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Okayama University

Academic institutionasia · jp
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Research library19linked papers
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Selected work

Representative Papers

Two-Stage Estimation of Population Abundance with Robust Inference under Interacting Survey Protocols

Jul 22, 2026

This study addresses the challenge of imperfect detection and sample-loss bias in estimating population abundance when multiple survey protocols interfere with one another. The authors propose a two-stage estimation framework that first calibrates for sample loss and then estimates detection probability, thereby avoiding the feedback loops and weak identifiability inherent in joint modeling approaches. A novel sandwich-type robust variance estimator is introduced to effectively propagate uncertainty from the first stage and mitigate the impact of potential model misspecification. Compared to Bayesian joint models, this method yields more reliable uncertainty quantification, as demonstrated by its robustness and practical utility in an analysis of squirrel ectoparasite data.

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Detection of LLM-assisted Code Plagiarism Using k-gram Software Birthmarks

Jul 03, 2026

This study addresses the challenge of detecting large language model (LLM)-generated code that evades conventional plagiarism detection through semantics-preserving rewrites. It presents the first systematic evaluation of Java bytecode-based k-gram software watermarking (with k ranging from 1 to 6) in the context of LLM-generated code. The approach integrates five similarity metrics—cosine similarity, Dice coefficient, Jaccard index, Simpson index, and edit distance–based similarity—and is evaluated on code produced by three prominent LLMs. Results demonstrate that the proposed watermarking technique effectively identifies LLM-assisted plagiarism, with code generated by domain-specialized models (e.g., ChatGPT-5.1-Codex-Mini) exhibiting greater stealthiness, thereby confirming that model specialization enhances the concealment of plagiarized content.

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Latest Papers

Two-Stage Estimation of Population Abundance with Robust Inference under Interacting Survey Protocols

Jul 22, 2026

This study addresses the challenge of imperfect detection and sample-loss bias in estimating population abundance when multiple survey protocols interfere with one another. The authors propose a two-stage estimation framework that first calibrates for sample loss and then estimates detection probability, thereby avoiding the feedback loops and weak identifiability inherent in joint modeling approaches. A novel sandwich-type robust variance estimator is introduced to effectively propagate uncertainty from the first stage and mitigate the impact of potential model misspecification. Compared to Bayesian joint models, this method yields more reliable uncertainty quantification, as demonstrated by its robustness and practical utility in an analysis of squirrel ectoparasite data.

0 citationsRead paper

Detection of LLM-assisted Code Plagiarism Using k-gram Software Birthmarks

Jul 03, 2026

This study addresses the challenge of detecting large language model (LLM)-generated code that evades conventional plagiarism detection through semantics-preserving rewrites. It presents the first systematic evaluation of Java bytecode-based k-gram software watermarking (with k ranging from 1 to 6) in the context of LLM-generated code. The approach integrates five similarity metrics—cosine similarity, Dice coefficient, Jaccard index, Simpson index, and edit distance–based similarity—and is evaluated on code produced by three prominent LLMs. Results demonstrate that the proposed watermarking technique effectively identifies LLM-assisted plagiarism, with code generated by domain-specialized models (e.g., ChatGPT-5.1-Codex-Mini) exhibiting greater stealthiness, thereby confirming that model specialization enhances the concealment of plagiarized content.

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