Ou Deng

16 papers C 1Journal 10Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CMSA: Addressing semantic discrepancy and context dependency in multimodal sentiment analysis. Neurocomputing Jianing Zhao, Ou Deng, Qun Jin
2026	J	`jnl`	Statistical-Neural Interaction Networks for Interpretable Mixed-Type Data Imputation. CoRR Ou Deng, Shoji Nishimura, Atsushi Ogihara, Qun Jin
2025	—	`conf`	Detection of Shock Risk Triggers Using Time-Aware Dynamic-Window RAG with LLMs. BDA (Astronomy, Science, and Engineering) Jianting Xu, Ou Deng, Qun Jin
2025	J	`jnl`	Logistic-Gated Operators Enable Auditable Unit-Aware Thresholds in Symbolic Regression. CoRR Ou Deng, Ruichen Cong, Jianting Xu, Shoji Nishimura, Atsushi Ogihara, Qun Jin
2024	C	`conf`	Analyzing Lifestyle and Behavior with Causal Discovery in Health Data from Wearable Devices and Self-Assessments. HealthCom Jing Zhang, Ruichen Cong, Ou Deng, Yuxi Li, Keishin Lam, Qun Jin
2024	J	`jnl`	Evolutionary Causal Discovery with Relative Impact Stratification for Interpretable Data Analysis. CoRR Ou Deng, Shoji Nishimura, Atsushi Ogihara, Qun Jin
2024	—	`conf`	Improving Multimodal Sentiment Analysis with Unimodal Pseudo-label Generation. BDA (Astronomy, Science, and Engineering) Jianing Zhao, Ou Deng, Qun Jin
2024	J	`jnl`	Multiple feature selection based on an optimization strategy for causal analysis of health data. Health Inf. Sci. Syst. Ruichen Cong, Ou Deng, Shoji Nishimura, Atsushi Ogihara, Qun Jin
2024	J	`jnl`	Phenome-wide causal proteomics enhance systemic lupus erythematosus flare prediction: A study in Asian populations. CoRR Liying Chen, Ou Deng, Ting Fang, Mei Chen, Xvfeng Zhang, Ruichen Cong, Dingqi Lu, Runrun Zhang, Qun Jin, Xinchang Wang
2023	—	`conf`	Causal Discovery of Health Features from Wearable Device and Traditional Chinese Medicine Diagnosis Data. HCI (49) Yuxi Li, Ou Deng, Atsushi Ogihara, Shoji Nishimura, Qun Jin
2023	J	`jnl`	FOSA: Full Information Maximum Likelihood (FIML) Optimized Self-Attention Imputation for Missing Data. CoRR Ou Deng, Qun Jin
2023	—	`conf`	Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling. HCI (19) Ou Deng, Qun Jin
2023	J	`jnl`	System Information Decomposition. CoRR Aobo Lyu, Bing Yuan, Ou Deng, Mingzhe Yang, Andrew Clark, Jiang Zhang
2022	J	`jnl`	Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling. CoRR Ou Deng, Qun Jin
2021	J	`jnl`	An Extended Epidemic Model on Interconnected Networks for COVID-19 to Explore the Epidemic Dynamics. CoRR Ou Deng, Kiichi Tago, Qun Jin
2013	—	`conf`	Forest Fire Risk Mapping Based on Spatial Logistic Model of Northeastern China Forest Zone. GRMSE (2) Ou Deng, Guofeng Su, Quanyi Huang, Yiqiu Li

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.