F. Claire Rind

16 papers A* 1Misc 1Journal 14

Year	Rank	Type	Title / Venue / Authors
2020	J	`jnl`	A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects. IEEE Trans. Cybern. Qinbing Fu, Cheng Hu, Jigen Peng, F. Claire Rind, Shigang Yue
2019	Misc	`conf`	A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios. AIAI Qinbing Fu, Nicola Bellotto, Huatian Wang, F. Claire Rind, Hongxin Wang, Shigang Yue
2019	J	`jnl`	A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios. CoRR Qinbing Fu, Nicola Bellotto, Huatian Wang, F. Claire Rind, Hongxin Wang, Shigang Yue
2013	J	`jnl`	Postsynaptic organisations of directional selective visual neural networks for collision detection. Neurocomputing Shigang Yue, F. Claire Rind
2013	J	`jnl`	Redundant Neural Vision Systems - Competing for Collision Recognition Roles. IEEE Trans. Auton. Ment. Dev. Shigang Yue, F. Claire Rind
2010	J	`jnl`	Reactive direction control for a mobile robot: a locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated. Auton. Robots Shigang Yue, Roger D. Santer, Yoshifumi Yamawaki, F. Claire Rind
2007	J	`jnl`	A Synthetic Vision System Using Directionally Selective Motion Detectors to Recognize Collision. Artif. Life Shigang Yue, F. Claire Rind
2007	J	`jnl`	A bio-inspired visual collision detection mechanism for cars: Combining insect inspired neurons to create a robust system. Biosyst. Richard Stafford, Roger D. Santer, F. Claire Rind
2007	J	`jnl`	Data mining neural spike trains for the identification of behavioural triggers using evolutionary algorithms. Neurocomputing Richard Stafford, F. Claire Rind
2006	J	`jnl`	A bio-inspired visual collision detection mechanism for cars: Optimisation of a model of a locust neuron to a novel environment. Neurocomputing Shigang Yue, F. Claire Rind, Matthias S. Keil, Jorge Cuadri, Richard Stafford
2006	J	`jnl`	Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement. IEEE Trans. Neural Networks Shigang Yue, F. Claire Rind
2006	J	`jnl`	Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes. Comput. Vis. Image Underst. Shigang Yue, F. Claire Rind
2005	A*	`conf`	A Collision Detection System for a Mobile Robot Inspired by the Locust Visual System. ICRA Shigang Yue, F. Claire Rind
2001	J	`jnl`	How accurate need sensory coding be for behaviour? Experiments using a mobile robot. Neurocomputing Mark Blanchard, F. Claire Rind, Paul F. M. J. Verschure
2000	J	`jnl`	Collision avoidance using a model of the locust LGMD neuron. Robotics Auton. Syst. Mark Blanchard, F. Claire Rind, Paul F. M. J. Verschure
1999	J	`jnl`	Using a Mobile Robot to Study Locust Collision Avoidance Responses. Int. J. Neural Syst. Mark Blanchard, Paul F. M. J. Verschure, F. Claire Rind

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.