Nonverbal cues in text-based computer-mediated communication (CMC), initially introduced to compensate for the lack of social and emotional cues, have evolved beyond their original purpose to express user identity. In particular, embodied identity cues—such as a user’s real face—remain relatively underexplored in text-based CMC despite their potential as richer cues. Recent advances in generative AI have lowered the barrier to AI-mediated self-presentation, yet empirical research is still needed to understand how these cues operate in real interactions and how users experience and accept them. To address this gap, we investigate the social and emotional effects of face-swapped GIFs (FSGIFs) created via generative AI. In a two-phase within-subjects experiment with 32 participants (16 dyads of close acquaintances), we find that FSGIFs significantly enhance relational benefits, including greater co-presence and intimacy compared to generic GIFs. Based on these findings and insights from interviews, we discuss design implications for AI-mediated self-presentation in text-based CMC.

Abstract Designers often create visualizations to achieve specific high‐level analytical or communication goals. These goals require people to extract complex and interconnected data patterns. Prior perceptual studies of visualization effectiveness have focused on low‐level tasks, such as estimating statistical quantities, and have recently explored high‐level comprehension of visualization. Despite the growing use of Large Language Models (LLMs) as visualization interpreters, how their interpretations relate to human understanding or what reasoning processes underlie their responses remains insufficiently understood. In this work, we explore LLMs' comprehension of visualization, examining the alignment between designers' communicative goals and what their audience sees. We have conducted a qualitative study to investigate the gap between human interpretative strategies and the reasoning pathways of LLMs across three types of visualizations, line graphs, bar graphs, and scatterplots, to identify the high‐level patterns generated by LLMs using three prompt conditions. Our analysis results indicate that LLMs exhibit a consistent interpretative strategy that remains unchanged across prompt constraints. Furthermore, we observe two distinct approaches: humans naturally synthesize data into trend‐centric narratives, whereas LLMs persist with a structural enumeration of comparisons and numerical ranges. Lastly, we see LLMs achieve visualization comprehension through mechanisms distinct from human intuition, pointing to critical challenges and new opportunities for visualization design.

Traffic forecasting represents a crucial problem within intelligent transportation systems.In recent research, Large Language Models (LLMs) have emerged as a promising method, but their intrinsic design, tailored primarily for sequential token processing, introduces notable challenges in effectively capturing spatial dependencies.Specifically, the inherent limitations of LLMs in modeling spatial relationships and their architectural incompatibility with graphstructured spatial data remain largely unaddressed.To overcome these limitations, we introduce ST-LINK, a novel framework that enhances the capability of Large Language Models to capture spatiotemporal dependencies.Its key components are Spatially-Enhanced Attention (SE-Attention) and the Memory Retrieval Feed-Forward Network (MRFFN).SE-Attention extends rotary position embeddings to integrate spatial correlations as direct rotational transformations within the attention mechanism.This approach maximizes spatial learning while preserving the LLM's inherent sequential processing structure.Meanwhile, MRFFN dynamically retrieves and utilizes key historical patterns to capture complex temporal dependencies and improve the stability of long-term forecasting.Comprehensive experiments on benchmark datasets demonstrate that ST-LINK surpasses conventional deep learning and LLM approaches, and effectively captures both regular traffic patterns and abrupt changes.

Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patterns were prevalent, but their structural vagueness and scale ambiguity hindered generalization. In contrast, deep learning models can effectively capture complex dynamics, yet often offer limited transparency. To bridge this gap, we propose a two-stage framework that integrates unsupervised pattern extracion with interpretable forecasting. (i) SIMPC segments and clusters multivariate time series, extracting recurrent patterns that are invariant to amplitude scaling and temporal distortion, even under varying window sizes. (ii) JISC-Net is a shapelet-based classifier that uses the initial part of extracted patterns as input and forecasts subsequent partial sequences for short-term directional movement. Experiments on Bitcoin and three S&P 500 equities demonstrate that our method ranks first or second in 11 out of 12 metric--dataset combinations, consistently outperforming baselines. Unlike conventional deep learning models that output buy-or-sell signals without interpretable justification, our approach enables transparent decision-making by revealing the underlying pattern structures that drive predictive outcomes.

Automatically assessing handwritten mathematical solutions is an important problem in educational technology with practical applications, but it remains a significant challenge due to the diverse formats, unstructured layouts, and symbolic complexity of student work.To address this challenge, we introduce VEHME-a Vision-Language Model for Evaluating Handwritten Mathematics Expressions-designed to assess open-form handwritten math responses with high accuracy and interpretable reasoning traces.VEHME integrates a two-phase training pipeline: (i) supervised fine-tuning using structured reasoning data, and (ii) reinforcement learning that aligns model outputs with multi-dimensional grading objectives, including correctness, reasoning depth, and error localization.To enhance spatial understanding, we propose an Expression-Aware Visual Prompting Module, trained on our synthesized multi-line math expressions dataset to robustly guide attention in visually heterogeneous inputs.Evaluated on AIHub and FERMAT datasets, VEHME achieves state-of-the-art performance among open-source models and approaches the accuracy of proprietary systems, demonstrating its potential as a scalable and accessible tool for automated math assessment.Our training and experiment code is publicly available at our GitHub repository.

Although rate-splitting multiple access (RSMA) has been widely investigated as one of the promising candidate schemes for 6-th generation wireless communication systems, detailed solutions on hybrid beamforming for RSMA have not been revealed so far. Given the situation that a number of commercial wireless networks are adopting massive MIMO systems across low to high frequency bands, a hybrid beamforming framework needs to be identified to enable RSMA in real networks. In this regard, we suggest a practical hybrid beamforming for RSMA comprising beam optimization procedures consisting of two phases. In the first phase, two-stage learning framework to design the RF precoder for RSMA systems is proposed. The first stage adopts supervised learning for private message transmission, while the second stage employs an unsupervised learning approach for common message transmission, utilizing a customized loss function to address a max-min fairness (MMF) criterion in the absence of a closed-form solution.In the second phase, baseband precoders for common and private messages can be derived based on the different criteria, and transmission power is properly allocated to common and private messages. The proposed hybrid beamforming framework does not require more feedback than conventional multiple access schemes, despite supporting common message transmission. Numerical results show that the proposed scheme guarantees high spectral and energy efficiency, even in the presence of imperfect channel state information (CSI) in both analog and digital domains.

Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs-graphs whose vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone. In this paper, we propose DG COMICS, a novel comic authoring tool for dynamic graphs that allows users to semi-automatically build and annotate comics. The tool uses a newly developed hierarchical clustering algorithm to segment consecutive snapshots of dynamic graphs while preserving their chronological order. It also presents rich information on both individuals and communities extracted from dynamic graphs in multiple views, where users can explore dynamic graphs and choose what to tell in comics. For evaluation, we provide an example and report the results of a user study and an expert review.

Accurate traffic forecasting is challenging due to the complex dependency on road networks, various types of roads, and the abrupt speed change due to the events. Recent works mainly focus on dynamic spatial modeling with adaptive graph embedding or graph attention having less consideration for temporal characteristics and in-situ modeling. In this paper, we propose a novel deep learning model named TESTAM, which individually models recurring and non-recurring traffic patterns by a mixture-of-experts model with three experts on temporal modeling, spatio-temporal modeling with static graph, and dynamic spatio-temporal dependency modeling with dynamic graph. By introducing different experts and properly routing them, TESTAM could better model various circumstances, including spatially isolated nodes, highly related nodes, and recurring and non-recurring events. For the proper routing, we reformulate a gating problem into a classification problem with pseudo labels. Experimental results on three public traffic network datasets, METR-LA, PEMS-BAY, and EXPY-TKY, demonstrate that TESTAM achieves a better indication and modeling of recurring and non-recurring traffic.

Visual clustering is a common perceptual task in scatterplots that supports diverse analytics tasks (e.g., cluster identification). However, even with the same scatterplot, the ways of perceiving clusters (i.e., conducting visual clustering) can differ due to the differences among individuals and ambiguous cluster boundaries. Although such perceptual variability casts doubt on the reliability of data analysis based on visual clustering, we lack a systematic way to efficiently assess this variability. In this research, we study perceptual variability in conducting visual clustering, which we call Cluster Ambiguity. To this end, we introduce CLAMS, a data-driven visual quality measure for automatically predicting cluster ambiguity in monochrome scatterplots. We first conduct a qualitative study to identify key factors that affect the visual separation of clusters (e.g., proximity or size difference between clusters). Based on study findings, we deploy a regression module that estimates the human-judged separability of two clusters. Then, CLAMS predicts cluster ambiguity by analyzing the aggregated results of all pairwise separability between clusters that are generated by the module. CLAMS outperforms widely-used clustering techniques in predicting ground truth cluster ambiguity. Meanwhile, CLAMS exhibits performance on par with human annotators. We conclude our work by presenting two applications for optimizing and benchmarking data mining techniques using CLAMS. The interactive demo of CLAMS is available at clusterambiguity.dev.

With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the traffic domain have also designed and improved DL models for forecasting tasks such as estimation of traffic speed and time of arrival. However, there exist many challenges in analyzing DL models due to the black-box property of DL models and complexity of traffic data (i.e., spatio-temporal dependencies). Collaborating with domain experts, we design a visual analytics system, AttnAnalyzer, that enables users to explore how DL models make predictions by allowing effective spatio-temporal dependency analysis. The system incorporates dynamic time warping (DTW) and Granger causality tests for computational spatio-temporal dependency analysis while providing map, table, line chart, and pixel views to assist user to perform dependency and model behavior analysis. For the evaluation, we present three case studies showing how AttnAnalyzer can effectively explore model behaviors and improve model performance in two different road networks. We also provide domain expert feedback.

Music creation is difficult because one must express one's creativity while following strict rules. The advancement of deep learning technologies has diversified the methods to automate complex processes and express creativity in music composition. However, prior research has not paid much attention to exploring the audiences' subjective satisfaction to improve music generation models. In this paper, we evaluate human satisfaction with the state-of-the-art automatic symbolic music generation models using deep learning. In doing so, we define a taxonomy for music generation models and suggest nine subjective evaluation metrics. Through an evaluation study, we obtained more than 700 evaluations from 100 participants, using the suggested metrics. Our evaluation study reveals that the token representation method and models' characteristics affect subjective satisfaction. Through our qualitative analysis, we deepen our understanding of AI-generated music and suggested evaluation metrics. Lastly, we present lessons learned and discuss future research directions of deep learning models for music creation.

To tackle ever-increasing city traffic congestion problems, researchers have proposed deep learning models to aid decision-makers in the traffic control domain. Although the proposed models have been remarkably improved in recent years, there are still questions that need to be answered before deploying models. For example, it is difficult to figure out which models provide state-of-the-art performance, as recently proposed models have often been evaluated with different datasets and experiment environments. It is also difficult to determine which models would work when traffic conditions change abruptly (e.g., rush hour). In this work, we conduct two experiments to answer the two questions. In the first experiment, we conduct an experiment with the state-of-the-art models and the identical public datasets to compare model performance under a consistent experiment environment. We then extract a set of temporal regions in the datasets, whose speeds change abruptly and use these regions to explore model performance with difficult intervals. The experiment results indicate that Graph-WaveNet and GMAN show better performance in general. We also find that prediction models tend to have varying performances with data and intervals, which calls for in-depth analysis of models on difficult intervals for real-world deployment.

As online marketplaces adopt new technologies to encourage consumers’ purchases (e.g., one-click purchases), the number of consumers who impulsively buy products also increases. Although some interventions have been introduced for consumers’ self-controlled purchases, there have been few studies that evaluate the effectiveness of the techniques in the real environment. In this paper, we conducted an online survey with 118 consumers in their 20s to investigate their impulse buying behaviors and self-control strategies. Based on the survey results and literature surveys, we developed interventions that can assist consumers in controlling their online purchase habits, including Reflection, Distraction, Desire Reduction, and Salient Cost. For evaluation, we enrolled 107 participants in a user study on a real-world e-commerce site. The results indicate that all interventions were effective in reducing impulse buying urges, with variations in user experiences. Our findings and design implications are discussed.

This paper proposes a novel switch control for the series elastic actuator (SEA). This switch control provides more robust position control along with better impulse dissipation performance. SEA is an actuator technology commonly used in robotic applications that require safe and precise interaction control. The SEA has built-in spring elements to inherently measure or estimate the transmitted force. It is often used for the application where the relevant action between motion and force is needed. The switching problem between position and torque control is one of the major issues, especially in situations where both position and torque information are critical, such as collisions. To deal with this problem, we discuss the conventional switching method and suggest a new method. Besides, this paper describes the criteria for comparing and analyzing them and presents a new approach that utilizes the concept of power to evaluate the performance of switching algorithms.

This paper proposes a practical and less conservative passivity analysis for series elastic actuators (SEAs) by introducing load port definition and shows that the achievable stiffness by the impedance control of SEA can be set higher than the inherent stiffness of SEA depending on the condition of the load dynamics. Since SEA can inherently measure or estimate a transmitted force thanks to its embedded spring element, impedance control is often exploited to render compliant behaviors related between the motion and the force. Although the stability of the SEA control system is of great importance, the conventional passivity analysis gives conservative criteria, and indeed limits the actual actuator performance. To tackle the conservatism of the conventional passivity in SEAs, we first explore the dynamic characteristics of SEA including load dynamics, which has been ignored for the sake of simplicity of the passivity analysis by excluding uncertain load dynamics. The inclusion of the load dynamics into the passivity analysis allows us to properly derive the less conservative limit of achievable stiffness by impedance control and the factors that determine the limit. The proposed analysis is verified by numerical simulations and applied to a passivity observer design for experimental validation on an actual SEA setup.

This paper proposes a novel parallel robot - Virtual Ground Robot (VGR) - that is driven by three Series Elastic Actuators (SEAs) to interact with a human. The proposed Virtual Ground Robot provides a virtual ground on which a human can stand on and interact in three directions: the pitch, the roll and the height directions. The most significant features of the proposed VGR are that 1) it is driven by RFSEAs (Reaction Force-sensing Series Elastic Actuator), and thus it can provide precise forces and torques, 2) the size of the VGR is small enough for a human to stand on with ease, and 3) it can generate torque/force large to support a weight of a human. Taking advantage of RFSEAs utilized in the proposed VGR, Spatial Force control algorithm is proposed in this paper. In order to design this controller, the motions of VGR are defined in the task space, the joint space and the RFSEA level. Based on the Kinematics, force control of VGR in the task level, which is named Spatial Force Control is designed and verified using experiments.