arXiv subjects

Yuanchun Shi

Publications and source records attributed to Yuanchun Shi.

At least 73 records · Page 4Linked to original sources

arXiv2024

Say Your Reason: Extract Contextual Rules In Situ for Context-aware Service Recommendation

This paper introduces SayRea, an interactive system that facilitates the extraction of contextual rules for personalized context-aware service recommendations in mobile scenarios. The system monitors a user's execution of registered services on their smartphones (via accessibility service) and proactively requests a single-sentence reason from the user. By utilizing a Large Language Model (LLM), SayRea parses the reason and predicts contextual relationships between the observed service and potential contexts (such as setting the alarm clock deep in the evening). In this way, SayRea can significantly reduce the cognitive load on users in anticipating future needs and selecting contextual attributes. A 10-day field study involving 20 participants showed that SayRea accumulated an average of 62.4 rules per user and successfully recommended 45% of service usage. The participants provided positive feedback on the system's usability, interpretability, and controllability. The findings highlight SayRea's effectiveness in personalized service recommendations and its potential to enhance user experience in mobile scenarios.

cs.HC↗

arXiv2024

AngleSizer: Enhancing Spatial Scale Perception for the Visually Impaired with an Interactive Smartphone Assistant

Spatial perception, particularly at small and medium scales, is an essential human sense but poses a significant challenge for the blind and visually impaired (BVI). Traditional learning methods for BVI individuals are often constrained by the limited availability of suitable learning environments and high associated costs. To tackle these barriers, we conducted comprehensive studies to delve into the real-world challenges faced by the BVI community. We have identified several key factors hindering their spatial perception, including the high social cost of seeking assistance, inefficient methods of information intake, cognitive and behavioral disconnects, and a lack of opportunities for hands-on exploration. As a result, we developed AngleSizer, an innovative teaching assistant that leverages smartphone technology. AngleSizer is designed to enable BVI individuals to use natural interaction gestures to try, feel, understand, and learn about sizes and angles effectively. This tool incorporates dual vibration-audio feedback, carefully crafted teaching processes, and specialized learning modules to enhance the learning experience. Extensive user experiments validated its efficacy and applicability with diverse abilities and visual conditions. Ultimately, our research not only expands the understanding of BVI behavioral patterns but also greatly improves their spatial perception capabilities, in a way that is both cost-effective and allows for independent learning.

cs.HC↗

arXiv2024

Bridging the gap between natural user expression with complex automation programming in smart homes

A long-standing challenge in end-user programming (EUP) is to trade off between natural user expression and the complexity of programming tasks. As large language models (LLMs) are empowered to handle semantic inference and natural language understanding, it remains under-explored how such capabilities can facilitate end-users to configure complex automation more naturally and easily. We propose AwareAuto, an EUP system that standardizes user expression and finishes two-step inference with the LLMs to achieve automation generation. AwareAuto allows contextual, multi-modality, and flexible user expression to configure complex automation tasks (e.g., dynamic parameters, multiple conditional branches, and temporal constraints), which are non-manageable in traditional EUP solutions. By studying realistic, complex rules data, AwareAuto gains 91.7% accuracy in matching user intentions and feasibility. We introduced user interaction to ensure system controllability and usability. We discuss the opportunities and challenges of incorporating LLMs in end-user programming techniques and grounding complex smart home contexts.

cs.HC↗

arXiv2024

SonarWatch: Field sensing technique for smartwatches based on ultrasound and motion

A smartwatch worn continuously on the wrist has the potential to perceive rich interactive gestures and natural behaviors of the user. Unfortunately, the current interaction functionality of smartwatches is primarily limited by the small touch screen. This paper proposes SonarWatch, a novel sensing technique that uses the acoustic field generated by the transceiver on the opposite sides of the watch to detect the presence of nearby objects and their shapes. This enables a range of gesture interactions and natural behavior perception. We designed an algorithm combining IMU and acoustic fields to identify these actions and optimize power consumption. We tested the performance of SonarWatch in different noise environments, achieving an overall accuracy of 93.7%. Its power consumption is close to that of physiological sensors. SonarWatch can achieve the above capabilities by utilizing the existing built-in sensors, making it a technology with solid practical value.

cs.HC↗

arXiv2024

CasualGaze: Towards Modeling and Recognizing Casual Gaze Behavior for Efficient Gaze-based Object Selection

We present CasualGaze, a novel eye-gaze-based target selection technique to support natural and casual eye-gaze input. Unlike existing solutions that require users to keep the eye-gaze center on the target actively, CasualGaze allows users to glance at the target object to complete the selection simply. To understand casual gaze behavior, we studied the spatial distribution of casual gaze for different layouts and user behavior in a simulated real-world environment. Results revealed the impacts of object parameters, the speed and randomness features of casual gaze, and special gaze behavior patterns in "blurred areas". Based on the results, we devised CasualGaze algorithms, employing a bivariate Gaussian distribution model along with temporal compensation and voting algorithms for robust target prediction. Usability evaluation study showed significant improvements in recognition and selection speed for CasualGaze compared with two baseline techniques. Subjective ratings and comments further supported the preference for CasualGaze regarding efficiency, accuracy, and stability.

cs.HC↗

arXiv2024

Leveraging Large Language Models for Generating Mobile Sensing Strategies in Human Behavior Modeling

Mobile sensing plays a crucial role in generating digital traces to understand human daily lives. However, studying behaviours like mood or sleep quality in smartphone users requires carefully designed mobile sensing strategies such as sensor selection and feature construction. This process is time-consuming, burdensome, and requires expertise in multiple domains. Furthermore, the resulting sensing framework lacks generalizability, making it difficult to apply to different scenarios. In the research, we propose an automated mobile sensing strategy for human behaviour understanding. First, we establish a knowledge base and consolidate rules for data collection and effective feature construction. Then, we introduce the multi-granular human behaviour representation and design procedures for leveraging large language models to generate strategies. Our approach is validated through blind comparative studies and usability evaluation. Ultimately, our approach holds the potential to revolutionise the field of mobile sensing and its applications.

cs.HC↗

arXiv2024

Asymmetric interaction preference induces cooperation in human-agent hybrid game

With the development of artificial intelligence, human beings are increasingly interested in human-agent collaboration, which generates a series of problems about the relationship between agents and humans, such as trust and cooperation. This inevitably induces the inherent human characteristic that there are subjective interaction preferences for different groups, especially in human-agent hybrid systems where human-human interaction, agent-agent interaction, and human-agent interaction coexist. However, understanding how individual interaction preferences affect the cooperation of the system remains a major challenge. Therefore, this paper proposes a human-agent hybrid prisoner's dilemma game system under the framework of evolutionary game. In spatial networks, the most significant difference between agents and humans is the flexibility of decision, where humans have higher adaptive capabilities, follow link dynamics, and adopt free decision rules, which enable them to choose different strategies for different neighbors. However, agents follow node dynamics and adopt consistent decision rules, applying the same strategy to different neighbors. We give the subjective preferences of any individual to different groups, involving the interaction preferences between homogeneous groups and heterogeneous groups respectively. The simulation results show that both human and agent have asymmetric interaction preferences for groups with different identities, which can significantly improve the cooperative behavior of the system. In the hybrid system, human groups show more stable prosocial behavior. Agent groups can form highly cooperative clusters under the condition of strong interaction preference for human groups. In addition, giving agents the ability to identify opponents can effectively alleviate the interaction dilemma of agents.

physics.soc-ph↗

arXiv2024

G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarios

Modern information querying systems are progressively incorporating multimodal inputs like vision and audio. However, the integration of gaze -- a modality deeply linked to user intent and increasingly accessible via gaze-tracking wearables -- remains underexplored. This paper introduces a novel gaze-facilitated information querying paradigm, named G-VOILA, which synergizes users' gaze, visual field, and voice-based natural language queries to facilitate a more intuitive querying process. In a user-enactment study involving 21 participants in 3 daily scenarios (p = 21, scene = 3), we revealed the ambiguity in users' query language and a gaze-voice coordination pattern in users' natural query behaviors with G-VOILA. Based on the quantitative and qualitative findings, we developed a design framework for the G-VOILA paradigm, which effectively integrates the gaze data with the in-situ querying context. Then we implemented a G-VOILA proof-of-concept using cutting-edge deep learning techniques. A follow-up user study (p = 16, scene = 2) demonstrates its effectiveness by achieving both higher objective score and subjective score, compared to a baseline without gaze data. We further conducted interviews and provided insights for future gaze-facilitated information querying systems.

cs.HC↗

arXiv2024

A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples

The capabilities of a single large language model (LLM) agent for solving a complex task are limited. Connecting multiple LLM agents to a network can effectively improve overall performance. However, building an LLM agent network (LAN) requires a substantial amount of time and effort. In this paper, we introduce EasyLAN, a human-computer collaborative tool that helps developers construct LANs. EasyLAN initially generates a LAN containing only one agent based on the description of the desired task. Subsequently, EasyLAN leverages a few training examples to update the LAN. For each example, EasyLAN models the gap between the output and the ground truth and identifies the causes of the errors. These errors are addressed through carefully designed strategies. Users can intervene in EasyLAN's workflow or directly modify the LAN. Eventually, the LAN evolves from a single agent to a network of LLM agents. The experimental results indicate that developers can rapidly construct LANs with good performance.

cs.HC↗

arXiv2024

A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis

Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillary.

eess.IV↗

arXiv2024

Complex Pathways to Cooperation Emergent from Asymmetry in Heterogeneous Populations

Cooperation within asymmetric populations has garnered significant attention in evolutionary games. This paper explores cooperation evolution in populations with weak and strong players, using a game model where players choose between cooperation and defection. Asymmetry stems from different benefits for strong and weak cooperators, with their benefit ratio indicating the degree of asymmetry. Varied rankings of parameters including the asymmetry degree, cooperation costs, and benefits brought by weak players give rise to scenarios including the prisoner's dilemma (PDG) for both player types, the snowdrift game (SDG), and mixed PDG-SDG interactions. Our results indicate that in an infinite well-mixed population, defection remains the dominant strategy when strong players engage in the prisoner's dilemma game. However, if strong players play snowdrift games, global cooperation increases with the proportion of strong players. In this scenario, strong cooperators can prevail over strong defectors when the proportion of strong players is low, but the prevalence of cooperation among strong players decreases as their proportion increases. In contrast, within a square lattice, the optimum global cooperation emerges at intermediate proportions of strong players with moderate degrees of asymmetry. Additionally, weak players protect cooperative clusters from exploitation by strong defectors. This study highlights the complex dynamics of cooperation in asymmetric interactions, contributing to the theory of cooperation in asymmetric games.

nlin.AO↗

arXiv2024

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention

Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explainable JITAI system that leverages machine learning to identify optimal intervention timings, introduces interventions with transparent AI explanations, and collects user feedback to establish a human-AI loop and adapt the intervention model over time. We conducted an 8-week field experiment (N=71) to evaluate the effectiveness of both the adaptation and explanation aspects of Time2Stop. Our results indicate that our adaptive models significantly outperform the baseline methods on intervention accuracy (>32.8\% relatively) and receptivity (>8.0\%). In addition, incorporating explanations further enhances the effectiveness by 53.8\% and 11.4\% on accuracy and receptivity, respectively. Moreover, Time2Stop significantly reduces overuse, decreasing app visit frequency by 7.0$\sim$8.9\%. Our subjective data also echoed these quantitative measures. Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust. We envision our work can inspire future research on JITAI systems with a human-AI loop to evolve with users.

cs.HC↗

arXiv2024

MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention

Problematic smartphone use negatively affects physical and mental health. Despite the wide range of prior research, existing persuasive techniques are not flexible enough to provide dynamic persuasion content based on users' physical contexts and mental states. We first conducted a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia. This informs our design of four persuasion strategies: understanding, comforting, evoking, and scaffolding habits. We leveraged large language models (LLMs) to enable the automatic and dynamic generation of effective persuasion content. We developed MindShift, a novel LLM-powered problematic smartphone use intervention technique. MindShift takes users' in-the-moment app usage behaviors, physical contexts, mental states, goals \& habits as input, and generates personalized and dynamic persuasive content with appropriate persuasion strategies. We conducted a 5-week field experiment (N=25) to compare MindShift with its simplified version (remove mental states) and baseline techniques (fixed reminder). The results show that MindShift improves intervention acceptance rates by 4.7-22.5% and reduces smartphone usage duration by 7.4-9.8%. Moreover, users have a significant drop in smartphone addiction scale scores and a rise in self-efficacy scale scores. Our study sheds light on the potential of leveraging LLMs for context-aware persuasion in other behavior change domains.

cs.CL↗

arXiv2024

UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language

We introduce UbiPhysio, a milestone framework that delivers fine-grained action description and feedback in natural language to support people's daily functioning, fitness, and rehabilitation activities. This expert-like capability assists users in properly executing actions and maintaining engagement in remote fitness and rehabilitation programs. Specifically, the proposed UbiPhysio framework comprises a fine-grained action descriptor and a knowledge retrieval-enhanced feedback module. The action descriptor translates action data, represented by a set of biomechanical movement features we designed based on clinical priors, into textual descriptions of action types and potential movement patterns. Building on physiotherapeutic domain knowledge, the feedback module provides clear and engaging expert feedback. We evaluated UbiPhysio's performance through extensive experiments with data from 104 diverse participants, collected in a home-like setting during 25 types of everyday activities and exercises. We assessed the quality of the language output under different tuning strategies using standard benchmarks. We conducted a user study to gather insights from clinical physiotherapists and potential users about our framework. Our initial tests show promise for deploying UbiPhysio in real-life settings without specialized devices.

cs.HC↗

arXiv2023

AutoTask: Executing Arbitrary Voice Commands by Exploring and Learning from Mobile GUI

Voice command interfaces (VCIs) have gained increasing importance, enabling hands-free and eyes-free interaction with digital devices. However, the inherent complexity in constructing effective voice interfaces has limited the VCIs' functionalities to only a small fraction of GUI applications and tasks. This paper presents AutoTask, a VCI capable of automating any task in any mobile application without configuration or modification from developers or end users. The primary challenge for AutoTask is the lack of knowledge, as it needs to accomplish unknown tasks (e.g., user commands) within an unknown environment (e.g., GUI). To address this challenge, AutoTask employs two strategies: (1) trial and error: AutoTask explores the GUI, attempts potential operation sequences, and recovers from errors through backtracking; (2) learning from the environment: AutoTask accumulates experiences during exploration and summarizes correct knowledge from these experiences. We implemented AutoTask on Android devices and conducted an evaluation study, which proved the feasibility of AutoTask.

cs.HC↗

arXiv2023

MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition

Multimodal sensors provide complementary information to develop accurate machine-learning methods for human activity recognition (HAR), but introduce significantly higher computational load, which reduces efficiency. This paper proposes an efficient multimodal neural architecture for HAR using an RGB camera and inertial measurement units (IMUs) called Multimodal Temporal Segment Attention Network (MMTSA). MMTSA first transforms IMU sensor data into a temporal and structure-preserving gray-scale image using the Gramian Angular Field (GAF), representing the inherent properties of human activities. MMTSA then applies a multimodal sparse sampling method to reduce data redundancy. Lastly, MMTSA adopts an inter-segment attention module for efficient multimodal fusion. Using three well-established public datasets, we evaluated MMTSA's effectiveness and efficiency in HAR. Results show that our method achieves superior performance improvements 11.13% of cross-subject F1-score on the MMAct dataset than the previous state-of-the-art (SOTA) methods. The ablation study and analysis suggest that MMTSA's effectiveness in fusing multimodal data for accurate HAR. The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.

cs.CV↗

arXiv2023

Interactive diversity disrupts cyclic dominance but maintains cooperation in spatial social dilemma games

Cyclic dominance has become a pivotal factor in sustaining cooperation within structured populations. However, this comprehension has predominantly revolved around node dynamics, where players are confined to employing the same strategy with all their neighbors. What has been largely overlooked is the profound influence of interactive diversity, where players can adapt distinct responses to different neighbors, on the dynamics of cyclic dominance and the broader patterns of cooperation. This investigation delves into the often-neglected role of interactive diversity in cyclic dominance and cooperation, utilizing a volunteer prisoner's dilemma model deployed on various network structures. Within this framework, we differentiate between `node players,' who adhere to a consistent strategy with all their neighbors, and `link players,' who adjust their strategies based on specific interactions, influenced by both direct and indirect emotional factors. Direct emotion governs the strategy between two interacting players, while indirect emotion encompasses the impact of third-party influences on strategic decisions. Through Monte Carlo simulations, we unveil a multifaceted relationship: interactive diversity generally disrupts cyclic dominance, yet its impact on cooperation varies, contingent on the prevalence of indirect strategy formulation. These findings suggest that the significance of cyclic dominance in fostering cooperation may have been overemphasized, as cooperation can persist even in the absence of strong cyclic dominance, owing to the presence of interactive diversity.

physics.soc-ph↗

arXiv2023

Facilitating Cooperation in Human-Agent Hybrid Populations through Autonomous Agents

Cooperation is a vital social behavior that plays a crucial role in human prosperity, enabling conflict resolution and averting disastrous outcomes. With the increasing presence of autonomous agents (AAs), human-agent interaction becomes more frequent in modern society. We investigate the impact of cooperative and defective AAs on human cooperation within the framework of evolutionary game theory, particularly in one-shot social dilemma games. Our findings reveal that cooperative AAs have a limited impact on prisoner's dilemma, but facilitate cooperation in stag hunt games. Surprisingly, defective AAs, rather than cooperative AAs, promote complete dominance of cooperation in snowdrift games. Meanwhile, in scenarios with weak imitation strength, cooperative AAs are able to maintain or even promote cooperation in all these games. Additionally, the results obtained from structured populations also imply that the effectiveness of AAs in promoting cooperation can be maximized by carefully considering their design and application in a given context.

physics.soc-ph↗