Investigating Performance and Usage of Input Methods for Soft Keyboard Hotkeys

Touch-based devices, despite their mainstream availability, do not support a unified and efficient command selection mechanism, available on every platform and application. We advocate that hotkeys, conventionally used as a shortcut mechanism on desktop computers, could be generalized as a command selection mechanism for touch-based devices, even for keyboard-less applications. In this paper, we investigate the performance and usage of soft keyboard shortcuts or hotkeys (abbreviated SoftCuts) through two studies comparing different input methods across sitting, standing and walking conditions. Our results suggest that SoftCuts not only are appreciated by participants but also support rapid command selection with different devices and hand configurations. We also did not find evidence that walking deters their performance when using the Once input method.Comment: 17+2 pages, published at Mobile HCI 202

Bonjour! Greeting Gestures for Collocated Interaction with Wearables

International audienceWearable devices such as smartwatches (SW) and head-worn displays (HWD) are gaining popularity. To improve the collocated capabilities of wearables, we need to facilitate collocated interaction in a socially acceptable manner. In this paper we propose to explore widespread used greeting gestures such as handshakes or head gestures to perform collocated interactions with wearables. These include pairing devices or information exchange. We analyze the properties of greetings and how they can map to different levels of wearable pairing (family, friend, work, stranger). This paper also suggest how these gestures could be detected with SWs and HWDs

Nouvelles techniques d'interaction pour les dispositifs miniaturisés de l'informatique mobile

Thanks to the availability of powerful miniaturized electronic coponents, this last decade have seen the popularization of small mobile devices such as smartphones, and even smaller devices for wearable computing. These new devices bring new interaction problems, such as the small size of the screen and the "fat-finger" problem (a relatively large portion of the sreen is occluded and pointing lacks precision). the objective of the work reported here is twofold : 1) to aquire a better understanding of pointing on small devices, thanks to an advanced study of Fitts' law, 2) to design new interaction techniques for increasing the interaction bandwidth between the user and the device.Du fait de la disponibilité de capteurs éléctroniques de plus en plus puissant, la dernière décennie a vu la popularisation de nouveaux dispositifs mobiles, comme les téléphones intelligents (smartphone), et même des dispositifs miniatures comme ceux de l'informatique portée. Ces nouveaux dispositifs apportent de nouveaux problèmes interactionnels, du fait de la petite taille de l'écran et du problème du "fat-finger" (lors de l'interaction, une large portion de l'écran se retrouve occultée par le doigt, et les tâches de pointage perdent en précision. L'objectif de ce travail est double : 1) d'acquérir une meilleure compréhension du pointage sur les petits dispositifs mobiles, grâce à une étude poussée de la loi Fitts, 2) de créer de nouvelles techniques d'interaction afin d'augmenter la bande passante interactionnelle entre l'utilisateur et le dispositi

Early multifocal stenosis after coronary artery snaring during off-pump coronary artery bypass in a patient with diabetes

AbstractJ Thorac Cardiovasc Surg 2001;122:1044-

CoAIcoder: Examining the Effectiveness of AI-assisted Collaborative Qualitative Analysis

While the domain of individual-level AI-assisted analysis has been extensively explored in previous studies, the field of AI-assisted collaborative qualitative analysis remains relatively unexplored. After identifying CQA practices and design opportunities through formative interviews, we introduce our collaborative qualitative coding tool, CoAIcoder, and designed the four different collaboration methods. We subsequently implemented a between-subject design involving 32 pairs of users who have undergone training in CQA across three commonly utilized phases under four methods. Our results suggest that CoAIcoder, which employs AI and a Shared Model, could potentially improve the efficiency of the coding process in CQA by fostering a quicker shared understanding and promoting early-stage discussions. However, this may come with the potential downside of reduced code diversity. We also underscored the existence of a trade-off between the level of independence and the coding outcome when humans collaborate during the early coding stages. Lastly, we identify design implications that could inspire and inform the future design of CQA systems

Investigating Performance and Usage of Input Methods for Soft Keyboard Hotkeys

International audienceTouch-based devices, despite their mainstream availability, do not support a unified and efficient command selection mechanism, available on every platform and application. We advocate that hotkeys, conventionally used as a shortcut mechanism on desktop computers, could be generalized as a command selection mechanism for touch-based devices, even for keyboard-less applications. In this paper, we investigate the performance and usage of soft keyboard shortcuts or hotkeys (abbreviated SoftCuts) through two studies comparing different input methods across sitting, standing and walking conditions. Our results suggest that SoftCuts not only are appreciated by participants but also support rapid command selection with different devices and hand configurations. We also did not find evidence that walking deters their performance when using the Once input method

CollabCoder: A GPT-Powered Workflow for Collaborative Qualitative Analysis

The Collaborative Qualitative Analysis (CQA) process can be time-consuming and resource-intensive, requiring multiple discussions among team members to refine codes and ideas before reaching a consensus. To address these challenges, we introduce CollabCoder, a system leveraging Large Language Models (LLMs) to support three CQA stages: independent open coding, iterative discussions, and the development of a final codebook. In the independent open coding phase, CollabCoder provides AI-generated code suggestions on demand, and allows users to record coding decision-making information (e.g. keywords and certainty) as support for the process. During the discussion phase, CollabCoder helps to build mutual understanding and productive discussion by sharing coding decision-making information with the team. It also helps to quickly identify agreements and disagreements through quantitative metrics, in order to build a final consensus. During the code grouping phase, CollabCoder employs a top-down approach for primary code group recommendations, reducing the cognitive burden of generating the final codebook. An evaluation involving 16 users confirmed the usability and effectiveness of CollabCoder and offered empirical insights into the LLMs' roles in CQA