Fatemeh Bazikar

11 papers A 1Journal 10

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space. Ann. Math. Artif. Intell. Hossein Moosaei, Saeed Khosravi, Fatemeh Bazikar, Milan Hladík, Mario Rosario Guarracino
2025	J	`jnl`	A novel method for solving universum twin bounded support vector machine in the primal space. Ann. Math. Artif. Intell. Hossein Moosaei, Saeed Khosravi, Fatemeh Bazikar, Milan Hladík, Mario Rosario Guarracino
2025	J	`jnl`	An improved multi-task least squares twin support vector machine. Ann. Math. Artif. Intell. Hossein Moosaei, Fatemeh Bazikar, Panos M. Pardalos
2025	J	`jnl`	Enhanced multi-task learning using optimization methods. Int. J. Data Sci. Anal. Fatemeh Bazikar, Bahram Sadeghi Bigham, Hossein Moosaei
2025	J	`jnl`	Multi-Task Learning Based on Support Vector Machines and Twin Support Vector Machines: A Comprehensive Survey. CoRR Fatemeh Bazikar, Hossein Moosaei, Atefeh Hemmati, Panos M. Pardalos
2024	J	`jnl`	Multi-task twin support vector machine with Universum data. Eng. Appl. Artif. Intell. Hossein Moosaei, Fatemeh Bazikar, Milan Hladík
2024	J	`jnl`	Sparse least-squares Universum twin bounded support vector machine with adaptive Lp-norms and feature selection. Expert Syst. Appl. Hossein Moosaei, Fatemeh Bazikar, Milan Hladík, Panos M. Pardalos
2023	A	`conf`	Augmented Lagrangian Method for Linear Programming Using Smooth Approximation. DIS Hossein Moosaei, Saeed Ketabchi, Mujahid N. Syed, Fatemeh Bazikar
2022	J	`jnl`	Multi-task twin support vector machine with Universum data. CoRR Hossein Moosaei, Fatemeh Bazikar, Milan Hladík
2022	J	`jnl`	Universum parametric-margin ν-support vector machine for classification using the difference of convex functions algorithm. Appl. Intell. Hossein Moosaei, Fatemeh Bazikar, Saeed Ketabchi, Milan Hladík
2020	J	`jnl`	DC programming and DCA for parametric-margin ν-support vector machine. Appl. Intell. Fatemeh Bazikar, Saeed Ketabchi, Hossein Moosaei

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.