and more. We also report the results of a user study evaluating SnapToReality, confirming that aligning objects to the real world is significantly faster when assisted by snapping to dynamically extracted constraints. Perhaps more importantly, we also found that snapping in AR enables a fresh and expressive form of AR content creation.
Supporting Comment Moderators in Identifying High Quality Online News Comments
Authors: Deokgun Park 0001, Simranjit Singh Sachar, Nicholas Diakopoulos, Niklas Elmqvist
Published at CHI, 2016

Online comments submitted by readers of news articles can provide valuable feedback and critique, personal views and perspectives, and opportunities for discussion. The varying quality of these comments necessitates that publishers remove the low quality ones, but there is also a growing awareness that by identifying and highlighting high quality contributions this can promote the general quality of the community. In this paper we take a user-centered design approach towards developing a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments using a combination of comment analytic scores as well as visualizations and flexible UI components. We evaluated this system with professional comment moderators working at local and national news outlets and provide insights into the utility and appropriateness of features for journalistic tasks, as well as how the system may enable or transform journalistic practices around online comments.
Taking 5: Work-Breaks, Productivity, and Opportunities for Personal Informatics for Knowledge Workers
Authors: Daniel A. Epstein, Daniel Avrahami, Jacob T. Biehl
Published at CHI, 2016

Taking breaks from work is an essential and universal practice. In this paper, we extend current research on productivity in the workplace to consider the break habits of knowledge workers and explore opportunities of break logging for personal informatics. We report on three studies. Through a survey of 147 U.S.-based knowledge workers, we investigate what activities respondents consider to be breaks from work, and offer an understanding of the benefit workers desire when they take breaks. We then present results from a two-week in-situ diary study with 28 participants in the U.S. who logged 800 breaks, offering insights into the effect of work breaks on productivity. We finally explore the space of information visualization of work breaks and productivity in a third study. We conclude with a discussion of implications for break recommendation systems, availability and interuptibility research, and the quantified workplace.
Telling Stories about Dynamic Networks with Graph Comics
Authors: Benjamin Bach, Natalie Kerracher, Kyle Wm. Hall, Sheelagh Carpendale, Jessie Kennedy, Nathalie Henry Riche
Published at CHI, 2016

In this paper, we explore graph comics as a medium to communicate changes in dynamic networks. While previous re- search has focused on visualizing dynamic networks for data exploration, we want to see if we can take advantage of the visual expressiveness and familiarity of comics to present and explain temporal changes in networks to an audience. To understand the potential of comics as a storytelling medium, we first created a variety of comics during a 3 month structured design process, involving domain experts from public education and neuroscience. This process led to the definition of 8 design factors for creating graph comics and propose design solutions for each. Results from a qualitative study suggest that a general audience is quickly able understand complex temporal changes through graph comics, provided with minimal textual annotations and no training.
The Effect of Richer Visualizations on Code Comprehension
Authors: Dimitar Asenov, Otmar Hilliges, Peter Müller 0001
Published at CHI, 2016

Researchers often introduce visual tools to programming environments in order to facilitate program comprehension, reduce navigation times, and help developers answer difficult questions. Syntax highlighting is the main visual lens through which developers perceive their code, and yet its effects and the effects of richer code presentations on code comprehension have not been evaluated systematically. We present a rigorous user study comparing mainstream syntax highlighting to two visually-enhanced presentations of code. Our results show that: (1) richer code visualizations reduce the time necessary to answer questions about code features, and (2) contrary to the subjective perception of developers, richer code visualizations do not lead to visual overload. Based on our results we outline practical recommendations for tool designers.
Unsupervised Clickstream Clustering for User Behavior Analysis
Authors: Gang Wang 0011, Xinyi Zhang, Shiliang Tang, Haitao Zheng 0001, Ben Y. Zhao
Published at CHI, 2016

Online services are increasingly dependent on user participation. Whether it's online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner. Our system identifies "clusters" of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool.
UX Heatmaps: Mapping User Experience on Visual Interfaces
Authors: Vanessa Georges, François Courtemanche, Sylvain Senecal, Thierry Baccino, Marc Fredette, Pierre-Majorique Léger
Published at CHI, 2016

In this paper, we present an off-the-shelf UX evaluation tool which contextualizes users' physiological and behavioral signals while interacting with a system. The proposed tool triangulates users' gaze data with inferred users' cognitive and emotional states to produce user experience (UX) heatmaps, which show where users were looking when they experienced specific cognitive and emotional states. Results show that for a given cognitive state (i.e., cognitive load), the proposed UX heatmap was able to effectively highlight the areas where users experienced different levels of cognitive load on an interface. The proposed tool enables the visual analysis of users' various emotional and cognitive states for specific areas on a given interface, and also to compare users' states across multiple interfaces, which should be useful for both UX researchers and practitioners.
When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems
Authors: Matthew Kay 0001, Tara Kola, Jessica R. Hullman, Sean A. Munson
Published at CHI, 2016

Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty. Existing uncertainty visualizations may not align with user needs or how they naturally reason about probability. We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes. To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application, and 3) an iterative design process. We present several candidate visualizations of uncertainty for realtime transit predictions in a mobile context, and we propose a novel discrete representation of continuous outcomes designed for small screens, quantile dotplots. In a controlled experiment we find that quantile dotplots reduce the variance of probabilistic estimates by ~1.15 times compared to density plots and facilitate more confident estimation by end-users in the context of realtime transit prediction scenarios.
3D Regression Heat Map Analysis of Population Study Data
Authors: Paul Klemm, Kai Lawonn, Sylvia Saalfeld, Uli Niemann, Katrin Hegenscheid, Henry Völzke, Bernhard Preim
Published at VAST, 2015

Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.
A Case Study Using Visualization Interaction Logs and Insight Metrics to Understand How Analysts Arrive at Insights
Authors: Hua Guo, Steven R. Gomez, Caroline Ziemkiewicz, David H. Laidlaw
Published at VAST, 2015

We present results from an experiment aimed at using logs of interactions with a visual analytics application to better understand how interactions lead to insight generation. We performed an insight-based user study of a visual analytics application and ran post hoc quantitative analyses of participants' measured insight metrics and interaction logs. The quantitative analyses identified features of interaction that were correlated with insight characteristics, and we confirmed these findings using a qualitative analysis of video captured during the user study. Results of the experiment include design guidelines for the visual analytics application aimed at supporting insight generation. Furthermore, we demonstrated an analysis method using interaction logs that identified which interaction patterns led to insights, going beyond insight-based evaluations that only quantify insight characteristics. We also discuss choices and pitfalls encountered when applying this analysis method, such as the benefits and costs of applying an abstraction framework to application-specific actions before further analysis. Our method can be applied to evaluations of other visualization tools to inform the design of insight-promoting interactions and to better understand analyst behaviors.
A software developer's guide to informal evaluation of Visual Analytics environments using VAST Challenge information
Authors: Kristin A. Cook, Jean Scholtz, Mark A. Whiting
Published at VAST, 2015

The VAST Challenge has been a popular venue for academic and industry participants for over ten years. Many participants comment that the majority of their time in preparing VAST Challenge entries is discovering elements in their software environments that need to be redesigned in order to solve the given task. Fortunately, there is no need to wait until the VAST Challenge is announced to test out software systems. The Visual Analytics Benchmark Repository contains all past VAST Challenge tasks, data, solutions and submissions. In this poster we describe how developers can perform informal evaluations of various aspects of their visual analytics environments using VAST Challenge information.
A System for visual exploration of caution spots from vehicle recorder data
Authors: Masahiko Itoh, Daisaku Yokoyama, Masashi Toyoda, Masaru Kitsuregawa
Published at VAST, 2015

It is vital for the transportation industry, which performs most of its work by automobiles, to reduce its accident rate. This paper proposes a 3D visual interaction method for exploring caution areas from large-scale vehicle recorder data. Our method provides (i) a flexible filtering interface for driving operations such as braking or handling operations by various combinations of their attribute values such as velocity and acceleration, and (ii) a 3D visual environment for spatio-temporal exploration of caution areas. The proposed method was able to extract caution areas where some accidents have actually occurred or that are on very narrow roads with bad visibility by using real data given by one of the biggest transportation companies in Japan.
An Uncertainty-Aware Approach for Exploratory Microblog Retrieval
Authors: Mengchen Liu, Shixia Liu, Xizhou Zhu, Qinying Liao, Furu Wei, Shimei Pan
Published at VAST, 2015

Although there has been a great deal of interest in analyzing customer opinions and breaking news in microblogs, progress has been hampered by the lack of an effective mechanism to discover and retrieve data of interest from microblogs. To address this problem, we have developed an uncertainty-aware visual analytics approach to retrieve salient posts, users, and hashtags. We extend an existing ranking technique to compute a multifaceted retrieval result: the mutual reinforcement rank of a graph node, the uncertainty of each rank, and the propagation of uncertainty among different graph nodes. To illustrate the three facets, we have also designed a composite visualization with three visual components: a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and the uncertainty analysis together enable analysts to effectively find the most uncertain results and interactively refine them. We have applied our approach to several Twitter datasets. Qualitative evaluation and two real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data.
BiSet: Semantic Edge Bundling with Biclusters for Sensemaking
Authors: Maoyuan Sun, Peng Mi, Chris North 0001, Naren Ramakrishnan
Published at VAST, 2015

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, “in-between”, to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.