Eduardo Paluzo-Hidalgo

24 papers C 1Journal 20Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases. CoRR Eduardo Paluzo-Hidalgo, Yuichi Ike
2025	J	`jnl`	Latent Space Topology Evolution in Multilayer Perceptrons. CoRR Eduardo Paluzo-Hidalgo
2024	J	`jnl`	An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learning. CoRR Víctor Toscano-Durán, Javier Perera-Lago, Eduardo Paluzo-Hidalgo, Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo, Matteo Rucco
2024	—	`conf`	Application of the Representative Measure Approach to Assess the Reliability of Decision Trees in Dealing with Unseen Vehicle Collision Data. xAI (4) Javier Perera-Lago, Víctor Toscano-Durán, Eduardo Paluzo-Hidalgo, Sara Narteni, Matteo Rucco
2024	J	`jnl`	Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision data. CoRR Javier Perera-Lago, Víctor Toscano-Durán, Eduardo Paluzo-Hidalgo, Sara Narteni, Matteo Rucco
2024	J	`jnl`	SIMAP: A simplicial-map layer for neural networks. CoRR Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo, Eduardo Paluzo-Hidalgo
2024	J	`jnl`	Trainable and explainable simplicial map neural networks. Inf. Sci. Eduardo Paluzo-Hidalgo, Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo
2023	J	`jnl`	A Survey of Vectorization Methods in Topological Data Analysis. IEEE Trans. Pattern Anal. Mach. Intell. Dashti Ali, Aras Asaad, María José Jiménez, Vidit Nanda, Eduardo Paluzo-Hidalgo, Manuel Soriano-Trigueros
2023	J	`jnl`	A Topological Approach to Measuring Training Data Quality. CoRR Álvaro Torras-Casas, Eduardo Paluzo-Hidalgo, Rocío González-Díaz
2023	J	`jnl`	Explainability in Simplicial Map Neural Networks. CoRR Eduardo Paluzo-Hidalgo, Miguel Angel Gutiérrez-Naranjo, Rocío González-Díaz
2023	—	`conf`	The Metric-aware Kernel-width Choice for LIME. xAI (Late-breaking Work, Demos, Doctoral Consortium) Aurelio Barrera-Vicent, Eduardo Paluzo-Hidalgo, Miguel Angel Gutiérrez-Naranjo
2022	J	`jnl`	Strong Euler well-composedness. J. Comb. Optim. Nicolas Boutry, Rocío González-Díaz, María José Jiménez, Eduardo Paluzo-Hidalgo
2022	J	`jnl`	Topology-based representative datasets to reduce neural network training resources. Neural Comput. Appl. Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo, Eduardo Paluzo-Hidalgo
2021	J	`jnl`	Emotion recognition in talking-face videos using persistent entropy and neural networks. CoRR Eduardo Paluzo-Hidalgo, Guillermo Aguirre-Carrazana, Rocío González-Díaz
2021	J	`jnl`	Optimizing the Simplicial-Map Neural Network Architecture. J. Imaging Eduardo Paluzo-Hidalgo, Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo, Jónathan Heras
2020	J	`jnl`	Approximating lower-star persistence via 2D combinatorial map simplification. Pattern Recognit. Lett. Guillaume Damiand, Eduardo Paluzo-Hidalgo, Ryan Slechta, Rocío González-Díaz
2020	C	`conf`	Euler Well-Composedness. IWCIA Nicolas Boutry, Rocío González-Díaz, María José Jiménez, Eduardo Paluzo-Hidalgo
2020	J	`jnl`	Two-hidden-layer feed-forward networks are universal approximators: A constructive approach. Neural Networks Eduardo Paluzo-Hidalgo, Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo
2019	J	`jnl`	Representative Datasets: The Perceptron Case. CoRR Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo, Eduardo Paluzo-Hidalgo
2019	—	`conf`	Towards Emotion Recognition: A Persistent Entropy Application. CTIC Rocío González-Díaz, Eduardo Paluzo-Hidalgo, José F. Quesada
2019	J	`jnl`	Towards a Philological Metric through a Topological Data Analysis Approach. CoRR Eduardo Paluzo-Hidalgo, Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo
2019	J	`jnl`	Two-hidden-layer Feedforward Neural Networks are Universal Approximators: A Constructive Approach. CoRR Rocío González-Díaz, Miguel Angel Gutiérrez-Naranjo, Eduardo Paluzo-Hidalgo
2018	J	`jnl`	Representative datasets for neural networks. Electron. Notes Discret. Math. Rocío González-Díaz, Eduardo Paluzo-Hidalgo, Miguel Angel Gutiérrez-Naranjo
2018	J	`jnl`	Towards Emotion Recognition: A Persistent Entropy Application. CoRR Rocío González-Díaz, Eduardo Paluzo-Hidalgo, José F. Quesada

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.