In today's globalized world, there are increasing opportunities for individuals to communicate using a common non-native language (lingua franca). Non-native speakers often have opportunities to listen to foreign languages, but may not comprehend them as fully as native speakers do. To aid real-time comprehension, live transcription of subtitles is frequently used in everyday life (e.g., during Zoom conversations, watching YouTube videos, or on social networking sites). However, simultaneously reading subtitles while listening can increase cognitive load. In this study, we propose Dynamik, a system that reduces cognitive load during reading by decreasing the size of less important words and enlarging important ones, thereby enhancing sentence contrast. Our results indicate that Dynamik can reduce certain aspects of cognitive load, specifically, participants' perceived performance and effort among individuals with low proficiency in English, as well as enhance the users' sense of comprehension, especially among people with low English ability. We further discuss our methods' applicability to other languages and potential improvements and further research directions.

As the Internet develops, social networking and other communication tools have transformed people’s relationships into something fast, visible, and geographically huge. However, these communication tools have not expanded opportunities for acquainting oneself with neighbors outside one’s social network; rather, they have comparatively diminished occasions for interacting with unfamiliar neighbors by prioritizing communication with existing friends. Therefore, we invented the medium Ichiyo to increase the opportunities to think of neighbors walking along the same street or in the same neighborhood and to expand the imagination of those who pass by and those who used to be there. Thus, users can engage in indirect interaction. We used commercially available laser cutters to engrave QR codes on leaves that are naturally found in our living space to prevent environmental invasion. The QR codes lead to a communal space on the web where users can freely leave messages. By engraving QR codes, information can be virtually expanded to be presented. To get the feedback of Ichiyo, we let a total of several thousand people experience a new way of communication as a part of the exhibition “iii Exhibition 2022”, an art exhibition at the University of Tokyo. A total of more than 1,000 leaves engraved with QR codes were prepared and scattered at the exhibition site and along the road from the nearest station to the venue.

The Steering Law is a robust model to predict the movement time (MT) for steering through a constrained path, and the most representative example in human-computer interaction (HCI) is navigating cascaded menus. In typical implementations of cascaded menus, however, users can deviate from the path for a short time; we call this error-accepting delay, or Tdelay. Yamanaka modified the Steering Law to predict MT under several Tdelay conditions, and our goal is to investigate the reproducibility of his model with more various Tdelay values. In addition, HCI researchers have recently formed a consensus that the goodness of models should be judged by the prediction accuracy for future (untested) task conditions. Thus, for the sake of completeness, we conducted two analyses: a shuffle-split cross-validation and leave-one-Tdelay-out cross-validation. The results showed that, regardless of the all-data and cross-validation analyses, Yamanaka’s modified model outperformed the baseline Steering Law, which strengthened his original experimental report.

Touch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and common operations on them. The detection of single or double taps causes the single-tap latency problem, which creates a bottleneck in terms of the sensitivity of touch inputs. To reduce the single-tap latency, we propose a novel machine-learning-based tap prediction method called PredicTaps. Our method predicts whether a detected tap is a single tap or the first contact of a double tap without having to wait for the hundreds of milliseconds conventionally required. We present three evaluations and one user evaluation that demonstrate its broad applicability and usability for various tap situations on two form factors (touchpad and smartphone). The results showed PredicTaps reduces the single-tap latency from 150--500 ms to 12 ms on laptops and to 17.6 ms on smartphones without reducing usability.

Among various methods to learn a second language (L2), such as listening and shadowing, Extensive Viewing involves learning L2 by watching many videos. However, it is difficult for many L2 learners to smoothly and effortlessly comprehend video contents made for native speakers at the original speed. Therefore, we developed a language learning assistance system that automatically adjusts the playback speed according to the learner's comprehension. Our system judges that learners understand the contents if they laugh at the punchlines of comedy dramas, and vice versa. Experimental results show that this system supports learners with relatively low L2 ability (under 700 in TOEIC Score in the experimental condition) to understand video contents. Our system can widen learners' possible options of native speakers' videos as Extensive Viewing material.