False Alarm Reduction Method for Weakness Static Analysis Using BERT Model

Nguyen, Dinh Huong; Seo, Aria; Nnamdi, Nnubia Pascal; Son, Yunsik

doi:10.3390/app13063502

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The integration of deep learning technologies into pedestrian trajectory prediction has signi cantly advanced the eld, moving away from traditional methodologies to embrace sophisticated, data-driven approaches. Deep learning is a machine learning method that allows arti cial neural networks to train sample data [12]. Central to this transformation is PECNet, a model that epitomizes the application of deep learning by utilizing advanced neural networks, such as Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and Graph Neural Networks (GNN).…”

Section: Deep Learning-based Pedestrian Trajectory Predictionmentioning

confidence: 99%

Robust Prediction Method for Pedestrian Trajectories in Occluded Video Scenarios

Seo,

Jeon,

Son

2024

Preprint

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In smart surveillance and intelligent transportation systems, accurately predicting pedestrian movements, especially under occlusions, remains a significant challenge. Traditional surveillance methods, primarily based on fixed CCTV footage, often overlook occlusions, noise, and camera angle changes, leading to inaccuracies in pedestrian trajectory predictions. Addressing this gap, our research introduces a novel trajectory prediction system optimized for various walking and filming conditions. By leveraging the ETH/UCY dataset, we simulate scenarios where pedestrians are temporarily obscured, employing re-identification techniques upon their reappearance to ensure path continuity. Our system utilizes linear prediction to reconstruct missing paths, integrating these with the PECNET baseline model for future path forecasting. This methodology allows for the effective handling of occlusions, a common yet underaddressed issue in current studies. Performance evaluation using Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics reveals our system achieves an ADE of 0.25 meters and an FDE of 0.50 meters, demonstrating comparable accuracy to predictions based on fully visible paths within the same dataset. These results highlight our approach's efficacy in complex environments, marking a significant step forward in occlusion-aware pedestrian movement analysis and prediction for enhanced surveillance system accuracy.

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Section: Deep Learning-based Pedestrian Trajectory Predictionmentioning

confidence: 99%

Robust Prediction Method for Pedestrian Trajectories in Occluded Video Scenarios

Seo,

Jeon,

Son

2024

Preprint

Self Cite

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“…In image processing, numerous innovative applications have emerged that leverage OpenCV's capabilities. These applications address crucial challenges in domains such as digital crime evidence management [9], personal portfolio authentication [10], and weakness static analysis [11]. By incorporating cutting-edge technologies like blockchain, Hyperledger Fabric, and Bidirectional Encoder Representations from Transformers (BERT) models, these advancements push the boundaries of what is possible in their respective fields.…”

Section: B Image Processing Using Opencvmentioning

confidence: 99%

Motility Analysis of Diaphragm in Patients With Chronic Pulmonary Lung Disease Based on Computed Tomography Technique

Yu,

Nnamdi,

Seo

et al. 2023

IEEE Access

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The diaphragm plays a crucial role in respiration, and diaphragm dysfunction is common in COPD, contributing to worsening symptoms and higher mortality rates. Many methods have been implemented to evaluate the function of the diaphragm, but each comes with its unique set of limitations. To overcome this challenge, a novel approach was introduced to assess diaphragm function in patients with chronic obstructive pulmonary disease (COPD) using thoracic computed tomography scans. This novel approach involves generating a simulation of the diaphragm from lung DICOM slice images and calculating the dynamic change in respiratory motion by quantifying the difference in the simulated diaphragm mesh area during inhalation and exhalation. The experimental design incorporates various image processing algorithms, computational geometry algorithms, and a surface fitting algorithm optimized to minimize the potential sources of error. When the proposed technique was applied to detect diaphragmatic dysfunction in patients with COPD, the results of the Pearson correlation analysis showed a strong relationship between the variables ratio of exhalation to inhalation surface and average z-value difference inhalation and exhalation surfaces, with a coefficient of 0.731. The results suggest that the proposed technique is highly accurate and beneficial in scenarios where diaphragm function is essential, such as respiratory disorders, neuromuscular diseases like Amyotrophic Lateral Sclerosis or muscular dystrophy, spinal cord injuries, and conditions such as sleep apnea. By providing specific numerical data on sensitivity and specificity, this approach offers a quantitative evaluation of its effectiveness in detecting diaphragmatic dysfunction in patients with COPD. In conclusion, the proposed approach is currently semi-automated, however, future study may investigate fully automated approaches using this technique.INDEX TERMS Chronic obstructive pulmonary disease, computed tomography, diaphragm segmentation, digital image processing, medical image computing.

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Software Weakness Detection in Solidity Smart Contracts Using Control and Data Flow Analysis: A Novel Approach with Graph Neural Networks

Seo,

Kim,

Yang

et al. 2024

Electronics

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Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution.

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False Alarm Reduction Method for Weakness Static Analysis Using BERT Model

Cited by 4 publications

References 13 publications

Robust Prediction Method for Pedestrian Trajectories in Occluded Video Scenarios

Robust Prediction Method for Pedestrian Trajectories in Occluded Video Scenarios

Motility Analysis of Diaphragm in Patients With Chronic Pulmonary Lung Disease Based on Computed Tomography Technique

Software Weakness Detection in Solidity Smart Contracts Using Control and Data Flow Analysis: A Novel Approach with Graph Neural Networks

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