Home-based care (HBC) delivers medical and care services in patients’ living environments, offering unique opportunities for patient-centered care. However, patient agency is often inadequately represented in shared HBC planning processes. Through 23 multi-stakeholder interviews with HBC patients, healthcare professionals, and care workers, alongside 60 hours of ethnographic observations, we examined how patient agency manifests in HBC and why this representation gap occurs. Our findings reveal that patient agency is not a static individual attribute but a relational capacity shaped through maintaining everyday continuity, mutual recognition from care providers, and engagement with material home environments. Furthermore, we identified that structured documentation systems filter out contextual knowledge, informal communication channels fragment patient voices, and doctor-centered hierarchies position patients as passive recipients. Drawing on these insights, we propose design considerations to bridge this representation gap and to integrate patient agency into shared HBC plans.

Journaling has long been recognized for fostering emotional awareness and self-reflection, and recent advancements in generative AI offer new opportunities to create personalized music that can enhance these practices.In this study, we explore how AI-generated music can augment the journaling experience.Through a formative study, we examined journal writers' writing patterns, purposes, emotional regulation strategies, and the design requirements for the system that augments journaling experience by journal-based AI-generated music.Based on these insights, we developed NoRe, a system that transforms journal entries into personalized music using generative AI.In a seven-day in-the-wild study (N=15), we investigated user engagement and perceived emotional effectiveness through system logs, surveys, and interviews.Our findings suggest that journal-based music generation could support emotional reflection and provide vivid reminiscence of daily experiences.Drawing from these findings, we discuss design implications for tailoring music to journal writers' emotional states and preferences.

Techno-spiritual practices refer to the use of digital technologies to support various spiritual activities, such as scripture reading. While prior human-computer interaction studies largely focus on understanding techno-spiritual practices in both personal and ministry environments, there is a lack of design research that explores novel design opportunities, based on longitudinal field deployment. As an important techno-spiritual practice, this work focuses on scripture reading and investigates the design space of "social scripture reading," as it is often organized into small groups for successful behavior maintenance. We designed and evaluated BibleCell, a social scripture reading tool that supports personalized reading plans, scripture reading, and social sharing. After the third year of deployment, we performed a two-month user study in Korean Protestant churches to deepen our understanding of techno-spiritual practices in social contexts via in-depth interviews (n = 27). We report the major themes of social techno-spiritual practices, such as social motivators, social interaction patterns, and leadership roles. We discuss our findings using a novel design concept of social-spiritual awareness that considers both the social and spiritual aspects of interactions in social computing systems.

User-driven intervention tools such as self-tracking help users to self-regulate problematic smartphone usage. These tools basically assume active user engagement, but prior studies warned a lack of user engagement over time. This paper proposes GoldenTime, a mobile app that promotes self-regulated usage behavior via system-driven proactive timeboxing and micro-financial incentives framed as gain or loss for behavioral reinforcement. We conducted a large-scale user study (n = 210) to explore how our proactive timeboxing and micro-financial incentives influence users’ smartphone usage behaviors. Our findings show that GoldenTime’s timeboxing based micro-financial incentives are effective in self-regulating smartphone usage, and incentive framing has a significant impact on user behavior. We provide practical design guidelines for persuasive technology design related to promoting digital wellbeing.

JavaScript has become one of the most widely used programming languages for web development, server-side programming, and even micro-controllers for IoT. However, its extremely functional and dynamic features degrade the performance and precision of static analysis. Moreover, the variety of built-in functions and host environments requires excessive manual modeling of their behaviors. To alleviate these problems, researchers have proposed various ways to leverage dynamic analysis during JavaScript static analysis. However, they do not fully utilize the high performance of dynamic analysis and often sacrifice the soundness of static analysis. In this paper, we present dynamic shortcuts, a new technique to flexibly switch between abstract and concrete execution during JavaScript static analysis in a sound way. It can significantly improve the analysis performance and precision by using highly-optimized commercial JavaScript engines and lessen the modeling efforts for opaque code. We actualize the technique via SAFEDS, an extended combination of SAFE and Jalangi, a static analyzer and a dynamic analyzer, respectively. We evaluated SAFEDS using 269 official tests of Lodash 4 library. Our experiment shows that SAFEDS is 7.81x faster than the baseline static analyzer, and it improves the precision to reduce failed assertions by 12.31% on average for 22 opaque functions.

Instant access and gratification make it difficult for us to self-limit the use of smartphone apps. We hypothesize that a slight increase in the interaction cost of accessing an app could successfully discourage app use. We propose a proactive intervention that requests users to perform a simple lockout task (e.g., typing a fixed length number) whenever a target app is launched. We investigate how a lockout task with varying workloads (i.e., pause only without number input, 10-digit input, and 30-digit input) influence a user's decision making, by a 3-week, in-situ experiment with 40 participants. Our findings show that even the pause-only task that requires a user to press a button to proceed discouraged an average of 13.1% of app use, and the 30-digit-input task discouraged 47.5%. We derived determinants of app use and non-use decision making for a given lockout task. We further provide implications for persuasive technology design for discouraging undesired behaviors.

Static program analysis often encounters problems in analyzing library code. Most real-world programs use library functions intensively, and library functions are usually written in different languages. For example, static analysis of JavaScript programs requires analysis of the standard built-in library implemented in host environments. A common approach to analyze such opaque code is for analysis developers to build models that provide the semantics of the code. Models can be built either manually, which is time consuming and error prone, or automatically, which may limit application to different languages or analyzers. In this paper, we present a novel mechanism to support automatic modeling of opaque code, which is applicable to various languages and analyzers. For a given static analysis, our approach automatically computes analysis results of opaque code via dynamic testing during static analysis. By using testing techniques, the mechanism does not guarantee sound over-approximation of program behaviors in general. However, it is fully automatic, is scalable in terms of the size of opaque code, and provides more precise results than conventional over-approximation approaches. Our evaluation shows that although not all functionalities in opaque code can (or should) be modeled automatically using our technique, a large number of JavaScript built-in functions are approximated soundly yet more precisely than existing manual models.

JavaScript web applications (apps) are prevalent these days, and quality assurance of web apps gets even more important. Even though researchers have studied various analysis techniques and software industries have developed code analyzers for their own code repositories, statically analyzing web apps in a sound and scalable manner is challenging. On top of dynamic features of JavaScript, abundant execution flows triggered by user events make a sound static analysis difficult. In this paper, we propose a novel EventHandler ( EH )-based static analysis for web apps using dynamically collected state information. Unlike traditional whole-program analyses, the EH -based analysis intentionally analyzes partial execution flows using concrete user events. Such analyses surely miss execution flows in the entire program, but they analyze less infeasible flows reporting less false positives. Moreover, they can finish analyzing partial flows of web apps that whole-program analyses often fail to finish analyzing, and produce partial bug reports. Our experimental results show that the EH -based analysis improves the precision dramatically compared with a state-of-the-art JavaScript whole-program analyzer, and it can finish analysis of partial execution flows in web apps that the whole-program analyzer fails to analyze within a timeout.

There is a growing tendency to use the Internet as the transmission medium of the telerobotic system since the Internet is inexpensive and available all over the world. However, the bilateral real-time teleoperation can be unstable due to the random time delays of the Internet. The event-based teleoperation is one of the fine approaches to overcome the ransom time delays. Since the event based control uses a non-time action reference, the stability and response performance of the system are affected by the time, which an event is held for. This study introduces the variable holding time to secure stability and response performance at once. The proper holding time for each event is obtained depending on the characteristics of the task through the fuzzy logic. The proposed control scheme was implemented on a robot arm over the Internet to show its effectiveness.

Computation of the characteristic points such as inflection points or cusp on a curve is often necessary in CAGD applications. When a curve is represented in a B-spline form, such computations can be made easier once it is transformed in a set of polynomial curves in a power form. Once a curve is represented in a power form, a point evaluation can be also made faster due to Horner's rule even though some issues of stability remains. In addition, the implicitization process of a parametric curve using a resultant usually requires the geometry represented in a power form. Usual practice of the transformation of a B-spline curve into a set of piecewise polynomial curves in a power form is done by either a knot refinement followed by basis conversions, or applying a Taylor expansion on the B-spline curve for each knot span. Presented in this paper is a new algorithm, called direct expansion, for the problem. The algorithm first locates the coefficients of all the linear terms that make up the basis functions in a knot span, and then the algorithm directly obtains the power form representation of basis functions by expanding the summation of products of appropriate linear terms. Then, a polynomial segment of a knot span can be easily obtained by the summation of products of the basis functions within the knot span with corresponding control points. Repeating this operation for each knot span, all of the polynomials of the B-spline curve can be transformed into a power form.