Institution profile

Foundation for Research and Technology - Hellas

Academic institutioneurope · gr
Official website
Research library94linked papers
Opportunities0open roles
Selected work

Representative Papers

Semantic Intelligence Against CSAM: The PreventCSA@EU Ontology Framework for Classification and Investigation

Aug 13, 2026

This study addresses the significant challenges posed by inconsistent legal definitions and classification standards for child sexual abuse and exploitation material (CSAM/CSEM) across jurisdictions, which impede cross-institutional collaboration and automated processing. To overcome this, the work proposes PreventCSA@EU, a semantic-driven ontology framework that integrates, for the first time, INHOPE UCS labels, Dublin Core-DMCI metadata, and Schema.org to construct a hierarchical ontology model tailored for CSA/CSE investigations. Centered on core entities—such as media objects, content, persons, depictions, and investigative reports—the framework systematically harmonizes existing ontologies and international metadata standards. This integration enables consistent CSAM/CSEM categorization, supports child identification, and facilitates case analysis, thereby substantially enhancing interoperability across systems and laying a critical technical foundation for the European Union’s planned CSAR central database.

0 citationsRead paper

Quantum One-Way Functions and Related Cryptographic Primitives

Aug 06, 2026

This work systematically investigates fundamental cryptographic primitives in quantum cryptography beyond key distribution, with a focus on quantum one-way functions and their associated constructs—such as one-way state generators and pseudorandom quantum states. By integrating quantum information theory, computational complexity, and quantum state preparation techniques, the study clarifies the mechanisms of one-wayness, physical realizability, and noise resilience under both computational and information-theoretic security frameworks across various adversarial models. The paper delineates conceptual relationships among diverse quantum cryptographic primitives, reviews and compares existing constructions, and identifies key open problems, thereby laying a theoretical foundation for practical quantum cryptographic systems that extend beyond quantum key distribution.

0 citationsRead paper

OliveGemma: A 3 Billion Visual Language Model for Recognising the Mediterranean & European Diet

Aug 04, 2026

This study addresses the challenge of fine-grained food recognition, which is hindered by high intra-class variation and strong visual similarity among dishes, thereby limiting the accuracy of image-based dietary assessment. Building upon the PaliGemma-2-3B architecture, the authors employ parameter-efficient fine-tuning via LoRA and integrate three European dietary datasets into a unified dish vocabulary to train a vision-language model capable of multi-task instruction-based question answering. This approach achieves state-of-the-art performance in a domain-specific setting, surpassing large proprietary models such as Gemini, GPT, and Claude. In 3-fold cross-validation, the model attains 92.96% Top-1 dish recognition accuracy and 90.79% Exact-Set ingredient accuracy, significantly outperforming both CNN baselines and existing state-of-the-art methods.

0 citationsRead paper
Recent publications

Latest Papers

Semantic Intelligence Against CSAM: The PreventCSA@EU Ontology Framework for Classification and Investigation

Aug 13, 2026

This study addresses the significant challenges posed by inconsistent legal definitions and classification standards for child sexual abuse and exploitation material (CSAM/CSEM) across jurisdictions, which impede cross-institutional collaboration and automated processing. To overcome this, the work proposes PreventCSA@EU, a semantic-driven ontology framework that integrates, for the first time, INHOPE UCS labels, Dublin Core-DMCI metadata, and Schema.org to construct a hierarchical ontology model tailored for CSA/CSE investigations. Centered on core entities—such as media objects, content, persons, depictions, and investigative reports—the framework systematically harmonizes existing ontologies and international metadata standards. This integration enables consistent CSAM/CSEM categorization, supports child identification, and facilitates case analysis, thereby substantially enhancing interoperability across systems and laying a critical technical foundation for the European Union’s planned CSAR central database.

0 citationsRead paper

Quantum One-Way Functions and Related Cryptographic Primitives

Aug 06, 2026

This work systematically investigates fundamental cryptographic primitives in quantum cryptography beyond key distribution, with a focus on quantum one-way functions and their associated constructs—such as one-way state generators and pseudorandom quantum states. By integrating quantum information theory, computational complexity, and quantum state preparation techniques, the study clarifies the mechanisms of one-wayness, physical realizability, and noise resilience under both computational and information-theoretic security frameworks across various adversarial models. The paper delineates conceptual relationships among diverse quantum cryptographic primitives, reviews and compares existing constructions, and identifies key open problems, thereby laying a theoretical foundation for practical quantum cryptographic systems that extend beyond quantum key distribution.

0 citationsRead paper

OliveGemma: A 3 Billion Visual Language Model for Recognising the Mediterranean & European Diet

Aug 04, 2026

This study addresses the challenge of fine-grained food recognition, which is hindered by high intra-class variation and strong visual similarity among dishes, thereby limiting the accuracy of image-based dietary assessment. Building upon the PaliGemma-2-3B architecture, the authors employ parameter-efficient fine-tuning via LoRA and integrate three European dietary datasets into a unified dish vocabulary to train a vision-language model capable of multi-task instruction-based question answering. This approach achieves state-of-the-art performance in a domain-specific setting, surpassing large proprietary models such as Gemini, GPT, and Claude. In 3-fold cross-validation, the model attains 92.96% Top-1 dish recognition accuracy and 90.79% Exact-Set ingredient accuracy, significantly outperforming both CNN baselines and existing state-of-the-art methods.

0 citationsRead paper