Selected work

Representative Papers

A fairness-aware extension of Stochastic Multicriteria Acceptability Analysis for ranking

Jun 16, 2026

This study addresses the frequent neglect of group fairness in traditional multicriteria ranking methods under preference uncertainty, which often leads to underrepresentation of disadvantaged groups. To bridge this gap, the authors propose SMAA-Fair, the first approach to embed fairness mechanisms directly into the Stochastic Multicriteria Acceptability Analysis (SMAA) framework. SMAA-Fair reweights simulated rankings according to fairness metrics—namely statistical parity, normalized discounted KL divergence (rKL), and nDKL—so that fairer rankings receive higher weight in both acceptability indices and central weights. Notably, the method is agnostic to any specific aggregation model, thereby preserving robustness while promoting fairness. Experimental results demonstrate that SMAA-Fair significantly enhances the representation of protected groups in top ranking positions without compromising robustness to preference uncertainty.

0 citationsRead paper

Quantum Genetic Optimization for Negative Selection Algorithms in Anomaly Detection

May 21, 2026

This study addresses the inefficiency of detector generation in traditional negative selection algorithms for anomaly detection by introducing a quantum genetic algorithm into the EvoSeedRNSA framework for the first time. Leveraging quantum superposition and probability amplitude adjustment mechanisms, the proposed approach significantly enhances both exploration of the search space and convergence efficiency during detector generation. The method not only improves anomaly detection performance on high-dimensional data but also demonstrates increased robustness to hyperparameter variations. Experimental results on a Metaverse financial transaction dataset show that the proposed scheme achieves substantially higher detection accuracy compared to classical methods, thereby validating the effectiveness and potential of integrating quantum computing with artificial immune systems.

0 citationsRead paper

Mamute: high-performance computing for geophysical methods

Feb 17, 2025

Geophysical simulations—particularly wave-equation-based seismic forward modeling and full-waveform inversion (FWI)—suffer from prohibitive computational costs and low supercomputing resource utilization. To address these challenges, this paper introduces a high-performance, open-source software framework. Methodologically, it pioneers the integration of fault-tolerant execution, automated parallel loop scheduling, and dynamic load balancing; implemented in C++, it supports hybrid MPI/OpenMP parallelism and adaptive distributed task scheduling. Experimental validation on large-scale real-world seismic modeling and FWI tasks demonstrates exceptional strong and weak scalability, robustness against hardware failures, and substantial improvements in supercomputer resource efficiency and computational stability. The framework simultaneously achieves high numerical accuracy and engineering practicality. Its source code is publicly available under the MIT License.

0 citationsRead paper

Recent publications

Latest Papers

A fairness-aware extension of Stochastic Multicriteria Acceptability Analysis for ranking

Jun 16, 2026

This study addresses the frequent neglect of group fairness in traditional multicriteria ranking methods under preference uncertainty, which often leads to underrepresentation of disadvantaged groups. To bridge this gap, the authors propose SMAA-Fair, the first approach to embed fairness mechanisms directly into the Stochastic Multicriteria Acceptability Analysis (SMAA) framework. SMAA-Fair reweights simulated rankings according to fairness metrics—namely statistical parity, normalized discounted KL divergence (rKL), and nDKL—so that fairer rankings receive higher weight in both acceptability indices and central weights. Notably, the method is agnostic to any specific aggregation model, thereby preserving robustness while promoting fairness. Experimental results demonstrate that SMAA-Fair significantly enhances the representation of protected groups in top ranking positions without compromising robustness to preference uncertainty.

0 citationsRead paper

Quantum Genetic Optimization for Negative Selection Algorithms in Anomaly Detection

May 21, 2026

This study addresses the inefficiency of detector generation in traditional negative selection algorithms for anomaly detection by introducing a quantum genetic algorithm into the EvoSeedRNSA framework for the first time. Leveraging quantum superposition and probability amplitude adjustment mechanisms, the proposed approach significantly enhances both exploration of the search space and convergence efficiency during detector generation. The method not only improves anomaly detection performance on high-dimensional data but also demonstrates increased robustness to hyperparameter variations. Experimental results on a Metaverse financial transaction dataset show that the proposed scheme achieves substantially higher detection accuracy compared to classical methods, thereby validating the effectiveness and potential of integrating quantum computing with artificial immune systems.

0 citationsRead paper

Mamute: high-performance computing for geophysical methods

Feb 17, 2025

Geophysical simulations—particularly wave-equation-based seismic forward modeling and full-waveform inversion (FWI)—suffer from prohibitive computational costs and low supercomputing resource utilization. To address these challenges, this paper introduces a high-performance, open-source software framework. Methodologically, it pioneers the integration of fault-tolerant execution, automated parallel loop scheduling, and dynamic load balancing; implemented in C++, it supports hybrid MPI/OpenMP parallelism and adaptive distributed task scheduling. Experimental validation on large-scale real-world seismic modeling and FWI tasks demonstrates exceptional strong and weak scalability, robustness against hardware failures, and substantial improvements in supercomputer resource efficiency and computational stability. The framework simultaneously achieves high numerical accuracy and engineering practicality. Its source code is publicly available under the MIT License.

0 citationsRead paper