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University of North Florida

Academic institutionnorthamerica · us
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Research library13linked papers
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Selected work

Representative Papers

Hybrid Machine Learning and Physical Modeling of Feedstock Deformation During Robotic 3D Printing of Continuous Fiber Thermoplastic Composites

May 04, 2026

This study addresses the deformation of continuous fiber-reinforced thermoplastic composites during robotic 3D printing, which arises from residual stress relaxation, drying, crystallization, and thermal stresses. To tackle this challenge, a hybrid modeling approach integrating physical mechanisms with data-driven techniques is proposed. The mechanical behavior of the prepreg is captured using a Kelvin–Voigt viscoelastic constitutive model, while a stabilized neural ordinary differential equation (Neural ODE) framework is introduced to describe the coupled drying and crystallization kinetics. Validated through dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC) experiments, and full-scale printing trials, the resulting model accurately reproduces real-world deformation phenomena and demonstrates strong generalization and robustness—even beyond the temperature ranges observed during training.

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Topo-ADV: Generating Topology-Driven Imperceptible Adversarial Point Clouds

Apr 10, 2026

This work addresses a critical limitation in existing 3D point cloud adversarial attacks, which predominantly focus on geometric perturbations while neglecting the influence of topological structure on model robustness. To bridge this gap, the paper introduces the first topology-aware adversarial attack framework that treats topological features as an explicit attack dimension. The proposed method employs an end-to-end differentiable architecture leveraging differentiable persistent homology representations and persistence diagram embeddings to jointly optimize a composite objective comprising topological discrepancy loss, misclassification loss, and geometric imperceptibility constraints. By enabling gradient-guided perturbations that alter semantic interpretation without compromising geometric fidelity, the approach challenges the conventional assumption that geometric preservation entails semantic consistency. Extensive experiments demonstrate state-of-the-art performance, achieving up to 100% attack success rates against PointNet and DGCNN across ModelNet40, ShapeNet Part, and ScanObjectNN benchmarks, while significantly outperforming existing methods across multiple perceptual metrics.

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Identifying Privacy Concerns in Upcoming Software Release: A Peek into the Future

Apr 01, 2026

This work addresses the limitation of existing approaches that rely on post-deployment user feedback to identify privacy issues, which prevents proactive mitigation prior to software release. To overcome this, the authors propose Pre-PI, a novel method that enables pre-release prediction of privacy concerns for the first time. Pre-PI achieves this by semantically aligning candidate features with existing ones, mapping historical privacy-related user reviews, and simulating user feedback to automatically generate privacy risk summaries. By shifting from reactive to proactive analysis, Pre-PI facilitates early privacy risk mitigation. Experimental evaluation on three real-world applications demonstrates that Pre-PI outperforms the state-of-the-art method Hark by identifying valid privacy concerns earlier and more comprehensively, significantly enhancing the ability to detect privacy issues before deployment.

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Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensive Defensive Framework

Mar 17, 2026

This study addresses the absence of a unified safety evaluation benchmark for current large language models (LLMs), which hinders the quantification of deployment risks in critical applications. To bridge this gap, we propose the first standardized, cross-architecture safety evaluation framework that systematically assesses the vulnerability of five representative LLMs under six categories of adversarial attacks. Furthermore, we introduce a deployable multi-layered external defense mechanism. Experimental results reveal that existing models exhibit vulnerability rates ranging from 11.9% to 29.8%, whereas our defense framework achieves an average detection accuracy of 83% with only a 5% false positive rate. Notably, the findings demonstrate no direct correlation between a model’s general capabilities and its safety robustness, offering empirical evidence and a practical solution for safer LLM deployment.

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Recent publications

Latest Papers

Hybrid Machine Learning and Physical Modeling of Feedstock Deformation During Robotic 3D Printing of Continuous Fiber Thermoplastic Composites

May 04, 2026

This study addresses the deformation of continuous fiber-reinforced thermoplastic composites during robotic 3D printing, which arises from residual stress relaxation, drying, crystallization, and thermal stresses. To tackle this challenge, a hybrid modeling approach integrating physical mechanisms with data-driven techniques is proposed. The mechanical behavior of the prepreg is captured using a Kelvin–Voigt viscoelastic constitutive model, while a stabilized neural ordinary differential equation (Neural ODE) framework is introduced to describe the coupled drying and crystallization kinetics. Validated through dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC) experiments, and full-scale printing trials, the resulting model accurately reproduces real-world deformation phenomena and demonstrates strong generalization and robustness—even beyond the temperature ranges observed during training.

0 citationsRead paper

Topo-ADV: Generating Topology-Driven Imperceptible Adversarial Point Clouds

Apr 10, 2026

This work addresses a critical limitation in existing 3D point cloud adversarial attacks, which predominantly focus on geometric perturbations while neglecting the influence of topological structure on model robustness. To bridge this gap, the paper introduces the first topology-aware adversarial attack framework that treats topological features as an explicit attack dimension. The proposed method employs an end-to-end differentiable architecture leveraging differentiable persistent homology representations and persistence diagram embeddings to jointly optimize a composite objective comprising topological discrepancy loss, misclassification loss, and geometric imperceptibility constraints. By enabling gradient-guided perturbations that alter semantic interpretation without compromising geometric fidelity, the approach challenges the conventional assumption that geometric preservation entails semantic consistency. Extensive experiments demonstrate state-of-the-art performance, achieving up to 100% attack success rates against PointNet and DGCNN across ModelNet40, ShapeNet Part, and ScanObjectNN benchmarks, while significantly outperforming existing methods across multiple perceptual metrics.

0 citationsRead paper

Identifying Privacy Concerns in Upcoming Software Release: A Peek into the Future

Apr 01, 2026

This work addresses the limitation of existing approaches that rely on post-deployment user feedback to identify privacy issues, which prevents proactive mitigation prior to software release. To overcome this, the authors propose Pre-PI, a novel method that enables pre-release prediction of privacy concerns for the first time. Pre-PI achieves this by semantically aligning candidate features with existing ones, mapping historical privacy-related user reviews, and simulating user feedback to automatically generate privacy risk summaries. By shifting from reactive to proactive analysis, Pre-PI facilitates early privacy risk mitigation. Experimental evaluation on three real-world applications demonstrates that Pre-PI outperforms the state-of-the-art method Hark by identifying valid privacy concerns earlier and more comprehensively, significantly enhancing the ability to detect privacy issues before deployment.

0 citationsRead paper

Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensive Defensive Framework

Mar 17, 2026

This study addresses the absence of a unified safety evaluation benchmark for current large language models (LLMs), which hinders the quantification of deployment risks in critical applications. To bridge this gap, we propose the first standardized, cross-architecture safety evaluation framework that systematically assesses the vulnerability of five representative LLMs under six categories of adversarial attacks. Furthermore, we introduce a deployable multi-layered external defense mechanism. Experimental results reveal that existing models exhibit vulnerability rates ranging from 11.9% to 29.8%, whereas our defense framework achieves an average detection accuracy of 83% with only a 5% false positive rate. Notably, the findings demonstrate no direct correlation between a model’s general capabilities and its safety robustness, offering empirical evidence and a practical solution for safer LLM deployment.

0 citationsRead paper