Selected work

Representative Papers

Robustness and User-Perceived Value of Popularity Calibration in Music Recommendation: A User Study

Aug 05, 2026

This study investigates whether popularity calibration genuinely enhances user experience in music recommendation and examines its reliability across varying levels of user listening history and item familiarity. The authors construct three types of playlists—high-popularity, low-popularity, and calibrated—and employ a controlled naive recommender to generate personalized lists. Calibration is quantified using Jensen–Shannon divergence (JSD), and subjective user feedback is collected through controlled experiments. This work presents the first systematic validation of JSD’s stability with respect to real users’ perceived calibration. Results indicate that while users can discern differences in popularity, they do not exhibit a significant preference for calibrated recommendations. Moreover, computed popularity labels show only weak alignment with users’ subjective judgments, and the relationship between JSD and perceived calibration is significantly moderated by item familiarity, playlist composition, and the availability of historical interaction data.

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Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series

May 20, 2026

This study addresses the challenge that existing methods struggle to effectively audit how diverse entities respond to historical signals under heterogeneous time lags. To tackle this, the problem is formulated as a temporal panel mining task, and the AC-GATE framework is proposed. AC-GATE uniquely treats effective time lags as structured outputs rather than post-hoc interpretations, introduces a scale-invariant lag gating mechanism, and integrates an adaptive conditional encoder with a hierarchical auditing protocol to decouple prediction calibration from lag discovery. Experiments demonstrate that the method accurately recovers ground-truth lag structures in synthetic data and yields non-degenerate, externally consistent heterogeneous lags on real-world national panel data, thereby enabling auditable lag discovery.

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Using Petri Nets for Context-Adaptive Robot Explanations

Sep 17, 2025

Robot explanations in human-robot interaction often lack contextual adaptivity, undermining transparency and user trust. Method: This paper proposes a Petri net–based context-adaptive explanation generation framework—the first to integrate Petri nets into robotic explanation systems. It formally models dynamic contextual cues (e.g., user attention, co-presence) to precisely capture concurrency, causal dependencies, and state transitions; formal verification ensures deadlock-freedom, boundedness, liveness, and context-sensitive reachability. Results: Experiments demonstrate strong robustness and real-time responsiveness across diverse interaction scenarios. The framework safely and reliably generates natural-language explanations aligned with the current context, significantly enhancing explanation transparency and user trust.

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Optimizing Password Cracking for Digital Investigations

Apr 04, 2025

To address the low efficiency and high resource consumption of password cracking in digital forensics, this paper proposes a lightweight rule-optimization framework grounded in policy compliance and empirical user behavior. Methodologically, it introduces the first dynamic rule-generation mechanism based on policy structures—such as the NCSC’s three-word password guidelines—integrated with password statistical modeling, analysis of public datasets, and user surveys to construct multi-granularity dictionaries and quantitatively assess the real-world vulnerability of three-word passwords under varying high-frequency word ratios. Key contributions include: (1) the first systematic revelation of the inherent tension between usability and security in three-word passwords; (2) a 40% reduction in rule-set size via iterative compression, yielding significantly faster cracking performance; and (3) empirical validation that a compact sub-dictionary comprising only the top 30% most frequent words successfully cracks 77.5% of real-world three-word passwords, confirming substantial practical risk.

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Recent publications

Latest Papers

Robustness and User-Perceived Value of Popularity Calibration in Music Recommendation: A User Study

Aug 05, 2026

This study investigates whether popularity calibration genuinely enhances user experience in music recommendation and examines its reliability across varying levels of user listening history and item familiarity. The authors construct three types of playlists—high-popularity, low-popularity, and calibrated—and employ a controlled naive recommender to generate personalized lists. Calibration is quantified using Jensen–Shannon divergence (JSD), and subjective user feedback is collected through controlled experiments. This work presents the first systematic validation of JSD’s stability with respect to real users’ perceived calibration. Results indicate that while users can discern differences in popularity, they do not exhibit a significant preference for calibrated recommendations. Moreover, computed popularity labels show only weak alignment with users’ subjective judgments, and the relationship between JSD and perceived calibration is significantly moderated by item familiarity, playlist composition, and the availability of historical interaction data.

0 citationsRead paper

Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series

May 20, 2026

This study addresses the challenge that existing methods struggle to effectively audit how diverse entities respond to historical signals under heterogeneous time lags. To tackle this, the problem is formulated as a temporal panel mining task, and the AC-GATE framework is proposed. AC-GATE uniquely treats effective time lags as structured outputs rather than post-hoc interpretations, introduces a scale-invariant lag gating mechanism, and integrates an adaptive conditional encoder with a hierarchical auditing protocol to decouple prediction calibration from lag discovery. Experiments demonstrate that the method accurately recovers ground-truth lag structures in synthetic data and yields non-degenerate, externally consistent heterogeneous lags on real-world national panel data, thereby enabling auditable lag discovery.

0 citationsRead paper

Using Petri Nets for Context-Adaptive Robot Explanations

Sep 17, 2025

Robot explanations in human-robot interaction often lack contextual adaptivity, undermining transparency and user trust. Method: This paper proposes a Petri net–based context-adaptive explanation generation framework—the first to integrate Petri nets into robotic explanation systems. It formally models dynamic contextual cues (e.g., user attention, co-presence) to precisely capture concurrency, causal dependencies, and state transitions; formal verification ensures deadlock-freedom, boundedness, liveness, and context-sensitive reachability. Results: Experiments demonstrate strong robustness and real-time responsiveness across diverse interaction scenarios. The framework safely and reliably generates natural-language explanations aligned with the current context, significantly enhancing explanation transparency and user trust.

0 citationsRead paper

Optimizing Password Cracking for Digital Investigations

Apr 04, 2025

To address the low efficiency and high resource consumption of password cracking in digital forensics, this paper proposes a lightweight rule-optimization framework grounded in policy compliance and empirical user behavior. Methodologically, it introduces the first dynamic rule-generation mechanism based on policy structures—such as the NCSC’s three-word password guidelines—integrated with password statistical modeling, analysis of public datasets, and user surveys to construct multi-granularity dictionaries and quantitatively assess the real-world vulnerability of three-word passwords under varying high-frequency word ratios. Key contributions include: (1) the first systematic revelation of the inherent tension between usability and security in three-word passwords; (2) a 40% reduction in rule-set size via iterative compression, yielding significantly faster cracking performance; and (3) empirical validation that a compact sub-dictionary comprising only the top 30% most frequent words successfully cracks 77.5% of real-world three-word passwords, confirming substantial practical risk.

0 citationsRead paper