Journaling is a well-established practice for reflecting on life events and constructing meaning; yet, finding concrete meaning from them remains challenging. Recent advances in generative AI demonstrate the potential to generate materials that support reflection, suggesting opportunities to assist meaning-making in journaling. However, prior approaches often center on ambiguous outputs and reflections of a single experience, leaving unexplored how concrete interpretations from AI can help self-reflection in journaling. In this study, we designed Augmentiary, an LLM-based journaling system that suggests candidate interpretations of experiences with concrete meaning while preserving users’ agency and voice, based on insights from a formative study with eight journal writers. We then conducted a four-week deployment study with 25 participants. Our findings show that AI’s interpretive feedback helped connect fragmented experiences and supported self-understanding through comparison with their own thoughts. Moreover, tensions between user agency and constructive reflection were revealed. We conclude by discussing design implications for AI-supported systems for fostering meaning-making without replacing users’ thinking.

Multi-agent AI systems are increasingly prevalent across digital environments, yet their social influence dynamics remain underexplored beyond basic compliance. This study investigates how different multi-agent configurations affect human decision-making through compliance and conversion mechanisms. We conducted a controlled experiment with 127 participants interacting with three LLM-powered agents across three conditions: Majority (all agents opposing participant), Minority (one dissenting agent), and Diffusion (gradual spread of minority position). Participants completed normative and informative tasks while reporting stance and confidence at five time points. Results demonstrate distinct influence patterns by condition and task type. In informative tasks, majority consensus drove largest immediate opinion changes, while minority dissent showed potential for delayed but deeper attitude shifts consistent with conversion-like processes. The diffusion condition revealed how temporal dynamics serve as persuasive signals. These findings extend social psychology theories to human-AI interaction, highlighting risks of synthetic consensus manipulation and opportunities for structured dissent to promote critical thinking.

Group dance, a sub-genre characterized by intricate motions made by a cohort of performers in tight synchronization, has a longstanding and culturally significant history and, in modern forms such as cheerleading, a broad base of current adherents. However, despite its popularity, learning group dance routines remains challenging. Based on the prior success of interactive systems to support individual dance learning, this paper argues that group dance settings are fertile ground for augmentation by interactive aids. To better understand these design opportunities, this paper presents a sequence of user-centered studies of and with amateur cheerleading troupes, spanning from the formative (interviews, observations) through the generative (an ideation workshop) to concept validation (technology probes and speed dating). The outcomes are a nuanced understanding of the lived practice of group dance learning, a set of interactive concepts to support those practices, and design directions derived from validating the proposed concepts. Through this empirical work, we expand the design space of interactive dance practice systems from the established context of single-user practice (primarily focused on gesture recognition) to a multi-user, group-based scenario focused on feedback and communication.

Touches between the fingers of an unencumbered hand represent a ready-to-use, eyes-free and expressive input space suitable for interacting with wearable devices such as smart glasses or watches. While prior work has focused on touches to the inner surface of the hand, touches to the nails, a practical site for mounting sensing hardware, have been comparatively overlooked. We extend prior implementations of single touch sensing nails to a full set of five and explore their potential for wearable input. We present design ideas and an input space of 144 touches (taps, flicks and swipes) derived from an ideation workshop. We complement this with data from two studies characterizing the subjective comfort and objective characteristics (task time, accuracy) of each touch. We conclude by synthesizing this material into a set of 29 viable nail touches, assessing their performance in a final study and illustrating how they could be used by presenting, and qualitatively evaluating, two example applications.

In order to utilize network resources efficiently and reduce regional congestion, associating mobile stations (MSs) with proper base stations (BSs) is of crucial importance in wireless cellular networks. There have been several load-aware proposals in literature, where most are classified into so-called closed-form approaches. In such approaches, each MS independently and deterministically selects the BS which is expected to provide the highest throughput. The throughput is estimated by a closed-form equation based on the assumption of the Proportional Fair (PF) user scheduler that ensures temporal fairness. However, the closed-form approaches do not perform well when the closed-form equation is not available, e.g., general α-fair user scheduler, where temporal fairness is not guaranteed, or deterministic BS association may make wrong decisions, e.g., under the dynamics of mobility or flow arrivals/departures. In this paper, we propose a novel BS association scheme, called ViSE (Virtual Scheduling based Emulation) to tackle such challenges. It emulates an optimal BS association by running a notion of virtual scheduler, and each MS randomly determines its associated BS with the probability proportional to the throughput virtually allocated by the virtual scheduler. We demonstrate through extensive simulations under various practical scenarios that ViSE outperforms the existing algorithms in terms of user schedulers with diverse fairness and robustness to network dynamics.

Due to the increasing demand of capacity in wireless cellular networks, the small cells such as pico and femto cells are becoming more popular to enjoy a spatial reuse gain, and thus cells with different sizes are expected to coexist in a complex manner. In such a heterogeneous environment, the role of interference management (IM) becomes of more importance, but technical challenges also increase, since the number of cell-edge users, suffering from severe interference from the neighboring cells, will naturally grow. In order to overcome low performance and/or high complexity of existing static and other dynamic IM algorithms, we propose a novel low-complex and fully distributed IM scheme, called REFIM (REFerence based Interference Management), in the downlink of heterogeneous multi-cell networks. We first formulate a general optimization problem that turns out to require intractable computation complexity for global optimality. To have a practical solution with low computational and signaling overhead, which is crucial for low-cost small-cell solutions, e.g., femto cells, in REFIM, we decompose it into per-BS (base station) problems based on the notion of reference user and reduce feedback overhead over backhauls both temporally and spatially. We evaluate REFIM through extensive simulations under various configurations, including the scenarios from a real deployment of BSs. We show that, compared to the schemes without IM, REFIM can yield more than 40% throughput improvement of cell-edge users while increasing the overall performance by 10~107%. This is equal to about 95% performance of the existing centralized IM algorithm (MC-IIWF) that is known to be near-optimal but hard to implement in practice due to prohibitive complexity. We also present that as long as interference is managed well, the spectrum sharing policy can outperform the best spectrum splitting policy where the number of subchannels is optimally divided between macro and femto cells.