Institution profile

Alexandru Ioan Cuza University

Academic institutioneurope · ro
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Research library18linked papers
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Selected work

Representative Papers

An Efficient Algorithm for Computing Mountain Prominence in Almost Linear Time

Jul 29, 2026

This study addresses the computational challenges posed by high-resolution digital elevation models (DEMs) in calculating topographic prominence at a global scale. The authors propose an approximately linear-time algorithm that efficiently computes the prominence of all peaks worldwide. The key insight is that the prominence of the vast majority of peaks is determined solely by local terrain, with only a small fraction influenced by distant higher summits. Leveraging this observation, the method refines classical algorithms and incorporates memoization to drastically reduce redundant computations and memory usage. Validation on real-world 3-arcsecond SRTM data demonstrates that the approach achieves substantial gains in computational efficiency while preserving the correctness of prominence values, enabling scalable prominence analysis across massive DEM datasets.

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Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions

May 11, 2026

This study evaluates the reliability of large language models (LLMs) in static security analysis of smart contracts, investigating whether they can replace or merely complement traditional tools. To this end, we introduce the first automated evaluation framework that systematically assesses LLM performance in vulnerability detection, quantitatively revealing— for the first time—high false positive rates stemming from lexical biases (e.g., identifier naming) and insufficient semantic validation. Through extensive experiments with diverse prompting strategies, we observe a pronounced trade-off between precision and recall. Our framework achieves 92% accuracy in classifying model outputs, demonstrating that current LLMs are ill-suited for standalone security auditing but show promise as collaborative aids to conventional static analysis tools, thereby underscoring the necessity of hybrid approaches.

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Enhancing Genetic Algorithms with Graph Neural Networks: A Timetabling Case Study

Feb 09, 2026

This work proposes a hybrid optimization framework that integrates a multimodal genetic algorithm with graph neural networks (GNNs) to address the challenge of balancing search efficiency and solution quality in workforce scheduling. For the first time, a GNN is embedded within the genetic algorithm as an enhancement operator, leveraging its capacity to model domain-specific knowledge—such as scheduling constraints and employee preferences—in a structured manner to guide the evolutionary search and improve solution quality. Experimental results on real-world employee scheduling tasks demonstrate that the proposed approach significantly outperforms standalone genetic algorithms or GNNs in both solution quality and computational efficiency, with performance gains confirmed to be statistically significant.

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Recent publications

Latest Papers

An Efficient Algorithm for Computing Mountain Prominence in Almost Linear Time

Jul 29, 2026

This study addresses the computational challenges posed by high-resolution digital elevation models (DEMs) in calculating topographic prominence at a global scale. The authors propose an approximately linear-time algorithm that efficiently computes the prominence of all peaks worldwide. The key insight is that the prominence of the vast majority of peaks is determined solely by local terrain, with only a small fraction influenced by distant higher summits. Leveraging this observation, the method refines classical algorithms and incorporates memoization to drastically reduce redundant computations and memory usage. Validation on real-world 3-arcsecond SRTM data demonstrates that the approach achieves substantial gains in computational efficiency while preserving the correctness of prominence values, enabling scalable prominence analysis across massive DEM datasets.

0 citationsRead paper

Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions

May 11, 2026

This study evaluates the reliability of large language models (LLMs) in static security analysis of smart contracts, investigating whether they can replace or merely complement traditional tools. To this end, we introduce the first automated evaluation framework that systematically assesses LLM performance in vulnerability detection, quantitatively revealing— for the first time—high false positive rates stemming from lexical biases (e.g., identifier naming) and insufficient semantic validation. Through extensive experiments with diverse prompting strategies, we observe a pronounced trade-off between precision and recall. Our framework achieves 92% accuracy in classifying model outputs, demonstrating that current LLMs are ill-suited for standalone security auditing but show promise as collaborative aids to conventional static analysis tools, thereby underscoring the necessity of hybrid approaches.

0 citationsRead paper

Enhancing Genetic Algorithms with Graph Neural Networks: A Timetabling Case Study

Feb 09, 2026

This work proposes a hybrid optimization framework that integrates a multimodal genetic algorithm with graph neural networks (GNNs) to address the challenge of balancing search efficiency and solution quality in workforce scheduling. For the first time, a GNN is embedded within the genetic algorithm as an enhancement operator, leveraging its capacity to model domain-specific knowledge—such as scheduling constraints and employee preferences—in a structured manner to guide the evolutionary search and improve solution quality. Experimental results on real-world employee scheduling tasks demonstrate that the proposed approach significantly outperforms standalone genetic algorithms or GNNs in both solution quality and computational efficiency, with performance gains confirmed to be statistically significant.

0 citationsRead paper