Raffaele Scaringi

14 papers A 1Journal 3Unranked 10

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	GraphCLIP: Image-graph contrastive learning for multimodal artwork classification. Knowl. Based Syst. Raffaele Scaringi, Giuseppe Fiameni, Gennaro Vessio, Giovanna Castellano
2025	A	`conf`	Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts. ECAI Pasquale De Marinis, Nicola Fanelli, Raffaele Scaringi, Emanuele Colonna, Giuseppe Fiameni, Gennaro Vessio, Giovanna Castellano
2025	—	`conf`	Multimodal Artwork Topic Modeling via Fine-Tuned Clip and Knowledge-Driven Prompts. MLSP Raffaele Scaringi, Giovanni Stea, Nicola Fanelli, Gennaro Vessio, Giovanna Castellano
2025	—	`conf`	Unveiling Visual Features in Artwork Classification: Towards Explainable Vision Transformers in the Arts. ICIAP (Workshops 1) Raffaele Scaringi, Nicola Fanelli, Gennaro Vessio, Giovanna Castellano
2025	—	`conf`	Using Heterogeneous Graph Neural Networks for Explainable Next-Activity Prediction. SEBD Vincenzo Pasquadibisceglie, Raffaele Scaringi, Annalisa Appice, Giovanna Castellano, Donato Malerba
2024	J	`jnl`	${\sf PROPHET}$PROPHET: Explainable Predictive Process Monitoring With Heterogeneous Graph Neural Networks. IEEE Trans. Serv. Comput. Vincenzo Pasquadibisceglie, Raffaele Scaringi, Annalisa Appice, Giovanna Castellano, Donato Malerba
2024	—	`conf`	Integrating Graph Neural Networks and Fuzzy Logic to Enhance Deep Learning Interpretability. EXPLIMED@ECAI Giovanna Castellano, Raffaele Scaringi, Gennaro Vessio, Gianluca Zaza
2024	J	`jnl`	Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts. CoRR Pasquale De Marinis, Nicola Fanelli, Raffaele Scaringi, Emanuele Colonna, Giuseppe Fiameni, Gennaro Vessio, Giovanna Castellano
2024	—	`conf`	Using LLMs to explain AI-generated art classification via Grad-CAM heatmaps. XAI.it@AI*IA Giovanna Castellano, Maria Grazia Miccoli, Raffaele Scaringi, Gennaro Vessio, Gianluca Zaza
2023	—	`conf`	Automatic analysis of artistic heritage through Artificial Intelligence. IRCDL Giovanna Castellano, Raffaele Scaringi, Gennaro Vessio
2023	—	`conf`	Exploring the Synergy Between Vision-Language Pretraining and ChatGPT for Artwork Captioning: A Preliminary Study. ICIAP Workshops (2) Giovanna Castellano, Nicola Fanelli, Raffaele Scaringi, Gennaro Vessio
2023	—	`conf`	Identifying AI-Generated Art with Deep Learning. CREAI@AI*IA Tommaso Bianco, Giovanna Castellano, Raffaele Scaringi, Gennaro Vessio
2023	—	`conf`	Recognizing the Style, Genre, and Emotion of a Work of Art Through Visual and Knowledge Graph Embeddings. AI*IA Giovanna Castellano, Raffaele Scaringi, Gennaro Vessio
2022	—	`conf`	Recognizing the Emotions Evoked by Artworks Through Visual Features and Knowledge Graph-Embeddings. ICIAP Workshops (1) Sinem Aslan, Giovanna Castellano, Vincenzo Digeno, Giuseppe Migailo, Raffaele Scaringi, Gennaro Vessio

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.