Takato Tatsumi

18 papers A 1B 5Journal 4Unranked 8

Year	Rank	Type	Title / Venue / Authors
2020	B	`conf`	Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS. CEC Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama
2019	B	`conf`	Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments. CEC Takato Tatsumi, Keiki Takadama
2019	B	`conf`	Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models. CEC Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama
2019	—	`conf`	XCS-CR for handling input, output, and reward noise. GECCO (Companion) Takato Tatsumi, Keiki Takadama
2018	—	`conf`	Classifier generalization for comprehensive classifiers subsumption in XCS. GECCO (Companion) Caili Zhang, Takato Tatsumi, Hiyoyuki Sato, Tim Kovacs, Keiki Takadama
2018	—	`conf`	Study of Analytical Methods on the Relationship between Sleep Quality and Stress with a focus on Human Circadian Rhythm. AAAI Spring Symposia Ryo Takano, Satoshi Hasegawa, Yuta Umenai, Takato Tatsumi, Keiki Takadama, Toru Shimuta, Toru Yabe, Hideo Matsumoto
2018	—	`conf`	XCS for Missing Attributes in Data. SCIS&ISIS Takato Tatsumi, Keiki Takadama
2018	—	`conf`	XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise. GECCO (Companion) Takato Tatsumi, Tim Kovacs, Keiki Takadama
2018	—	`conf`	XCSR based on compressed input by deep neural network for high dimensional data. GECCO (Companion) Kazuma Matsumoto, Ryo Takano, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
2017	B	`conf`	Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution. CEC Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
2017	J	`jnl`	Approach to Clustering with Variance-Based XCS. J. Adv. Comput. Intell. Intell. Informatics Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama
2017	A	`conf`	Automatic adjustment of selection pressure based on range of reward in learning classifier system. GECCO Takato Tatsumi, Hiroyuki Sato, Keiki Takadama
2017	J	`jnl`	Learning Classifier System Based on Mean of Reward. J. Adv. Comput. Intell. Intell. Informatics Takato Tatsumi, Hiroyuki Sato, Keiki Takadama
2017	J	`jnl`	Supporting the Exploration of the Learning Goals for a Continuous Learner Toward Creative Learning. J. Adv. Comput. Intell. Intell. Informatics Takato Okudo, Tomohiro Yamaguchi, Akinori Murata, Takato Tatsumi, Fumito Uwano, Keiki Takadama
2017	J	`jnl`	XCSR Learning from Compressed Data Acquired by Deep Neural Network. J. Adv. Comput. Intell. Intell. Informatics Kazuma Matsumoto, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
2016	—	`conf`	Extracting Different Abstracted Level Rule with Variance-Based LCS. SCIS&ISIS Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama, Hiroyuki Sato, Tim Kovacs
2016	—	`conf`	Variance-based Learning Classifier System without Convergence of Reward Estimation. GECCO (Companion) Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
2015	B	`conf`	Handling different level of unstable reward environment through an estimation of reward distribution in XCS. CEC Takato Tatsumi, Takahiro Komine, Hiroyuki Sato, Keiki Takadama

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.