Integrating recent advancements in resilient algorithms and techniques into existing codes is a singular challenge in fault tolerance - in part due to the underlying complexity of implementing resilience in the first place, but also due to the difficulty introduced when integrating the functionality of a standalone new strategy with the preexisting resilience layers of an application. We propose that the answer is not to build integrated solutions for users, but runtimes designed to integrate into a larger comprehensive resilience system and thereby enable the necessary jump to multi-layered recovery. Our work designs, implements, and verifies one such comprehensive system of runtimes. Utilizing Fenix, a process resilience tool with integration into preexisting resilience systems as a design priority, we update Kokkos Resilience and the use pattern of VeloC to support application-level integration of resilience runtimes. Our work shows that designing integrable systems rather than integrated systems allows for user-designed optimization and upgrading of resilience techniques while maintaining the simplicity and performance of all-in-one resilience solutions. More application-specific choice in resilience strategies allows for better long-term flexibility, performance, and - importantly - simplicity.

Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and human-computer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics.

Collaborative human-robot field operations rely on timely decision-making and coordination, which can be challenging for heterogeneous teams operating in large-scale deployments. In this work, we present the design of an immersive, mixed reality (MR) interface to support sense-making and situational awareness based on the data collection capabilities of both human and robotic team members. Our solution integrates state-of-the-art methods in environment mapping and MR so that users may gain rapid insights regarding the working environment, the current and previous locations of human and robot team members, and the environment data such team members have collected. We describe the implementation of our system, share lessons learned in collaborating with emergency responders throughout our design process, and offer a vision for the use of immersive displays for human-robot field team deployments in large-scale outdoor environments.

Augmented Reality (AR) can assist with physical tasks such as object assembly through the use of situated instructions. These instructions can be in the form of videos, pictures, text or guiding animations, where the most helpful media among these is highly dependent on both the user and the nature of the task. Our work supports the authoring of AR tutorials for assembly tasks with little overhead beyond simply performing the task itself. The presented system, AuthAR reduces the time and effort required to build interactive AR tutorials by automatically generating key components of the AR tutorial while the author is assembling the physical pieces. Further, the system guides authors through the process of adding videos, pictures, text and animations to the tutorial. This concurrent assembly and tutorial generation approach allows for authoring of portable tutorials that fit the preferences of different end users.

When delivering presentations to a co-located audience, we typically use slides with text and 2D graphics to complement the spoken narrative. Though presentations have largely been explored on 2D media, augmented reality (AR) allows presentation designers to add data and augmentations to existing physical infrastructure on display. This coupling could provide a more engaging experience to the audience and support comprehension. With HydrogenAR, we present a novel application that leverages the benefits of data-driven storytelling with those of AR to explain the unique challenges of hydrogen dispenser reliability. Utilizing physical props, situated data, and virtual augmentations and animations, HydrogenAR serves as a unique presentation tool, particularly critical for stakeholders, tour groups, and VIPs. HydrogenAR is a product of multiple collaborative design iterations with a local Hydrogen Fuel research team and is evaluated through interviews with team members and a user study with end-users to evaluate the usability and quality of the interactive AR experience. Through this work, we provide design considerations for AR data-driven presentations and discuss how AR could be used for innovative content delivery beyond traditional slide-based presentations.

Immersive Analytics (IA) uses immersive virtual and augmented reality displays for data visualization and visual analytics. Designers rely on studies of how accurately people interpret data in different visualizations to make effective visualization choices. However, these studies focus on data analysis in traditional desktop environments. We lack empirical grounding for how to best visualize data in immersive environments. This study explores how people interpret data visualizations across different display types by measuring how quickly and accurately people conduct three analysis tasks over five visual channels: color, size, height, orientation, and depth. We identify key quantitative differences in performance and user behavior, indicating that stereo viewing resolves some of the challenges of visualizations in 3D space. We also find that while AR displays encourage increased navigation, they decrease performance with color-based visualizations. Our results provide guidelines on how to tailor visualizations to different displays in order to better leverage the affordances of IA modalities.

Data collection and analysis in the field is critical for operations in domains such as environmental science and public safety. However, field workers currently face data- and platform-oriented issues in efficient data collection and analysis in the field, such as limited connectivity, screen space, and attentional resources. In this paper, we explore how visual analytics tools might transform field practices by more deeply integrating data into these operations. We use a design probe coupling mobile, cloud, and immersive analytics components to guide interviews with ten experts from five domains to explore how visual analytics could support data collection and analysis needs in the field. The results identify shortcomings of current approaches and target scenarios and design considerations for future field analysis systems. We embody these findings in FieldView, an extensible, open-source prototype designed to support critical use cases for situated field analysis. Our findings suggest the potential for integrating mobile and immersive technologies to enhance data's utility for various field operations and new directions for visual analytics tools to transform fieldwork.

This paper explores a new wearable system, called TYTH, that enables a novel form of human computer interaction based on the relative location and interaction between the user's tongue and teeth. TYTH allows its user to interact with a computing system by tapping on their teeth. This form of interaction is analogous to using a finger to type on a keypad except that the tongue substitutes for the finger and the teeth for the keyboard. We study the neurological and anatomical structures of the tongue to design TYTH so that the obtrusiveness and social awkwardness caused by the wearable is minimized while maximizing its accuracy and sensing sensitivity. From behind the user's ears, TYTH senses the brain signals and muscle signals that control tongue movement sent from the brain and captures the miniature skin surface deformation caused by tongue movement. We model the relationship between tongue movement and the signals recorded, from which a tongue localization technique and tongue-teeth tapping detection technique are derived. Through a prototyping implementation and an evaluation with 15 subjects, we show that TYTH can be used as a form of hands-free human computer interaction with 88.61% detection rate and promising adoption rate by users.

Augmented reality (AR) applications can leverage the full space of an environment to create immersive experiences. However, most empirical studies of interaction in AR focus on interactions with objects close to the user, generally within arms reach. As objects move farther away, the efficacy and usability of different interaction modalities may change. This work explores AR interactions at a distance, measuring how applications may support fluid, efficient, and intuitive interactive experiences in room-scale augmented reality. We conducted an empirical study (N = 20) to measure trade-offs between three interaction modalities-multimodal voice, embodied freehand gesture, and handhelds devices-for selecting, rotating, and translating objects at distances ranging from 8 to 16 feet (2.4m-4.9m). Though participants performed comparably with embodied freehand gestures and handheld remotes, they perceived embodied gestures as significantly more efficient and usable than device-mediated interactions. Our findings offer considerations for designing efficient and intuitive interactions in room-scale AR applications.