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.
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.
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.
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.
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.