Qing Li

12 papers C 1Journal 10Unranked 1

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism. Neural Networks Can Liu, Kaige Wang, Qing Li, Fazhan Zhao, Kun Zhao, Hongtu Ma
2024	J	`jnl`	Remote-Sensing Image Stitching Based on Infinite Homography and Global Seam Finding. J. Circuits Syst. Comput. Jiayi Chang, Qing Li, Yanju Liang, Liguo Zhou
2024	J	`jnl`	SC-AOF: A Sliding Camera and Asymmetric Optical-Flow-Based Blending Method for Image Stitching. Sensors Jiayi Chang, Qing Li, Yanju Liang, Liguo Zhou
2023	J	`jnl`	Denseformer for single image deraining. Int. J. Appl. Math. Comput. Sci. Tianming Wang, Kaige Wang, Qing Li
2023	J	`jnl`	Real-Time 3D Object Detection on Crowded Pedestrians. Sensors Bin Lu, Qing Li, Yanju Liang
2023	J	`jnl`	When dual contrastive learning meets disentangled features for unpaired image deraining. Mach. Vis. Appl. Tianming Wang, Kaige Wang, Qing Li
2022	J	`jnl`	An Adaptive Fusion Algorithm for Depth Completion. Sensors Long Chen, Qing Li
2022	J	`jnl`	An End-to-End Cascaded Image Deraining and Object Detection Neural Network. CoRR Kaige Wang, Tianming Wang, Jianchuang Qu, Huatao Jiang, Qing Li, Lin Chang
2022	J	`jnl`	An End-to-End Cascaded Image Deraining and Object Detection Neural Network. IEEE Robotics Autom. Lett. Kaige Wang, Tianming Wang, Jianchuang Qu, Huatao Jiang, Qing Li, Lin Chang
2022	J	`jnl`	Guided Spatial Propagation Network for Depth Completion. IEEE Robotics Autom. Lett. Long Chen, Qing Li
2020	C	`conf`	Deep Transfer Learning Enable End-to-End Steering Angles Prediction for Self-driving Car. IV Huatao Jiang, Lin Chang, Qing Li, Dapeng Chen
2019	—	`conf`	Trajectory Prediction of Vehicles Based on Deep Learning. ICITE Huatao Jiang, Lin Chang, Qing Li, Dapeng Chen

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.