Federico Sabbatini

36 papers A* 2A 1Journal 12Unranked 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Four Decades of Symbolic Knowledge Extraction from Sub-Symbolic Predictors. A Survey. ACM Comput. Surv. Federico Sabbatini
2025	—	`conf`	A Fairness-Oriented Visual Extension for the PSyKE Platform. AEQUITAS@ECAI Federico Sabbatini
2025	J	`jnl`	Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review. CoRR Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo, Matteo Magnini, Andrea Omicini
2025	—	`conf`	Symbolic Knowledge Quality Evaluation with WInd. WOA Federico Sabbatini, Roberta Calegari
2024	—	`conf`	A Front-End Circuit in 28 nm CMOS for Hydrogenated Amorphous Silicon Detectors in Clinical Dosimetry. MOCAST Giovanni Mazza, Lorenzo Piccolo, Luca Antognini, Saba Aziz, Aishah Bashiri, Lucio Calcagnile, Daniela Calvo, Domenico Caputo, Anna Paola Caricato, Roberto Catalano, Roberto Cirio, Giuseppe Antonio Pablo Cirrone, Tommaso Croci, Giacomo Cuttone, Gianpiero De Cesare, Paolo De Remigis, Sylvain Dunand, Michele Fabi, Luca Frontini, Catia Grimani, Mariacristina Guarrera, Maria Ionica, Keida Kanxheri, Matthew Large, Francesca Lenta, Valentino Liberali, Nicola Lovecchio, Maurizio Martino, Giuseppe Maruccio, Mauro Menichelli, Anna Grazia Monteduro, Arianna Morozzi, Francesco Moscatelli, Augusto Nascetti, Stefania Pallotta, Daniele Passeri, Maddalena Pedio, Marco Petasecca, Giada Petringa, Francesca Peverini, Pisana Placidi, Gianluca Quarta, Silvia Rizzato, Federico Sabbatini, Leonello Servoli, Alberto Stabile, Cinzia Talamonti, Jonathan Emanuel Thomet, Luca Tosti, Mattia Villani, Richard James Wheadon, Nicolas Wyrsch, Nicola Zema
2024	—	`conf`	Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care. EXPLIMED@ECAI Christel Sirocchi, Muhammad Suffian Nizami, Federico Sabbatini, Alessandro Bogliolo, Sara Montagna
2024	J	`jnl`	Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care. CoRR Christel Sirocchi, Muhammad Suffian Nizami, Federico Sabbatini, Alessandro Bogliolo, Sara Montagna
2024	—	`conf`	Hierarchical Knowledge Extraction from Opaque Machine Learning Predictors. AI*IA Federico Sabbatini, Roberta Calegari
2024	—	`conf`	ICE: An Evaluation Metric to Assess Symbolic Knowledge Quality. AI*IA Federico Sabbatini, Roberta Calegari
2024	J	`jnl`	On the evaluation of the symbolic knowledge extracted from black boxes. AI Ethics Federico Sabbatini, Roberta Calegari
2024	—	`conf`	Symbolic Knowledge Comparison: Metrics and Methodologies for Multi-Agent Systems. WOA Federico Sabbatini, Christel Sirocchi, Roberta Calegari
2024	J	`jnl`	Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review. ACM Comput. Surv. Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo, Matteo Magnini, Andrea Omicini
2024	—	`conf`	Unmasking the Shadows: Leveraging Symbolic Knowledge Extraction to Discover Biases and Unfairness in Opaque Predictive Models. AEQUITAS@ECAI Federico Sabbatini, Roberta Calegari
2024	J	`jnl`	Untying black boxes with clustering-based symbolic knowledge extraction. Intelligenza Artificiale Federico Sabbatini, Roberta Calegari
2023	—	`conf`	Achieving Complete Coverage with Hypercube-Based Symbolic Knowledge-Extraction Techniques. ECAI Workshops (1) Federico Sabbatini, Roberta Calegari
2023	—	`conf`	Bottom-Up and Top-Down Workflows for Hypercube- And Clustering-Based Knowledge Extractors. EXTRAAMAS Federico Sabbatini, Roberta Calegari
2023	—	`conf`	ExACT Explainable Clustering: Unravelling the Intricacies of Cluster Formation. KoDis+CAKR@KR Federico Sabbatini, Roberta Calegari
2023	A*	`conf`	Explainable Clustering with CREAM. KR Federico Sabbatini, Roberta Calegari
2023	J	`jnl`	Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA. CoRR Federico Sabbatini, Catia Grimani
2023	A	`conf`	Symbolic Knowledge-Extraction Evaluation Metrics: The FiRe Score. ECAI Federico Sabbatini, Roberta Calegari
2023	J	`jnl`	Towards a unified model for symbolic knowledge extraction with hypercube-based methods. Intelligenza Artificiale Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini
2023	—	`conf`	Unlocking Insights and Trust: The Value of Explainable Clustering Algorithms for Cognitive Agents. WOA Federico Sabbatini, Roberta Calegari
2023	—	`conf`	Unveiling Opaque Predictors via Explainable Clustering: The CReEPy Algorithm. BEWARE@AI*IA Federico Sabbatini, Roberta Calegari
2023	—	`conf`	X-ray qualification of hydrogenated amorphous silicon sensors on flexible substrate. IWASI Mauro Menichelli, Luca Antognini, Aishah Bashiri, Marco Bizzarri, Lucio Calcagnile, M. Caprai, Anna Paola Caricato, Roberto Catalano, Giuseppe Antonio Pablo Cirrone, Tommaso Croci, Giacomo Cuttone, Sylvain Dunand, Michele Fabi, Luca Frontini, Catia Grimani, Maria Ionica, Keida Kanxheri, Matthew Large, Valentino Liberali, Maurizio Martino, Giuseppe Maruccio, Giovanni Mazza, A. G. Monteduro, Arianna Morozzi, Francesco Moscatelli, Stefania Pallotta, A. Papi, Daniele Passeri, Maddalena Pedio, Marco Petasecca, Giada Petringa, Francesca Peverini, Lorenzo Piccolo, Pisana Placidi, Gianluca Quarta, Silvia Rizzato, G. Rossi, Federico Sabbatini, Alberto Stabile, Leonello Servoli, Cinzia Talamonti, Luca Tosti, Mattia Villani, Richard James Wheadon, Nicolas Wyrsch, Nicola Zema
2022	J	`jnl`	Clustering-Based Approaches for Symbolic Knowledge Extraction. CoRR Federico Sabbatini, Roberta Calegari
2022	J	`jnl`	Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper. CoRR Federico Sabbatini, Roberta Calegari
2022	—	`conf`	Hypercube-based methods for symbolic knowledge extraction: towards a unified model. WOA Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini
2022	—	`conf`	Semantic Web-Based Interoperability for Intelligent Agents with PSyKE. EXTRAAMAS@AAMAS Federico Sabbatini, Giovanni Ciatto, Andrea Omicini
2022	A*	`conf`	Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO. KR Federico Sabbatini, Roberta Calegari
2022	J	`jnl`	Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder. CoRR Federico Sabbatini, Catia Grimani
2022	J	`jnl`	Symbolic knowledge extraction from opaque ML predictors in PSyKE: Platform design & experiments. Intelligenza Artificiale Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini
2022	—	`conf`	The PSyKE Technology for Trustworthy Artificial Intelligence. AI*IA Roberta Calegari, Federico Sabbatini
2021	—	`conf`	GridEx: An Algorithm for Knowledge Extraction from Black-Box Regressors. EXTRAAMAS@AAMAS Federico Sabbatini, Giovanni Ciatto, Andrea Omicini
2021	—	`conf`	On the Design of PSyKE: A Platform for Symbolic Knowledge Extraction. WOA Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini
2020	—	`conf`	Improvements to the G-Lorep Federation of Learning Object Repositories. ICCSA (7) Federico Sabbatini, Sergio Tasso, Simonetta Pallottelli, Osvaldo Gervasi
2019	—	`conf`	Cloud and Local Servers for a Federation of Molecular Science Learning Object Repositories. ICCSA (6) Sergio Tasso, Simonetta Pallottelli, Osvaldo Gervasi, Federico Sabbatini, Valentina Franzoni, Antonio Laganà

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.