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2026
Journal article
Open Access
Quantifying query fairness under unawareness
Jaenich Thomas, Moreo Alejandro, Fabris Alessandro, Mcdonald Graham, Esuli Andrea, Ounis Iadh, Fabrizio Sebastiani
Traditional ranking algorithms are designed to retrieve the most relevant items for a user’s query, but they often inherit biases from data that can unfairly disadvantage vulnerable groups. Fairness in information access systems (IAS) is typically assessed by comparing the distribution of groups in a ranking to a target distribution, such as the overall group distribution in the dataset. These fairness metrics depend on knowing the true group labels for each item. However, when groups are defined by demographic or sensitive attributes, these labels are often unknown, leading to a setting known as “fairness under unawareness.” To address this, group membership can be inferred using machine-learned classifiers, and group prevalence is estimated by counting the predicted labels. Unfortunately, such an estimation is known to be unreliable under dataset shift, compromising the accuracy of fairness evaluations. In this paper, we introduce a robust fairness estimator based on quantification that effectively handles multiple sensitive attributes beyond binary classifications. Our method outperforms existing baselines across various sensitive attributes and, to the best of our knowledge, is the first to establish a reliable protocol for measuring fairness under unawareness across multiple queries and groups.Source: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, vol. 85 (issue articolo 7)
DOI: 10.1613/jair.1.17675
Metrics:
Jaenich Thomas, Moreo Alejandro, Fabris Alessandro, Mcdonald Graham, Esuli Andrea, Ounis Iadh, Fabrizio Sebastiani
Traditional ranking algorithms are designed to retrieve the most relevant items for a user’s query, but they often inherit biases from data that can unfairly disadvantage vulnerable groups. Fairness in information access systems (IAS) is typically assessed by comparing the distribution of groups in a ranking to a target distribution, such as the overall group distribution in the dataset. These fairness metrics depend on knowing the true group labels for each item. However, when groups are defined by demographic or sensitive attributes, these labels are often unknown, leading to a setting known as “fairness under unawareness.” To address this, group membership can be inferred using machine-learned classifiers, and group prevalence is estimated by counting the predicted labels. Unfortunately, such an estimation is known to be unreliable under dataset shift, compromising the accuracy of fairness evaluations. In this paper, we introduce a robust fairness estimator based on quantification that effectively handles multiple sensitive attributes beyond binary classifications. Our method outperforms existing baselines across various sensitive attributes and, to the best of our knowledge, is the first to establish a reliable protocol for measuring fairness under unawareness across multiple queries and groups.Source: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, vol. 85 (issue articolo 7)
DOI: 10.1613/jair.1.17675
Metrics:
See at:
CNR IRIS 
| www.jair.org | CNR IRIS 
2026
Conference article
Open Access
ViSketch-GPT: collaborative multi-scale feature extraction for hand-drawn sketch retrieval
Federico Giulio, Carrara Fabio, Gennaro Claudio, Di Benedetto Marco
Understanding the nature of hand-drawn sketches is challenging due to the wide variation in their creation. Federico et al. [10] demonstrated that recognizing complex structural patterns enhances both sketch recognition and generation. Building on this foundation, we explore how the extracted features can also be leveraged for hand-drawn sketch retrieval. In this work, we extend ViSketch-GPT, a multi-scale context extraction model originally designed for classification and generation, to the task of retrieval. The model’s ability to capture intricate details at multiple scales allows it to learn highly discriminative representations, making it well-suited for retrieval applications. Through extensive experiments on the QuickDraw and TU-Berlin datasets, we show that ViSketch-GPT surpasses state-of-the-art methods in sketch retrieval, achieving substantial improvements across multiple evaluation metrics. Our results show that the extracted feature representations, originally designed for classification and generation, are also highly effective for retrieval tasks. This highlights ViSketch-GPT as a versatile and high-powerful framework for various applications in computer vision and sketch analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16134, pp. 3-13. Reykjavik, Iceland, 1–3 october 2025
DOI: 10.1007/978-3-032-06069-3_1
Project(s): Italian Strengthening of ESFRI RI RESILIENCE, SUN
Metrics:
Federico Giulio, Carrara Fabio, Gennaro Claudio, Di Benedetto Marco
Understanding the nature of hand-drawn sketches is challenging due to the wide variation in their creation. Federico et al. [10] demonstrated that recognizing complex structural patterns enhances both sketch recognition and generation. Building on this foundation, we explore how the extracted features can also be leveraged for hand-drawn sketch retrieval. In this work, we extend ViSketch-GPT, a multi-scale context extraction model originally designed for classification and generation, to the task of retrieval. The model’s ability to capture intricate details at multiple scales allows it to learn highly discriminative representations, making it well-suited for retrieval applications. Through extensive experiments on the QuickDraw and TU-Berlin datasets, we show that ViSketch-GPT surpasses state-of-the-art methods in sketch retrieval, achieving substantial improvements across multiple evaluation metrics. Our results show that the extracted feature representations, originally designed for classification and generation, are also highly effective for retrieval tasks. This highlights ViSketch-GPT as a versatile and high-powerful framework for various applications in computer vision and sketch analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16134, pp. 3-13. Reykjavik, Iceland, 1–3 october 2025
DOI: 10.1007/978-3-032-06069-3_1
Project(s): Italian Strengthening of ESFRI RI RESILIENCE, SUN
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Pupillometry and brain-wide c-Fos mapping uncover multimodal mirror emotional contagion related networks of mice
Caldarelli Matteo, Zucca Stefano, Viglione Aurelia, Stella Alessandra, Nisar Rida, Sagona Giulia, Papini Ester M., Carrara Fabio, Bovetti Serena, Mazziotti Raffaele M., Pizzorusso Tommaso
Emotional contagion (ECo) represents a fundamental form of empathy. In this study, we used pupillometry to quantify ECo by assessing pupil responses of a mouse watching another mouse receive a tail shock. Pupil dilation effectively measured both direct and vicarious emotional response thresholds at the individual level through psychometric curve analysis. The pupillary ECo response diminished when the observer could not see the demonstrator, suggesting a multisensory process involving vision. Viewing videos of tail-shocked mice elicited a pupil response in the observer. Brain-wide c-Fos mapping revealed a broad network of 88 brain regions activated during ECo, with all areas activated in the demonstrator also engaged in the observer. Additionally, in some brain regions, correlated activation was detected between each observer-demonstrator pair, indicating that ECo promotes a shared neural state. These findings advance our understanding of the neural basis of shared emotions, with implications for analyzing neuropsychiatric disorder models.Source: ISCIENCE
DOI: 10.1016/j.isci.2026.114827
Metrics:
Caldarelli Matteo, Zucca Stefano, Viglione Aurelia, Stella Alessandra, Nisar Rida, Sagona Giulia, Papini Ester M., Carrara Fabio, Bovetti Serena, Mazziotti Raffaele M., Pizzorusso Tommaso
Emotional contagion (ECo) represents a fundamental form of empathy. In this study, we used pupillometry to quantify ECo by assessing pupil responses of a mouse watching another mouse receive a tail shock. Pupil dilation effectively measured both direct and vicarious emotional response thresholds at the individual level through psychometric curve analysis. The pupillary ECo response diminished when the observer could not see the demonstrator, suggesting a multisensory process involving vision. Viewing videos of tail-shocked mice elicited a pupil response in the observer. Brain-wide c-Fos mapping revealed a broad network of 88 brain regions activated during ECo, with all areas activated in the demonstrator also engaged in the observer. Additionally, in some brain regions, correlated activation was detected between each observer-demonstrator pair, indicating that ECo promotes a shared neural state. These findings advance our understanding of the neural basis of shared emotions, with implications for analyzing neuropsychiatric disorder models.Source: ISCIENCE
DOI: 10.1016/j.isci.2026.114827
Metrics:
See at:
CNR IRIS | www.cell.com | CNR IRIS
2026
Conference article
Open Access
JoinPap: Learning-based matching for the reconstruction of fragmentary papyri
Carrara Fabio, Corsini Massimiliano, Falchi Fabrizio, Messina Nicola
Reconstructing ancient papyri from fragmented pieces is a demanding task, posing significant challenges for papyrologists due to degraded material, subtle texture cues, and a lack of distinct landmarks. This paper introduces JoinPap, an intelligent interactive system designed to foster human-machine collaboration in this specialized domain. JoinPap leverages a self-supervised convolutional autoencoder, trained with a contrastive learning objective on high-resolution papyri scans, to acquire robust and discriminative texture-aware embeddings. These representations capture the continuity of fiber patterns across fragments, enabling a specialized matching algorithm to propose optimal vertical and horizontal alignments. We elaborate on data preparation, network design, training methodology, and integration of the matcher into a user-centered interface that supports fragment manipulation and annotation. JoinPap effectively supports expert-in-the-loop reconstruction by offering high-quality alignment suggestions grounded in visual texture continuity.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16170, pp. 296-306. Roma, Italy, 15–19 september 2025
DOI: 10.1007/978-3-032-11381-8_25
Project(s): FAIR - "Future Artificial Intelligence Research" - Spoke 1 "Human-centered AI", JoinPap – Reconstructing Fragmentary Papyri through Human-Machine Interaction
Metrics:
Carrara Fabio, Corsini Massimiliano, Falchi Fabrizio, Messina Nicola
Reconstructing ancient papyri from fragmented pieces is a demanding task, posing significant challenges for papyrologists due to degraded material, subtle texture cues, and a lack of distinct landmarks. This paper introduces JoinPap, an intelligent interactive system designed to foster human-machine collaboration in this specialized domain. JoinPap leverages a self-supervised convolutional autoencoder, trained with a contrastive learning objective on high-resolution papyri scans, to acquire robust and discriminative texture-aware embeddings. These representations capture the continuity of fiber patterns across fragments, enabling a specialized matching algorithm to propose optimal vertical and horizontal alignments. We elaborate on data preparation, network design, training methodology, and integration of the matcher into a user-centered interface that supports fragment manipulation and annotation. JoinPap effectively supports expert-in-the-loop reconstruction by offering high-quality alignment suggestions grounded in visual texture continuity.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16170, pp. 296-306. Roma, Italy, 15–19 september 2025
DOI: 10.1007/978-3-032-11381-8_25
Project(s): FAIR - "Future Artificial Intelligence Research" - Spoke 1 "Human-centered AI", JoinPap – Reconstructing Fragmentary Papyri through Human-Machine Interaction
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Interactive story maps for historical musical instruments: a 3D and semantic web tool for cultural heritage preservation
Bartalesi Valentina, Lenzi Emanuele, De Martino Claudio, Coro Gianpaolo
Ancient stringed instruments from the 17th and 18th centuries are essential for global cultural heritage but pose preservation and study challenges due to their fragility and rarity. Digital reproductions and interactive story maps offer new ways to analyse these instruments, providing historical, mechanical, and acoustic insights while aiding modern luthiers in replication. This paper presents a novel methodology and software that integrates 3D models of historical instruments into interactive story maps using Semantic Web technologies. The system enables users to explore 3D models with embedded annotations from Sketchfab and supports the creation of formal narratives enriched with geospatial and multimedia content. Each narrative event is linked to a backend knowledge base (KB) structured with established ontologies. The tool is open-source and web-based, and allows interoperability between its story maps and external KBs such as Wikidata and Europeana. It supports multiple levels of descriptive details, from precise 3D model annotations to broad geographic and temporal references. An evaluation case study featured 25 participants exploring a story map about a 1737 Antonio Stradivari violin, connected to a KB of 1716 triples. The results highlight the tool’s usability and effectiveness in representing the violin’s spatiotemporal and cultural context, showcasing the value of combining 3D models and geospatial data to enhance cultural heritage preservation.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 22 (issue 1)
DOI: 10.1007/s41060-025-01014-4
Metrics:
Bartalesi Valentina, Lenzi Emanuele, De Martino Claudio, Coro Gianpaolo
Ancient stringed instruments from the 17th and 18th centuries are essential for global cultural heritage but pose preservation and study challenges due to their fragility and rarity. Digital reproductions and interactive story maps offer new ways to analyse these instruments, providing historical, mechanical, and acoustic insights while aiding modern luthiers in replication. This paper presents a novel methodology and software that integrates 3D models of historical instruments into interactive story maps using Semantic Web technologies. The system enables users to explore 3D models with embedded annotations from Sketchfab and supports the creation of formal narratives enriched with geospatial and multimedia content. Each narrative event is linked to a backend knowledge base (KB) structured with established ontologies. The tool is open-source and web-based, and allows interoperability between its story maps and external KBs such as Wikidata and Europeana. It supports multiple levels of descriptive details, from precise 3D model annotations to broad geographic and temporal references. An evaluation case study featured 25 participants exploring a story map about a 1737 Antonio Stradivari violin, connected to a KB of 1716 triples. The results highlight the tool’s usability and effectiveness in representing the violin’s spatiotemporal and cultural context, showcasing the value of combining 3D models and geospatial data to enhance cultural heritage preservation.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 22 (issue 1)
DOI: 10.1007/s41060-025-01014-4
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2026
Contribution to book
Open Access
Deep learning-based structural health monitoring of historical towers
Girardi Maria, Gurioli Gianmarco, Messina Nicola, Padovani Cristina, Pellegrini Daniele
Artificial intelligence is transforming the traditional approach to science, technology, and industry, and deep learning (DL) techniques are increasingly used in various applications. The possibility of storing, analyzing, and processing big data recorded by sensor networks allows scientists and technicians to train data-driven models that reproduce the complexity of real-world phenomena.DOI: 10.1201/9781003516941-9
Project(s): REVOLUTION
Metrics:
Girardi Maria, Gurioli Gianmarco, Messina Nicola, Padovani Cristina, Pellegrini Daniele
Artificial intelligence is transforming the traditional approach to science, technology, and industry, and deep learning (DL) techniques are increasingly used in various applications. The possibility of storing, analyzing, and processing big data recorded by sensor networks allows scientists and technicians to train data-driven models that reproduce the complexity of real-world phenomena.DOI: 10.1201/9781003516941-9
Project(s): REVOLUTION
Metrics:
2026
Journal article
Open Access
Vi-SketchGPT: a novel multi-scale and context-aware representation for sketch generation and classification
Federico Giulio, Amato Giuseppe, Carrara Fabio, Gennaro Claudio, Di Benedetto Marco
Human sketches exhibit substantial variability across individuals in terms of line style, abstraction level and drawing conventions. Unlike realistic images, they provide limited contextual information and rely on highly simplified concept representations. Recognizing and generating sketches therefore requires efficient use of the available information, identification of the most informative local features, interpretation of their meaning within a minimal context, and understanding of the spatial relationships that define the overall structure. In this study, we introduce ViSketch-GPT, a representation and model that can extract these local features, contextualize them within the sketch and encode spatial relationships, thereby enabling a deeper understanding of the sketch structure. Guided by the intuition of the void as information, we leverage Signed Distance Functions (SDF) to reveal this potentially hidden information, organizing it via quadtree decomposition and processing it with a hierarchical Transformer to capture multi-scale dependencies. This structured representation allows the model to support both high-fidelity generation and accurate classification. Experiments on the QuickDraw and TU-Berlin datasets demonstrated that the model classifies sketches with high accuracy while generating outputs that preserve structural coherence, respect part relationships, and capture essential conceptual patterns despite the scarcity of information in the original sketches.Source: IEEE ACCESS
DOI: 10.1109/access.2026.3659732
Project(s): Italian Strengthening of ESFRI RI RESILIENCE, SUN
Metrics:
Federico Giulio, Amato Giuseppe, Carrara Fabio, Gennaro Claudio, Di Benedetto Marco
Human sketches exhibit substantial variability across individuals in terms of line style, abstraction level and drawing conventions. Unlike realistic images, they provide limited contextual information and rely on highly simplified concept representations. Recognizing and generating sketches therefore requires efficient use of the available information, identification of the most informative local features, interpretation of their meaning within a minimal context, and understanding of the spatial relationships that define the overall structure. In this study, we introduce ViSketch-GPT, a representation and model that can extract these local features, contextualize them within the sketch and encode spatial relationships, thereby enabling a deeper understanding of the sketch structure. Guided by the intuition of the void as information, we leverage Signed Distance Functions (SDF) to reveal this potentially hidden information, organizing it via quadtree decomposition and processing it with a hierarchical Transformer to capture multi-scale dependencies. This structured representation allows the model to support both high-fidelity generation and accurate classification. Experiments on the QuickDraw and TU-Berlin datasets demonstrated that the model classifies sketches with high accuracy while generating outputs that preserve structural coherence, respect part relationships, and capture essential conceptual patterns despite the scarcity of information in the original sketches.Source: IEEE ACCESS
DOI: 10.1109/access.2026.3659732
Project(s): Italian Strengthening of ESFRI RI RESILIENCE, SUN
Metrics:
2026
Journal article
Open Access
Automatic annotation of legal references (Allegationes) in the Liber Extra’s Ordinary Gloss
Esuli Andrea, Imperia Vincenzo Roberto, Puccetti Giovanni
The study of normative corpora of the past is a key activity in the fields of Religious Studies and Legal History. The development of intelligent software tools that support this activity is of paramount importance to support the digital transformation of the community. We present an interdisciplinary activity that leads to an accurate automatic annotation of legal references in the Liber Extra’s Ordinary Gloss. An index of legal references has been derived from the annotations enabling the creation of novel navigation and data analysis tools. The contribution of this work is twofold: the actual index is already by itself valuable resource for the discipline, and we detail the process that leads to its production, showing that an effective result can be delivered by a small team with limited resources. Both the index and the code are made publicly available.Source: UMANISTICA DIGITALE, vol. 22, pp. 139-156
DOI: 10.60923/issn.2532-8816/22163
Project(s): Italian Strengthening of ESFRI RI RESILIENCE
Metrics:
Esuli Andrea, Imperia Vincenzo Roberto, Puccetti Giovanni
The study of normative corpora of the past is a key activity in the fields of Religious Studies and Legal History. The development of intelligent software tools that support this activity is of paramount importance to support the digital transformation of the community. We present an interdisciplinary activity that leads to an accurate automatic annotation of legal references in the Liber Extra’s Ordinary Gloss. An index of legal references has been derived from the annotations enabling the creation of novel navigation and data analysis tools. The contribution of this work is twofold: the actual index is already by itself valuable resource for the discipline, and we detail the process that leads to its production, showing that an effective result can be delivered by a small team with limited resources. Both the index and the code are made publicly available.Source: UMANISTICA DIGITALE, vol. 22, pp. 139-156
DOI: 10.60923/issn.2532-8816/22163
Project(s): Italian Strengthening of ESFRI RI RESILIENCE
Metrics:
See at:
CNR IRIS | umanisticadigitale.unibo.it | CNR IRIS
2026
Journal article
Open Access
Decentralized edge learning: a comparative study of distillation strategies and dissimilarity measures
Molo Mbasa J., Vadicamo Lucia, Gennaro Claudio, Carlini Emanuele
Decentralized learning is emerging as a scalable and privacy-preserving alternative to centralized machine learning, particularly in distributed systems where data cannot be centrally shared among multiple nodes or clients. While Federated Learning is widely adopted in this context, Knowledge Distillation (KD) is emerging as a flexible and scalable alternative where model output is used to share knowledge among distributed clients. However, existing studies often overlook the efficiency and effectiveness of various knowledge transfer strategies in KD, especially in decentralized environments where data is non-IID. This study provides key insights by examining the impact of network topology and distillation strategies in KD-based decentralized learning approaches. Our evaluation spans several dissimilarity measures, including Cross-Entropy, Kullback-Leibler divergence, Triangular Divergence, Jensen-Shannon divergence, Structural Entropic Distance, and Multi-way SED, assessed under both pairwise and holistic distillation schemes. In the pairwise approach, distillation is performed by summing the client-wise dissimilarities between a client's output and each neighbor's prediction individually, while the holistic approach computes dissimilarity with respect to the average of the output predictions received from neighboring clients. We also analyze performance across client connectivity levels to explore the trade-off between convergence speed and model accuracy. The results indicate that the holistic distillation approach, which averages client predictions, outperforms the sum of pairwise distillation, especially when employing alternative measures like TD, SED, and JS. These measures offer improved performance over conventional metrics such as CE and KL divergence.Source: FUTURE GENERATION COMPUTER SYSTEMS, vol. 176
DOI: 10.1016/j.future.2025.108171
Project(s): National Centre for HPC, Big Data and Quantum Computing, Sustainable Mobility Center
Metrics:
Molo Mbasa J., Vadicamo Lucia, Gennaro Claudio, Carlini Emanuele
Decentralized learning is emerging as a scalable and privacy-preserving alternative to centralized machine learning, particularly in distributed systems where data cannot be centrally shared among multiple nodes or clients. While Federated Learning is widely adopted in this context, Knowledge Distillation (KD) is emerging as a flexible and scalable alternative where model output is used to share knowledge among distributed clients. However, existing studies often overlook the efficiency and effectiveness of various knowledge transfer strategies in KD, especially in decentralized environments where data is non-IID. This study provides key insights by examining the impact of network topology and distillation strategies in KD-based decentralized learning approaches. Our evaluation spans several dissimilarity measures, including Cross-Entropy, Kullback-Leibler divergence, Triangular Divergence, Jensen-Shannon divergence, Structural Entropic Distance, and Multi-way SED, assessed under both pairwise and holistic distillation schemes. In the pairwise approach, distillation is performed by summing the client-wise dissimilarities between a client's output and each neighbor's prediction individually, while the holistic approach computes dissimilarity with respect to the average of the output predictions received from neighboring clients. We also analyze performance across client connectivity levels to explore the trade-off between convergence speed and model accuracy. The results indicate that the holistic distillation approach, which averages client predictions, outperforms the sum of pairwise distillation, especially when employing alternative measures like TD, SED, and JS. These measures offer improved performance over conventional metrics such as CE and KL divergence.Source: FUTURE GENERATION COMPUTER SYSTEMS, vol. 176
DOI: 10.1016/j.future.2025.108171
Project(s): National Centre for HPC, Big Data and Quantum Computing, Sustainable Mobility Center
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | Future Generation Computer Systems | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Wordnet and word ladders: climbing the abstraction taxonomy with LLMs
Puccetti G., Bolognesi M., Esuli A.
WordNet has long served as a benchmark for approximating the mechanisms of semantic categorization in the human mind, particularly through its hierarchical structure of word synsets, most notably the IS-A relation. However, these semantic relations have traditionally been curated manually by expert lexicographers, relying on external resources like dictionaries and corpora. In this paper, we explore whether large language models (LLMs) can be leveraged to approximate these hierarchical semantic relations, potentially offering a scalable and more dynamic alternative for maintaining and updating the WordNet taxonomy. This investigation addresses the feasibility and implications of automating this process with LLMs by testing a set of prompts encoding different sociodemographic traits and finds that adding age and job information to the prompt affects the model ability to generate text in agreement with hierarchical semantic relations while gender does not have a statistically significant impact.
Puccetti G., Bolognesi M., Esuli A.
WordNet has long served as a benchmark for approximating the mechanisms of semantic categorization in the human mind, particularly through its hierarchical structure of word synsets, most notably the IS-A relation. However, these semantic relations have traditionally been curated manually by expert lexicographers, relying on external resources like dictionaries and corpora. In this paper, we explore whether large language models (LLMs) can be leveraged to approximate these hierarchical semantic relations, potentially offering a scalable and more dynamic alternative for maintaining and updating the WordNet taxonomy. This investigation addresses the feasibility and implications of automating this process with LLMs by testing a set of prompts encoding different sociodemographic traits and finds that adding age and job information to the prompt affects the model ability to generate text in agreement with hierarchical semantic relations while gender does not have a statistically significant impact.
See at:
CNR IRIS | unipv-larl.github.io | CNR IRIS
2025
Journal article
Restricted
Criticality in neural cultures: insights into memory and connectivity in entorhinal-hippocampal networks
Iannello L., Tonelli F., Cremisi F., Calcagnile L. Maria, Mannella R., Amato G., Di Garbo A.
The brain is a complex system of interconnected regions that underlie memory, cognition, and perception. Today, our understanding of the brain's dynamic processes remains incomplete, particularly regarding differences in electrophysiological activity and inter-regional connectivity among specific areas. To explore this, we investigated the electrical activity, functional connectivity, and interactions of neural cultures differentiated into hippocampal, isocortical, and entorhinal networks using multi-electrode arrays (MEAs) to record extracellular local field potentials. Our results showed that collective synchronization events, or network bursts, were present in all cultures except for the hippocampal networks. Interestingly, introducing entorhinal neuron spheroids onto hippocampal cultures induced synchronized activity. Furthermore, Self-organized criticality analysis confirmed that all networks, except hippocampal cultures, were in a critical regime. Moreover, we found that entorhinal-hippocampal coupling facilitated criticality, promoting recurrent synchronized activity patterns. The consistent scaling exponents across configurations underscore the universality of criticality in biological networks. Finally, power spectrum analysis revealed a theta band peak in connected entorhinal-hippocampal cultures, consistent with in vivo studies, highlighting the role of theta oscillations in memory consolidation. Our findings provide more insights into brain functioning and offer an in vitro model for studying learning and memory.Source: CHAOS, SOLITONS AND FRACTALS, vol. 194
DOI: 10.1016/j.chaos.2025.116184
Project(s): AICult: Artificial Intelligence with Cultured Neuronal Networks, Tuscany Health Ecosystem
Metrics:
Iannello L., Tonelli F., Cremisi F., Calcagnile L. Maria, Mannella R., Amato G., Di Garbo A.
The brain is a complex system of interconnected regions that underlie memory, cognition, and perception. Today, our understanding of the brain's dynamic processes remains incomplete, particularly regarding differences in electrophysiological activity and inter-regional connectivity among specific areas. To explore this, we investigated the electrical activity, functional connectivity, and interactions of neural cultures differentiated into hippocampal, isocortical, and entorhinal networks using multi-electrode arrays (MEAs) to record extracellular local field potentials. Our results showed that collective synchronization events, or network bursts, were present in all cultures except for the hippocampal networks. Interestingly, introducing entorhinal neuron spheroids onto hippocampal cultures induced synchronized activity. Furthermore, Self-organized criticality analysis confirmed that all networks, except hippocampal cultures, were in a critical regime. Moreover, we found that entorhinal-hippocampal coupling facilitated criticality, promoting recurrent synchronized activity patterns. The consistent scaling exponents across configurations underscore the universality of criticality in biological networks. Finally, power spectrum analysis revealed a theta band peak in connected entorhinal-hippocampal cultures, consistent with in vivo studies, highlighting the role of theta oscillations in memory consolidation. Our findings provide more insights into brain functioning and offer an in vitro model for studying learning and memory.Source: CHAOS, SOLITONS AND FRACTALS, vol. 194
DOI: 10.1016/j.chaos.2025.116184
Project(s): AICult: Artificial Intelligence with Cultured Neuronal Networks, Tuscany Health Ecosystem
Metrics:
See at:
Chaos Solitons & Fractals | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR IRIS | CNR IRIS | CNR IRIS | www.sciencedirect.com
2025
Conference article
Open Access
Transductive model selection under prior probability shift
Volpi L., Moreo Fernandez A., Sebastiani F.
Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are available at training time. Similarly to inductive learning contexts, transductive learning contexts may be affected by dataset shift, i.e., may be such that the assumption according to which the training data and the unlabelled data are independently and identically distributed (IID), does not hold. We here propose a method, tailored to transductive classification contexts, for performing model selection (i.e., hyperparameter optimisation) when the data exhibit prior probability shift, an important type of dataset shift typical of anti-causal learning problems. In our proposed method the hyperparameters can be optimised directly on the unlabelled data to which the trained classifier must be applied; this is unlike traditional model selection methods, that are based on performing cross-validation on the labelled training data. By tailoring model selection to the actual test distribution, our approach contributes to the trustworthiness of AI systems, as it enables more reliable and robust classifier deployment under changed conditions. We provide experimental results that show the benefits brought about by our method.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4132, pp. 256-265. Bologna, Italy, 25-26 October 2025
Project(s): Future Artificial Intelligence Research
Volpi L., Moreo Fernandez A., Sebastiani F.
Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are available at training time. Similarly to inductive learning contexts, transductive learning contexts may be affected by dataset shift, i.e., may be such that the assumption according to which the training data and the unlabelled data are independently and identically distributed (IID), does not hold. We here propose a method, tailored to transductive classification contexts, for performing model selection (i.e., hyperparameter optimisation) when the data exhibit prior probability shift, an important type of dataset shift typical of anti-causal learning problems. In our proposed method the hyperparameters can be optimised directly on the unlabelled data to which the trained classifier must be applied; this is unlike traditional model selection methods, that are based on performing cross-validation on the labelled training data. By tailoring model selection to the actual test distribution, our approach contributes to the trustworthiness of AI systems, as it enables more reliable and robust classifier deployment under changed conditions. We provide experimental results that show the benefits brought about by our method.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4132, pp. 256-265. Bologna, Italy, 25-26 October 2025
Project(s): Future Artificial Intelligence Research
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Stress-testing machine generated text detection: shifting language models writing style to fool detectors
Pedrotti A., Papucci M., Ciaccio C., Miaschi A., Puccetti G., Dell'Orletta F., Esuli A.
Recent advancements in Generative AI and Large Language Models (LLMs) have enabled the creation of highly realistic synthetic content, raising concerns about the potential for malicious use, such as misinformation and manipulation. Moreover, detecting Machine-Generated Text (MGT) remains challenging due to the lack of robust benchmarks that assess generalization to real-world scenarios. In this work, we evaluate the resilience of state-of-the-art MGT detectors (e.g., Mage, Radar, LLM-DetectAIve) to linguistically informed adversarial attacks. We develop a pipeline that fine-tunes language models using Direct Preference Optimization (DPO) to shift the MGT style toward human-written text (HWT), obtaining generations more challenging to detect by current models. Additionally, we analyze the linguistic shifts induced by the alignment and how detectors rely on “linguistic shortcuts” to detect texts. Our results show that detectors can be easily fooled with relatively few examples, resulting in a significant drop in detecting performances. This highlights the importance of improving detection methods and making them robust to unseen in-domain texts. We release code, models, and data to support future research on more robust MGT detection benchmarks.DOI: 10.18653/v1/2025.findings-acl.156
Project(s): SoBigData
Metrics:
Pedrotti A., Papucci M., Ciaccio C., Miaschi A., Puccetti G., Dell'Orletta F., Esuli A.
Recent advancements in Generative AI and Large Language Models (LLMs) have enabled the creation of highly realistic synthetic content, raising concerns about the potential for malicious use, such as misinformation and manipulation. Moreover, detecting Machine-Generated Text (MGT) remains challenging due to the lack of robust benchmarks that assess generalization to real-world scenarios. In this work, we evaluate the resilience of state-of-the-art MGT detectors (e.g., Mage, Radar, LLM-DetectAIve) to linguistically informed adversarial attacks. We develop a pipeline that fine-tunes language models using Direct Preference Optimization (DPO) to shift the MGT style toward human-written text (HWT), obtaining generations more challenging to detect by current models. Additionally, we analyze the linguistic shifts induced by the alignment and how detectors rely on “linguistic shortcuts” to detect texts. Our results show that detectors can be easily fooled with relatively few examples, resulting in a significant drop in detecting performances. This highlights the importance of improving detection methods and making them robust to unseen in-domain texts. We release code, models, and data to support future research on more robust MGT detection benchmarks.DOI: 10.18653/v1/2025.findings-acl.156
Project(s): SoBigData
Metrics:
See at:
aclanthology.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
DIACU: a dataset for the DIAchronic analysis of Church Slavonic
Cassese M., Puccetti G., Napolitano M., Esuli A.
The Church Slavonic language has evolved over time without being formalized into a precise grammar. Therefore, there is currently no clearly outlined history of this language tracing its evolution. However, in recent years, there has been a greater effort to digitize these resources, partly motivated by increased sensitivity with respect to the need to preserve multilingual knowledge. To exploit them, we propose DIACU (DIAchronic Analysis of Church Slavonic), a comprehensive collection of several existing corpora in Church Slavonic. In this work, we thoroughly describe the collection of this novel dataset and test its effectiveness as a training set for attributing Slavonic texts to specific periods. The dataset and the code of the experiments is available at https://github.com/MariaCassese/DIACU.DOI: 10.18653/v1/2025.bsnlp-1.12
Metrics:
Cassese M., Puccetti G., Napolitano M., Esuli A.
The Church Slavonic language has evolved over time without being formalized into a precise grammar. Therefore, there is currently no clearly outlined history of this language tracing its evolution. However, in recent years, there has been a greater effort to digitize these resources, partly motivated by increased sensitivity with respect to the need to preserve multilingual knowledge. To exploit them, we propose DIACU (DIAchronic Analysis of Church Slavonic), a comprehensive collection of several existing corpora in Church Slavonic. In this work, we thoroughly describe the collection of this novel dataset and test its effectiveness as a training set for attributing Slavonic texts to specific periods. The dataset and the code of the experiments is available at https://github.com/MariaCassese/DIACU.DOI: 10.18653/v1/2025.bsnlp-1.12
Metrics:
See at:
aclanthology.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
ARTEMIS: animal recognition through enhanced multimodal integration system
Fazzari E., Romano D., Falchi F., Stefanini C.
This paper introduces Animal Recognition Through Enhanced Multimodal Integration System (ARTEMIS), a transformer-based framework designed for multilabel animal action recognition by fusing video, image, and textual modalities. ARTEMIS utilizes state-of-the-art captioning and language models, such as BLIP2 and Llama 3, to generate textual descriptions from video frames, which are input to the model, significantly enhancing its performance unlikely previous results that do not consider this modality. Through comprehensive ablation studies, we explore the contribution of various model components and propose optimization strategies, including genetic algorithms and reinforcement learning, to dynamically adjust ensemble weights. Our feature alignment techniques-using contrastive and cosine similarity losses-further improve multimodal integration. Evaluations on the Animal Kingdom dataset, which includes 30,100 clips across 140 action classes, demonstrate that ARTEMIS achieves a new state-of-the-art mAP of 79.82, outperforming existing methods. The combination of multimodal fusion and ensemble strategies makes ARTEMIS a robust solution for complex animal action recognition tasks. The code of our fusion method is available at https://github.com/edofazza/ARTEMIS.Source: INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
DOI: 10.1007/s13042-025-02602-3
Project(s): Robocoenosis
Metrics:
Fazzari E., Romano D., Falchi F., Stefanini C.
This paper introduces Animal Recognition Through Enhanced Multimodal Integration System (ARTEMIS), a transformer-based framework designed for multilabel animal action recognition by fusing video, image, and textual modalities. ARTEMIS utilizes state-of-the-art captioning and language models, such as BLIP2 and Llama 3, to generate textual descriptions from video frames, which are input to the model, significantly enhancing its performance unlikely previous results that do not consider this modality. Through comprehensive ablation studies, we explore the contribution of various model components and propose optimization strategies, including genetic algorithms and reinforcement learning, to dynamically adjust ensemble weights. Our feature alignment techniques-using contrastive and cosine similarity losses-further improve multimodal integration. Evaluations on the Animal Kingdom dataset, which includes 30,100 clips across 140 action classes, demonstrate that ARTEMIS achieves a new state-of-the-art mAP of 79.82, outperforming existing methods. The combination of multimodal fusion and ensemble strategies makes ARTEMIS a robust solution for complex animal action recognition tasks. The code of our fusion method is available at https://github.com/edofazza/ARTEMIS.Source: INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
DOI: 10.1007/s13042-025-02602-3
Project(s): Robocoenosis
Metrics:
See at:
Archivio della ricerca della Scuola Superiore Sant'Anna | Archivio della ricerca della Scuola Superiore Sant'Anna | CNR IRIS | link.springer.com | GitHub | CNR IRIS
2025
Conference article
Open Access
Automatic annotation of legal references (Allegationes) in the Liber Extra's Ordinary Gloss
Esuli A., Imperia V. R., Puccetti G.
The study of normative corpora of the past is a key activity in the fields of Religious Studies and Legal History. The development of intelligent software tools that support this activity is of paramount importance to support the digital transformation of the community. We present an interdisciplinary activity that lead to an accurate automatic annotation of legal references in the Liber Extra’s Ordinary Gloss. An index of legal references as been derived from the annotations enabling the creation of novel navigation and data analysis tools. The contribution of this work is twofold: the actual index is already by itself valuable resource for the discipline, and we detail the process that lead to its production, showing that an effective result can be delivered by a small team with limited resources. Both the index and the code are made publicly available.
Esuli A., Imperia V. R., Puccetti G.
The study of normative corpora of the past is a key activity in the fields of Religious Studies and Legal History. The development of intelligent software tools that support this activity is of paramount importance to support the digital transformation of the community. We present an interdisciplinary activity that lead to an accurate automatic annotation of legal references in the Liber Extra’s Ordinary Gloss. An index of legal references as been derived from the annotations enabling the creation of novel navigation and data analysis tools. The contribution of this work is twofold: the actual index is already by itself valuable resource for the discipline, and we detail the process that lead to its production, showing that an effective result can be delivered by a small team with limited resources. Both the index and the code are made publicly available.
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
From neurons to computation: biological reservoir computing for pattern recognition
Iannello L., Ciampi L., Lagani G., Tonelli F., Crocco E., Calcagnile L. M., Di Garbo A., Cremisi F., Amato G.
In this paper, we introduce a paradigm for reservoir computing (RC) that leverages a pool of cultured biological neurons as the reservoir substrate, creating a biological reservoir computing (BRC). This system operates similarly to an echo state network (ESN), with the key distinction that the neural activity is generated by a network of cultured neurons, rather than being modeled by traditional artificial computational units. The neuronal activity is recorded using a multi-electrode array (MEA), which enables high-throughput recording of neural signals. In our approach, inputs are introduced into the network through a subset of the MEA electrodes, while the remaining electrodes capture the resulting neural activity. This generates a nonlinear mapping of the input data to a high-dimensional biological feature space, where distinguishing between data becomes more efficient and straightforward, allowing a simple linear classifier to perform pattern recognition tasks effectively. To evaluate the performance of our proposed system, we present an experimental study that includes various input patterns, such as positional codes, bars with different orientations, and a digit recognition task. The results demonstrate the feasibility of using biological neural networks to perform tasks traditionally handled by artificial neural networks, paving the way for further exploration of biologically-inspired computing systems, with potential applications in neuromorphic engineering and bio-hybrid computing.Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 2757, pp. 114-131. Okinawa, Japan, 20-24 november 2025
DOI: 10.1007/978-981-95-4100-3_9
DOI: 10.48550/arxiv.2505.03510
Project(s): AICult, Tuscany Health Ecosystem
Metrics:
Iannello L., Ciampi L., Lagani G., Tonelli F., Crocco E., Calcagnile L. M., Di Garbo A., Cremisi F., Amato G.
In this paper, we introduce a paradigm for reservoir computing (RC) that leverages a pool of cultured biological neurons as the reservoir substrate, creating a biological reservoir computing (BRC). This system operates similarly to an echo state network (ESN), with the key distinction that the neural activity is generated by a network of cultured neurons, rather than being modeled by traditional artificial computational units. The neuronal activity is recorded using a multi-electrode array (MEA), which enables high-throughput recording of neural signals. In our approach, inputs are introduced into the network through a subset of the MEA electrodes, while the remaining electrodes capture the resulting neural activity. This generates a nonlinear mapping of the input data to a high-dimensional biological feature space, where distinguishing between data becomes more efficient and straightforward, allowing a simple linear classifier to perform pattern recognition tasks effectively. To evaluate the performance of our proposed system, we present an experimental study that includes various input patterns, such as positional codes, bars with different orientations, and a digit recognition task. The results demonstrate the feasibility of using biological neural networks to perform tasks traditionally handled by artificial neural networks, paving the way for further exploration of biologically-inspired computing systems, with potential applications in neuromorphic engineering and bio-hybrid computing.Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 2757, pp. 114-131. Okinawa, Japan, 20-24 november 2025
DOI: 10.1007/978-981-95-4100-3_9
DOI: 10.48550/arxiv.2505.03510
Project(s): AICult, Tuscany Health Ecosystem
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | link.springer.com | doi.org | doi.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Animal behavior analysis methods using deep learning: a survey
Fazzari E., Romano D., Falchi F., Stefanini C.
Animal behavior serves as a reliable indicator of the adaptation of organisms to their environment and their overall well-being. Through rigorous observation of animal actions and interactions, researchers and observers can glean valuable insights into diverse facets of their lives, encompassing health, social dynamics, ecological relationships, and neuroethological dimensions. Although state-of-the-art deep learning models have demonstrated remarkable accuracy in classifying various forms of animal data, their adoption in animal behavior studies remains limited. This survey article endeavors to comprehensively explore deep learning architectures and strategies applied to the identification of animal behavior, spanning auditory, visual, and audiovisual methodologies. The survey categorizes techniques into pose estimation-based and non-pose estimation-based methods, analyzing their applications, effectiveness, and limitations. Furthermore, the manuscript scrutinizes extant animal behavior datasets, offering a detailed examination of the principal challenges confronting this research domain. The article culminates in a comprehensive discussion of key research directions within deep learning that hold potential for advancing the field of animal behavior studies.Source: EXPERT SYSTEMS WITH APPLICATIONS, vol. 289 (issue 128330)
DOI: 10.1016/j.eswa.2025.128330
DOI: 10.48550/arxiv.2405.14002
Metrics:
Fazzari E., Romano D., Falchi F., Stefanini C.
Animal behavior serves as a reliable indicator of the adaptation of organisms to their environment and their overall well-being. Through rigorous observation of animal actions and interactions, researchers and observers can glean valuable insights into diverse facets of their lives, encompassing health, social dynamics, ecological relationships, and neuroethological dimensions. Although state-of-the-art deep learning models have demonstrated remarkable accuracy in classifying various forms of animal data, their adoption in animal behavior studies remains limited. This survey article endeavors to comprehensively explore deep learning architectures and strategies applied to the identification of animal behavior, spanning auditory, visual, and audiovisual methodologies. The survey categorizes techniques into pose estimation-based and non-pose estimation-based methods, analyzing their applications, effectiveness, and limitations. Furthermore, the manuscript scrutinizes extant animal behavior datasets, offering a detailed examination of the principal challenges confronting this research domain. The article culminates in a comprehensive discussion of key research directions within deep learning that hold potential for advancing the field of animal behavior studies.Source: EXPERT SYSTEMS WITH APPLICATIONS, vol. 289 (issue 128330)
DOI: 10.1016/j.eswa.2025.128330
DOI: 10.48550/arxiv.2405.14002
Metrics:
See at:
arXiv.org e-Print Archive | Expert Systems with Applications | Archivio della ricerca della Scuola Superiore Sant'Anna | CNR IRIS | www.sciencedirect.com | doi.org | CNR IRIS
2025
Book
Open Access
SUN: Social and hUman ceNtered XR - A Horizon Europe Project Paving the Way for the Widespread Adoption of Extended and Virtual Worlds
Vairo C., Caracciolo G., Giorgi D., Leonardis D., Vadicamo L.
Extended Reality (XR) is a rapidly growing technology that bridges physical and virtual worlds, opening up new possibilities in healthcare, communications, and security. The European project SUN – Social and hUman ceNtered XR, funded by the Horizon Europe program, addresses the ongoing challenges of making XR more accessible, usable, and realistic. SUN develops technologies and models that enhance social interaction and immersive perception, while keeping an ethical and human-centered design, by introducing new wearable sensors, haptic interfaces, and high-performance streaming solutions. Through new 3D acquisition techniques and the use of artificial intelligence, SUN explores innovative ways to connect physical objects and digital counterparts, creating coherent and immersive environments. The project’s innovations were validated in three real-world piloting scenarios: rehabilitation therapy, workplace safety and social interaction, and assistive technologies for individuals with severe mobility or communication impairments. This volume presents the results of three years of research and development, offering a solid vision of how XR can evolve in a sustainable, ethical, and human-centered way.DOI: 10.32079/isti-book-2025/001
Project(s): SUN
Metrics:
Vairo C., Caracciolo G., Giorgi D., Leonardis D., Vadicamo L.
Extended Reality (XR) is a rapidly growing technology that bridges physical and virtual worlds, opening up new possibilities in healthcare, communications, and security. The European project SUN – Social and hUman ceNtered XR, funded by the Horizon Europe program, addresses the ongoing challenges of making XR more accessible, usable, and realistic. SUN develops technologies and models that enhance social interaction and immersive perception, while keeping an ethical and human-centered design, by introducing new wearable sensors, haptic interfaces, and high-performance streaming solutions. Through new 3D acquisition techniques and the use of artificial intelligence, SUN explores innovative ways to connect physical objects and digital counterparts, creating coherent and immersive environments. The project’s innovations were validated in three real-world piloting scenarios: rehabilitation therapy, workplace safety and social interaction, and assistive technologies for individuals with severe mobility or communication impairments. This volume presents the results of three years of research and development, offering a solid vision of how XR can evolve in a sustainable, ethical, and human-centered way.DOI: 10.32079/isti-book-2025/001
Project(s): SUN
Metrics:
See at:
CNR IRIS | www.cnr.it | CNR IRIS
2025
Other
Open Access
Linking Dante UI: manuale d’uso
Trupiano L., Concordia C., Aloia N., Tomazzoli G., Meghini C.
Questo documento descrive l’interfaccia grafica per l’accesso ai dati del grafo di conoscenza Linking Dante (LiDa). L’interfaccia grafica (GUI), accessibile all’indirizzo https://lida.dantenetwork.it, è stata progettata e sviluppata per fornire a utenti con vari gradi di esperienza un accesso semplificato ai dati del grafo di conoscenza LiDa.
Trupiano L., Concordia C., Aloia N., Tomazzoli G., Meghini C.
Questo documento descrive l’interfaccia grafica per l’accesso ai dati del grafo di conoscenza Linking Dante (LiDa). L’interfaccia grafica (GUI), accessibile all’indirizzo https://lida.dantenetwork.it, è stata progettata e sviluppata per fornire a utenti con vari gradi di esperienza un accesso semplificato ai dati del grafo di conoscenza LiDa.
See at:
CNR IRIS | lida.dantenetwork.it | CNR IRIS