Selected work

Representative Papers

The Topp-Leone XLindley Distribution: Properties, Estimation and Applications to Lifetime Data

Oct 06, 2026

This study addresses the limited flexibility of existing two-parameter lifetime distribution models in characterizing complex tail behaviors by constructing a novel XLindley distribution based on the Topp-Leone family. The statistical properties of the proposed model, including moments, entropy, and reliability functions, are systematically derived, with parameters estimated via maximum likelihood. Monte Carlo simulations validate the robustness of the estimators, while empirical analysis using a bank customer waiting time dataset demonstrates that the new model achieves significantly superior fitting accuracy compared to conventional distributions. This work provides a mathematically rigorous tool with favorable tail behavior for lifetime data modeling.

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Women Sport Actions Dataset for Visual Classification Using Small Scale Training Data

Jul 15, 2025

To address the scarcity of female sports action imagery and insufficient modeling of intra-class and inter-class variations—key bottlenecks in few-shot action recognition—this work introduces WomenSports, the first dedicated benchmark dataset for visual classification of women’s sports actions, featuring fine-grained samples across diverse scenes, poses, and attire. Methodologically, we propose a Local Context Region-based Channel Attention (LCRA) mechanism, integrated into ResNet-50 to enhance discriminative feature learning. On WomenSports, our approach achieves 89.15% Top-1 accuracy. Cross-dataset evaluation further demonstrates strong generalization capability, significantly outperforming baseline methods. This work bridges dual gaps in the field: it provides the first large-scale, gender-specific action dataset and a tailored attention architecture, thereby establishing a foundational resource for fair, robust, and inclusive sports motion analysis.

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Recent publications

Latest Papers

The Topp-Leone XLindley Distribution: Properties, Estimation and Applications to Lifetime Data

Oct 06, 2026

This study addresses the limited flexibility of existing two-parameter lifetime distribution models in characterizing complex tail behaviors by constructing a novel XLindley distribution based on the Topp-Leone family. The statistical properties of the proposed model, including moments, entropy, and reliability functions, are systematically derived, with parameters estimated via maximum likelihood. Monte Carlo simulations validate the robustness of the estimators, while empirical analysis using a bank customer waiting time dataset demonstrates that the new model achieves significantly superior fitting accuracy compared to conventional distributions. This work provides a mathematically rigorous tool with favorable tail behavior for lifetime data modeling.

0 citationsRead paper

Women Sport Actions Dataset for Visual Classification Using Small Scale Training Data

Jul 15, 2025

To address the scarcity of female sports action imagery and insufficient modeling of intra-class and inter-class variations—key bottlenecks in few-shot action recognition—this work introduces WomenSports, the first dedicated benchmark dataset for visual classification of women’s sports actions, featuring fine-grained samples across diverse scenes, poses, and attire. Methodologically, we propose a Local Context Region-based Channel Attention (LCRA) mechanism, integrated into ResNet-50 to enhance discriminative feature learning. On WomenSports, our approach achieves 89.15% Top-1 accuracy. Cross-dataset evaluation further demonstrates strong generalization capability, significantly outperforming baseline methods. This work bridges dual gaps in the field: it provides the first large-scale, gender-specific action dataset and a tailored attention architecture, thereby establishing a foundational resource for fair, robust, and inclusive sports motion analysis.

0 citationsRead paper