Farid Saberi-Movahed

22 papers A* 1Journal 20

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Dual Autoencoder-like NMF with higher-order graph regularization for topic modeling. Knowl. Based Syst. Maryam Majidi, Farid Saberi-Movahed, Mohammad Ghasemzadeh, Sahar Vahdati
2026	J	`jnl`	Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey. ACM Comput. Surv. Farid Saberi-Movahed, Kamal Berahmand, Razieh Sheikhpour, Yuefeng Li, Shirui Pan, Mahdi Jalili
2026	J	`jnl`	Robust oblique projection and weighted NMF for hyperspectral unmixing. Pattern Recognit. Yasin Hashemi-Nazari, Azita Tajaddini, Farid Saberi-Movahed, Fernando Alonso-Fernandez, Prayag Tiwari
2026	J	`jnl`	Semi-supervised feature selection with concept factorization and robust label learning. Pattern Recognit. Razieh Sheikhpour, Farid Saberi-Movahed, Mahdi Jalili, Kamal Berahmand
2026	J	`jnl`	Unsupervised feature selection via graph-based proximity and structured autoencoder-like NMF. Inf. Process. Manag. Mehri Pakmanesh, Farid Saberi-Movahed, Abbas Salemi, Prayag Tiwari
2025	J	`jnl`	A Comprehensive Survey on Spectral Clustering with Graph Structure Learning. CoRR Kamal Berahmand, Farid Saberi-Movahed, Razieh Sheikhpour, Yuefeng Li, Mahdi Jalili
2025	J	`jnl`	A similarity measure based on subspace distance for spectral clustering. Neurocomputing Nadimeh Naseri, Mahdi Eftekhari, Farid Saberi-Movahed, Mehdi Radjabalipour, Lluis A. Belanche
2025	J	`jnl`	Bilinear Self-Representation for Unsupervised Feature Selection with Structure Learning. Neurocomputing Hossein Nasser Assadi, Faranges Kyanfar, Farid Saberi-Movahed, Abbas Salemi
2025	A*	`conf`	OA ICDM Kamal Berahmand, Razieh Sheikhpour, Farid Saberi-Movahed, Mahdi Jalili
2025	J	`jnl`	Robust semi-supervised multi-label feature selection based on shared subspace and manifold learning. Inf. Sci. Razieh Sheikhpour, Mehrnoush Mohammadi, Kamal Berahmand, Farid Saberi-Movahed, Hassan Khosravi
2025	J	`jnl`	Simultaneous outlier detection and elimination in hyperspectral unmixing via weighted non-negative matrix tri-factorization. Mach. Learn. Yasin Hashemi-Nazari, Farid Saberi-Movahed, Azita Tajaddini, Catarina Moreira, Xin Ning, Prayag Tiwari
2024	J	`jnl`	Deep Nonnegative Matrix Factorization with Joint Global and Local Structure Preservation. Expert Syst. Appl. Farid Saberi-Movahed, Bitasta Biswas, Prayag Tiwari, Jens Lehmann, Sahar Vahdati
2024	J	`jnl`	Low-Redundant Unsupervised Feature Selection based on Data Structure Learning and Feature Orthogonalization. Expert Syst. Appl. Mahsa Samareh Jahani, Farid Saberi-Movahed, Mahdi Eftekhari, Gholamreza Aghamollaei, Prayag Tiwari
2024	J	`jnl`	Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey. CoRR Farid Saberi-Movahed, Kamal Berahmand, Razieh Sheikhpour, Yuefeng Li, Shirui Pan
2024	J	`jnl`	On applying deflation and flexible preconditioning to the adaptive Simpler GMRES method for Sylvester tensor equations. J. Frankl. Inst. Azita Tajaddini, Farid Saberi-Movahed, Xian-Ming Gu, Mohammed Heyouni
2023	J	`jnl`	Deep Metric Learning with Soft Orthogonal Proxies. CoRR Farshad Saberi-Movahed, Mohammad Kazem Ebrahimpour, Farid Saberi-Movahed, Monireh Moshavash, Dorsa Rahmatian, Mahvash Mohazzebi, Mahdi Shariatzadeh, Mahdi Eftekhari
2023	J	`jnl`	Graph Regularized Nonnegative Matrix Factorization for Community Detection in Attributed Networks. IEEE Trans. Netw. Sci. Eng. Kamal Berahmand, Mehrnoush Mohammadi, Farid Saberi-Movahed, Yuefeng Li, Yue Xu
2023	J	`jnl`	Unsupervised feature selection based on variance-covariance subspace distance. Neural Networks Saeed Karami, Farid Saberi-Movahed, Prayag Tiwari, Pekka Marttinen, Sahar Vahdati
2023	J	`jnl`	Unsupervised feature selection guided by orthogonal representation of feature space. Neurocomputing Mahsa Samareh Jahani, Gholamreza Aghamollaei, Mahdi Eftekhari, Farid Saberi-Movahed
2022	J	`jnl`	Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods. Comput. Biol. Medicine Farshad Saberi-Movahed, Mahyar Mohammadifard, Adel Mehrpooya, Mohammad Rezaei-Ravari, Kamal Berahmand, Mehrdad Rostami, Saeed Karami, Mohammad Najafzadeh, Davood Hajinezhad, Mina Jamshidi, Farshid Abedi, Mahtab Mohammadifard, Elnaz Farbod, Farinaz Safavi, Mohammadreza Dorvash, Negar Mottaghi-Dastjerdi, Shahrzad Vahedi, Mahdi Eftekhari, Farid Saberi-Movahed, Hamid Alinejad-Rokny, Shahab S. Band, Iman Tavassoly
2022	J	`jnl`	Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy with application in gene selection. Knowl. Based Syst. Farid Saberi-Movahed, Mehrdad Rostami, Kamal Berahmand, Saeed Karami, Prayag Tiwari, Mourad Oussalah, Shahab S. Band
2022	—	`book`	How Fuzzy Concepts Contribute to Machine Learning Mahdi Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, Vicenç Torra

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.