Selected work

Representative Papers

Diffusion-Driven Deceptive Patches: Adversarial Manipulation and Forensic Detection in Facial Identity Verification

Jan 14, 2026

This work addresses the vulnerability of facial biometric systems to adversarial attacks by proposing a highly realistic and interpretable adversarial patch generation method. By integrating diffusion models with the Fast Gradient Sign Method (FGSM), the approach simultaneously optimizes adversarial perturbations while performing luminance correction and Gaussian smoothing, achieving high visual fidelity (SSIM of 0.95). The framework innovatively incorporates a ViT-GPT2 architecture to translate identity information into semantic descriptions, thereby enhancing forensic interpretability. Furthermore, it systematically evaluates the robustness of recognition models under adversarial conditions through a fusion of perceptual hashing and image segmentation techniques. This end-to-end pipeline balances high attack efficacy with strong interpretability, offering a novel direction for improving the security of biometric authentication systems.

0 citationsRead paper

Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review

Oct 11, 2025

This paper addresses the persistent gap between theoretical advances in reinforcement learning (RL) and their practical deployment in robotics and control systems. To bridge this divide, we propose a structured taxonomy tailored to real-world robotic applications, grounded in the Markov decision process (MDP) framework and systematically incorporating mainstream deep RL algorithms—including DDPG, TD3, PPO, and SAC—across canonical domains such as motion control, dexterous manipulation, and multi-agent coordination. The taxonomy explicitly integrates training paradigms and deployment maturity metrics. Crucially, we identify recurring design patterns and evolutionary trends in high-dimensional continuous control tasks, thereby unifying theoretical insights with engineering constraints. Our framework advances reproducibility, transferability, and robustness in RL deployment on physical robots, offering both a methodological foundation and actionable guidelines for practitioners. (149 words)

0 citationsRead paper

Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey

Aug 27, 2025

To address the challenges of dynamics, non-stationarity, and scalability in multi-agent collaborative decision-making within intelligent transportation systems (ITS), this paper proposes a unified classification framework for multi-agent reinforcement learning (MARL) tailored to ITS. The framework systematically categorizes MARL approaches into four paradigms: value-based, policy-gradient-based, actor-critic-based, and communication-enhanced methods. It further maps these to key ITS applications—including traffic signal control, cooperative autonomous driving, logistics dispatching, and on-demand mobility. Empirical evaluation is conducted across mainstream simulation platforms (SUMO, CARLA, CityFlow), identifying critical bottlenecks such as sim-to-real transfer, credit assignment, and environmental non-stationarity. This work establishes the first structured taxonomy that jointly considers algorithmic principles and traffic-domain semantics, providing a comprehensive survey, standardized benchmarks, and actionable research directions for advancing both the theoretical foundations and real-world deployment of MARL in ITS.

0 citationsRead paper

Factors Influencing Change Orders in Horizontal Construction Projects: A Comparative Analysis of Unit Price and Lump Sum Contracts

Jun 30, 2025

This study addresses the pervasive issue of horizontal change orders (COs) under the Design–Bid–Build (DBB) delivery method, which frequently trigger cost overruns and schedule delays. Leveraging historical data from 770 Florida Department of Transportation projects (581 unit-price and 189 lump-sum contracts), a discrete choice model is developed to quantitatively identify key determinants of CO frequency—including project size, duration, and work type—and to assess the differential impact of contract type on CO incidence. Its novelty lies in the first application of discrete choice modeling to inform DBB contract-type selection, moving beyond heuristic, experience-based matching. Empirical validation demonstrates that the model outperforms current practice, yielding higher accuracy in contract-type recommendation and improved precision in CO frequency prediction. The framework thus provides a practical, quantitative decision-support tool for transportation agencies to optimize procurement strategies and mitigate change-order risk.

0 citationsRead paper

Recent publications

Latest Papers

Diffusion-Driven Deceptive Patches: Adversarial Manipulation and Forensic Detection in Facial Identity Verification

Jan 14, 2026

This work addresses the vulnerability of facial biometric systems to adversarial attacks by proposing a highly realistic and interpretable adversarial patch generation method. By integrating diffusion models with the Fast Gradient Sign Method (FGSM), the approach simultaneously optimizes adversarial perturbations while performing luminance correction and Gaussian smoothing, achieving high visual fidelity (SSIM of 0.95). The framework innovatively incorporates a ViT-GPT2 architecture to translate identity information into semantic descriptions, thereby enhancing forensic interpretability. Furthermore, it systematically evaluates the robustness of recognition models under adversarial conditions through a fusion of perceptual hashing and image segmentation techniques. This end-to-end pipeline balances high attack efficacy with strong interpretability, offering a novel direction for improving the security of biometric authentication systems.

0 citationsRead paper

Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review

Oct 11, 2025

This paper addresses the persistent gap between theoretical advances in reinforcement learning (RL) and their practical deployment in robotics and control systems. To bridge this divide, we propose a structured taxonomy tailored to real-world robotic applications, grounded in the Markov decision process (MDP) framework and systematically incorporating mainstream deep RL algorithms—including DDPG, TD3, PPO, and SAC—across canonical domains such as motion control, dexterous manipulation, and multi-agent coordination. The taxonomy explicitly integrates training paradigms and deployment maturity metrics. Crucially, we identify recurring design patterns and evolutionary trends in high-dimensional continuous control tasks, thereby unifying theoretical insights with engineering constraints. Our framework advances reproducibility, transferability, and robustness in RL deployment on physical robots, offering both a methodological foundation and actionable guidelines for practitioners. (149 words)

0 citationsRead paper

Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey

Aug 27, 2025

To address the challenges of dynamics, non-stationarity, and scalability in multi-agent collaborative decision-making within intelligent transportation systems (ITS), this paper proposes a unified classification framework for multi-agent reinforcement learning (MARL) tailored to ITS. The framework systematically categorizes MARL approaches into four paradigms: value-based, policy-gradient-based, actor-critic-based, and communication-enhanced methods. It further maps these to key ITS applications—including traffic signal control, cooperative autonomous driving, logistics dispatching, and on-demand mobility. Empirical evaluation is conducted across mainstream simulation platforms (SUMO, CARLA, CityFlow), identifying critical bottlenecks such as sim-to-real transfer, credit assignment, and environmental non-stationarity. This work establishes the first structured taxonomy that jointly considers algorithmic principles and traffic-domain semantics, providing a comprehensive survey, standardized benchmarks, and actionable research directions for advancing both the theoretical foundations and real-world deployment of MARL in ITS.

0 citationsRead paper

Factors Influencing Change Orders in Horizontal Construction Projects: A Comparative Analysis of Unit Price and Lump Sum Contracts

Jun 30, 2025

This study addresses the pervasive issue of horizontal change orders (COs) under the Design–Bid–Build (DBB) delivery method, which frequently trigger cost overruns and schedule delays. Leveraging historical data from 770 Florida Department of Transportation projects (581 unit-price and 189 lump-sum contracts), a discrete choice model is developed to quantitatively identify key determinants of CO frequency—including project size, duration, and work type—and to assess the differential impact of contract type on CO incidence. Its novelty lies in the first application of discrete choice modeling to inform DBB contract-type selection, moving beyond heuristic, experience-based matching. Empirical validation demonstrates that the model outperforms current practice, yielding higher accuracy in contract-type recommendation and improved precision in CO frequency prediction. The framework thus provides a practical, quantitative decision-support tool for transportation agencies to optimize procurement strategies and mitigate change-order risk.

0 citationsRead paper