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Rajshahi University of Engineering and Technology

Academic institutionasia · bd
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Research library42linked papers
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Selected work

Representative Papers

Faster Releases, Fewer Risks: A Study on Maven Artifact Vulnerabilities and Lifecycle Management

Mar 31, 2025

The impact of release practices on software supply chain security and dependency health remains poorly understood. Method: We conduct a large-scale empirical study of 203,000 releases across 10,000 Maven Central artifacts and 1.7 million dependency relationships, integrating time-series dependency evolution modeling, statistical testing of CVE associations, and metadata mining. Contribution/Results: We uncover, for the first time, a strong negative correlation between release velocity and dependency staleness duration (p < 0.001), as well as a significant negative association with CVE counts. High-frequency releasing reduces average direct-dependency staleness by 62% and decreases CVE prevalence in transitive dependencies by 47%. These findings establish “rapid releasing” as a quantifiable, generalizable security practice—providing novel empirical evidence and methodological foundations for dependency management and software supply chain risk governance.

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Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm

Sep 05, 2026International Conference on Pattern Recognition

This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and imbalancing the data processing in the passes is considered an imitation of the cognitive processes observed in humans suffering from sleep deprivation. Previous research has demonstrated that sleep deprivation in the Forward-Forward algorithm has a catastrophic effect on learning efficacy. To mitigate this issue, we explore several approaches; these include alternative activation, optimized loss function, and threshold tuning. To simulate periodic rest, we reduce the number of positive passes in alternating epochs, creating short break phases. We additionally investigate the potential of caffeine-induced stimulation to enhance performance during sleep-deprived conditions. Experimental evaluations conducted on the MNIST and Fashion-MNIST datasets demonstrate that these modifications improve accuracy under the context of sleep deprivation. For example, a 2%-62% accuracy gain is observed in a severe sleep deprivation setting (16 positive or awake periods and 1 negative or sleep period). The approaches also enhance the resilience of the algorithm and its alignment with the adaptive mechanisms of human cognition.

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Latest Papers

Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm

Sep 05, 2026International Conference on Pattern Recognition

This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and imbalancing the data processing in the passes is considered an imitation of the cognitive processes observed in humans suffering from sleep deprivation. Previous research has demonstrated that sleep deprivation in the Forward-Forward algorithm has a catastrophic effect on learning efficacy. To mitigate this issue, we explore several approaches; these include alternative activation, optimized loss function, and threshold tuning. To simulate periodic rest, we reduce the number of positive passes in alternating epochs, creating short break phases. We additionally investigate the potential of caffeine-induced stimulation to enhance performance during sleep-deprived conditions. Experimental evaluations conducted on the MNIST and Fashion-MNIST datasets demonstrate that these modifications improve accuracy under the context of sleep deprivation. For example, a 2%-62% accuracy gain is observed in a severe sleep deprivation setting (16 positive or awake periods and 1 negative or sleep period). The approaches also enhance the resilience of the algorithm and its alignment with the adaptive mechanisms of human cognition.

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TRIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification

Aug 14, 2026

This study addresses the high annotation costs and asymmetric error risks in semi-supervised retinal OCT classification by proposing a risk-controllable semi-supervised framework. The method integrates a hierarchical classifier, a context-aware Transformer teacher model, and a patient-level conformal risk controller, while incorporating an asymmetric cost matrix to mitigate under-grading. Experimental results demonstrate that with only 20% labeled data, the model achieves 89.66% accuracy and reduces the under-grading rate by 42.7% compared to state-of-the-art methods. This work effectively resolves clinical safety concerns in low-resource settings, significantly enhancing the reliability of computer-aided diagnosis systems for retinal diseases.

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