Qianting Ma

16 papers Journal 13Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Contrast-Saturation Adaptive Model for Low-Light Image Enhancement. SIAM J. Imaging Sci. Qianting Ma, Yang Wang, Tieyong Zeng
2025	J	`jnl`	The impact of natural disasters on agricultural credit risk: A theoretical model and empirical test. Comput. Ind. Eng. Qianting Ma, Weizhong Wang, Ruiqi Leng, Muhammet Deveci, Renjia Liu, Dursun Delen
2024	J	`jnl`	Dynamic Evolution Model of Internet Financial Public Opinion. Inf. Chao Yu, Jianmin He, Qianting Ma, Xinyu Liu
2024	J	`jnl`	MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion. Comput. Medical Imaging Graph. Mengqi Xu, Qianting Ma, Huajie Zhang, Dexing Kong, Tieyong Zeng
2024	—	`conf`	Momentum on Sports Performance: Machine Learning and Multi-Aspect Analysis. ICBAR Junxiao Li, Xin Tan, Lanfang Yi, Qianting Ma
2023	J	`jnl`	A novel prediction based portfolio optimization model using deep learning. Comput. Ind. Eng. Yilin Ma, Weizhong Wang, Qianting Ma
2023	J	`jnl`	Can green innovation subsidies reduce the systemic risk of green innovative enterprises? A simulation study. Technol. Anal. Strateg. Manag. Wenke Yang, Qianting Ma, Jianmin He, Shuai Lu, Xi Chen
2023	J	`jnl`	Low-Light Image Enhancement via Implicit Priors Regularized Illumination Optimization. IEEE Trans. Computational Imaging Qianting Ma, Yang Wang, Tieyong Zeng
2023	J	`jnl`	Retinex-Based Variational Framework for Low-Light Image Enhancement and Denoising. IEEE Trans. Multim. Qianting Ma, Yang Wang, Tieyong Zeng
2020	—	`conf`	A Complex Network Model for Credit and Debt Relationships Analysis of Bank-Firm System. CNIOT Qianting Ma, Wenke Yang, Jianmin He
2020	—	`conf`	Complex Network and Systemic Risk Analysis of Financial systems Based on Computational Experiment Method. CNIOT Hong Yao, Qianting Ma, Jianmin He
2019	J	`jnl`	Single image blind deblurring based on the fractional-order differential. Comput. Math. Appl. Fangfang Dong, Qianting Ma
2018	J	`jnl`	A new variational model for joint restoration and segmentation based on the Mumford-Shah model. J. Vis. Commun. Image Represent. Qianting Ma, Dexing Kong
2018	J	`jnl`	Image Segmentation via Mean Curvature Regularized Mumford-Shah Model and Thresholding. Neural Process. Lett. Qianting Ma, Jialin Peng, Dexing Kong
2017	J	`jnl`	A fractional differential fidelity-based PDE model for image denoising. Mach. Vis. Appl. Qianting Ma, Fang-Fang Dong, Dexing Kong
2016	J	`jnl`	An endogenous network model of banking systems. Complex. Shouwei Li, Xin Sui, Qianting Ma

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.