DevLan/vispubs huggingface dataset:
PicMe: Interactive Visual Guidance for Taking Requested Photo Composition
Authors: Minju Kim, Jungjin Lee
Published at CHI, 2019

PicMe is a mobile application that provides interactive on-screen guidance that helps the user take pictures of a composition that another person requires. Once the requester captures a picture of the desired composition and delivers it to the user (photographer), a 2.5D guidance system, called the virtual frame, guides the user in real-time by showing a three-dimensional composition of the target image (i.e., size and shape). In addition, according to the matching accuracy rate, we provide a small-sized target image in an inset window as feedback and edge visualization for further alignment of the detail elements. We implemented PicMe to work fully in mobile environments. We then conducted a preliminary user study to evaluate the effectiveness of PicMe compared to traditional 2D guidance methods. The results show that PicMe helps users reach their target images more accurately and quickly by giving participants more confidence in their tasks.
Ranked-List Visualization: A Graphical Perception Study
Authors: Pranathi Mylavarapu, Adil Yalçin, Xan Gregg, Niklas Elmqvist
Published at CHI, 2019

Visualization of ranked lists is a common occurrence, but many in-the-wild solutions fly in the face of vision science and visualization wisdom. For example, treemaps and bubble charts are commonly used for this purpose, despite the fact that the data is not hierarchical and that length is easier to perceive than area. Furthermore, several new visual representations have recently been suggested in this area, including wrapped bars, packed bars, piled bars, and Zvinca plots. To quantify the differences and trade-offs for these ranked-list visualizations, we here report on a crowdsourced graphical perception study involving six such visual representations, including the ubiquitous scrolled barchart, in three tasks: ranking (assessing a single item), comparison (two items), and average (assessing global distribution). Results show that wrapped bars may be the best choice for visualizing ranked lists, and that treemaps are surprisingly accurate despite the use of area rather than length to represent value.
Saliency Deficit and Motion Outlier Detection in Animated Scatterplots
Authors: Rafael Veras, Christopher Collins 0001
Published at CHI, 2019

We report the results of a crowdsourced experiment that measured the accuracy of motion outlier detection in multivariate, animated scatterplots. The targets were outliers either in speed or direction of motion, and were presented with varying levels of saliency in dimensions that are irrelevant to the task of motion outlier detection (e.g., color, size, position). We found that participants had trouble finding the outlier when it lacked irrelevant salient features and that visual channels contribute unevenly to the odds of an outlier being correctly detected. Direction of motion contributes the most to accurate detection of speed outliers, and position contributes the most to accurate detection of direction outliers. We introduce the concept of saliency deficit in which item importance in the data space is not reflected in the visualization due to a lack of saliency. We conclude that motion outlier detection is not well supported in multivariate animated scatterplots.
Scaptics and Highlight-Planes: Immersive Interaction Techniques for Finding Occluded Features in 3D Scatterplots
Authors: Arnaud Prouzeau, Maxime Cordeil, Clement Robin, Barrett Ens, Bruce H. Thomas, Tim Dwyer
Published at CHI, 2019

Three-dimensional scatterplots suffer from well-known perception and usability problems. In particular, overplotting and occlusion, mainly due to density and noise, prevent users from properly perceiving the data. Thanks to accurate head and hand tracking, immersive Virtual Reality (VR) setups provide new ways to interact and navigate with 3D scatterplots. VR also supports additional sensory modalities such as haptic feedback. Inspired by methods commonly used in Scientific Visualisation to visually explore volumes, we propose two techniques that leverage the immersive aspects of VR: first, a density-based haptic vibration technique (Scaptics) which provides feedback through the controller; and second, an adaptation of a cutting plane for 3D scatterplots (Highlight-Plane). We evaluated both techniques in a controlled study with two tasks involving density (finding high- and low-density areas). Overall, Scaptics was the most time-efficient and accurate technique, however, in some conditions, it was outperformed by Highlight-Plane.
TalkTraces: Real-Time Capture and Visualization of Verbal Content in Meetings
Authors: Senthil K. Chandrasegaran, Chris Bryan, Hidekazu Shidara, Tung-Yen Chuang, Kwan-Liu Ma
Published at CHI, 2019

Group Support Systems provide ways to review and edit shared content during meetings, but typically require participants to explicitly generate the content. Recent advances in speech-to-text conversion and language processing now make it possible to automatically record and review spoken information. We present the iterative design and evaluation of TalkTraces, a real-time visualization that helps teams identify themes in their discussions and obtain a sense of agenda items covered. We use topic modeling to identify themes within the discussions and word embeddings to compute the discussion "relatedness" to items in the meeting agenda. We evaluate TalkTraces iteratively: we first conduct a comparative between-groups study between two teams using TalkTraces and two teams using traditional notes, over four sessions. We translate the findings into changes in the interface, further evaluated by one team over four sessions. Based on our findings, we discuss design implications for real-time displays of discussion content.
Thinking Too Classically: Research Topics in Human-Quantum Computer Interaction
Authors: Zahra Ashktorab, Justin D. Weisz, Maryam Ashoori
Published at CHI, 2019

Quantum computing is a fundamentally different way of performing computation than classical computing. Many problems that are considered hard for classical computers may have efficient solutions using quantum computers. Recently, technology companies including IBM, Microsoft, and Google have invested in developing both quantum computing hardware and software to explore the potential of quantum computing. Because of the radical shift in computing paradigms that quantum represents, we see an opportunity to study the unique needs people have when interacting with quantum systems, what we call Quantum HCI (QHCI). Based on interviews with experts in quantum computing, we identify four areas in which HCI researchers can contribute to the field of quantum computing. These areas include understanding current and future quantum users, tools for programming and debugging quantum algorithms, visualizations of quantum states, and educational materials to train the first generation of "quantum native" programmers.
Towards Collaboration Translucence: Giving Meaning to Multimodal Group Data
Authors: Vanessa Echeverría, Roberto Martínez Maldonado, Simon Buckingham Shum
Published at CHI, 2019

Collocated, face-to-face teamwork remains a pervasive mode of working, which is hard to replicate online. Team members' embodied, multimodal interaction with each other and artefacts has been studied by researchers, but due to its complexity, has remained opaque to automated analysis. However, the ready availability of sensors makes it increasingly affordable to instrument work spaces to study teamwork and groupwork. The possibility of visualising key aspects of a collaboration has huge potential for both academic and professional learning, but a frontline challenge is the enrichment of quantitative data streams with the qualitative insights needed to make sense of them. In response, we introduce the concept of collaboration translucence, an approach to make visible selected features of group activity. This is grounded both theoretically (in the physical, epistemic, social and affective dimensions of group activity), and contextually (using domain-specific concepts). We illustrate the approach from the automated analysis of healthcare simulations to train nurses, generating four visual proxies that fuse multimodal data into higher order patterns.
Towards Effective Foraging by Data Scientists to Find Past Analysis Choices
Authors: Mary Beth Kery, Bonnie E. John, Patrick O'Flaherty, Amber Horvath, Brad A. Myers
Published at CHI, 2019

Data scientists are responsible for the analysis decisions they make, but it is hard for them to track the process by which they achieved a result. Even when data scientists keep logs, it is onerous to make sense of the resulting large number of history records full of overlapping variants of code, output, plots, etc. We developed algorithmic and visualization techniques for notebook code environments to help data scientists forage for information in their history. To test these interventions, we conducted a think-aloud evaluation with 15 data scientists, where participants were asked to find specific information from the history of another person's data science project. The participants succeed on a median of 80% of the tasks they performed. The quantitative results suggest promising aspects of our design, while qualitative results motivated a number of design improvements. The resulting system, called Verdant, is released as an open-source extension for JupyterLab.
Trust and Recall of Information across Varying Degrees of Title-Visualization Misalignment
Authors: Ha Kyung Kong, Zhicheng Liu 0001, Karrie Karahalios
Published at CHI, 2019

Visualizations are emerging as a means of spreading digital misinformation. Prior work has shown that visualization interpretation can be manipulated through slanted titles that favor only one side of the visual story, yet people still think the visualization is impartial. In this work, we study whether such effects continue to exist when titles and visualizations exhibit greater degrees of misalignment: titles whose message differs from the visually cued message in the visualization, and titles whose message contradicts the visualization. We found that although titles with a contradictory slant triggered more people to identify bias compared to titles with a miscued slant, visualizations were persistently perceived as impartial by the majority. Further, people's recall of the visualization's message more frequently aligned with the titles than the visualization. Based on these results, we discuss the potential of leveraging textual components to detect and combat visual-based misinformation with text-based slants.
Understanding Law Enforcement Strategies and Needs for Combating Human Trafficking
Authors: Julia Deeb-Swihart, Alex Endert, Amy S. Bruckman
Published at CHI, 2019

In working to rescue victims of human trafficking, law enforcement officers face a host of challenges. Working in complex, layered organizational structures, they face challenges of collaboration and communication. Online information is central to every phase of a human-trafficking investigation. With terabytes of available data such as sex work ads, policing is increasingly a big-data research problem. In this study, we interview sixteen law enforcement officers working to rescue victims of human trafficking to try to understand their computational needs. We highlight three major areas where future work in human-computer interaction can help. First, combating human trafficking requires advances in information visualization of large, complex, geospatial data, as victims are frequently forcibly moved across jurisdictions. Second, the need for unified information databases raises critical research issues of usable security and privacy. Finally, the archaic nature of information systems available to law enforcement raises policy issues regarding resource allocation for software development.
Understanding Visual Cues in Visualizations Accompanied by Audio Narrations
Authors: Ha Kyung Kong, Wenjie Zhu, Zhicheng Liu 0001, Karrie Karahalios
Published at CHI, 2019

It is often assumed that visual cues, which highlight specific parts of a visualization to guide the audience's attention, facilitate visualization storytelling and presentation. This assumption has not been systematically studied. We present an in-lab experiment and a Mechanical Turk study to examine the effects of integral and separable visual cues on the recall and comprehension of visualizations that are accompanied by audio narration. Eye-tracking data in the in-lab experiment confirm that cues helped the viewers focus on relevant parts of the visualization faster. We found that in general, visual cues did not have a significant effect on learning outcomes, but for specific cue techniques (e.g. glow) or specific chart types (e.g heatmap), cues significantly improved comprehension. Based on these results, we discuss how presenters might select visual cues depending on the role of the cues and the visualization type.
Vistribute: Distributing Interactive Visualizations in Dynamic Multi-Device Setups
Authors: Tom Horak, Andreas Mathisen, Clemens Nylandsted Klokmose, Raimund Dachselt, Niklas Elmqvist
Published at CHI, 2019

We present Vistribute, a framework for the automatic distribution of visualizations and UI components across multiple heterogeneous devices. Our framework consists of three parts: (i) a design space considering properties and relationships of interactive visualizations, devices, and user preferences in multi-display environments; (ii) specific heuristics incorporating these dimensions for guiding the distribution for a given interface and device ensemble; and (iii) a web-based implementation instantiating these heuristics to automatically generate a distribution as well as providing interaction mechanisms for user-defined adaptations. In contrast to existing UI distribution systems, we are able to infer all required information by analyzing the visualizations and devices without relying on additional input provided by users or programmers. In a qualitative study, we let experts create their own distributions and rate both other manual distributions and our automatic ones. We found that all distributions provided comparable quality, hence validating our framework.
Visualizing Uncertainty and Alternatives in Event Sequence Predictions
Authors: Shunan Guo, Fan Du, Sana Malik, Eunyee Koh, Sungchul Kim, Zhicheng Liu 0001, Donghyun Kim 0007, Hongyuan Zha, Nan Cao 0001
Published at CHI, 2019

Data analysts apply machine learning and statistical methods to timestamped event sequences to tackle various problems but face unique challenges when interpreting the results. Especially in event sequence prediction, it is difficult to convey uncertainty and possible alternative paths or outcomes. In this work, informed by interviews with five machine learning practitioners, we iteratively designed a novel visualization for exploring event sequence predictions of multiple records where users are able to review the most probable predictions and possible alternatives alongside uncertainty information. Through a controlled study with 18 participants, we found that users are more confident in making decisions when alternative predictions are displayed and they consider the alternatives more when deciding between two options with similar top predictions.
Visually Encoding the Lived Experience of Bipolar Disorder
Authors: Jaime Snyder, Elizabeth L. Murnane, Caitlin Lustig, Stephen Voida
Published at CHI, 2019

Issues of social identity, attitudes towards self-disclosure, and potentially biased approaches to what is considered "typical" or "normal" are critical factors when designing visualizations for personal informatics systems. This is particularly true when working with vulnerable populations like those who self-track to manage serious mental illnesses like bipolar disorder (BD). We worked with individuals diagnosed with BD to 1) better understand sense-making challenges related to the representation and interpretation of personal data and 2) probe the benefits, risks, and limitations of participatory approaches to designing personal data visualizations that better reflect their lived experiences. We describe our co-design process, present a series of emergent visual encoding schemas resulting from these activities, and report on the assessment of these speculative designs by participants. We conclude by summarizing important considerations and implications for designing personal data visualizations for (and with) people who self-track to manage serious mental illness.
VizML: A Machine Learning Approach to Visualization Recommendation
Authors: Kevin Zeng Hu, Michiel A. Bakker, Stephen Li, Tim Kraska, César A. Hidalgo 0001
Published at CHI, 2019

Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select, rather than manually specify. Here, we demonstrate a novel machine learning-based approach to visualization recommendation that learns visualization design choices from a large corpus of datasets and associated visualizations. First, we identify five key design choices made by analysts while creating visualizations, such as selecting a visualization type and choosing to encode a column along the X- or Y-axis. We train models to predict these design choices using one million dataset-visualization pairs collected from a popular online visualization platform. Neural networks predict these design choices with high accuracy compared to baseline models. We report and interpret feature importances from one of these baseline models. To evaluate the generalizability and uncertainty of our approach, we benchmark with a crowdsourced test set, and show that the performance of our model is comparable to human performance when predicting consensus visualization type, and exceeds that of other visualization recommender systems.
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Authors: Kevin Zeng Hu, Snehalkumar (Neil) S. Gaikwad, Madelon Hulsebos, Michiel A. Bakker, Emanuel Zgraggen, César A. Hidalgo 0001, Tim Kraska, Guoliang Li 0001, Arvind Satyanarayan, Çagatay Demiralp
Published at CHI, 2019

Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs. These exemplars often lack the characteristics of real-world datasets, and their one-off nature makes it difficult to compare different techniques. In this paper, we present VizNet: a large-scale corpus of over 31 million datasets compiled from open data repositories and online visualization galleries. On average, these datasets comprise 17 records over 3 dimensions and across the corpus, we find 51% of the dimensions record categorical data, 44% quantitative, and only 5% temporal. VizNet provides the necessary common baseline for comparing visualization design techniques, and developing benchmark models and algorithms for automating visual analysis. To demonstrate VizNet's utility as a platform for conducting online crowdsourced experiments at scale, we replicate a prior study assessing the influence of user task and data distribution on visual encoding effectiveness, and extend it by considering an additional task: outlier detection. To contend with running such studies at scale, we demonstrate how a metric of perceptual effectiveness can be learned from experimental results, and show its predictive power across test datasets.
A Visual Analytics Framework for Spatiotemporal Trade Network Analysis
Authors: Hong Wang, Yafeng Lu, Shade T. Shutters, Michael Steptoe, Feng Wang 0012, Steven Landis, Ross Maciejewski
Published at VAST, 2018

Economic globalization is increasing connectedness among regions of the world, creating complex interdependencies within various supply chains. Recent studies have indicated that changes and disruptions within such networks can serve as indicators for increased risks of violence and armed conflicts. This is especially true of countries that may not be able to compete for scarce commodities during supply shocks. Thus, network-induced vulnerability to supply disruption is typically exported from wealthier populations to disadvantaged populations. As such, researchers and stakeholders concerned with supply chains, political science, environmental studies, etc. need tools to explore the complex dynamics within global trade networks and how the structure of these networks relates to regional instability. However, the multivariate, spatiotemporal nature of the network structure creates a bottleneck in the extraction and analysis of correlations and anomalies for exploratory data analysis and hypothesis generation. Working closely with experts in political science and sustainability, we have developed a highly coordinated, multi-view framework that utilizes anomaly detection, network analytics, and spatiotemporal visualization methods for exploring the relationship between global trade networks and regional instability. Requirements for analysis and initial research questions to be investigated are elicited from domain experts, and a variety of visual encoding techniques for rapid assessment of analysis and correlations between trade goods, network patterns, and time series signatures are explored. We demonstrate the application of our framework through case studies focusing on armed conflicts in Africa, regional instability measures, and their relationship to international global trade.
A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications
Authors: Cong Xie, Wei Xu 0020, Klaus Mueller 0001
Published at VAST, 2018

Anomalous runtime behavior detection is one of the most important tasks for performance diagnosis in High Performance Computing (HPC). Most of the existing methods find anomalous executions based on the properties of individual functions, such as execution time. However, it is insufficient to identify abnormal behavior without taking into account the context of the executions, such as the invocations of children functions and the communications with other HPC nodes. We improve upon the existing anomaly detection approaches by utilizing the call stack structures of the executions, which record rich temporal and contextual information. With our call stack tree (CSTree) representation of the executions, we formulate the anomaly detection problem as finding anomalous tree structures in a call stack forest. The CSTrees are converted to vector representations using our proposed stack2vec embedding. Structural and temporal visualizations of CSTrees are provided to support users in the identification and verification of the anomalies during an active anomaly detection process. Three case studies of real-world HPC applications demonstrate the capabilities of our approach.
An Information-Theoretic Approach to the Cost-benefit Analysis of Visualization in Virtual Environments
Authors: Min Chen 0001, Kelly P. Gaither, Nigel W. John, Brian C. McCann
Published at VAST, 2018

Visualization and virtual environments (VEs) have been two interconnected parallel strands in visual computing for decades. Some VEs have been purposely developed for visualization applications, while many visualization applications are exemplary showcases in general-purpose VEs. Because of the development and operation costs of VEs, the majority of visualization applications in practice have yet to benefit from the capacity of VEs. In this paper, we examine this status quo from an information-theoretic perspective. Our objectives are to conduct cost-benefit analysis on typical VE systems (including augmented and mixed reality, theater-based systems, and large powerwalls), to explain why some visualization applications benefit more from VEs than others, and to sketch out pathways for the future development of visualization applications in VEs. We support our theoretical propositions and analysis using theories and discoveries in the literature of cognitive sciences and the practical evidence reported in the literatures of visualization and VEs.