Existing guidelines for designing targets on smartphones often focus on single-tap operations for accurate selection. However, smartphone interfaces can support both tap and swipe actions. We explored user-performance differences between tap and swipe in two crowdsourced experiments using bar and square targets. Results indicated longer operation times, higher error rates, and significantly shifted touch points for swipe compared to tap. Our findings imply that current target-size guidelines may not apply to swipe-operated targets, and they reveal new research opportunities for swipeable-target designs.

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.

A prior work has recommended adding a 4-mm gap between a target and the edge of a screen, as tapping a target located at the screen edge takes longer than tapping non-edge targets. However, it is possible that this recommendation was created based on statistical errors, and unexplored situations existed in the prior work. In this study, we re-examine the recommendation by utilizing crowdsourced experiments to resolve the issues. If we observe the same results as the prior work through experiments including diversities, we can verify that the recommendation is suitable. We found that increasing the gap between the target and the screen edge decreased the movement time, which was consistent with the prior work. In addition, we newly found that increasing the gap decreased the error rate as well. On the basis of these results, we discuss how the gap and the target should be designed.

We propose a method that predicts the success rate in pointing to 2D rectangular targets by using 1D vertical-bar and horizontal-bar task results. The method can predict the success rates for more practical situations under fewer experimental conditions. This shortens the duration of experiments, thus saving costs for researchers and practitioners. We verified the method through two experiments: laboratory-based and crowdsourced ones. In the laboratory-based experiment, we found that using 1D task results to predict the success rate for 2D targets slightly decreases the prediction accuracy. In the crowdsourced experiment, this method scored better than using 2D task results. Thus, we recommend that researchers use the method properly depending on the situation.

These days, many apps acquire location data as a way of estimating the user's behavior. As such, there are privacy concerns in using location data. In particular, users who are concerned about privacy may reduce the frequency of location acquisition or turn off the function, even though it degrades the quality of service. On the other hand, the only options available to users are yes-no or either-or ones such as "Always permit background acquisition" or "Permit only while using the app". For example, users who give permission to "Permit only while using the app" are themselves unable to understand how far their own veil of privacy will be lifted. That is, there are no metrics that can help users to understand the value of their own location data. How should the value of location data be determined? This study attempts to answer that question. The difficulty is that the value of a single point of location data depends on the context, such as how much other location data the app holds or when the location data was obtained. We propose a "Location YardStick" (LYS) that calculates the value of location information fairly in context. We confirmed that the LYS score is close to the user's expectations by comparing its results with those of a large online survey of 1300 people, and we conducted case studies in which we calculated LYS on location data acquired in various actual contexts.

The analysis of crowd flow in urban regions (urban dynamics) from GPS traces has been actively explored over the last decade. However, the existing prediction models assume that the population density in the analysis area is almost uniform, making it difficult to analyze fine-grained urban dynamics on a nationwide scale, where urban and rural areas coexist. In this paper, we propose a predictive model, called mixed-order Poisson regression (MOIRE), to capture changes in active populations nationwide by combining lower-order patterns and higher-order interaction effects. The proposed method utilizes multiple pieces of contextual information that greatly affect crowd flows (e.g., time-of-day, day-of-the-week, weather situation, holiday calendar information). We evaluated MOIRE on two massive GPS datasets gathered in urban regions at different scales. The results show that it has better predictive performance than the state-of-the- art method. Moreover, we implemented an anomaly detection system in urban dynamics for the whole nation of Japan in accordance with MOIRE specifications. This application enabled us to confirm MOIRE's performance intuitively.

Maximum entropy inverse reinforcement learning (MaxEnt IRL) is an effective approach for learning the underlying rewards of demonstrated human behavior, while it is intractable in high-dimensional state space due to the exponential growth of calculation cost. In recent years, a few works on approximating MaxEnt IRL in large state spaces by graphs provide successful results, however, types of state space models are quite limited. In this work, we extend them to more generic large state space models with graphs where time interval consistency of Markov decision processes are guaranteed. We validate our proposed method in the context of driving behavior prediction. Experimental results using actual driving data confirm the superiority of our algorithm in both prediction performance and computational cost over other existing IRL frameworks.