Zheng Wu - RankMe

18 papers A* 3Journal 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	DBL: Dual-Level balanced learning for long-Tailed classification. Pattern Recognit. Zheng Wu, Kehua Guo, Sheng Ren, Bin Hu, Xiangyuan Zhu, Rui Ding
2026	J	`jnl`	Multi-feature aggregation attention for efficient image super-resolution. Vis. Comput. Xiangyuan Zhu, Wei Zhao, Xuchong Liu, Zheng Wu, Sheng Ren
2026	J	`jnl`	ReE3D: Boosting Novel View Synthesis for Monocular Images Using Residual Encoders. IEEE Trans. Multim. Kehua Guo, Tianyu Chen, Sheng Ren, Bin Hu, Zheng Wu, Shaojun Guo, Hui Fang
2025	J	`jnl`	Backdoor Defense in Transportation Cyber-Physical Systems Using Frequency Domain Hybrid Distillation. IEEE Trans. Intell. Transp. Syst. Bin Hu, Kehua Guo, Zheng Wu, Xianhong Wen, Xiaokang Zhou
2025	J	`jnl`	GAN Prior-Enhanced Novel View Synthesis From Monocular Degraded Images. IEEE Trans. Multim. Kehua Guo, Zheng Wu, Xianhong Wen, Shaojun Guo, Zhipeng Xi, Tianyu Chen
2025	J	`jnl`	Lightweight image super-resolution with tokenized dynamic embedding network. Knowl. Based Syst. Xiangyuan Zhu, Xuchong Liu, Zheng Wu
2024	J	`jnl`	GRTR: Gradient Rebalanced Traffic Sign Recognition for Autonomous Vehicles. IEEE Trans Autom. Sci. Eng. Kehua Guo, Zheng Wu, Weizheng Wang, Sheng Ren, Xiaokang Zhou, Thippa Reddy Gadekallu, Entao Luo, Chao Liu
2024	J	`jnl`	Medical long-tailed learning for imbalanced data: Bibliometric analysis. Comput. Methods Programs Biomed. Zheng Wu, Kehua Guo, Entao Luo, Tian Wang, Shoujin Wang, Yi Yang, Xiangyuan Zhu, Rui Ding
2024	J	`jnl`	Modal adaptive super-resolution for medical images via continual learning. Signal Process. Zheng Wu, Feihong Zhu, Kehua Guo, Sheng Ren, Chao Liu, Hui Fang
2024	J	`jnl`	Progressive Diversity Generation for Single Domain Generalization. IEEE Trans. Multim. Rui Ding, Kehua Guo, Xiangyuan Zhu, Zheng Wu, Hui Fang
2023	J	`jnl`	GREB: gradient re-balanced loss for long-tailed multi-lable classification. J. Ambient Intell. Humaniz. Comput. Zheng Wu, Kehua Guo, Sheng Ren, Xiangyuan Zhu, Rui Ding, Liwei Wang
2023	A*	`conf`	Gradient-Based Graph Attention for Scene Text Image Super-resolution. AAAI Xiangyuan Zhu, Kehua Guo, Hui Fang, Rui Ding, Zheng Wu, Gerald Schaefer
2023	J	`jnl`	NC Neural Comput. Appl. Zheng Wu, Changchun Shen, Kehua Guo, Entao Luo, Liwei Wang
2023	A*	`conf`	Single Domain Generalization via Unsupervised Diversity Probe. ACM Multimedia Kehua Guo, Rui Ding, Tian Qiu, Xiangyuan Zhu, Zheng Wu, Liwei Wang, Hui Fang
2023	J	`jnl`	Stereoscopic image super-resolution with interactive memory learning. Expert Syst. Appl. Xiangyuan Zhu, Kehua Guo, Tian Qiu, Hui Fang, Zheng Wu, Xuyang Tan, Chao Liu
2022	J	`jnl`	Background selection schema on deep learning-based classification of dermatological disease. Comput. Biol. Medicine Jiancun Zhou, Zheng Wu, Zixi Jiang, Kai Huang, Kehua Guo, Shuang Zhao
2022	A*	`conf`	ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition. NeurIPS Rui Ding, Kehua Guo, Xiangyuan Zhu, Zheng Wu, Liwei Wang
2021	J	`jnl`	BSSPD: A Blockchain-Based Security Sharing Scheme for Personal Data with Fine-Grained Access Control. Wirel. Commun. Mob. Comput. Hongmin Gao, Zhaofeng Ma, Shoushan Luo, Yanping Xu, Zheng Wu

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.