Selected work

Representative Papers

Rotating-Memory Fibonacci Numbers and Periodic Tilings

Sep 11, 2026

We introduce and study the rotating-memory Fibonacci numbers, a periodic variable-order analogue of the Fibonacci sequence in which the number of preceding terms used in the recurrence changes cyclically with the index. Despite this varying memory, the resulting sequences exhibit a remarkably rigid structure. We derive closed forms, rational generating functions, arithmetic properties, and exact growth behavior, and show that the sequence decomposes naturally into geometric subsequences. We also develop combinatorial interpretations in terms of periodically constrained tilings, and restricted compositions, including bijective explanations for the multiplicative structure of the sequence. In addition, the first two nonclassical periods admit natural geometry-driven realizations: the period-2 sequence arises from monomer--dimer tilings of a triangular chain, while the period-3 sequence is related to tilings of a double hexagon strip by single and double hexagons. These connections provide geometric interpretations of the rotating recurrence in which the periodic behavior is induced by the underlying structures themselves, and suggest a broader problem of constructing analogous models for higher periods.

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Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)

Sep 02, 2026

为解决不受限环境中面部识别挑战，本文提出DQM-Face框架，结合特征幅度与语义质量学习机制，通过优化吸引-排斥边缘增强类内紧凑性和类间区分度。

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An Interactive Vision Language Platform for Cognitive Remediation in Schizophrenia

Jul 21, 2026

This study addresses the limitations of manual, observer-based assessment of patient actions in cognitive rehabilitation for schizophrenia, which suffers from high subjectivity and poor scalability. To overcome these challenges, this work proposes the first automated evaluation platform leveraging a fine-tuned vision-language model. Operating within a customized tabletop miniature environment, the system integrates video recordings with audio instructions to enable end-to-end mapping from hand–object motion trajectories to high-level clinical semantics. By analyzing video sequences, tracking movements, and generating semantic interpretations, the framework automatically validates the correctness of goal-directed behaviors. Evaluated on a dataset comprising 4,634 videos, the method achieves high-precision semantic-level assessment, substantially enhancing objectivity and scalability, and offers an innovative automated solution for cognitive rehabilitation.

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FAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks

Jul 13, 2026

This study addresses the challenge of hate speech detection in Algerian Arabic Darija on social media, where linguistic complexity arises from the mixing of Arabic, French, and Arabizi. To tackle this issue, the authors propose a hybrid GRU-based architecture that integrates multi-source embeddings—specifically DZ FastText, DZ AraVec, and DziriBERT—and enhances sequence modeling through a self-attention mechanism to improve semantic representation. Evaluated on a Darija dataset under low-resource conditions, the model achieves 93.2% accuracy, 92.1% F1-score, and 97.0% ROC-AUC, significantly outperforming baseline approaches including traditional machine learning models, RNNs, and Transformers. These results demonstrate the effectiveness of combining multiple dialectal embeddings with attention mechanisms for hate speech detection in resource-constrained, linguistically heterogeneous settings.

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Which Metric Reflects the Spelling Rate Accuracy in Event-Related Potential-Based Brain-Computer Interfaces?

Jul 01, 2026

In event-related potential (ERP)-based brain–computer interfaces (BCIs), conventional accuracy metrics often fail to adequately reflect users’ actual spelling performance, particularly due to limitations in handling class imbalance and evaluating spelling speed. This study systematically evaluates the correlation of 13 performance metrics—including Brier score, Matthews correlation coefficient (MCC), ROC AUC, PR AUC, average precision (AP), and partial AUC (pAUC)—with spelling rate across varying numbers of trial repetitions, using the publicly available LARESI and OpenBMI datasets. The work reveals, for the first time, that the Brier score, MCC, and several metrics designed to address class imbalance exhibit strong correlations with spelling rate. These findings advocate for prioritizing such metrics in ERP-BCI studies to enhance comparability and practical relevance.

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Recent publications

Latest Papers

Rotating-Memory Fibonacci Numbers and Periodic Tilings

Sep 11, 2026

We introduce and study the rotating-memory Fibonacci numbers, a periodic variable-order analogue of the Fibonacci sequence in which the number of preceding terms used in the recurrence changes cyclically with the index. Despite this varying memory, the resulting sequences exhibit a remarkably rigid structure. We derive closed forms, rational generating functions, arithmetic properties, and exact growth behavior, and show that the sequence decomposes naturally into geometric subsequences. We also develop combinatorial interpretations in terms of periodically constrained tilings, and restricted compositions, including bijective explanations for the multiplicative structure of the sequence. In addition, the first two nonclassical periods admit natural geometry-driven realizations: the period-2 sequence arises from monomer--dimer tilings of a triangular chain, while the period-3 sequence is related to tilings of a double hexagon strip by single and double hexagons. These connections provide geometric interpretations of the rotating recurrence in which the periodic behavior is induced by the underlying structures themselves, and suggest a broader problem of constructing analogous models for higher periods.

0 citationsRead paper

Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)

Sep 02, 2026

为解决不受限环境中面部识别挑战，本文提出DQM-Face框架，结合特征幅度与语义质量学习机制，通过优化吸引-排斥边缘增强类内紧凑性和类间区分度。

0 citationsRead paper

An Interactive Vision Language Platform for Cognitive Remediation in Schizophrenia

Jul 21, 2026

This study addresses the limitations of manual, observer-based assessment of patient actions in cognitive rehabilitation for schizophrenia, which suffers from high subjectivity and poor scalability. To overcome these challenges, this work proposes the first automated evaluation platform leveraging a fine-tuned vision-language model. Operating within a customized tabletop miniature environment, the system integrates video recordings with audio instructions to enable end-to-end mapping from hand–object motion trajectories to high-level clinical semantics. By analyzing video sequences, tracking movements, and generating semantic interpretations, the framework automatically validates the correctness of goal-directed behaviors. Evaluated on a dataset comprising 4,634 videos, the method achieves high-precision semantic-level assessment, substantially enhancing objectivity and scalability, and offers an innovative automated solution for cognitive rehabilitation.

0 citationsRead paper

FAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks

Jul 13, 2026

This study addresses the challenge of hate speech detection in Algerian Arabic Darija on social media, where linguistic complexity arises from the mixing of Arabic, French, and Arabizi. To tackle this issue, the authors propose a hybrid GRU-based architecture that integrates multi-source embeddings—specifically DZ FastText, DZ AraVec, and DziriBERT—and enhances sequence modeling through a self-attention mechanism to improve semantic representation. Evaluated on a Darija dataset under low-resource conditions, the model achieves 93.2% accuracy, 92.1% F1-score, and 97.0% ROC-AUC, significantly outperforming baseline approaches including traditional machine learning models, RNNs, and Transformers. These results demonstrate the effectiveness of combining multiple dialectal embeddings with attention mechanisms for hate speech detection in resource-constrained, linguistically heterogeneous settings.

0 citationsRead paper

Which Metric Reflects the Spelling Rate Accuracy in Event-Related Potential-Based Brain-Computer Interfaces?

Jul 01, 2026

In event-related potential (ERP)-based brain–computer interfaces (BCIs), conventional accuracy metrics often fail to adequately reflect users’ actual spelling performance, particularly due to limitations in handling class imbalance and evaluating spelling speed. This study systematically evaluates the correlation of 13 performance metrics—including Brier score, Matthews correlation coefficient (MCC), ROC AUC, PR AUC, average precision (AP), and partial AUC (pAUC)—with spelling rate across varying numbers of trial repetitions, using the publicly available LARESI and OpenBMI datasets. The work reveals, for the first time, that the Brier score, MCC, and several metrics designed to address class imbalance exhibit strong correlations with spelling rate. These findings advocate for prioritizing such metrics in ERP-BCI studies to enhance comparability and practical relevance.

0 citationsRead paper