Federica Amato

15 papers B 1Journal 7Unranked 7
YearRankTypeTitle / Venue / Authors
2026 J jnl
Voice classification in Parkinson's disease: A deep learning approach using transformers and error rate metrics.
Biomed. Signal Process. Control.
Benedetta Perrone, Federica Amato, Gabriella Olmo
2025 J jnl
Beyond breathalyzers: AI-powered speech analysis for alcohol intoxication detection.
Expert Syst. Appl.
Federica Amato, Valerio Cesarini, Gabriella Olmo, Giovanni Saggio, Giovanni Costantini
2024 — conf
Features of 3-D Phase Space Attractors as Descriptors of Chaotic Laws in Voice Signals for the AI-based Detection of Dysphonia.
MetroInd4.0 & IoT
Valerio Cesarini, Mattia Magliocchetti, Diego Calicchia, Federica Amato, Antonio Suppa, Francesco Asci, Luca Marsili, Giovanni Saggio, Giovanni Costantini
2023 J jnl
Artificial Intelligence-Based Voice Assessment of Patients with Parkinson's Disease Off and On Treatment: Machine vs. Deep-Learning Comparison.
Sensors
Giovanni Costantini, Valerio Cesarini, Pietro Di Leo, Federica Amato, Antonio Suppa, Francesco Asci, Antonio Pisani, Alessandra Calculli, Giovanni Saggio
2023 — conf
Automatic Detection of Myotonia using a Sensory Glove with Resistive Flex Sensors and Machine Learning Techniques.
MetroInd4.0&IoT
Valerio Cesarini, Giovanni Costantini, Federica Amato, Vito Errico, Luca Pietrosanti, Alexandre Calado, Roberto Massa, Erica Frezza, Fernanda Irrera, Alessandro Manoni, Giovanni Saggio
2023 J jnl
Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson's disease: A systematic review.
Expert Syst. Appl.
Luis Sigcha, Luigi Borzì, Federica Amato, Irene Rechichi, Carlos Ramos-Romero, Andrés Cárdenas, Luis Gascó, Gabriella Olmo
2023 — conf
Hallmarks of Parkinson's disease progression determined by temporal evolution of speech attractors in the reconstructed phase-space.
MetroInd4.0&IoT
Federica Amato, Valerio Cesarini, Luca Pietrosanti, Giovanni Costantini, Gabriella Olmo, Giovanni Saggio
2023 J jnl
Machine learning- and statistical-based voice analysis of Parkinson's disease patients: A survey.
Expert Syst. Appl.
Federica Amato, Giovanni Saggio, Valerio Cesarini, Gabriella Olmo, Giovanni Costantini
2022 — conf
Obesity and Gastro-Esophageal Reflux voice disorders: a Machine Learning approach.
MeMeA
Federica Amato, Maria Fasani, Glauco Raffaelli, Valerio Cesarini, Gabriella Olmo, Nicola Di Lorenzo, Giovanni Costantini, Giovanni Saggio
2022 — conf
Single-Channel EEG Detection of REM Sleep Behaviour Disorder: The Influence of REM and Slow Wave Sleep.
IWBBIO (1)
Irene Rechichi, Federica Amato, Alessandro Cicolin, Gabriella Olmo
2022 — conf
Sleep Quality through Vocal Analysis: a Telemedicine Application.
PerCom Workshops
Federica Amato, Irene Rechichi, Luigi Borzì, Gabriella Olmo
2021 J jnl
An algorithm for Parkinson's disease speech classification based on isolated words analysis.
Health Inf. Sci. Syst.
Federica Amato, Luigi Borzì, Gabriella Olmo, Juan Rafael Orozco-Arroyave
2021 — conf
Socially Assistive Robotics combined with Artificial Intelligence for ADHD.
CCNC
Federica Amato, Marianna Di Gregorio, Clara Monaco, Monica Sebillo, Genoveffa Tortora, Giuliana Vitiello
2021 J jnl
Speech Impairment in Parkinson's Disease: Acoustic Analysis of Unvoiced Consonants in Italian Native Speakers.
IEEE Access
Federica Amato, Luigi Borzì, Gabriella Olmo, Carlo Alberto Artusi, Gabriele Imbalzano, Leonardo Lopiano
2020 B conf
The Therapeutic Use of Humanoid Robots for Behavioral Disorders.
AVI
Federica Amato, Marianna Di Gregorio, Clara Monaco, Monica Sebillo, Genoveffa Tortora, Giuliana Vitiello

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.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

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.