not yet published
Conference article
Open Access
Exploring scientometrics with the OpenAIRE Graph: introducing the OpenAIRE Beginner's Kit
Mannocci A., Baglioni M.
The OpenAIRE Graph is an extensive resource housing diverse information onresearch products, including literature, datasets, and software, alongsideresearch projects and other scholarly outputs and context. It stands as acornerstone among contemporary research information databases, offeringinvaluable insights for scientometric investigations. Despite its wealth ofdata, its sheer size may initially appear daunting, potentially hindering itswidespread adoption. To address this challenge, this paper introduces theOpenAIRE Beginner's Kit, a user-friendly solution providing access to a subsetof the OpenAIRE Graph within a sandboxed environment coupled with a Jupyternotebook for analysis. The OpenAIRE Beginner's Kit is meticulously designed todemocratise research and data exploration, offering accessibility from standarddesktop and laptop setups. Within this paper, we provide a brief overview ofthe included dataset and offer guidance on leveraging the kit through aselection of illustrative queries tailored to address common scientometricinquiries.
Mannocci A., Baglioni M.
The OpenAIRE Graph is an extensive resource housing diverse information onresearch products, including literature, datasets, and software, alongsideresearch projects and other scholarly outputs and context. It stands as acornerstone among contemporary research information databases, offeringinvaluable insights for scientometric investigations. Despite its wealth ofdata, its sheer size may initially appear daunting, potentially hindering itswidespread adoption. To address this challenge, this paper introduces theOpenAIRE Beginner's Kit, a user-friendly solution providing access to a subsetof the OpenAIRE Graph within a sandboxed environment coupled with a Jupyternotebook for analysis. The OpenAIRE Beginner's Kit is meticulously designed todemocratise research and data exploration, offering accessibility from standarddesktop and laptop setups. Within this paper, we provide a brief overview ofthe included dataset and offer guidance on leveraging the kit through aselection of illustrative queries tailored to address common scientometricinquiries.
not yet published
Journal article
Metadata Only Access
A classification-aware super-resolution framework for ship targets in SAR imagery
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Oktay Karakus
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, super-resolution (SR) techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image quality based on pixel-level metrics, leaving the relationship between super-resolved image fidelity and downstream classification performance largely underexplored. This raises a key question: can integrating classification objectives directly into the super-resolution process further improve classification accuracy? In this paper, we try to respond to this question by investigating the relationship between super-resolution and classification through the deployment of a specialised algorithmic strategy. We propose a novel methodology that increases the resolution of synthetic aperture radar imagery by optimising loss functions that account for both image quality and classification performance. Our approach improves image quality, as measured by scientifically ascertained image quality indicators, while also enhancing classification accuracy.Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Oktay Karakus
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, super-resolution (SR) techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image quality based on pixel-level metrics, leaving the relationship between super-resolved image fidelity and downstream classification performance largely underexplored. This raises a key question: can integrating classification objectives directly into the super-resolution process further improve classification accuracy? In this paper, we try to respond to this question by investigating the relationship between super-resolution and classification through the deployment of a specialised algorithmic strategy. We propose a novel methodology that increases the resolution of synthetic aperture radar imagery by optimising loss functions that account for both image quality and classification performance. Our approach improves image quality, as measured by scientifically ascertained image quality indicators, while also enhancing classification accuracy.Source: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
not yet published
Conference article
Open Access
Talking to DINO: bridging self-supervised vision backbones with language for open-vocabulary segmentation
Barsellotti L., Bianchi L., Messina N., Carrara F., Cornia M., Baraldi L., Falchi F., Cucchiara R.
Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localization due to their global alignment of image and text features. Conversely, self-supervised visual models like DINO excel in fine-grained visual encoding but lack integration with language. To bridge this gap, we present Talk2DINO, a novel hybrid approach that combines the spatial accuracy of DINOv2 with the language understanding of CLIP. Our approach aligns the textual embeddings of CLIP to the patch-level features of DINOv2 through a learned mapping function without the need to fine-tune the underlying backbones. At training time, we exploit the attention maps of DINOv2 to selectively align local visual patches with textual embeddings. We show that the powerful semantic and localization abilities of Talk2DINO can enhance the segmentation process, resulting in more natural and less noisy segmentations, and that our approach can also effectively distinguish foreground objects from the background. Experimental results demonstrate that Talk2DINO achieves state-of-the-art performance across several unsupervised OVS benchmarks.Source: PROCEEDINGS IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, pp. 22025-22035. Honolulu, Hawaii (USA), 19-23/10/2025
Project(s): Future Artificial Intelligence Research, Italian Strengthening of ESFRI RI RESILIENCE, SUN
, a MUltimedia platform for Content Enrichment and Search in audiovisual archives
Barsellotti L., Bianchi L., Messina N., Carrara F., Cornia M., Baraldi L., Falchi F., Cucchiara R.
Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localization due to their global alignment of image and text features. Conversely, self-supervised visual models like DINO excel in fine-grained visual encoding but lack integration with language. To bridge this gap, we present Talk2DINO, a novel hybrid approach that combines the spatial accuracy of DINOv2 with the language understanding of CLIP. Our approach aligns the textual embeddings of CLIP to the patch-level features of DINOv2 through a learned mapping function without the need to fine-tune the underlying backbones. At training time, we exploit the attention maps of DINOv2 to selectively align local visual patches with textual embeddings. We show that the powerful semantic and localization abilities of Talk2DINO can enhance the segmentation process, resulting in more natural and less noisy segmentations, and that our approach can also effectively distinguish foreground objects from the background. Experimental results demonstrate that Talk2DINO achieves state-of-the-art performance across several unsupervised OVS benchmarks.Source: PROCEEDINGS IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, pp. 22025-22035. Honolulu, Hawaii (USA), 19-23/10/2025
Project(s): Future Artificial Intelligence Research, Italian Strengthening of ESFRI RI RESILIENCE, SUN
, a MUltimedia platform for Content Enrichment and Search in audiovisual archives
See at:
CNR IRIS 
| openaccess.thecvf.com | CNR IRIS 
not yet published
Journal article
Open Access
A time penalty for the Global South? Inequalities in visa appointment wait times at german embassies and consulates worldwide
Deutschmann Emanuel, Gabrielli Lorenzo, Orlova Alexandra, Harder Niklas, Recchi Ettore
Visas are a key tool for states to regulate incoming mobility from abroad, which can have ramifications for the establishment and perpetuation of global inequalities. In this article, we systematically analyze visa appointment wait times in German embassies and consulates worldwide. Using computational methods, we collect—and publish—fine-grained longitudinal data on the closest available appointment dates for various visa types, covering a total of 16,182 visa appointment requests. Our analysis reveals strong and systematic variance: the poorer the country a diplomatic mission is based in, the longer the wait time and the lower the chances of finding an available appointment (which ranges from almost 0 to 100 percent). We also argue that Germany's system is quite opaque compared to other established immigration countries such as the U.S. These core findings raise important questions in light of current debates about global justice, legal pathways to migration, and efforts to attract foreign talent.Source: POLITICAL GEOGRAPHY
DOI: 10.1016/j.polgeo.2025.103440
Metrics:
Deutschmann Emanuel, Gabrielli Lorenzo, Orlova Alexandra, Harder Niklas, Recchi Ettore
Visas are a key tool for states to regulate incoming mobility from abroad, which can have ramifications for the establishment and perpetuation of global inequalities. In this article, we systematically analyze visa appointment wait times in German embassies and consulates worldwide. Using computational methods, we collect—and publish—fine-grained longitudinal data on the closest available appointment dates for various visa types, covering a total of 16,182 visa appointment requests. Our analysis reveals strong and systematic variance: the poorer the country a diplomatic mission is based in, the longer the wait time and the lower the chances of finding an available appointment (which ranges from almost 0 to 100 percent). We also argue that Germany's system is quite opaque compared to other established immigration countries such as the U.S. These core findings raise important questions in light of current debates about global justice, legal pathways to migration, and efforts to attract foreign talent.Source: POLITICAL GEOGRAPHY
DOI: 10.1016/j.polgeo.2025.103440
Metrics:
See at:
Political Geography | Cadmus, EUI Research Repository | CNR IRIS | www.sciencedirect.com | CNR IRIS
not yet published
Conference article
Open Access
Enhancing interoperability of SPARQL endpoints: RESTful and OAI-PMH API for the DH-ATLAS project
Rubin Giorgia, Bardi Alessia, Del Gratta Riccardo, Del Grosso Angelo Mario
Digital repositories leveraging RDF and Linked Data for Cultural Heritage metadata face a critical challenge: their native SPARQL endpoints often create silos because major aggregators rely on the OAI-PMH protocol for harvesting, and developers prefer RESTful APIs. This divergence undermines the visibility and FAIRness (Findable, Accessible, Interoperable, Reusable) of scholarly resources. This paper details the unified access strategy implemented for the DH-ATLAS project, which generated an Ontology and Knowledge Graph focused on Italian Digital Cultural Heritage research. To ensure broad dissemination and resource reuse without duplicating data, DH-ATLAS developed two configurable and modular software components to implement compatibility with the OpenAIRE guidelines and REST clients. Together, these components establish a cohesive and reusable solution for integrating semantic repositories with diverse metadata consumers, promoting the long-term sustainability and broader accessibility of the DH-ATLAS Knowledge Graph and serving as a model for other RDF infrastructures.Project(s): The ATLAS of Italian Digital Humanities: a dynamic knowledge graph of digital scholarly research on Italian Cultural Heritage
Rubin Giorgia, Bardi Alessia, Del Gratta Riccardo, Del Grosso Angelo Mario
Digital repositories leveraging RDF and Linked Data for Cultural Heritage metadata face a critical challenge: their native SPARQL endpoints often create silos because major aggregators rely on the OAI-PMH protocol for harvesting, and developers prefer RESTful APIs. This divergence undermines the visibility and FAIRness (Findable, Accessible, Interoperable, Reusable) of scholarly resources. This paper details the unified access strategy implemented for the DH-ATLAS project, which generated an Ontology and Knowledge Graph focused on Italian Digital Cultural Heritage research. To ensure broad dissemination and resource reuse without duplicating data, DH-ATLAS developed two configurable and modular software components to implement compatibility with the OpenAIRE guidelines and REST clients. Together, these components establish a cohesive and reusable solution for integrating semantic repositories with diverse metadata consumers, promoting the long-term sustainability and broader accessibility of the DH-ATLAS Knowledge Graph and serving as a model for other RDF infrastructures.Project(s): The ATLAS of Italian Digital Humanities: a dynamic knowledge graph of digital scholarly research on Italian Cultural Heritage
See at:
CNR IRIS | ircdl2026.unimore.it | CNR IRIS
not yet published
Conference article
Open Access
Classifying the ripeness of mangoes using image processing and deep learning
Jirapattarasakul S., Thanyacharoen S., Leone G. R., Akkaralaertsest T., Yingthawornsuk T.
Mango is a popular fruit that comes in many different varieties. Each variety has a different flavor, aroma, and texture. Selecting mangoes at the appropriate level of ripeness is therefore important to both consumers and producers. Problems frequently encountered include inconsistencies in sorting mangoes using traditional methods, which often rely on human experience and eyesight. This can affect the quality of mango product. This research focuses on developing a ripeness-rawness screening system for Okrong and Mahachanok mango varieties using Deep Learning and Image Processing techniques. Two CNN (Convolutional Neural Network) models, VGG16, MobileNetV2, and CNN1D, were used to analyze mango images and distinguish between ripe and raw levels. The test results showed that the VGG16 model achieved the highest performance in screening ripeness-rawness of both mango varieties, with an accuracy of 98%, followed by MobileNetV2 at 96% and CNN1D at 92%. For the ripeness-rawness classification of only the Okrong mango variety, the VGG16 model still achieved the highest performance, with an accuracy of 99%, followed by MobileNetV2 at 96% and CNN1D at 95%. These results indicate that CNN models, particularly VGG16, have great potential for application in developing automated mango sorting system based on ripeness-rawness levels. This proposed work can significantly improve efficiency in managing and selecting mango product.
Jirapattarasakul S., Thanyacharoen S., Leone G. R., Akkaralaertsest T., Yingthawornsuk T.
Mango is a popular fruit that comes in many different varieties. Each variety has a different flavor, aroma, and texture. Selecting mangoes at the appropriate level of ripeness is therefore important to both consumers and producers. Problems frequently encountered include inconsistencies in sorting mangoes using traditional methods, which often rely on human experience and eyesight. This can affect the quality of mango product. This research focuses on developing a ripeness-rawness screening system for Okrong and Mahachanok mango varieties using Deep Learning and Image Processing techniques. Two CNN (Convolutional Neural Network) models, VGG16, MobileNetV2, and CNN1D, were used to analyze mango images and distinguish between ripe and raw levels. The test results showed that the VGG16 model achieved the highest performance in screening ripeness-rawness of both mango varieties, with an accuracy of 98%, followed by MobileNetV2 at 96% and CNN1D at 92%. For the ripeness-rawness classification of only the Okrong mango variety, the VGG16 model still achieved the highest performance, with an accuracy of 99%, followed by MobileNetV2 at 96% and CNN1D at 95%. These results indicate that CNN models, particularly VGG16, have great potential for application in developing automated mango sorting system based on ripeness-rawness levels. This proposed work can significantly improve efficiency in managing and selecting mango product.
See at:
gcmm2024.rmutk.ac.th | CNR IRIS | CNR IRIS | CNR IRIS
not yet published
Conference article
Open Access
Breaking the 2D dependency: what limits 3D-only open-vocabulary scene understanding
D’orsi Domenico, Carrara Fabio, Falchi Fabrizio, Tonellotto Nicola
Open-vocabulary 3D scene understanding, i.e., recognizing and classifying objects in 3D scenes without being limited to a predefined set of classes, is a foundational task for robotics and extended reality applications. Current leading methods often rely on 2D foundation models to extract semantics, then projected in 3D. This paper investigates the viability of a purely 3D-native pipeline, thereby eliminating dependencies on 2D models and reprojections. We systematically explored various architectural combinations using established 3D components. However, our extensive experiments on benchmark datasets reveal significant performance limitations with this direct 3D-native approach, with performance metrics falling short of expectations. Rather than a simple failure, these outcomes provide critical insights into the current deficiencies of existing 3D models when cascaded for complex open-vocabulary tasks. We highlight the lessons learned, identify the pipeline's limitations (e.g., segmenter-encoder domain gap, robustness to imperfect segmentations), and posit future research directions. We argue that a fundamental rethinking of model design and interplay is necessary to realize the potential of truly 3D-native open-vocabulary understanding.DOI: 10.5281/zenodo.17338754
DOI: 10.5281/zenodo.17338755
Project(s): Social and Human Centered XR
Metrics:
D’orsi Domenico, Carrara Fabio, Falchi Fabrizio, Tonellotto Nicola
Open-vocabulary 3D scene understanding, i.e., recognizing and classifying objects in 3D scenes without being limited to a predefined set of classes, is a foundational task for robotics and extended reality applications. Current leading methods often rely on 2D foundation models to extract semantics, then projected in 3D. This paper investigates the viability of a purely 3D-native pipeline, thereby eliminating dependencies on 2D models and reprojections. We systematically explored various architectural combinations using established 3D components. However, our extensive experiments on benchmark datasets reveal significant performance limitations with this direct 3D-native approach, with performance metrics falling short of expectations. Rather than a simple failure, these outcomes provide critical insights into the current deficiencies of existing 3D models when cascaded for complex open-vocabulary tasks. We highlight the lessons learned, identify the pipeline's limitations (e.g., segmenter-encoder domain gap, robustness to imperfect segmentations), and posit future research directions. We argue that a fundamental rethinking of model design and interplay is necessary to realize the potential of truly 3D-native open-vocabulary understanding.DOI: 10.5281/zenodo.17338754
DOI: 10.5281/zenodo.17338755
Project(s): Social and Human Centered XR
Metrics:
See at:
CNR IRIS | zenodo.org | ZENODO | ZENODO | CNR IRIS
not yet published
Journal article
Open Access
A Local Lorentz invariance test with LAGEOS satellites
Lucchesi David, Visco Massimo, Peron Roberto, Rodriguez José C., Pucacco Giuseppe, Anselmo Luciano, Bassan Massimo, Appleby Graham, Cinelli Marco, Di Marco Alessandro, Lucente Marco, Magnafico Carmelo, Pardini Carmen, Sapio Feliciana
Strong theoretical arguments suggest that a breakdown of Lorentz Invariance could arise under some very particular conditions. From an experimental point of view, it is important to test the Local Lorentz Invariance with ever greater precision and in all contexts, regardless of the theoretical motivation for the possible violation. In this paper we discuss some aspects of the gravitational sector. Tests of Lorentz Invariance in the context of gravity are difficult and rare in the literature. Possible violations could arise from quantum physics applied to gravity or the presence of vector and tensor fields mediating the gravitational interaction together with the metric tensor of General Relativity. We present our results in the latter case. We analyzed the orbit of the LAGEOS and LAGEOS II satellites over a period of almost three decades. The effects of the possible preferred frame represented by the cosmic microwave background radiation on the mean argument of latitude of the satellites orbit were considered. These effects would manifest themselves mainly through the post-Newtonian parameter $α_1$, a parameter that has a null value in General Relativity. We constrain this parameterized post-Newtonian parameter down to the level of $α_1 \le 2\times10^{-5}$, improving a previous limit obtained through the Lunar Laser Ranging technique.Source: PHYSICAL REVIEW D
DOI: 10.1103/hj6p-bfyr
Metrics:
Lucchesi David, Visco Massimo, Peron Roberto, Rodriguez José C., Pucacco Giuseppe, Anselmo Luciano, Bassan Massimo, Appleby Graham, Cinelli Marco, Di Marco Alessandro, Lucente Marco, Magnafico Carmelo, Pardini Carmen, Sapio Feliciana
Strong theoretical arguments suggest that a breakdown of Lorentz Invariance could arise under some very particular conditions. From an experimental point of view, it is important to test the Local Lorentz Invariance with ever greater precision and in all contexts, regardless of the theoretical motivation for the possible violation. In this paper we discuss some aspects of the gravitational sector. Tests of Lorentz Invariance in the context of gravity are difficult and rare in the literature. Possible violations could arise from quantum physics applied to gravity or the presence of vector and tensor fields mediating the gravitational interaction together with the metric tensor of General Relativity. We present our results in the latter case. We analyzed the orbit of the LAGEOS and LAGEOS II satellites over a period of almost three decades. The effects of the possible preferred frame represented by the cosmic microwave background radiation on the mean argument of latitude of the satellites orbit were considered. These effects would manifest themselves mainly through the post-Newtonian parameter $α_1$, a parameter that has a null value in General Relativity. We constrain this parameterized post-Newtonian parameter down to the level of $α_1 \le 2\times10^{-5}$, improving a previous limit obtained through the Lunar Laser Ranging technique.Source: PHYSICAL REVIEW D
DOI: 10.1103/hj6p-bfyr
Metrics:
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
Journal article
Open Access
Experimental and numerical investigation on the seismic effective mass of an industrial steel silo tested on shaking table
Mansour Sulyman, Pellegrini Daniele, Girardi Maria, Marra Matteo, Palermo Michele, Silvestri Stefano
Silos are classified as non-building structures whose overall structural response has been extensively studied in the last years. Nevertheless, their dynamic and seismic response still presents open issues, mainly regarding the seismic effective mass of the stored product, the estimation of the fundamental frequency of the filled silo system, and the dependency of the damping ratio on the input nature and magnitude. The main objective of this work is to provide an estimation of the effective mass, taking advantage of a wide experimental shaking table campaign carried out on a full-scale flat-bottom corrugated-wall steel silo filled with soft wheat. The paper initially presents the results of Experimental Modal Analysis for the identification of the vibration frequencies and the calibration of a numerical model considering a no-tension material for the ensiled content. The calibrated model is able to well reproduce the actual response of the filled silo measured during the shaking table tests for both sinusoidal and earthquake inputs and it is thus used to retrieve information about the seismic effective mass beyond the limits of the experimental work. The reconstruction of the global overturning moment at the silo base from the numerical model indicates an average mass participation of 91 % for an industrial silo with a common filling aspect ratio (H/D = 0.9). This highlights that the 80 % value of the effective mass prescribed by Eurocode 8 may reveal to be non-conservative for silos characterised by corrugated wall sections and aspect ratios close to or greater than 1.Source: STRUCTURES, vol. 84 (issue 111020)
DOI: 10.1016/j.istruc.2025.111020
Metrics:
Mansour Sulyman, Pellegrini Daniele, Girardi Maria, Marra Matteo, Palermo Michele, Silvestri Stefano
Silos are classified as non-building structures whose overall structural response has been extensively studied in the last years. Nevertheless, their dynamic and seismic response still presents open issues, mainly regarding the seismic effective mass of the stored product, the estimation of the fundamental frequency of the filled silo system, and the dependency of the damping ratio on the input nature and magnitude. The main objective of this work is to provide an estimation of the effective mass, taking advantage of a wide experimental shaking table campaign carried out on a full-scale flat-bottom corrugated-wall steel silo filled with soft wheat. The paper initially presents the results of Experimental Modal Analysis for the identification of the vibration frequencies and the calibration of a numerical model considering a no-tension material for the ensiled content. The calibrated model is able to well reproduce the actual response of the filled silo measured during the shaking table tests for both sinusoidal and earthquake inputs and it is thus used to retrieve information about the seismic effective mass beyond the limits of the experimental work. The reconstruction of the global overturning moment at the silo base from the numerical model indicates an average mass participation of 91 % for an industrial silo with a common filling aspect ratio (H/D = 0.9). This highlights that the 80 % value of the effective mass prescribed by Eurocode 8 may reveal to be non-conservative for silos characterised by corrugated wall sections and aspect ratios close to or greater than 1.Source: STRUCTURES, vol. 84 (issue 111020)
DOI: 10.1016/j.istruc.2025.111020
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Enhancing token boundary detection in disfluent speech
Srivastava Manu, Ferro Marcello, Pirrelli Vito, Coro Gianpaolo
This paper presents an open-source Automatic Speech Recognition (ASR) pipeline optimised for disfluent Italian read speech, designed to enhance both transcription accuracy and token boundary precision in low-resource settings. The study aims to address the difficulty that conventional ASR systems face in capturing the temporal irregularities of disfluent reading, which are crucial for psycholinguistic and clinical analyses of fluency. Building upon the WhisperX framework, the proposed system replaces the neural Voice Activity Detection module with an energy-based segmentation algorithm designed to preserve prosodic cues such as pauses and hesitations. A dual-alignment strategy integrates two complementary phoneme-level ASR models to correct onset–offset asymmetries, while a bias-compensation post-processing step mitigates systematic timing errors. Evaluation on the READLET (child read speech) and CLIPS (adult read speech) corpora shows consistent improvements over baseline systems, confirming enhanced robustness in boundary detection and transcription under disfluent conditions. The results demonstrate that the proposed architecture provides a general, language-independent framework for accurate alignment and disfluency-aware ASR. The approach can support downstream analyses of reading fluency and speech planning, contributing to both computational linguistics and clinical speech research.Source: INTELLIGENT SYSTEMS WITH APPLICATIONS, vol. 29
DOI: 10.1016/j.iswa.2025.200614
Project(s): READLET
Metrics:
Srivastava Manu, Ferro Marcello, Pirrelli Vito, Coro Gianpaolo
This paper presents an open-source Automatic Speech Recognition (ASR) pipeline optimised for disfluent Italian read speech, designed to enhance both transcription accuracy and token boundary precision in low-resource settings. The study aims to address the difficulty that conventional ASR systems face in capturing the temporal irregularities of disfluent reading, which are crucial for psycholinguistic and clinical analyses of fluency. Building upon the WhisperX framework, the proposed system replaces the neural Voice Activity Detection module with an energy-based segmentation algorithm designed to preserve prosodic cues such as pauses and hesitations. A dual-alignment strategy integrates two complementary phoneme-level ASR models to correct onset–offset asymmetries, while a bias-compensation post-processing step mitigates systematic timing errors. Evaluation on the READLET (child read speech) and CLIPS (adult read speech) corpora shows consistent improvements over baseline systems, confirming enhanced robustness in boundary detection and transcription under disfluent conditions. The results demonstrate that the proposed architecture provides a general, language-independent framework for accurate alignment and disfluency-aware ASR. The approach can support downstream analyses of reading fluency and speech planning, contributing to both computational linguistics and clinical speech research.Source: INTELLIGENT SYSTEMS WITH APPLICATIONS, vol. 29
DOI: 10.1016/j.iswa.2025.200614
Project(s): READLET
Metrics:
See at:
CNR IRIS | www.sciencedirect.com | CNR IRIS
2026
Book
Open Access
Preface to Journeys between formal methods and the railway industry. Essays dedicated to Alessandro Fantechi on the occasion of his 70th birthday
Ter Beek Maurice Henri, Gnesi Stefania, Haxthausen Anne E., Semini Laura
This Festschrift contains 18 contributions by collaborators, colleagues and friends of Alessandro Fantechi to celebrate his 70th birthday.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. i-xvi
DOI: 10.1007/978-3-032-12484-5
Project(s): Sustainable Mobility National Research Center
Metrics:
Ter Beek Maurice Henri, Gnesi Stefania, Haxthausen Anne E., Semini Laura
This Festschrift contains 18 contributions by collaborators, colleagues and friends of Alessandro Fantechi to celebrate his 70th birthday.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. i-xvi
DOI: 10.1007/978-3-032-12484-5
Project(s): Sustainable Mobility National Research Center
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS
2026
Journal article
Open Access
Detecting fishing areas from navigation radar detector data
Coro Gianpaolo, Bove Pasquale, Ellenbroek Anton
Detecting global fishing activity is essential for sustainable ocean governance, yet systems based on vessel-transmitted information, such as Automatic Identification System (AIS) and Vessel Monitoring Systems, are limited by access issues, coverage gaps, and the inability to detect non-cooperative vessels. To overcome these issues, this paper presents Point-to-Fishing (P2F), an AI-driven workflow to detect fishing areas and estimate fishing hours from Navigation Radar Detector (NRD) data of satellite or terrestrial systems, complemented with currents and bathymetry data from Copernicus and GEBCO. P2F integrates analytical components based on statistical analysis, machine learning, and deep learning to conduct vessel behaviour analysis, spatial feature extraction (vessel abundance, recurrence, current-driven interpolation, and bathymetric suitability), and anomaly detection. The workflow operates effectively with or without vessel identifiers, enabling the detection of fishing areas in data-sparse or AIS-denied regions, even using one satellite only. P2F is validated on data covering the North Sea, the Western Norwegian Sea, and the North Atlantic. The validation cases utilise terrestrial and satellite NRD data alternately, with the Global Fishing Watch fishing effort distributions as a validation reference. P2F achieves a consistent ~75% agreement in relevant fishing area classification and intense-fishing area identification, and ~93% accuracy in total fishing effort estimation.Source: INTERNATIONAL JOURNAL OF DIGITAL EARTH, vol. 19 (issue 1)
DOI: 10.1080/17538947.2026.2617004
Project(s): Accordo di Collaborazione N. 403036 del 25/10/2024 tra la Norwegian Defence Research Establishment (FFI) e il CNR-ISTI.
Metrics:
Coro Gianpaolo, Bove Pasquale, Ellenbroek Anton
Detecting global fishing activity is essential for sustainable ocean governance, yet systems based on vessel-transmitted information, such as Automatic Identification System (AIS) and Vessel Monitoring Systems, are limited by access issues, coverage gaps, and the inability to detect non-cooperative vessels. To overcome these issues, this paper presents Point-to-Fishing (P2F), an AI-driven workflow to detect fishing areas and estimate fishing hours from Navigation Radar Detector (NRD) data of satellite or terrestrial systems, complemented with currents and bathymetry data from Copernicus and GEBCO. P2F integrates analytical components based on statistical analysis, machine learning, and deep learning to conduct vessel behaviour analysis, spatial feature extraction (vessel abundance, recurrence, current-driven interpolation, and bathymetric suitability), and anomaly detection. The workflow operates effectively with or without vessel identifiers, enabling the detection of fishing areas in data-sparse or AIS-denied regions, even using one satellite only. P2F is validated on data covering the North Sea, the Western Norwegian Sea, and the North Atlantic. The validation cases utilise terrestrial and satellite NRD data alternately, with the Global Fishing Watch fishing effort distributions as a validation reference. P2F achieves a consistent ~75% agreement in relevant fishing area classification and intense-fishing area identification, and ~93% accuracy in total fishing effort estimation.Source: INTERNATIONAL JOURNAL OF DIGITAL EARTH, vol. 19 (issue 1)
DOI: 10.1080/17538947.2026.2617004
Project(s): Accordo di Collaborazione N. 403036 del 25/10/2024 tra la Norwegian Defence Research Establishment (FFI) e il CNR-ISTI.
Metrics:
2026
Contribution to book
Open Access
Towards dynamic classification in domain modeling with Jjodel
Ter Beek Maurice Henri, Bucchiarone Antonio, Pierantonio Alfonso, Selic Bran
This work addresses the limitations of traditional object-oriented classification in representing evolving systems. Conventional classification enforces rigid hierarchies that hinder dynamic reclassification, concurrent viewpoints, and transient or overlapping states, thus limiting their usefulness for systems that exhibit change, context sensitivity, or adaptation. We propose a modeling notation that extends UML class diagrams with declarative behavioral dynamics directly embedded in structural information. Unlike approaches that separate structure and behavior, our notation unifies them in a framework suitable for both conceptual/domain modeling and evolutionary, context-aware dynamics. The notation is fully defined, including abstract syntax, diagrammatic syntax, semantics, and simulation, within the Jjodel platform, ensuring rigor and tool support. Its structural nature enables the declarative specification of dynamics without imperative constructs. Moreover, the notation can be connected to frameworks for formal verification and model checking, enabling analysis of dynamic properties. Applicability is illustrated through a context-aware scenario in which enriched structural models can be simulated, reasoned about, and eventually verified.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. 193-215
DOI: 10.1007/978-3-032-12484-5_11
Metrics:
Ter Beek Maurice Henri, Bucchiarone Antonio, Pierantonio Alfonso, Selic Bran
This work addresses the limitations of traditional object-oriented classification in representing evolving systems. Conventional classification enforces rigid hierarchies that hinder dynamic reclassification, concurrent viewpoints, and transient or overlapping states, thus limiting their usefulness for systems that exhibit change, context sensitivity, or adaptation. We propose a modeling notation that extends UML class diagrams with declarative behavioral dynamics directly embedded in structural information. Unlike approaches that separate structure and behavior, our notation unifies them in a framework suitable for both conceptual/domain modeling and evolutionary, context-aware dynamics. The notation is fully defined, including abstract syntax, diagrammatic syntax, semantics, and simulation, within the Jjodel platform, ensuring rigor and tool support. Its structural nature enables the declarative specification of dynamics without imperative constructs. Moreover, the notation can be connected to frameworks for formal verification and model checking, enabling analysis of dynamic properties. Applicability is illustrated through a context-aware scenario in which enriched structural models can be simulated, reasoned about, and eventually verified.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16470, pp. 193-215
DOI: 10.1007/978-3-032-12484-5_11
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2026
Conference article
Restricted
Software product quality: some thoughts about its evolution and perspectives in the AI years
Buglione Luigi, Merola Francesco
“Quality is free” was the title of a famous 1979 book by Philip Crosby, one of the Total Quality Management (TQM) gurus that for many people could have been misleading. “Quality” is part of what currently are the so-called NFRs (Non- Functional Requirements), complementing FURs (Functional User Requirements) from a product-view perspective. Any requirement generates tasks/activities (thus efforts and costs, it’s not free at all...) and must be properly sized for improving project estimates from the early stages. Quality Models (QMs) constantly evolved from the mid ‘70s, from the FCM (Factor-Criteria-Model) until the current ISO models included in the SQuARe family (25000 series). The last model published in 2023 was the ISO/IEC 25059 about a revision of the 25010quality model in the light of these AI years. This paper will discuss from an evolutionary perspective what software quality has been, is and should/could be perceived and defined during next years, by a measurement perspective.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16362, pp. 353-365. Salerno, Italy, 1–3 december 2025
DOI: 10.1007/978-3-032-12092-2_30
Metrics:
Buglione Luigi, Merola Francesco
“Quality is free” was the title of a famous 1979 book by Philip Crosby, one of the Total Quality Management (TQM) gurus that for many people could have been misleading. “Quality” is part of what currently are the so-called NFRs (Non- Functional Requirements), complementing FURs (Functional User Requirements) from a product-view perspective. Any requirement generates tasks/activities (thus efforts and costs, it’s not free at all...) and must be properly sized for improving project estimates from the early stages. Quality Models (QMs) constantly evolved from the mid ‘70s, from the FCM (Factor-Criteria-Model) until the current ISO models included in the SQuARe family (25000 series). The last model published in 2023 was the ISO/IEC 25059 about a revision of the 25010quality model in the light of these AI years. This paper will discuss from an evolutionary perspective what software quality has been, is and should/could be perceived and defined during next years, by a measurement perspective.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16362, pp. 353-365. Salerno, Italy, 1–3 december 2025
DOI: 10.1007/978-3-032-12092-2_30
Metrics:
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doi.org | CNR IRIS | CNR IRIS | link.springer.com
2026
Journal article
Restricted
Enhancing randomized recurrent neural networks with explainable attribution methods
Spinnato Francesco, Ceni Andrea, Cossu Andrea, Guidotti Riccardo, Gallicchio Claudio, Bacciu Davide
Recurrent Neural Networks (RNNs) are well-suited for temporal data modeling but remain limited by their high training computational cost. As a lightweight alternative, randomized RNNs mitigate this issue by employing a fixed, randomly initialized recurrent layer combined with a simple, trainable output layer. To classify a given input sequence, randomized RNNs usually rely on the final reservoir state, which can be suboptimal when relevant temporal information is sparse or masked by noise. In this work, we investigate how explainable attribution methods can improve the performance of randomized RNNs in classification tasks. In particular, we adopt gradient-based attribution explainability techniques to weigh reservoir states according to their relevance to the final prediction. We theoretically justify the effectiveness of our approach through linear stability analysis, offering geometric intuition via an estimation of the variability of the recurrent dynamics by means of explainability techniques. Our experimental evaluation spans 30 binary and 10 multiclass time series classification tasks, comparing several randomized recurrent models. Results show that explainability-guided weighting can improve classification performance in noisy scenarios.Source: NEUROCOMPUTING, vol. 666
DOI: 10.1016/j.neucom.2025.132318
Metrics:
Spinnato Francesco, Ceni Andrea, Cossu Andrea, Guidotti Riccardo, Gallicchio Claudio, Bacciu Davide
Recurrent Neural Networks (RNNs) are well-suited for temporal data modeling but remain limited by their high training computational cost. As a lightweight alternative, randomized RNNs mitigate this issue by employing a fixed, randomly initialized recurrent layer combined with a simple, trainable output layer. To classify a given input sequence, randomized RNNs usually rely on the final reservoir state, which can be suboptimal when relevant temporal information is sparse or masked by noise. In this work, we investigate how explainable attribution methods can improve the performance of randomized RNNs in classification tasks. In particular, we adopt gradient-based attribution explainability techniques to weigh reservoir states according to their relevance to the final prediction. We theoretically justify the effectiveness of our approach through linear stability analysis, offering geometric intuition via an estimation of the variability of the recurrent dynamics by means of explainability techniques. Our experimental evaluation spans 30 binary and 10 multiclass time series classification tasks, comparing several randomized recurrent models. Results show that explainability-guided weighting can improve classification performance in noisy scenarios.Source: NEUROCOMPUTING, vol. 666
DOI: 10.1016/j.neucom.2025.132318
Metrics:
See at:
CNR IRIS | CNR IRIS | www.sciencedirect.com
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
Other
Restricted
Spoke 9 - AGRITECH 36-MONTH REPORT
Pucci Laura, Tomassi Elena, Arouna Nafiou, Gabriele Morena, Peres Fabbri Laryssa, Pozzo Luisa, Conte Giuseppe, Cremonesi Paola, Castiglioni Bianca, Moroni Davide, Martinelli Massimo
This document represents the 36-month report on products of animal origin intended for human consumption.Project(s): Spoke 9 AGRITECH
Pucci Laura, Tomassi Elena, Arouna Nafiou, Gabriele Morena, Peres Fabbri Laryssa, Pozzo Luisa, Conte Giuseppe, Cremonesi Paola, Castiglioni Bianca, Moroni Davide, Martinelli Massimo
This document represents the 36-month report on products of animal origin intended for human consumption.Project(s): Spoke 9 AGRITECH
2026
Conference article
Restricted
Critical Analysis of ASPICE® 4.0 Machine Learning Engineering Process Requirements
Lami Giuseppe, Falcini Fabio
The introduction of machine learning development paradigm into the automotive software industry has made necessary to update the applicable qual- ity evaluation standards such as Automotive SPICE®. As a result the Automotive SPICE® community timely tackled this challenge with the introduction of the ver- sion 4.0 containing a first baseline of process requirements for machine learning engineering. The paper provides a succinct critical analysis of related ASPICE® new content with particular reference to the current state of the art of machine learn- ing development practices. The outcome of this paper aims at being an input for the forthcoming improvement initiatives within the Automotive SPICE working groups.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16362, pp. 346-352. Salerno, Italy, 1-3 December 2025
DOI: 10.1007/978-3-032-12092-2_27
Metrics:
Lami Giuseppe, Falcini Fabio
The introduction of machine learning development paradigm into the automotive software industry has made necessary to update the applicable qual- ity evaluation standards such as Automotive SPICE®. As a result the Automotive SPICE® community timely tackled this challenge with the introduction of the ver- sion 4.0 containing a first baseline of process requirements for machine learning engineering. The paper provides a succinct critical analysis of related ASPICE® new content with particular reference to the current state of the art of machine learn- ing development practices. The outcome of this paper aims at being an input for the forthcoming improvement initiatives within the Automotive SPICE working groups.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16362, pp. 346-352. Salerno, Italy, 1-3 December 2025
DOI: 10.1007/978-3-032-12092-2_27
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | link.springer.com
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