JENGA: Exploiting Counter-Based RowHammer Countermeasures to Break Real-Time Predictability
研究通过分析基于硬件计数器的RowHammer防御措施对实时系统时间行为的影响，提出JENGA攻击方法，并提出安全分析界限以解决由此引发的WCET问题。
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Selected work
Representative Papers
The Nocturnity Scale: Measuring the Sense of Being at Night in Virtual Urban Environments
This study addresses the lack of effective instruments for quantifying the subjective sense of “nighttime atmosphere” evoked by scenes in virtual urban environments. To bridge this gap, it introduces the novel construct of “nocturnity” and develops a theoretical framework encompassing three dimensions: perception, activity, and internal state. Building upon a comprehensive literature review, expert evaluation, and psychometric methodology, the authors construct an initial 42-item Likert-scale questionnaire. Integrating domain-specific knowledge from urban lighting research, the instrument incorporates diagnostically meaningful subdimensions, thereby constituting the first multidimensional measurement tool tailored specifically to virtual nighttime settings. This foundational work enables future empirical validation and cross-scenario comparative analyses.
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Humor Style Drives Laughter, Topic Shapes Acceptability: Evaluating Bilingual Personal and Political Robot-Delivered AI Jokes
This study investigates how humor style, topic type, and language preference jointly influence users’ perceptions of funniness and appropriateness of AI-generated jokes delivered by a robot in group settings. Conducted in an authentic human–robot interaction classroom environment, the research employs a mixed-factorial experimental design, utilizing large language models to generate four humor styles—including aggressive and affiliative—and two topic categories (person-focused vs. political). User perceptions were assessed via structured questionnaires. This work represents the first empirical integration of humor style, topic, and bilingual preference within HRI research, revealing that aggressive and affiliative humor are perceived as significantly funnier, person-focused topics are deemed more appropriate, and language preference is modulated by content type, linguistic proficiency, and individual differences in humor engagement.
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Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry
This work addresses the failure of LiDAR-inertial SLAM in GNSS-denied environments, where geometrically sparse or repetitive terrain induces severe elevation drift. Building upon the LIO-SAM framework, we propose a novel factor graph architecture that integrates leg odometry—derived from proprioceptive gait control—as a lightweight vertical anchor within the graph optimization. This integration is achieved through a relative pose equality constraint with a selective noise model, tightly coupled with the primary LiDAR-inertial pipeline. Requiring no additional sensors, our method reduces elevation drift from over 30 meters to less than 30 centimeters in outdoor experiments exceeding one kilometer, and achieves stable convergence even in scenarios where baseline approaches completely fail.
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JENGA: Exploiting Counter-Based RowHammer Countermeasures to Break Real-Time Predictability
研究通过分析基于硬件计数器的RowHammer防御措施对实时系统时间行为的影响，提出JENGA攻击方法，并提出安全分析界限以解决由此引发的WCET问题。
0 citationsRead paper
The Nocturnity Scale: Measuring the Sense of Being at Night in Virtual Urban Environments
This study addresses the lack of effective instruments for quantifying the subjective sense of “nighttime atmosphere” evoked by scenes in virtual urban environments. To bridge this gap, it introduces the novel construct of “nocturnity” and develops a theoretical framework encompassing three dimensions: perception, activity, and internal state. Building upon a comprehensive literature review, expert evaluation, and psychometric methodology, the authors construct an initial 42-item Likert-scale questionnaire. Integrating domain-specific knowledge from urban lighting research, the instrument incorporates diagnostically meaningful subdimensions, thereby constituting the first multidimensional measurement tool tailored specifically to virtual nighttime settings. This foundational work enables future empirical validation and cross-scenario comparative analyses.
0 citationsRead paper
Humor Style Drives Laughter, Topic Shapes Acceptability: Evaluating Bilingual Personal and Political Robot-Delivered AI Jokes
This study investigates how humor style, topic type, and language preference jointly influence users’ perceptions of funniness and appropriateness of AI-generated jokes delivered by a robot in group settings. Conducted in an authentic human–robot interaction classroom environment, the research employs a mixed-factorial experimental design, utilizing large language models to generate four humor styles—including aggressive and affiliative—and two topic categories (person-focused vs. political). User perceptions were assessed via structured questionnaires. This work represents the first empirical integration of humor style, topic, and bilingual preference within HRI research, revealing that aggressive and affiliative humor are perceived as significantly funnier, person-focused topics are deemed more appropriate, and language preference is modulated by content type, linguistic proficiency, and individual differences in humor engagement.
0 citationsRead paper
Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry
This work addresses the failure of LiDAR-inertial SLAM in GNSS-denied environments, where geometrically sparse or repetitive terrain induces severe elevation drift. Building upon the LIO-SAM framework, we propose a novel factor graph architecture that integrates leg odometry—derived from proprioceptive gait control—as a lightweight vertical anchor within the graph optimization. This integration is achieved through a relative pose equality constraint with a selective noise model, tightly coupled with the primary LiDAR-inertial pipeline. Requiring no additional sensors, our method reduces elevation drift from over 30 meters to less than 30 centimeters in outdoor experiments exceeding one kilometer, and achieves stable convergence even in scenarios where baseline approaches completely fail.
0 citationsRead paper