Qin Zhong

13 papers A* 1Journal 10Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Development and validation of predictive models for prognostic assessment in patients with acute non-ST-segment elevation myocardial infarction: a multicentre real-world study from China. BMC Medical Informatics Decis. Mak. Chongyou Rao, Wanling Wang, Hebin Che, Qin Zhong, Zongren Li, Wei Liu, Liqiang Fu, Wei Qin, Jixian Gao, Bo Liu, Kunlun He
2025	J	`jnl`	Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care. CoRR Yuxiao Cheng, Xinxin Song, Ziqian Wang, Qin Zhong, Kunlun He, Jinli Suo
2025	J	`jnl`	Critical Systemic Risks in Multilayer Automotive Supply Networks: Static and Dynamic Network Perspectives. Syst. Xiongping Yue, Qin Zhong
2025	J	`jnl`	Ultra-Efficient Imaging of Hypersonic BFSAR Under Doppler Aliasing Via Time-Frequency Flow Diagram Analysis. IEEE Trans. Aerosp. Electron. Syst. Xuanqi Wang, Xuan Song, Qin Zhong, Yachao Li, Huaming Zhang, Yuzhou Liu
2024	J	`jnl`	An Implicit Transformer-based Fusion Method for Hyperspectral and Multispectral Remote Sensing Image. Int. J. Appl. Earth Obs. Geoinformation Chunyu Zhu, Tinghao Zhang, Qiong Wu, Yachao Li, Qin Zhong
2024	A*	`conf`	CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery. ICLR Yuxiao Cheng, Ziqian Wang, Tingxiong Xiao, Qin Zhong, Jinli Suo, Kunlun He
2023	J	`jnl`	CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery. CoRR Yuxiao Cheng, Ziqian Wang, Tingxiong Xiao, Qin Zhong, Jinli Suo, Kunlun He
2022	J	`jnl`	DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications. Bioinform. Zongren Li, Qin Zhong, Jing Yang, Yongjie Duan, Wenjun Wang, Chengkun Wu, Kunlun He
2022	J	`jnl`	Electricity Procurement Strategies under Supply Disruption and Price Fluctuation. Complex. Jie Tan, Qin Zhong
2022	J	`jnl`	Fuzzy closed-loop supply chain models with quality and marketing effort-dependent demand. Expert Syst. Appl. Huaige Zhang, Zongjun Wang, Xianpei Hong, Yeming (Yale) Gong, Qin Zhong
2019	—	`conf`	Attention Based Semi-Supervised Dictionary Learning for Diagnosis of Autism Spectrum Disorders. ICME Workshops Meng Yang, Qin Zhong, Lin Chen, Fanglin Huang, Baiying Lei
2018	J	`jnl`	Pricing Decisions in a Fuzzy Supply Chain System Considering Different Duopolistic Retailers' Competitive Behavior. Int. J. Fuzzy Syst. Qin Zhong, Zongjun Wang, Xianpei Hong
2018	—	`conf`	Semi-supervised Dictionary Active Learning for Pattern Classification. PRCV (3) Qin Zhong, Meng Yang, Tiancheng Zhang

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.