Edith Belise Kenmogne

12 papers B 2Journal 10

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	SSL-SLR: Self-Supervised Representation Learning for Sign Language Recognition. Trans. Mach. Learn. Res. Ariel Basso Madjoukeng, Jérôme Fink, Pierre Poitier, Edith Belise Kenmogne, Benoît Frénay
2025	B	`conf`	Local-Global Data Augmentation for Contrastive Learning in Static Sign Language Recognition. IDA Ariel Basso Madjoukeng, Edith Belise Kenmogne, Pierre Poitier, Benoît Frénay, Jérôme Fink
2025	J	`jnl`	Robust sparse k-means clustering against STOF observations. Int. J. Data Sci. Anal. Ariel Basso Madjoukeng, Edith Belise Kenmogne, Benoît Frénay
2025	J	`jnl`	SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition. CoRR Ariel Basso Madjoukeng, Jérôme Fink, Pierre Poitier, Edith Belise Kenmogne, Benoît Frénay
2025	J	`jnl`	Support estimation in frequent itemsets mining on Enriched Two Level Tree. Inf. Syst. Clémentin Tayou Djamégni, William Kery Branston Ndemaze, Edith Belise Kenmogne, Hervé Maradona Nana Kouassi, Arnauld Nzegha Fountsop, Idriss Tetakouchom, Laurent Cabrel Tabueu Fotso
2024	J	`jnl`	An Improved Algorithm for Extracting Frequent Gradual Patterns. Informatica Edith Belise Kenmogne, Idriss Tetakouchom, Clémentin Tayou Djamégni, Roger Nkambou, Laurent Cabrel Tabueu Fotso
2024	B	`conf`	Fast k-means with Stable Instance Sets. IJCNN Ariel Basso Madjoukeng, Edith Belise Kenmogne, Benoît Frénay
2023	J	`jnl`	A novel algorithm for mining maximal frequent gradual patterns. Eng. Appl. Artif. Intell. Edith Belise Kenmogne, Laurent Cabrel Tabueu Fotso, Clémentin Tayou Djamégni
2022	J	`jnl`	Efficient mining of intra-periodic frequent sequences. Array Edith Belise Kenmogne, Clémentin Tayou Djamégni, Roger Nkambou, Laurent Cabrel Tabueu Fotso, Tadmon Calvin
2017	J	`jnl`	A pattern growth-based sequential pattern mining algorithm called prefixSuffixSpan. EAI Endorsed Trans. Scalable Inf. Syst. Edith Belise Kenmogne, Tadmon Calvin, Roger Nkambou
2017	J	`jnl`	The Impact of the Pattern-Growth Ordering on the Performances of Pattern Growth-Based Sequential Pattern Mining Algorithms. Comput. Inf. Sci. Edith Belise Kenmogne
2016	J	`jnl`	A traditional-learning time predictive approach for e-learning systems in challenging environments. EAI Endorsed Trans. e Learn. Edith Belise Kenmogne

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.