DevLan/vispubs huggingface dataset:
LaserFactory: A Laser Cutter-based Electromechanical Assembly and Fabrication Platform to Make Functional Devices & Robots
Authors: Martin Nisser, Christina Chen Liao, Yuchen Chai, Aradhana Adhikari, Steve Hodges 0001, Stefanie Müller 0001
Published at CHI, 2021

LaserFactory is an integrated fabrication process that augments a commercially available fabrication machine to support the manufacture of fully functioning devices without human intervention. In addition to creating 2D and 3D mechanical structures, LaserFactory creates conductive circuit traces with arbitrary geometries, picks-and-places electronic and electromechanical components, and solders them in place. To enable this functionality, we make four contributions. First, we build a hardware add-on to the laser cutter head that can deposit silver circuit traces and assemble components. Second, we develop a new method to cure dispensed silver using a CO2 laser. Third, we build a motion-based signaling method that allows our system to be readily integrated with commercial laser cutters. Finally, we provide a design and visualization tool for making functional devices with LaserFactory. Having described the LaserFactory system, we demonstrate how it is used to fabricate devices such as a fully functioning quadcopter and a sensor-equipped wristband. Our evaluation shows that LaserFactory can assemble a variety of differently sized components (up to 65g), that these can be connected by narrow traces (down to 0.75mm) that become highly conductive after laser soldering (3.2Ω/m), and that our acceleration-based sensing scheme works reliably (to 99.5% accuracy).
Little Road Driving HUD: Heads-Up Display Complexity Influences Drivers' Perceptions of Automated Vehicles
Authors: Rebecca Currano, So Yeon Park, Dylan James Moore, Kent Lyons, David Sirkin
Published at CHI, 2021

Modern vehicles are using AI and increasingly sophisticated sensor suites to improve Advanced Driving Assistance Systems (ADAS) and support automated driving capabilities. Heads-Up-Displays (HUDs) provide an opportunity to visually inform drivers about vehicle perception and interpretation of the driving environment. One approach to HUD design may be to reveal to drivers the vehicle’s full contextual understanding, though it is not clear if the benefits of additional information outweigh the drawbacks of added complexity, or if this balance holds across drivers. We designed and tested an Augmented Reality (AR) HUD in an online study (N = 298), focusing on the influence of HUD visualizations on drivers’ situation awareness and perceptions. Participants viewed two driving scenes with one of three HUD conditions. Results were nuanced: situation awareness declined with increasing driving context complexity, and contrary to expectation, also declined with the presence of a HUD compared to no HUD. Significant differences were found by varying HUD complexity, which led us to explore different characterizations of complexity, including counts of scene items, item categories, and illuminated pixels. Our analysis finds that driving style interacts with driving context and HUD complexity, warranting further study.
Locomotion Vault: the Extra Mile in Analyzing VR Locomotion Techniques
Authors: Massimiliano Di Luca, Hasti Seifi, Simon Egan, Mar González-Franco
Published at CHI, 2021

Numerous techniques have been proposed for locomotion in virtual reality (VR). Several taxonomies consider a large number of attributes (e.g., hardware, accessibility) to characterize these techniques. However, finding the appropriate locomotion technique (LT) and identifying gaps for future designs in the high-dimensional space of attributes can be quite challenging. To aid analysis and innovation, we devised Locomotion Vault (https://locomotionvault.github.io/), a database and visualization of over 100 LTs from academia and industry. We propose similarity between LTs as a metric to aid navigation and visualization. We show that similarity based on attribute values correlates with expert similarity assessments (a method that does not scale). Our analysis also highlights an inherent trade-off between simulation sickness and accessibility across LTs. As such, Locomotion Vault shows to be a tool that unifies information on LTs and enables their standardization and large-scale comparison to help understand the space of possibilities in VR locomotion.
Mapping the Landscape of COVID-19 Crisis Visualizations
Authors: Yixuan Zhang 0001, Yifan Sun 0002, Lace M. K. Padilla, Sumit Barua, Enrico Bertini, Andrea G. Parker
Published at CHI, 2021

In response to COVID-19, a vast number of visualizations have been created to communicate information to the public. Information exposure in a public health crisis can impact people’s attitudes towards and responses to the crisis and risks, and ultimately the trajectory of a pandemic. As such, there is a need for work that documents, organizes, and investigates what COVID-19 visualizations have been presented to the public. We address this gap through an analysis of 668 COVID-19 visualizations. We present our findings through a conceptual framework derived from our analysis, that examines who, (uses) what data, (to communicate) what messages, in what form, under what circumstances in the context of COVID-19 crisis visualizations. We provide a set of factors to be considered within each component of the framework. We conclude with directions for future crisis visualization research.
MARVIS: Combining Mobile Devices and Augmented Reality for Visual Data Analysis
Authors: Ricardo Langner, Marc Satkowski, Wolfgang Büschel, Raimund Dachselt
Published at CHI, 2021

We present Marvis, a conceptual framework that combines mobile devices and head-mounted Augmented Reality (AR) for visual data analysis. We propose novel concepts and techniques addressing visualization-specific challenges. By showing additional 2D and 3D information around and above displays, we extend their limited screen space. AR views between displays as well as linking and brushing are also supported, making relationships between separated visualizations plausible. We introduce the design process and rationale for our techniques. To validate Marvis’ concepts and show their versatility and widespread applicability, we describe six implemented example use cases. Finally, we discuss insights from expert hands-on reviews. As a result, we contribute to a better understanding of how the combination of one or more mobile devices with AR can benefit visual data analysis. By exploring this new type of visualization environment, we hope to provide a foundation and inspiration for future mobile data visualizations.
MIRIA: A Mixed Reality Toolkit for the In-Situ Visualization and Analysis of Spatio-Temporal Interaction Data
Authors: Wolfgang Büschel, Anke Lehmann, Raimund Dachselt
Published at CHI, 2021

In this paper, we present MIRIA, a Mixed Reality Interaction Analysis toolkit designed to support the in-situ visual analysis of user interaction in mixed reality and multi-display environments. So far, there are few options to effectively explore and analyze interaction patterns in such novel computing systems. With MIRIA, we address this gap by supporting the analysis of user movement, spatial interaction, and event data by multiple, co-located users directly in the original environment. Based on our own experiences and an analysis of the typical data, tasks, and visualizations used in existing approaches, we identify requirements for our system. We report on the design and prototypical implementation of MIRIA, which is informed by these requirements and offers various visualizations such as 3D movement trajectories, position heatmaps, and scatterplots. To demonstrate the value of MIRIA for real-world analysis tasks, we conducted expert feedback sessions using several use cases with authentic study data.
Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis
Authors: Zhilan Zhou, Ximing Wen, Yue Wang 0035, David Gotz
Published at CHI, 2021

Visual analytics systems enable highly interactive exploratory data analysis. Across a range of fields, these technologies have been successfully employed to help users learn from complex data. However, these same exploratory visualization techniques make it easy for users to discover spurious findings. This paper proposes new methods to monitor a user’s analytic focus during visual analysis of structured datasets and use it to surface relevant articles that contextualize the visualized findings. Motivated by interactive analyses of electronic health data, this paper introduces a formal model of analytic focus, a computational approach to dynamically update the focus model at the time of user interaction, and a prototype application that leverages this model to surface relevant medical publications to users during visual analysis of a large corpus of medical records. Evaluation results with 24 users show that the modeling approach has high levels of accuracy and is able to surface highly relevant medical abstracts.
mTSeer: Interactive Visual Exploration of Models on Multivariate Time-series Forecast
Authors: Ke Xu, Jun Yuan, Yifang Wang 0001, Cláudio T. Silva, Enrico Bertini
Published at CHI, 2021

Time-series forecasting contributes crucial information to industrial and institutional decision-making with multivariate time-series input. Although various models have been developed to facilitate the forecasting process, they make inconsistent forecasts. Thus, it is critical to select the model appropriately. The existing selection methods based on the error measures fail to reveal deep insights into the model’s performance, such as the identification of salient features and the impact of temporal factors (e.g., periods). This paper introduces mTSeer, an interactive system for the exploration, explanation, and evaluation of multivariate time-series forecasting models. Our system integrates a set of algorithms to steer the process, and rich interactions and visualization designs to help interpret the differences between models in both model and instance level. We demonstrate the effectiveness of mTSeer through three case studies with two domain experts on real-world data, qualitative interviews with the two experts, and quantitative evaluation of the three case studies.
NBSearch: Semantic Search and Visual Exploration of Computational Notebooks
Authors: Xingjun Li, Yuanxin Wang, Hong Wang, Yang Wang, Jian Zhao 0010
Published at CHI, 2021

Code search is an important and frequent activity for developers using computational notebooks (e.g., Jupyter). The flexibility of notebooks brings challenges for effective code search, where classic search interfaces for traditional software code may be limited. In this paper, we propose, NBSearch, a novel system that supports semantic code search in notebook collections and interactive visual exploration of search results. NBSearch leverages advanced machine learning models to enable natural language search queries and intuitive visualizations to present complicated intra- and inter-notebook relationships in the returned results. We developed NBSearch through an iterative participatory design process with two experts from a large software company. We evaluated the models with a series of experiments and the whole system with a controlled user study. The results indicate the feasibility of our analytical pipeline and the effectiveness of NBSearch to support code search in large notebook collections.
PriView- Exploring Visualisations to Support Users' Privacy Awareness
Authors: Sarah Prange, Ahmed Shams, Robin Piening, Yomna Abdelrahman, Florian Alt
Published at CHI, 2021

We present PriView, a concept that allows privacy-invasive devices in the users’ vicinity to be visualised. PriView is motivated by an ever-increasing number of sensors in our environments tracking potentially sensitive data (e.g., audio and video). At the same time, users are oftentimes unaware of this, which violates their privacy. Knowledge about potential recording would enable users to avoid accessing such areas or not to disclose certain information. We built two prototypes: a) a mobile application capable of detecting smart devices in the environment using a thermal camera, and b) VR mockups of six scenarios where PriView might be useful (e.g., a rental apartment). In both, we included several types of visualisation. Results of our lab study (N=24) indicate that users prefer simple, permanent indicators while wishing for detailed visualisations on demand. Our exploration is meant to support future designs of privacy visualisations for varying smart environments.
Quantitative Data Visualisation on Virtual Globes
Authors: Kadek Ananta Satriadi, Barrett Ens, Tobias Czauderna, Maxime Cordeil, Bernhard Jenny
Published at CHI, 2021

Geographic data visualisation on virtual globes is intuitive and widespread, but has not been thoroughly investigated. We explore two main design factors for quantitative data visualisation on virtual globes: i) commonly used primitives (2D bar, 3D bar, circle) and ii) the orientation of these primitives (tangential, normal, billboarded). We evaluate five distinctive visualisation idioms in a user study with 50 participants. The results show that aligning primitives tangentially on the globe’s surface decreases the accuracy of area-proportional circle visualisations, while the orientation does not have a significant effect on the accuracy of length-proportional bar visualisations. We also find that tangential primitives induce higher perceived mental load than other orientations. Guided by these results we design a novel globe visualisation idiom, Geoburst, that combines a virtual globe and a radial bar chart. A preliminary evaluation reports potential benefits and drawbacks of the Geoburst visualisation.
RCEA-360VR: Real-time, Continuous Emotion Annotation in 360° VR Videos for Collecting Precise Viewport-dependent Ground Truth Labels
Authors: Tong Xue, Abdallah El Ali, Tianyi Zhang, Gangyi Ding, Pablo César
Published at CHI, 2021

Precise emotion ground truth labels for 360° virtual reality (VR) video watching are essential for fine-grained predictions under varying viewing behavior. However, current annotation techniques either rely on post-stimulus discrete self-reports, or real-time, continuous emotion annotations (RCEA) but only for desktop/mobile settings. We present RCEA for 360° VR videos (RCEA-360VR), where we evaluate in a controlled study (N=32) the usability of two peripheral visualization techniques: HaloLight and DotSize. We furthermore develop a method that considers head movements when fusing labels. Using physiological, behavioral, and subjective measures, we show that (1) both techniques do not increase users’ workload, sickness, nor break presence (2) our continuous valence and arousal annotations are consistent with discrete within-VR and original stimuli ratings (3) users exhibit high similarity in viewing behavior, where fused ratings perfectly align with intended labels. Our work contributes usable and effective techniques for collecting fine-grained viewport-dependent emotion labels in 360° VR.
Reconfiguration Strategies with Composite Data Physicalizations
Authors: Kim Sauvé, David Verweij, Jason Alexander, Steven Houben
Published at CHI, 2021

Composite data physicalizations allow for the physical reconfiguration of data points, creating new opportunities for interaction and engagement. However, there is a lack of understanding of people’s strategies and behaviors when directly manipulating physical data objects. In this paper, we systematically characterize different reconfiguration strategies using six exemplar physicalizations. We asked 20 participants to reorganize these exemplars with two levels of restriction: changing a single data object versus changing multiple data objects. Our findings show that there were two main reconfiguration strategies used: changes in proximity and changes in atomic orientation. We further characterize these using concrete examples of participant actions in relation to the structure of the physicalizations. We contribute an overview of reconfiguration strategies, which informs the design of future manually reconfigurable and dynamic composite physicalizations.
reVISit: Looking Under the Hood of Interactive Visualization Studies
Authors: Carolina Nobre, Dylan Wootton, Zach Cutler, Lane Harrison, Hanspeter Pfister, Alexander Lex
Published at CHI, 2021

Quantifying user performance with metrics such as time and accuracy does not show the whole picture when researchers evaluate complex, interactive visualization tools. In such systems, performance is often influenced by different analysis strategies that statistical analysis methods cannot account for. To remedy this lack of nuance, we propose a novel analysis methodology for evaluating complex interactive visualizations at scale. We implement our analysis methods in reVISit, which enables analysts to explore participant interaction performance metrics and responses in the context of users’ analysis strategies. Replays of participant sessions can aid in identifying usability problems during pilot studies and make individual analysis processes salient. To demonstrate the applicability of reVISit to visualization studies, we analyze participant data from two published crowdsourced studies. Our findings show that reVISit can be used to reveal and describe novel interaction patterns, to analyze performance differences between different analysis strategies, and to validate or challenge design decisions.
Soloist: Generating Mixed-Initiative Tutorials from Existing Guitar Instructional Videos Through Audio Processing
Authors: Bryan Wang, Mengyu Yang, Tovi Grossman
Published at CHI, 2021

Learning musical instruments using online instructional videos has become increasingly prevalent. However, pre-recorded videos lack the instantaneous feedback and personal tailoring that human tutors provide. In addition, existing video navigations are not optimized for instrument learning, making the learning experience encumbered. Guided by our formative interviews with guitar players and prior literature, we designed Soloist, a mixed-initiative learning framework that automatically generates customizable curriculums from off-the-shelf guitar video lessons. Soloist takes raw videos as input and leverages deep-learning based audio processing to extract musical information. This back-end processing is used to provide an interactive visualization to support effective video navigation and real-time feedback on the user's performance, creating a guided learning experience. We demonstrate the capabilities and specific use-cases of Soloist within the domain of learning electric guitar solos using instructional YouTube videos. A remote user study, conducted to gather feedback from guitar players, shows encouraging results as the users unanimously preferred learning with Soloist over unconverted instructional videos.
STREAM: Exploring the Combination of Spatially-Aware Tablets with Augmented Reality Head-Mounted Displays for Immersive Analytics
Authors: Sebastian Hubenschmid, Johannes Zagermann, Simon Butscher, Harald Reiterer
Published at CHI, 2021

Recent research in the area of immersive analytics demonstrated the utility of head-mounted augmented reality devices for visual data analysis. However, it can be challenging to use the by default supported mid-air gestures to interact with visualizations in augmented reality (e.g. due to limited precision). Touch-based interaction (e.g. via mobile devices) can compensate for these drawbacks, but is limited to two-dimensional input. In this work we present STREAM: Spatially-aware Tablets combined with Augmented Reality Head-Mounted Displays for the multimodal interaction with 3D visualizations. We developed a novel eyes-free interaction concept for the seamless transition between the tablet and the augmented reality environment. A user study reveals that participants appreciated the novel interaction concept, indicating the potential for spatially-aware tablets in augmented reality. Based on our findings, we provide design insights to foster the application of spatially-aware touch devices in augmented reality and research implications indicating areas that need further investigation.
Tele-Immersive Improv: Effects of Immersive Visualisations on Rehearsing and Performing Theatre Online
Authors: Boyd Branch, Christos Efstratiou, Piotr Mirowski, Kory W. Mathewson, Paul Allain
Published at CHI, 2021

Performers acutely need but lack tools to remotely rehearse and create live theatre, particularly due to global restrictions on social interactions during the Covid-19 pandemic. No studies, however, have heretofore examined how remote video-collaboration affects performance. This paper presents the findings of a field study with 16 domain experts over six weeks investigating how tele-immersion affects the rehearsal and performance of improvisational theatre. To conduct the study, an original media server was developed for co-locating remote performers into shared virtual 3D environments which were accessed through popular video conferencing software. The results of this qualitative study indicate that tele-immersive environments uniquely provide performers with a strong sense of co- presence, feelings of physical connection, and an increased ability to enter the social-flow states required for improvisational theatre. Based on our observations, we put forward design recommendations for video collaboration tools tailored to the unique demands of live performance.
The Public Life of Data: Investigating Reactions to Visualizations on Reddit
Authors: Tobias Kauer, Marian Dörk, Arran L. Ridley, Benjamin Bach
Published at CHI, 2021

This research investigates how people engage with data visualizations when commenting on the social platform Reddit. There has been considerable research on collaborative sensemaking with visualizations and the personal relation of people with data. Yet, little is known about how public audiences without specific expertise and shared incentives openly express their thoughts, feelings, and insights in response to data visualizations. Motivated by the extensive social exchange around visualizations in online communities, this research examines characteristics and motivations of people’s reactions to posts featuring visualizations. Following a Grounded Theory approach, we study 475 reactions from the /r/dataisbeautiful community, identify ten distinguishable reaction types, and consider their contribution to the discourse. A follow-up survey with 168 Reddit users clarified their intentions to react. Our results help understand the role of personal perspectives on data and inform future interfaces that integrate audience reactions into visualizations to foster a public discourse about data.
Towards an Understanding of Situated AR Visualization for Basketball Free-Throw Training
Authors: Tica Lin, Rishi Singh, Yalong Yang 0001, Carolina Nobre, Johanna Beyer, Maurice A. Smith, Hanspeter Pfister
Published at CHI, 2021

We present an observational study to compare co-located and situated real-time visualizations in basketball free-throw training. Our goal is to understand the advantages and concerns of applying immersive visualization to real-world skill-based sports training and to provide insights for designing AR sports training systems. We design both a situated 3D visualization on a head-mounted display and a 2D visualization on a co-located display to provide immediate visual feedback on a player’s shot performance. Using a within-subject study design with experienced basketball shooters, we characterize user goals, report on qualitative training experiences, and compare the quantitative training results. Our results show that real-time visual feedback helps athletes refine subsequent shots. Shooters in our study achieve greater angle consistency with our visual feedback. Furthermore, AR visualization promotes an increased focus on body form in athletes. Finally, we present suggestions for the design of future sports AR studies.