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
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
Open Access
A conversational assistant for geoscientists in virtual research environments
Peccerillo Biagio, Oliviero Alfredo, Procaccini Marco, Candela Leonardo, Frosini Luca, Mangiacrapa Francesco, Panichi Giancarlo, Assante Massimiliano, Pagano Pasquale
D4Science provides web-based Virtual Research Environments (VREs) that support FAIR, open, and reproducible science across multiple research domains, including Earth science. These environments integrate data access, computation, and collaboration services, offering powerful capabilities to researchers and enabling complex, data-intensive scientific activities within a shared digital infrastructure. This contribution introduces a conversational intelligent assistant integrated into D4Science VREs, designed to support Earth scientists in their research activity. The assistant provides a natural language interface that helps users interact with D4Science VREs' services, locate relevant datasets and research items, obtain guidance on common tasks, and support exploratory and operational activities within the VRE. The assistant is designed with a modular approach. The user interacts with a coordinator agent that orchestrates a multi-agent system, where specialized AI agents collaborate to perform a variety of tasks. This architecture allows the assistant to handle heterogeneous requests and to support users across different phases of their research activities, while also facilitating maintenance and extensibility. The conversational agent adopts a Retrieval-Augmented Generation (RAG) approach that leverages the knowledge already captured by the VRE through its regular use by research communities. In fact, as VREs naturally accumulate updated knowledge created and curated by researchers over time, the assistant's knowledge base evolves incorporating new information. This way, the assistant can ground its responses in domain-specific and up-to-date information, effectively acting as a domain-aware expert embedded within the research environment. By serving as an accessible entry point to the VRE, the assistant complements existing interfaces without altering established workflows. The presentation discusses the motivation, design choices, and integration strategy. It also presents various concrete use cases relevant to Earth scientists, demonstrating how the conversational assistant can be effectively employed to support their research activity.DOI: 10.5194/egusphere-egu26-13138
Metrics:
Peccerillo Biagio, Oliviero Alfredo, Procaccini Marco, Candela Leonardo, Frosini Luca, Mangiacrapa Francesco, Panichi Giancarlo, Assante Massimiliano, Pagano Pasquale
D4Science provides web-based Virtual Research Environments (VREs) that support FAIR, open, and reproducible science across multiple research domains, including Earth science. These environments integrate data access, computation, and collaboration services, offering powerful capabilities to researchers and enabling complex, data-intensive scientific activities within a shared digital infrastructure. This contribution introduces a conversational intelligent assistant integrated into D4Science VREs, designed to support Earth scientists in their research activity. The assistant provides a natural language interface that helps users interact with D4Science VREs' services, locate relevant datasets and research items, obtain guidance on common tasks, and support exploratory and operational activities within the VRE. The assistant is designed with a modular approach. The user interacts with a coordinator agent that orchestrates a multi-agent system, where specialized AI agents collaborate to perform a variety of tasks. This architecture allows the assistant to handle heterogeneous requests and to support users across different phases of their research activities, while also facilitating maintenance and extensibility. The conversational agent adopts a Retrieval-Augmented Generation (RAG) approach that leverages the knowledge already captured by the VRE through its regular use by research communities. In fact, as VREs naturally accumulate updated knowledge created and curated by researchers over time, the assistant's knowledge base evolves incorporating new information. This way, the assistant can ground its responses in domain-specific and up-to-date information, effectively acting as a domain-aware expert embedded within the research environment. By serving as an accessible entry point to the VRE, the assistant complements existing interfaces without altering established workflows. The presentation discusses the motivation, design choices, and integration strategy. It also presents various concrete use cases relevant to Earth scientists, demonstrating how the conversational assistant can be effectively employed to support their research activity.DOI: 10.5194/egusphere-egu26-13138
Metrics:
See at:
CNR IRIS | www.egu26.eu | CNR IRIS
2026
Book
Open Access
Design manual for accessibility and the inclusive use of cultural heritage: from human functioning to the functioning of cultural places
Bernacchia Monica, Biocca Luigi, Buzzi Marina, Capirci Olga, Caponera Federica, Carella Giuseppina, Ceccarani Patrizia, Cetorelli Gabriella, Contardi Anna, Della Fina Valentina, Di Renzo Alessio, Filetici Maria Grazia, Galesi Giulio, Genta Chiara, Gervasio Carmine Fernando, Grassini Aldo, Guido Manuel Roberto, Leporini Barbara, Luongo Mariajosè, Maglorio Massimo, Marino Carmen, Mezzelani Alessandra, Montuschi Carla, Pagano Alfonsina, Papi Luca, Pennacchi Barbara, Pietroni Eva, Ricci Enrico, Rossini Mauro, Russo Francesco Paolo, Schiavone Elisabetta, Schivo Flavia, Scianna Andrea, Trasatti Annalisa, Zanut Stefano
The English-language version of the Manual, presented in an expanded edition in March 2026, represents an update of the volume with two appendices that are part of the institutional series of Policy Briefs of the Department of Human and Social Sciences, Cultural Heritage (DSU) of the National Research Council (CNR). This new edition, in addition to gathering the salient aspects of "universal design," starting from the concept of the functioning of individuals to the functioning of cultural places, presents additional current aspects through documents that summarize the results of research on relevant topics and their implications for policy and social debate. In the first addendum, the choice was to explore the issues of appropriate, respectful, and correct language regarding accessibility based on Legislative Decree 62/2024, centered on a "Person First" vision, as enshrined by the World Health Organization (2001) and the Convention on the Rights of Persons with Disabilities (2006). The second document proposes an integrated approach to accessible trails, understood as landscape experiences to be enjoyed independently and safely, capable of meeting the needs of each individual
Bernacchia Monica, Biocca Luigi, Buzzi Marina, Capirci Olga, Caponera Federica, Carella Giuseppina, Ceccarani Patrizia, Cetorelli Gabriella, Contardi Anna, Della Fina Valentina, Di Renzo Alessio, Filetici Maria Grazia, Galesi Giulio, Genta Chiara, Gervasio Carmine Fernando, Grassini Aldo, Guido Manuel Roberto, Leporini Barbara, Luongo Mariajosè, Maglorio Massimo, Marino Carmen, Mezzelani Alessandra, Montuschi Carla, Pagano Alfonsina, Papi Luca, Pennacchi Barbara, Pietroni Eva, Ricci Enrico, Rossini Mauro, Russo Francesco Paolo, Schiavone Elisabetta, Schivo Flavia, Scianna Andrea, Trasatti Annalisa, Zanut Stefano
The English-language version of the Manual, presented in an expanded edition in March 2026, represents an update of the volume with two appendices that are part of the institutional series of Policy Briefs of the Department of Human and Social Sciences, Cultural Heritage (DSU) of the National Research Council (CNR). This new edition, in addition to gathering the salient aspects of "universal design," starting from the concept of the functioning of individuals to the functioning of cultural places, presents additional current aspects through documents that summarize the results of research on relevant topics and their implications for policy and social debate. In the first addendum, the choice was to explore the issues of appropriate, respectful, and correct language regarding accessibility based on Legislative Decree 62/2024, centered on a "Person First" vision, as enshrined by the World Health Organization (2001) and the Convention on the Rights of Persons with Disabilities (2006). The second document proposes an integrated approach to accessible trails, understood as landscape experiences to be enjoyed independently and safely, capable of meeting the needs of each individual
See at:
CNR IRIS | www.dsu.cnr.it | CNR IRIS
2026
Journal article
Open Access
Charting the landscape of Italian Diamond Open Access publishing
Angioni Simone, Baglioni Miriam, Bardi Alessia, Manghi Paolo, Mannocci Andrea, Pavone Gina
Diamond Open Access (DOA) is a non-commercial model of scholarly publishing that removes financial barriers for authors and readers. While international studies have outlined the global uptake of DOA, this paper investigates the presence and characteristics of the DOA landscape in Italy. We conducted a quantitative analysis on the 168 Italian journals classified as Diamond by EZB, and we studied their publishing volume, disciplinary distribution and citation impact by integrating information from the following open resources: DOAJ, OpenAIRE, ROAD, SCImago Journal Rank, and the ANVUR classification used in the Italian Research Assessment Framework (VQR). Key findings include the significant growth of DOA journals, particularly in the social sciences and humanities, as well as the high level of international citations, indicating strong global relevance. The study also highlights challenges such as the need for better indexing and comprehensive data to fully capture the DOA landscape.Source: QUANTITATIVE SCIENCE STUDIES
DOI: 10.1162/qss.a.466
Metrics:
Angioni Simone, Baglioni Miriam, Bardi Alessia, Manghi Paolo, Mannocci Andrea, Pavone Gina
Diamond Open Access (DOA) is a non-commercial model of scholarly publishing that removes financial barriers for authors and readers. While international studies have outlined the global uptake of DOA, this paper investigates the presence and characteristics of the DOA landscape in Italy. We conducted a quantitative analysis on the 168 Italian journals classified as Diamond by EZB, and we studied their publishing volume, disciplinary distribution and citation impact by integrating information from the following open resources: DOAJ, OpenAIRE, ROAD, SCImago Journal Rank, and the ANVUR classification used in the Italian Research Assessment Framework (VQR). Key findings include the significant growth of DOA journals, particularly in the social sciences and humanities, as well as the high level of international citations, indicating strong global relevance. The study also highlights challenges such as the need for better indexing and comprehensive data to fully capture the DOA landscape.Source: QUANTITATIVE SCIENCE STUDIES
DOI: 10.1162/qss.a.466
Metrics:
See at:
direct.mit.edu | CNR IRIS | 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
Building geospatial knowledge graphs for cultural heritage with the GeoLinks API
Pratelli Nicolo, De Martino Claudio, Lenzi Emanuele, Bartalesi Valentina
The Cultural Heritage domain is rich in contextual information, often found in unstructured texts. These sources frequently reference places but typically lack the semantic structure required for geospatial integration and analysis. We introduce the GeoLinks API, a lightweight web service that extracts and links geospatial entities from narrative texts to support the automatic construction of structured geospatial knowledge graphs. The API performs two core functions: (i) extracting geometries from GeoNames IRIs, and (ii) recognising and linking place entities to Wikidata. Outputs are expressed in GeoSPARQL, enabling spatial reasoning, semantic interoperability, and integration with Linked Data infrastructures. We demonstrate the performance of the GeoLinks API on a dataset of contextual narratives related to traditional craft production, illustrating how it transforms unstructured cultural texts into machine-readable, semantically enriched geospatial knowledge. This approach advances Cultural Heritage preservation and research by making spatial context explicit and supporting new forms of digital analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16297, pp. 445-452. Heraklion, Crete, Greece, 15–17 October 2025
DOI: 10.1007/978-981-95-5009-8_31
Project(s): Craeft
Metrics:
Pratelli Nicolo, De Martino Claudio, Lenzi Emanuele, Bartalesi Valentina
The Cultural Heritage domain is rich in contextual information, often found in unstructured texts. These sources frequently reference places but typically lack the semantic structure required for geospatial integration and analysis. We introduce the GeoLinks API, a lightweight web service that extracts and links geospatial entities from narrative texts to support the automatic construction of structured geospatial knowledge graphs. The API performs two core functions: (i) extracting geometries from GeoNames IRIs, and (ii) recognising and linking place entities to Wikidata. Outputs are expressed in GeoSPARQL, enabling spatial reasoning, semantic interoperability, and integration with Linked Data infrastructures. We demonstrate the performance of the GeoLinks API on a dataset of contextual narratives related to traditional craft production, illustrating how it transforms unstructured cultural texts into machine-readable, semantically enriched geospatial knowledge. This approach advances Cultural Heritage preservation and research by making spatial context explicit and supporting new forms of digital analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16297, pp. 445-452. Heraklion, Crete, Greece, 15–17 October 2025
DOI: 10.1007/978-981-95-5009-8_31
Project(s): Craeft
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Belief change based on knowledge measures
Casini Giovanni, Straccia Umberto
Belief Change (BC) is the process of changing beliefs (in our case, in terms of contraction, expansion and revision) taking into account a new piece of knowledge, which possibly may be in contradiction with the current belief. In this work, we present a novel quantitative BC framework based on the principle of minimizing the surprise from an information-theoretic perspective. Central to our approach is the Principle of Minimal Surprise (PMS), which asserts that when confronted with the uncertainty about which is the actual world, an agent should tend to favour the most expected, i.e., least surprising, outcomes. To formalize this, we make use of Knowledge Measures (KMs), which quantify the amount of information contained in a knowledge base. Guided by the PMS, our framework encourages belief change operations that minimize the informational amount deviation from the original belief, i.e., those that introduce the least surprise.Source: JOURNAL OF LOGIC AND COMPUTATION, vol. 36 (issue 2)
DOI: 10.1093/logcom/exag007
Project(s): STARWARS
Metrics:
Casini Giovanni, Straccia Umberto
Belief Change (BC) is the process of changing beliefs (in our case, in terms of contraction, expansion and revision) taking into account a new piece of knowledge, which possibly may be in contradiction with the current belief. In this work, we present a novel quantitative BC framework based on the principle of minimizing the surprise from an information-theoretic perspective. Central to our approach is the Principle of Minimal Surprise (PMS), which asserts that when confronted with the uncertainty about which is the actual world, an agent should tend to favour the most expected, i.e., least surprising, outcomes. To formalize this, we make use of Knowledge Measures (KMs), which quantify the amount of information contained in a knowledge base. Guided by the PMS, our framework encourages belief change operations that minimize the informational amount deviation from the original belief, i.e., those that introduce the least surprise.Source: JOURNAL OF LOGIC AND COMPUTATION, vol. 36 (issue 2)
DOI: 10.1093/logcom/exag007
Project(s): STARWARS
Metrics:
See at:
academic.oup.com | CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Editorial introducing the new editors-in-chief
Ter Beek Maurice Henri, Johnsen Einar Broch
Editorial Introducing the New Editors-in-ChiefSource: FORMAL ASPECTS OF COMPUTING, vol. 38 (issue 1), pp. 1-2
DOI: 10.1145/3787980
Metrics:
Ter Beek Maurice Henri, Johnsen Einar Broch
Editorial Introducing the New Editors-in-ChiefSource: FORMAL ASPECTS OF COMPUTING, vol. 38 (issue 1), pp. 1-2
DOI: 10.1145/3787980
Metrics:
See at:
dl.acm.org | CNR IRIS | CNR IRIS