UI Design & Development

会議の名前
CHI 2022
i-LaTeX: Manipulating Transitional Representations between LaTeX Code and Generated Documents
要旨

Document description languages such as LaTeX are used extensively to author scientific and technical documents, but editing them is cumbersome: code-based editors only provide generic features, while WYSIWYG interfaces only support a subset of the language. Our interviews with 11 LaTeX users highlighted their difficulties dealing with textually-encoded abstractions and with the mappings between source code and document output. To address some of these issues, we introduce Transitional Representations for document description languages, which enable the visualisation and manipulation of fragments of code in relation to their generated output. We present i-LaTeX, a LaTeX editor equipped with Transitional Representations of formulae, tables, images, and grid layouts. A 16-participant experiment shows that Transitional Representations let them complete common editing tasks significantly faster, with fewer compilations, and with a lower workload. We discuss how Transitional Representations affect editing strategies and conclude with directions for future work.

著者
Camille Gobert
Université Paris-Saclay, Orsay, France
Michel Beaudouin-Lafon
Université Paris-Saclay, CNRS, Inria, Orsay, France
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517494

動画
Overcoming Legacy Bias: Re-Designing Gesture Interactions in Virtual Reality With a San Community in Namibia
要旨

Recent improvements in hand-tracking technologies support novel applications and developments of gesture interactions in virtual reality (VR). Current implementations are mostly convention-based, originating in a Western technological context, thereby creating a legacy bias in gesture interaction implementations. With expanding application contexts and growing user groups and contexts, the design and selection of gestures need to be diversified. In this paper we present an exploration of natural gestures, followed by their implementation in a VR application and co-design of new gestures with a marginalized San community in Namibia. This study contributes to the still scarce empirical work in user-driven gesture design research, aiming to reduce legacy bias, on a methodological and technical level as well as through engaging non-WEIRD participants. Our findings confirm the applicability of our method, combined with Partner and Priming suggested by Morris et al., to the design of gestures inspired by natural interactions. We also consider the implementation of user-designed gestures to be necessary to asses usability, usefulness and technical issues in VR. Furthermore, the research directly advances the HCI agenda for diversity, through an ongoing research and design partnership with an indigenous community in Southern Africa, thereby challenging systemic bias and promoting design for the pluriverse.

著者
Emilie Maria Nybo. Arendttorp
Aalborg University, Aalborg, Denmark
Kasper Rodil
Aalborg University, Aalborg, Denmark
Heike Winschiers-Theophilus
Namibia University of Sceince and Technology, Windhoek, Namibia
Christof Magoath
Independent Researcher, Donkerbos, Namibia
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517549

動画
The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold Selection
要旨

Gesture recognition systems using nearest neighbor pattern matching are able to distinguish gesture from non-gesture actions by rejecting input whose recognition scores are poor. However, in the context of gesture customization, where training data is sparse, learning a tight rejection threshold that maximizes accuracy in the presence of continuous high activity (HA) data is a challenging problem. To this end, we present the Voight-Kampff Machine (VKM), a novel approach for rejection threshold selection. VKM uses new synthetic data techniques to select an initial threshold that the system thereafter adjusts based on the training set size and expected gesture production variability. We pair VKM with a state-of-the-art custom gesture segmenter and recognizer to evaluate our system across several HA datasets, where gestures are interleaved with non-gesture actions. Compared to alternative rejection threshold selection techniques, we show that our approach is the only one that consistently achieves high performance.

著者
Eugene Matthew. Taranta
University of Central Florida, Orlando, Florida, United States
Mykola Maslych
University of Central Florida, Orlando, Florida, United States
Ryan Ghamandi
UCF, Orlando, Florida, United States
Joseph LaViola
University of Central Florida, Orlando, Florida, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3502000

動画
Guided Bug Crush: Assist Manual GUI Testing of Android Apps via Hint Moves
要旨

Mobile apps are indispensable for people’s daily life. Complementing with automated GUI testing, manual testing is the last line of defence for app quality. However, the repeated actions and easily missing of functionalities make manual testing time-consuming and inefficient. Inspired by the game candy crush with flashy candies as hint moves for players, we propose an approach named NaviDroid for navigating testers via highlighted next operations for more effective and efficient testing. Within NaviDroid, we construct an enriched state transition graph with the triggering actions as the edges for two involved states. Based on it, we utilize the dynamic programming algorithm to plan the exploration path, and augment the GUI with visualized hints for testers to quickly explore untested activities and avoid duplicate explorations. The automated experiments demonstrate the high coverage and efficient path planning of NaviDroid and a user study further confirms its usefulness. The NaviDroid can help us develop more robust software that works in more mission-critical settings, not only by performing more thorough testing with the same effort that has been put in before, but also by integrating these techniques into different parts of development pipeline.

著者
Zhe Liu
Institute of Software, Chinese Academy of Sciences, Beijing, China
Chunyang Chen
Monash University, Melbourne, Victoria, Australia
Junjie Wang
Institute of Software, Chinese Academy of Sciences, Beijing, China
Yuekai Huang
University of Chinese Academy of Sciences, Beijing, China; Laboratory for Internet Software Technologies, Institute of Software Chinese Academy of Sciences, Beijing, China;, Beijing, China
Jun Hu
Institute of Software Chinese Academy of Sciences, Beijing, China
Qing Wang
Institute of Software Chinese Academy of Sciences, Beijing, Beijing, China
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501903

動画
Iterative Design of Gestures During Elicitation: Understanding the Role of Increased Production
要旨

Previous gesture elicitation studies have found that user proposals are influenced by legacy bias which may inhibit users from proposing gestures that are most appropriate for an interaction. Increasing production during elicitation studies has shown promise moving users beyond legacy gestures. However, variety decreases as more symbols are produced. While several studies have used increased production since its introduction, little research has focused on understanding the effect on the proposed gesture quality, on why variety decreases, and on whether increased production should be limited. In this paper, we present a gesture elicitation study aimed at understanding the impact of increased production. We show that users refine the most promising gestures and that how long it takes to find promising gestures varies by participant. We also show that gestural refinements provide insight into the gestural features that matter for users to assign semantic meaning and discuss implications for training gesture classifiers.

受賞
Honorable Mention
著者
Andreea Danielescu
Accenture Labs, San Francisco, California, United States
David Piorkowski
IBM Research, Yorktown Heights, New York, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501962

動画