Publications
OUR RESEARCH
Scientific Publications
Here you can find the comprehensive list of publications from the members of the Research Center on Computer Vision and eXtended Reality (xRAI).
Use the tag cloud to filter papers based on specific research topics, or use the menus to filter by year, type of publication, or authors.
For each paper, you have the option to view additional details such as the Abstract, Links, and BibTex record.
Research is formalized curiosity. It is poking and prying with a purpose
Zora Neale Hurston
2025
Mondal, Semanto; Ferraro, Antonino; Pecorelli, Fabiano; Pietro, Giuseppe De
A Logic Tensor Network-Based Neurosymbolic Framework for Explainable Diabetes Prediction Journal Article
In: Applied Sciences, vol. 15, no. 21, pp. 11806, 2025, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags:
@article{mondal_logic_2025,
title = {A Logic Tensor Network-Based Neurosymbolic Framework for Explainable Diabetes Prediction},
author = {Semanto Mondal and Antonino Ferraro and Fabiano Pecorelli and Giuseppe De Pietro},
url = {https://www.mdpi.com/2076-3417/15/21/11806},
doi = {10.3390/app152111806},
issn = {2076-3417},
year = {2025},
date = {2025-11-01},
urldate = {2025-11-25},
journal = {Applied Sciences},
volume = {15},
number = {21},
pages = {11806},
abstract = {Neurosymbolic AI is an emerging paradigm that combines neural network learning capabilities with the structured reasoning capacity of symbolic systems. Although machine learning has achieved cutting-edge outcomes in diverse fields, including healthcare, agriculture, and environmental science, it has potential limitations. Machine learning and neural models excel at identifying intricate data patterns, yet they often lack transparency, depend on large labelled datasets, and face challenges with logical reasoning and tasks that require explainability. These challenges reduce their reliability in high-stakes applications such as healthcare. To address these limitations, we propose a hybrid framework that integrates symbolic knowledge expressed in First-Order Logic into neural learning via a Logic Tensor Network (LTN). In this framework, expert-defined medical rules are embedded as logical axioms with learnable thresholds. As a result, the model gains predictive power, interpretability, and explainability through reasoning over the logical rules. We have utilized this neurosymbolic method for predicting diabetes by employing the Pima Indians Diabetes Dataset. Our experimental setup evaluates the LTN-based model against several conventional methods, including Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (K-NN), Random Forest Classifiers (RF), Naive Bayes (NB), and a Standalone Neural Network (NN). The findings demonstrate that the neurosymbolic framework not only surpasses traditional models in predictive accuracy but also offers improved explainability and robustness. Notably, the LTN-based neurosymbolic framework achieves an excellent balance between recall and precision, along with a higher AUC-ROC score. These results underscore its potential for trustworthy medical diagnostics. This work highlights how integrating symbolic reasoning with data-driven models can bridge the gap between explainability, interpretability, and performance, offering a promising direction for AI systems in domains where both accuracy and explainability are critical.},
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Neurosymbolic AI is an emerging paradigm that combines neural network learning capabilities with the structured reasoning capacity of symbolic systems. Although machine learning has achieved cutting-edge outcomes in diverse fields, including healthcare, agriculture, and environmental science, it has potential limitations. Machine learning and neural models excel at identifying intricate data patterns, yet they often lack transparency, depend on large labelled datasets, and face challenges with logical reasoning and tasks that require explainability. These challenges reduce their reliability in high-stakes applications such as healthcare. To address these limitations, we propose a hybrid framework that integrates symbolic knowledge expressed in First-Order Logic into neural learning via a Logic Tensor Network (LTN). In this framework, expert-defined medical rules are embedded as logical axioms with learnable thresholds. As a result, the model gains predictive power, interpretability, and explainability through reasoning over the logical rules. We have utilized this neurosymbolic method for predicting diabetes by employing the Pima Indians Diabetes Dataset. Our experimental setup evaluates the LTN-based model against several conventional methods, including Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (K-NN), Random Forest Classifiers (RF), Naive Bayes (NB), and a Standalone Neural Network (NN). The findings demonstrate that the neurosymbolic framework not only surpasses traditional models in predictive accuracy but also offers improved explainability and robustness. Notably, the LTN-based neurosymbolic framework achieves an excellent balance between recall and precision, along with a higher AUC-ROC score. These results underscore its potential for trustworthy medical diagnostics. This work highlights how integrating symbolic reasoning with data-driven models can bridge the gap between explainability, interpretability, and performance, offering a promising direction for AI systems in domains where both accuracy and explainability are critical.
Martino, Vincenzo De; Lambiase, Stefano; Pecorelli, Fabiano; Heuvel, Willem-Jan Van Den; Ferrucci, Filomena; Palomba, Fabio
Sustainability of Machine Learning-Enabled Systems: The Machine Learning Practitioner’s Perspective Journal Article
In: ACM Transactions on Software Engineering and Methodology, pp. 3777553, 2025, ISSN: 1049-331X, 1557-7392.
Abstract | Links | BibTeX | Tags:
@article{de_martino_sustainability_2025,
title = {Sustainability of Machine Learning-Enabled Systems: The Machine Learning Practitioner’s Perspective},
author = {Vincenzo De Martino and Stefano Lambiase and Fabiano Pecorelli and Willem-Jan Van Den Heuvel and Filomena Ferrucci and Fabio Palomba},
url = {https://dl.acm.org/doi/10.1145/3777553},
doi = {10.1145/3777553},
issn = {1049-331X, 1557-7392},
year = {2025},
date = {2025-11-01},
urldate = {2025-11-24},
journal = {ACM Transactions on Software Engineering and Methodology},
pages = {3777553},
abstract = {Software sustainability is a key multifaceted non-functional requirement that encompasses environmental, social, and economic concerns, yet its integration into the development of Machine Learning (ML)-enabled systems remains an open challenge. While previous research has explored high-level sustainability principles and policy recommendations, limited empirical evidence exists on how sustainability is practically managed in ML workflows. Existing studies predominantly focus on environmental sustainability, e.g., carbon footprint reduction, while missing
the broader spectrum of sustainability dimensions and the challenges practitioners face in real-world settings
. To address this gap, we conduct an empirical study to characterize sustainability in ML-enabled systems from a practitioner's perspective. We investigate (1) how ML engineers perceive and describe sustainability, (2) the software engineering practices they adopt to support it, and (3) the key challenges hindering its adoption. We first perform a qualitative analysis based on interviews with eight experienced ML engineers, followed by a large-scale quantitative survey with 203 ML practitioners. Our key findings reveal a significant disconnection between sustainability awareness and its systematic implementation, highlighting the need for more structured guidelines, measurement frameworks, and regulatory support.},
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Software sustainability is a key multifaceted non-functional requirement that encompasses environmental, social, and economic concerns, yet its integration into the development of Machine Learning (ML)-enabled systems remains an open challenge. While previous research has explored high-level sustainability principles and policy recommendations, limited empirical evidence exists on how sustainability is practically managed in ML workflows. Existing studies predominantly focus on environmental sustainability, e.g., carbon footprint reduction, while missing
the broader spectrum of sustainability dimensions and the challenges practitioners face in real-world settings
. To address this gap, we conduct an empirical study to characterize sustainability in ML-enabled systems from a practitioner's perspective. We investigate (1) how ML engineers perceive and describe sustainability, (2) the software engineering practices they adopt to support it, and (3) the key challenges hindering its adoption. We first perform a qualitative analysis based on interviews with eight experienced ML engineers, followed by a large-scale quantitative survey with 203 ML practitioners. Our key findings reveal a significant disconnection between sustainability awareness and its systematic implementation, highlighting the need for more structured guidelines, measurement frameworks, and regulatory support.
the broader spectrum of sustainability dimensions and the challenges practitioners face in real-world settings
. To address this gap, we conduct an empirical study to characterize sustainability in ML-enabled systems from a practitioner's perspective. We investigate (1) how ML engineers perceive and describe sustainability, (2) the software engineering practices they adopt to support it, and (3) the key challenges hindering its adoption. We first perform a qualitative analysis based on interviews with eight experienced ML engineers, followed by a large-scale quantitative survey with 203 ML practitioners. Our key findings reveal a significant disconnection between sustainability awareness and its systematic implementation, highlighting the need for more structured guidelines, measurement frameworks, and regulatory support.
Aversano, Lerina; Iammarino, Martina; Madau, Antonella; Montano, Debora; Verdone, Chiara
A Hybrid Approach Integrating Clinical Data and Tomography to Improve Diagnosis of Parkinson’s Disease Journal Article
In: ACM Transactions on Computing for Healthcare, vol. 6, no. 4, pp. 1–22, 2025, ISSN: 2691-1957, 2637-8051.
Abstract | Links | BibTeX | Tags:
@article{aversano_hybrid_2025,
title = {A Hybrid Approach Integrating Clinical Data and Tomography to Improve Diagnosis of Parkinson’s Disease},
author = {Lerina Aversano and Martina Iammarino and Antonella Madau and Debora Montano and Chiara Verdone},
url = {https://dl.acm.org/doi/10.1145/3735660},
doi = {10.1145/3735660},
issn = {2691-1957, 2637-8051},
year = {2025},
date = {2025-10-01},
urldate = {2025-11-04},
journal = {ACM Transactions on Computing for Healthcare},
volume = {6},
number = {4},
pages = {1–22},
abstract = {Parkinson’s Disease (PD) is a neurodegenerative condition primarily affecting the elderly but also occurring in younger individuals. It is caused by a progressive loss of nerve cells in the brain’s substantia nigra that release dopamine, essential for controlling movements. Dopamine deficiency results in symptoms affecting both motor and non-motor functions, which vary among individuals. Diagnosis relies on clinical symptoms and medical history, often supported by brain scans, as there is no specific diagnostic test available. Diagnosis is challenging due to vague initial symptoms resembling other conditions. Current research indicates that AI can significantly enhance data and image analysis, aiding in the diagnosis and monitoring of PD progression. To this aim, this study proposes a hybrid model allowing the integrated use of clinical data and single photon emission computed tomography images of a patient to predict the presence of the disease. The approach consists of a combination of two types of neural networks, an LSTM for clinical data and a CNN for images. The validation is performed on a widely validated dataset belonging to the Parkinson’s Progression Markers Initiative, from which the data recording visits of 1,814 patients were extracted. The obtained results are interesting and useful to address further investigations.},
keywords = {},
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Parkinson’s Disease (PD) is a neurodegenerative condition primarily affecting the elderly but also occurring in younger individuals. It is caused by a progressive loss of nerve cells in the brain’s substantia nigra that release dopamine, essential for controlling movements. Dopamine deficiency results in symptoms affecting both motor and non-motor functions, which vary among individuals. Diagnosis relies on clinical symptoms and medical history, often supported by brain scans, as there is no specific diagnostic test available. Diagnosis is challenging due to vague initial symptoms resembling other conditions. Current research indicates that AI can significantly enhance data and image analysis, aiding in the diagnosis and monitoring of PD progression. To this aim, this study proposes a hybrid model allowing the integrated use of clinical data and single photon emission computed tomography images of a patient to predict the presence of the disease. The approach consists of a combination of two types of neural networks, an LSTM for clinical data and a CNN for images. The validation is performed on a widely validated dataset belonging to the Parkinson’s Progression Markers Initiative, from which the data recording visits of 1,814 patients were extracted. The obtained results are interesting and useful to address further investigations.
Ferraro, Antonino; Orlando, Gian Marco; Russo, Diego
Generative Agent-Based Modeling with Large Language Models for insider threat detection Journal Article
In: Engineering Applications of Artificial Intelligence, vol. 157, pp. 111343, 2025, ISSN: 09521976.
Links | BibTeX | Tags: Cybersecurity, Generative Agent-Based Modeling, Insider threat detection, Large Language Models, Multi-Agent Systems
@article{ferraro_generative_2025,
title = {Generative Agent-Based Modeling with Large Language Models for insider threat detection},
author = {Antonino Ferraro and Gian Marco Orlando and Diego Russo},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0952197625013454},
doi = {10.1016/j.engappai.2025.111343},
issn = {09521976},
year = {2025},
date = {2025-10-01},
urldate = {2025-09-30},
journal = {Engineering Applications of Artificial Intelligence},
volume = {157},
pages = {111343},
keywords = {Cybersecurity, Generative Agent-Based Modeling, Insider threat detection, Large Language Models, Multi-Agent Systems},
pubstate = {published},
tppubtype = {article}
}
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
Advancing AI-driven surveillance systems in hospital: A fine-grained instance segmentation dataset for accurate in-bed patient monitoring Journal Article
In: Computers in Biology and Medicine, vol. 195, pp. 110550, 2025, ISSN: 00104825.
@article{mennella_advancing_2025,
title = {Advancing AI-driven surveillance systems in hospital: A fine-grained instance segmentation dataset for accurate in-bed patient monitoring},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482525009011},
doi = {10.1016/j.compbiomed.2025.110550},
issn = {00104825},
year = {2025},
date = {2025-09-01},
urldate = {2025-09-30},
journal = {Computers in Biology and Medicine},
volume = {195},
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Generosi, Andrea; Villafan, Josè Yuri; Ferretti, Maddalena; Mengoni, Maura
A recommender-based web platform to boost tourism in marginal territories Journal Article
In: Information Technology & Tourism, vol. 27, no. 3, pp. 797–831, 2025, ISSN: 1098-3058, 1943-4294.
@article{generosi_recommender-based_2025,
title = {A recommender-based web platform to boost tourism in marginal territories},
author = {Andrea Generosi and Josè Yuri Villafan and Maddalena Ferretti and Maura Mengoni},
url = {https://link.springer.com/10.1007/s40558-025-00327-1},
doi = {10.1007/s40558-025-00327-1},
issn = {1098-3058, 1943-4294},
year = {2025},
date = {2025-09-01},
urldate = {2025-09-30},
journal = {Information Technology & Tourism},
volume = {27},
number = {3},
pages = {797–831},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bruschi, Valeria; Generosi, Andrea; Dourou, Nefeli Aikaterini; Spinsante, Susanna; Mengoni, Maura; Cecchi, Stefania
Vehicle Sound Interaction: A Preliminary Study on Driver’s Experience Affected by Immersive Sound Reproduction Proceedings Article
In: 2025 Immersive and 3D Audio: from Architecture to Automotive (I3DA), pp. 1–9, IEEE, Bologna, Italy, 2025, ISBN: 9798331558284.
@inproceedings{bruschi_vehicle_2025,
title = {Vehicle Sound Interaction: A Preliminary Study on Driver’s Experience Affected by Immersive Sound Reproduction},
author = {Valeria Bruschi and Andrea Generosi and Nefeli Aikaterini Dourou and Susanna Spinsante and Maura Mengoni and Stefania Cecchi},
url = {https://ieeexplore.ieee.org/document/11202078/},
doi = {10.1109/I3DA65421.2025.11202078},
isbn = {9798331558284},
year = {2025},
date = {2025-09-01},
urldate = {2025-11-06},
booktitle = {2025 Immersive and 3D Audio: from Architecture to Automotive (I3DA)},
pages = {1–9},
publisher = {IEEE},
address = {Bologna, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Bruschi, Valeria; Generosi, Andrea; Terenzi, Alessandro; Mengoni, Maura; Cecchi, Stefania
A Preliminary Study on the Effect of Spatial Sound Reproduction based on Physiological Responses and Facial Expressions of the Listener Proceedings Article
In: 2025 Immersive and 3D Audio: from Architecture to Automotive (I3DA), pp. 1–7, IEEE, Bologna, Italy, 2025, ISBN: 9798331558284.
@inproceedings{bruschi_preliminary_2025,
title = {A Preliminary Study on the Effect of Spatial Sound Reproduction based on Physiological Responses and Facial Expressions of the Listener},
author = {Valeria Bruschi and Andrea Generosi and Alessandro Terenzi and Maura Mengoni and Stefania Cecchi},
url = {https://ieeexplore.ieee.org/document/11202118/},
doi = {10.1109/I3DA65421.2025.11202118},
isbn = {9798331558284},
year = {2025},
date = {2025-09-01},
urldate = {2025-11-06},
booktitle = {2025 Immersive and 3D Audio: from Architecture to Automotive (I3DA)},
pages = {1–7},
publisher = {IEEE},
address = {Bologna, Italy},
keywords = {},
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Gatto, Carola; Barba, Maria Cristina; Chiarello, Sofia; Corchia, Laura; Faggiano, Federica; Nuzzo, Benito Luigi; Panaro, Ileana Riera; Sumerano, Giada; Luca, Valerio De; Giorgi, Manuela De; Paolis, Lucio Tommaso De
In: Journal on Computing and Cultural Heritage, vol. 18, no. 3, pp. 1–31, 2025, ISSN: 1556-4673, 1556-4711.
Abstract | Links | BibTeX | Tags: Cultural Heritage, Extended reality, Human Computer Interaction, User experience
@article{gatto_improving_2025,
title = {Improving Accessibility to Cultural Heritage: Integration of Extended Reality, Tactile Prints and User Experience Analysis for the Church of Madonna dell’Itri},
author = {Carola Gatto and Maria Cristina Barba and Sofia Chiarello and Laura Corchia and Federica Faggiano and Benito Luigi Nuzzo and Ileana Riera Panaro and Giada Sumerano and Valerio De Luca and Manuela De Giorgi and Lucio Tommaso De Paolis},
url = {https://dl.acm.org/doi/10.1145/3733154},
doi = {10.1145/3733154},
issn = {1556-4673, 1556-4711},
year = {2025},
date = {2025-09-01},
urldate = {2025-09-30},
journal = {Journal on Computing and Cultural Heritage},
volume = {18},
number = {3},
pages = {1–31},
abstract = {The theme of accessibility is one of the most delicate aspects within the cultural heritage domain and can be approached in various dimensions, encompassing not only physical accessibility but also sensory and cognitive accessibility. This article presents the outcomes of the implementation of the ‘Intra l’Itri’ project, aimed to enhance the accessibility of the church of Madonna dell’Itri in Nociglia, Italy, using eXtended Reality (XR) technologies. The church harbours an ancient pictorial palimpsest with layers of historical significance, compounded by structural alterations over time. Funded by the Salento Interprovincial University Consortium (Consorzio Universitario Interprovinciale Salentino - CUIS 2020), the project engaged interdisciplinary collaboration involving the University of Salento’s Department of Engineering for Innovation and Department of Cultural Heritage, the Municipality of Nociglia, local companies, associations and professionals. Its objectives encompassed studying and conserving frescoes and the church’s structure, facilitating intelligent cultural immersion, enhancing visitor accessibility and fostering local identity. This contribution focuses on the developments of Augmented Reality (AR) and Virtual Reality (VR) applications, digital restoration visualisation, as well as 3D and tactile prints. It presents results and findings from the test campaign, validating the digital strategy aimed to enrich the accessibility of this historically significant artistic site.},
keywords = {Cultural Heritage, Extended reality, Human Computer Interaction, User experience},
pubstate = {published},
tppubtype = {article}
}
The theme of accessibility is one of the most delicate aspects within the cultural heritage domain and can be approached in various dimensions, encompassing not only physical accessibility but also sensory and cognitive accessibility. This article presents the outcomes of the implementation of the ‘Intra l’Itri’ project, aimed to enhance the accessibility of the church of Madonna dell’Itri in Nociglia, Italy, using eXtended Reality (XR) technologies. The church harbours an ancient pictorial palimpsest with layers of historical significance, compounded by structural alterations over time. Funded by the Salento Interprovincial University Consortium (Consorzio Universitario Interprovinciale Salentino - CUIS 2020), the project engaged interdisciplinary collaboration involving the University of Salento’s Department of Engineering for Innovation and Department of Cultural Heritage, the Municipality of Nociglia, local companies, associations and professionals. Its objectives encompassed studying and conserving frescoes and the church’s structure, facilitating intelligent cultural immersion, enhancing visitor accessibility and fostering local identity. This contribution focuses on the developments of Augmented Reality (AR) and Virtual Reality (VR) applications, digital restoration visualisation, as well as 3D and tactile prints. It presents results and findings from the test campaign, validating the digital strategy aimed to enrich the accessibility of this historically significant artistic site.
Mennella, Ciro; Esposito, Massimo; Pietro, Giuseppe De; Maniscalco, Umberto
Multiscale activity recognition algorithms to improve cross-subjects performance resilience in rehabilitation monitoring systems Journal Article
In: Computer Methods and Programs in Biomedicine, vol. 267, pp. 108792, 2025, ISSN: 01692607.
@article{mennella_multiscale_2025,
title = {Multiscale activity recognition algorithms to improve cross-subjects performance resilience in rehabilitation monitoring systems},
author = {Ciro Mennella and Massimo Esposito and Giuseppe De Pietro and Umberto Maniscalco},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0169260725002093},
doi = {10.1016/j.cmpb.2025.108792},
issn = {01692607},
year = {2025},
date = {2025-07-01},
urldate = {2025-09-30},
journal = {Computer Methods and Programs in Biomedicine},
volume = {267},
pages = {108792},
keywords = {},
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Pecorelli, Fabiano; Barletta, Vita Santa; Serrano, Manuel A.
Preface for “Quantum Programming for Software Engineering (QP4SE)” Journal Article
In: Science of Computer Programming, vol. 243, pp. 103257, 2025, ISSN: 01676423.
@article{pecorelli_preface_2025,
title = {Preface for “Quantum Programming for Software Engineering (QP4SE)”},
author = {Fabiano Pecorelli and Vita Santa Barletta and Manuel A. Serrano},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0167642324001801},
doi = {10.1016/j.scico.2024.103257},
issn = {01676423},
year = {2025},
date = {2025-07-01},
urldate = {2025-09-30},
journal = {Science of Computer Programming},
volume = {243},
pages = {103257},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Maggioli, Filippo; Melzi, Simone; Livesu, Marco
Volumetric Functional Maps Miscellaneous
2025, (arXiv:2506.13212 [cs]).
Abstract | Links | BibTeX | Tags: Computer graphics, Computer Vision and Pattern Recognition, Geometry Processing, Shape Analysis, Shape Matching, Spectral Geometry
@misc{maggioli_volumetric_2025,
title = {Volumetric Functional Maps},
author = {Filippo Maggioli and Simone Melzi and Marco Livesu},
url = {http://arxiv.org/abs/2506.13212},
doi = {10.48550/arXiv.2506.13212},
year = {2025},
date = {2025-07-01},
urldate = {2025-11-06},
publisher = {arXiv},
abstract = {The computation of volumetric correspondences between 3D shapes is a prominent tool for medical and industrial applications. In this work, we pave the way for spectral volume mapping, extending for the first time the functional maps framework from the surface to the volumetric setting. We show that the eigenfunctions of the volumetric Laplace operator define a functional space that is suitable for high-quality signal transfer. We also experiment with various techniques that edit this functional space, porting them to volume domains. We validate our method on novel volumetric datasets and on tetrahedralizations of well established surface datasets, also showcasing practical applications involving both discrete and continuous signal mapping, for segmentation transfer, mesh connectivity transfer and solid texturing. Last but not least, we show that considering the volumetric spectrum greatly improves the accuracy for classical shape matching tasks among surfaces, consistently outperforming existing surface-only spectral methods.},
note = {arXiv:2506.13212 [cs]},
keywords = {Computer graphics, Computer Vision and Pattern Recognition, Geometry Processing, Shape Analysis, Shape Matching, Spectral Geometry},
pubstate = {published},
tppubtype = {misc}
}
The computation of volumetric correspondences between 3D shapes is a prominent tool for medical and industrial applications. In this work, we pave the way for spectral volume mapping, extending for the first time the functional maps framework from the surface to the volumetric setting. We show that the eigenfunctions of the volumetric Laplace operator define a functional space that is suitable for high-quality signal transfer. We also experiment with various techniques that edit this functional space, porting them to volume domains. We validate our method on novel volumetric datasets and on tetrahedralizations of well established surface datasets, also showcasing practical applications involving both discrete and continuous signal mapping, for segmentation transfer, mesh connectivity transfer and solid texturing. Last but not least, we show that considering the volumetric spectrum greatly improves the accuracy for classical shape matching tasks among surfaces, consistently outperforming existing surface-only spectral methods.
Naeem, Tariq; Pirani, Massimiliano; Spalazzi, Luca
Evidence-Based Oracles Using Bayesian Network Proceedings Article
In: 2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), pp. 1–6, IEEE, Lucca, Italy, 2025, ISBN: 9798331543723.
@inproceedings{naeem_evidence-based_2025,
title = {Evidence-Based Oracles Using Bayesian Network},
author = {Tariq Naeem and Massimiliano Pirani and Luca Spalazzi},
url = {https://ieeexplore.ieee.org/document/11096167/},
doi = {10.1109/DCOSS-IoT65416.2025.00151},
isbn = {9798331543723},
year = {2025},
date = {2025-06-01},
urldate = {2025-09-30},
booktitle = {2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)},
pages = {1–6},
publisher = {IEEE},
address = {Lucca, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Buonaiuto, Giuseppe; Guarasci, Raffaele; Pietro, Giuseppe De; Esposito, Massimo
Multilingual multi-task quantum transfer learning Journal Article
In: Quantum Machine Intelligence, vol. 7, no. 1, pp. 46, 2025, ISSN: 2524-4906, 2524-4914.
Abstract | Links | BibTeX | Tags:
@article{buonaiuto_multilingual_2025,
title = {Multilingual multi-task quantum transfer learning},
author = {Giuseppe Buonaiuto and Raffaele Guarasci and Giuseppe De Pietro and Massimo Esposito},
url = {https://link.springer.com/10.1007/s42484-025-00260-w},
doi = {10.1007/s42484-025-00260-w},
issn = {2524-4906, 2524-4914},
year = {2025},
date = {2025-06-01},
urldate = {2025-09-30},
journal = {Quantum Machine Intelligence},
volume = {7},
number = {1},
pages = {46},
abstract = {Abstract
Hybrid quantum-classical algorithms have emerged as promising candidates for overcoming current limitations of deep learning techniques and recently have attracted a lot of attention for their application in natural language processing (NLP). Among the potential applications of quantum computing in this field, quantum transfer learning—using quantum circuits for fine-tuning pre-trained classical models specific to a task—is regarded as a potential avenue to exploit the potentiality of quantum computers. This study validates, both experimentally and with domain knowledge analysis, the efficacy of quantum transfer learning for two distinct NLP tasks—semantic and syntactic—and employ multilingual data encompassing both English and Italian. In particular is hereby demonstrated that embedded knowledge coming from pre-trained deep learning models can be effectively transferred into a quantum classifier, which shows good performances, either comparable or potentially better than their classical counterparts, with a further reduction of parameters compared to a purely classical classifier. Furthermore, a qualitative linguistic analysis of the results is presented, that elucidates two points: the lack of language dependence in the quantum models and the ability to discriminate with higher precision than standard classifiers, sub-types of linguistic structures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
Hybrid quantum-classical algorithms have emerged as promising candidates for overcoming current limitations of deep learning techniques and recently have attracted a lot of attention for their application in natural language processing (NLP). Among the potential applications of quantum computing in this field, quantum transfer learning—using quantum circuits for fine-tuning pre-trained classical models specific to a task—is regarded as a potential avenue to exploit the potentiality of quantum computers. This study validates, both experimentally and with domain knowledge analysis, the efficacy of quantum transfer learning for two distinct NLP tasks—semantic and syntactic—and employ multilingual data encompassing both English and Italian. In particular is hereby demonstrated that embedded knowledge coming from pre-trained deep learning models can be effectively transferred into a quantum classifier, which shows good performances, either comparable or potentially better than their classical counterparts, with a further reduction of parameters compared to a purely classical classifier. Furthermore, a qualitative linguistic analysis of the results is presented, that elucidates two points: the lack of language dependence in the quantum models and the ability to discriminate with higher precision than standard classifiers, sub-types of linguistic structures.
Hybrid quantum-classical algorithms have emerged as promising candidates for overcoming current limitations of deep learning techniques and recently have attracted a lot of attention for their application in natural language processing (NLP). Among the potential applications of quantum computing in this field, quantum transfer learning—using quantum circuits for fine-tuning pre-trained classical models specific to a task—is regarded as a potential avenue to exploit the potentiality of quantum computers. This study validates, both experimentally and with domain knowledge analysis, the efficacy of quantum transfer learning for two distinct NLP tasks—semantic and syntactic—and employ multilingual data encompassing both English and Italian. In particular is hereby demonstrated that embedded knowledge coming from pre-trained deep learning models can be effectively transferred into a quantum classifier, which shows good performances, either comparable or potentially better than their classical counterparts, with a further reduction of parameters compared to a purely classical classifier. Furthermore, a qualitative linguistic analysis of the results is presented, that elucidates two points: the lack of language dependence in the quantum models and the ability to discriminate with higher precision than standard classifiers, sub-types of linguistic structures.
Carere, Federico; Sardellitti, Alessandro; Sangiovanni, Silvia; Bernieri, Andrea; Laracca, Marco
Rotating Eddy Current Testing: a Probe Optimization Analysis to Improve Test Performance Proceedings Article
In: 2025 IEEE 12th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 774–779, IEEE, Naples, Italy, 2025, ISBN: 979-8-3315-0152-5.
Abstract | Links | BibTeX | Tags: Any orientation defects, Defect detection, Eddy current testing, Non-destructive testing, Probe optimization, Rotating eddy current
@inproceedings{carere_rotating_2025,
title = {Rotating Eddy Current Testing: a Probe Optimization Analysis to Improve Test Performance},
author = {Federico Carere and Alessandro Sardellitti and Silvia Sangiovanni and Andrea Bernieri and Marco Laracca},
url = {https://ieeexplore.ieee.org/document/11114503/},
doi = {10.1109/MetroAeroSpace64938.2025.11114503},
isbn = {979-8-3315-0152-5},
year = {2025},
date = {2025-06-01},
urldate = {2025-09-30},
booktitle = {2025 IEEE 12th International Workshop on Metrology for AeroSpace (MetroAeroSpace)},
pages = {774–779},
publisher = {IEEE},
address = {Naples, Italy},
abstract = {Rotating Eddy Current (REC) techniques have been widely adopted to improve defect detection capabilities in Non-Destructive Testing (NDT). However, the integration of ferromagnetic structures into REC-based probes remains an underexplored approach to further improve detection sensitivity. This paper presents a numerical analysis investigating the impact of different ferromagnetic structures (i.e. air-cored, ferromagnetic-shielded, ferromagnetic-cored and pot-cored) on the performance of REC probes. Finite element analysis (FEA) simulations were performed with COMSOL Multiphysics ®, evaluating each probe configuration in terms of reaction magnetic flux concentration and defect detectability. The study considered superficial and buried cracks in an aluminium alloy sample, analyzing variations in defect response, in terms of reaction magnetic flux, according to crack size and depth. The results indicate that the optimal probe design depends on defect location: while ferromagnetic-cored structures offer superior performance for surface cracks, pot-cored structures improve the detection of buried defects. These results highlight the potential of ferromagnetic structures adopted in REC methods, suggesting the need for further optimization adapted to the specific inspection scenario.},
keywords = {Any orientation defects, Defect detection, Eddy current testing, Non-destructive testing, Probe optimization, Rotating eddy current},
pubstate = {published},
tppubtype = {inproceedings}
}
Rotating Eddy Current (REC) techniques have been widely adopted to improve defect detection capabilities in Non-Destructive Testing (NDT). However, the integration of ferromagnetic structures into REC-based probes remains an underexplored approach to further improve detection sensitivity. This paper presents a numerical analysis investigating the impact of different ferromagnetic structures (i.e. air-cored, ferromagnetic-shielded, ferromagnetic-cored and pot-cored) on the performance of REC probes. Finite element analysis (FEA) simulations were performed with COMSOL Multiphysics ®, evaluating each probe configuration in terms of reaction magnetic flux concentration and defect detectability. The study considered superficial and buried cracks in an aluminium alloy sample, analyzing variations in defect response, in terms of reaction magnetic flux, according to crack size and depth. The results indicate that the optimal probe design depends on defect location: while ferromagnetic-cored structures offer superior performance for surface cracks, pot-cored structures improve the detection of buried defects. These results highlight the potential of ferromagnetic structures adopted in REC methods, suggesting the need for further optimization adapted to the specific inspection scenario.
De Luca, Valerio; Schena, Annamaria; Covino, Attilio; Di Bitonto, Pierpaolo; Potenza, Ada; Barba, Maria Cristina; D’Errico, Giovanni; De Paolis, Lucio Tommaso
Serious Games for the Treatment of Children with ADHD: The BRAVO Project Journal Article
In: Information Systems Frontiers, vol. 27, no. 3, pp. 841–863, 2025, ISSN: 1387-3326, 1572-9419.
Abstract | Links | BibTeX | Tags: ADHD, Extended reality, Gamification, Serious games
@article{deluca_serious_2025,
title = {Serious Games for the Treatment of Children with ADHD: The BRAVO Project},
author = {Valerio De Luca and Annamaria Schena and Attilio Covino and Pierpaolo Di Bitonto and Ada Potenza and Maria Cristina Barba and Giovanni D’Errico and Lucio Tommaso De Paolis},
url = {https://link.springer.com/10.1007/s10796-023-10457-8},
doi = {10.1007/s10796-023-10457-8},
issn = {1387-3326, 1572-9419},
year = {2025},
date = {2025-06-01},
urldate = {2025-09-30},
journal = {Information Systems Frontiers},
volume = {27},
number = {3},
pages = {841–863},
abstract = {Abstract
Children affected by attention-deficit hyperactivity disorder (ADHD) exhibit several symptoms characterized by inattention, impulsivity and motor hyperactivity that impair both school performance and everyday life. The BRAVO (Beyond the tReatment of the Attention deficit hyperactiVity disOrder) project dealt with the development of several serious games based on extended reality that help patients improve in self-control, respect for rules, attention and concentration. In order to achieve both logopaedic and behavioural educational goals, serious games were developed concerning three different categories:
Topological Categories
,
Infinite Runner
and
Planning
. Experimental tests conducted over a six-month period assessed the patients’ performance and the emotional impact of the games, also showing a general improvement in cognitive and behavioural functions.},
keywords = {ADHD, Extended reality, Gamification, Serious games},
pubstate = {published},
tppubtype = {article}
}
Abstract
Children affected by attention-deficit hyperactivity disorder (ADHD) exhibit several symptoms characterized by inattention, impulsivity and motor hyperactivity that impair both school performance and everyday life. The BRAVO (Beyond the tReatment of the Attention deficit hyperactiVity disOrder) project dealt with the development of several serious games based on extended reality that help patients improve in self-control, respect for rules, attention and concentration. In order to achieve both logopaedic and behavioural educational goals, serious games were developed concerning three different categories:
Topological Categories
,
Infinite Runner
and
Planning
. Experimental tests conducted over a six-month period assessed the patients’ performance and the emotional impact of the games, also showing a general improvement in cognitive and behavioural functions.
Children affected by attention-deficit hyperactivity disorder (ADHD) exhibit several symptoms characterized by inattention, impulsivity and motor hyperactivity that impair both school performance and everyday life. The BRAVO (Beyond the tReatment of the Attention deficit hyperactiVity disOrder) project dealt with the development of several serious games based on extended reality that help patients improve in self-control, respect for rules, attention and concentration. In order to achieve both logopaedic and behavioural educational goals, serious games were developed concerning three different categories:
Topological Categories
,
Infinite Runner
and
Planning
. Experimental tests conducted over a six-month period assessed the patients’ performance and the emotional impact of the games, also showing a general improvement in cognitive and behavioural functions.
Luca, Valerio De; Pascarelli, Claudio; Colucci, Mattia; Afrune, Paolo; Corallo, Angelo; Avanzini, Giulio
A Platform for Safe Operations of Unmanned Aircraft Systems in Critical Areas Journal Article
In: Engineering, vol. 49, pp. 314–331, 2025, ISSN: 20958099.
Links | BibTeX | Tags: Augmented Reality, Contingency Management, Situation awareness, UAS
@article{de_luca_platform_2025,
title = {A Platform for Safe Operations of Unmanned Aircraft Systems in Critical Areas},
author = {Valerio De Luca and Claudio Pascarelli and Mattia Colucci and Paolo Afrune and Angelo Corallo and Giulio Avanzini},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2095809925000980},
doi = {10.1016/j.eng.2025.02.004},
issn = {20958099},
year = {2025},
date = {2025-06-01},
urldate = {2025-09-30},
journal = {Engineering},
volume = {49},
pages = {314–331},
keywords = {Augmented Reality, Contingency Management, Situation awareness, UAS},
pubstate = {published},
tppubtype = {article}
}
Pirani, Massimiliano; Cucchiarelli, Alessandro; Naeem, Tariq; Spalazzi, Luca
Verifiable Actor Model Systems Through Relational-Model Multi-Agent System and Zero-Knowledge Proofs Proceedings Article
In: 2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 01–06, IEEE, Emden, Germany, 2025, ISBN: 9798331542993.
@inproceedings{pirani_verifiable_2025,
title = {Verifiable Actor Model Systems Through Relational-Model Multi-Agent System and Zero-Knowledge Proofs},
author = {Massimiliano Pirani and Alessandro Cucchiarelli and Tariq Naeem and Luca Spalazzi},
url = {https://ieeexplore.ieee.org/document/11087864/},
doi = {10.1109/ICPS65515.2025.11087864},
isbn = {9798331542993},
year = {2025},
date = {2025-05-01},
urldate = {2025-09-30},
booktitle = {2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS)},
pages = {01–06},
publisher = {IEEE},
address = {Emden, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ippolito, Adelaide; Barberà-Mariné, Maria Gloria; Zollo, Giuseppe; Cannavacciuolo, Lorella
How organisational factors and clinical decision support system affect nurses' knowledge for decisions in triage Journal Article
In: Knowledge Management Research & Practice, vol. 23, no. 3, pp. 249–262, 2025, ISSN: 1477-8238, 1477-8246.
Abstract | Links | BibTeX | Tags: Clinical Decision Support Systems, Cognitive heuristic, Organizational factors, Triage
@article{ippolitoHowOrganisationalFactors2024,
title = {How organisational factors and clinical decision support system affect nurses' knowledge for decisions in triage},
author = {Adelaide Ippolito and Maria Gloria Barberà-Mariné and Giuseppe Zollo and Lorella Cannavacciuolo},
url = {https://www.tandfonline.com/doi/full/10.1080/14778238.2024.2377973},
doi = {10.1080/14778238.2024.2377973},
issn = {1477-8238, 1477-8246},
year = {2025},
date = {2025-05-01},
urldate = {2024-07-16},
journal = {Knowledge Management Research & Practice},
volume = {23},
number = {3},
pages = {249–262},
abstract = {This paper delves into heuristic decision-making by nurses during the triage process, aiming to elucidate how organisational factors influence nurses' decision-making regarding the assignment of priority codes to patients, and to assess the effectiveness of Clinical Decision Support Systems (CDSS) in this context. Drawing on an experimental dataset of 25 triage cases evaluated by 35 nurses via interviews, the study was conducted in two Spanish Emergency Departments using CDSS. Findings indicate that organisational factors predominantly influence decisions in cases with complete and coherent information. However, in cases where information is incoherent or missing, individual nurse characteristics guide decision-making. Furthermore, it suggests that CDSS should be tailored to nurses' clinical reasoning to serve as effective support for individual decision-making processes.},
keywords = {Clinical Decision Support Systems, Cognitive heuristic, Organizational factors, Triage},
pubstate = {published},
tppubtype = {article}
}
This paper delves into heuristic decision-making by nurses during the triage process, aiming to elucidate how organisational factors influence nurses' decision-making regarding the assignment of priority codes to patients, and to assess the effectiveness of Clinical Decision Support Systems (CDSS) in this context. Drawing on an experimental dataset of 25 triage cases evaluated by 35 nurses via interviews, the study was conducted in two Spanish Emergency Departments using CDSS. Findings indicate that organisational factors predominantly influence decisions in cases with complete and coherent information. However, in cases where information is incoherent or missing, individual nurse characteristics guide decision-making. Furthermore, it suggests that CDSS should be tailored to nurses' clinical reasoning to serve as effective support for individual decision-making processes.
Carere, Federico; Sardellitti, Alessandro; Sangiovanni, Silvia; Bernieri, Andrea; Laracca, Marco
An improvement in defect detection through integration of Rotating Eddy Currents and multi-tone excitation Proceedings Article
In: 2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6, IEEE, Chemnitz, Germany, 2025, ISBN: 979-8-3315-0500-4.
Abstract | Links | BibTeX | Tags: Any orientation defects, Buried cracks, Defect detection, Eddy current testing, Multi-frequency signals, Non-destructive testing, Rotating eddy current, Signal Processing
@inproceedings{carere_improvement_2025,
title = {An improvement in defect detection through integration of Rotating Eddy Currents and multi-tone excitation},
author = {Federico Carere and Alessandro Sardellitti and Silvia Sangiovanni and Andrea Bernieri and Marco Laracca},
url = {https://ieeexplore.ieee.org/document/11079123/},
doi = {10.1109/I2MTC62753.2025.11079123},
isbn = {979-8-3315-0500-4},
year = {2025},
date = {2025-05-01},
urldate = {2025-09-30},
booktitle = {2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)},
pages = {1–6},
publisher = {IEEE},
address = {Chemnitz, Germany},
abstract = {In this paper, a novel improvement of the Rotating Eddy Current (REC) technique is proposed, integrating multi-tone excitation signals to enhance defect detection in conductive materials. The traditional REC method, which is effective in detecting defects with high spatial resolution, is enhanced by the use of multi-frequency signals to reduce measurement time and provide additional information on defect depth. The proposed method has been tested on an aluminum sample with different defect characteristics, including both superficial and buried cracks. Experimental results demonstrated the effectiveness, in terms of probe sensitivity to the defects, of the novel approach for the different case studies analyzed. The proposed technique provides a comprehensive solution for Non-Destructive Testing (NDT), balancing defect sensitivity, measurement time and defect depth characterization.},
keywords = {Any orientation defects, Buried cracks, Defect detection, Eddy current testing, Multi-frequency signals, Non-destructive testing, Rotating eddy current, Signal Processing},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, a novel improvement of the Rotating Eddy Current (REC) technique is proposed, integrating multi-tone excitation signals to enhance defect detection in conductive materials. The traditional REC method, which is effective in detecting defects with high spatial resolution, is enhanced by the use of multi-frequency signals to reduce measurement time and provide additional information on defect depth. The proposed method has been tested on an aluminum sample with different defect characteristics, including both superficial and buried cracks. Experimental results demonstrated the effectiveness, in terms of probe sensitivity to the defects, of the novel approach for the different case studies analyzed. The proposed technique provides a comprehensive solution for Non-Destructive Testing (NDT), balancing defect sensitivity, measurement time and defect depth characterization.
Pirani, Massimiliano; Cucchiarelli, Alessandro; Naeem, Tariq; Spalazzi, Luca
A Blockchain-Driven Cyber-Systemic Approach to Hybrid Reality Journal Article
In: Systems, vol. 13, no. 4, pp. 294, 2025, ISSN: 2079-8954.
Abstract | Links | BibTeX | Tags:
@article{pirani_blockchain-driven_2025,
title = {A Blockchain-Driven Cyber-Systemic Approach to Hybrid Reality},
author = {Massimiliano Pirani and Alessandro Cucchiarelli and Tariq Naeem and Luca Spalazzi},
url = {https://www.mdpi.com/2079-8954/13/4/294},
doi = {10.3390/systems13040294},
issn = {2079-8954},
year = {2025},
date = {2025-04-01},
urldate = {2025-09-30},
journal = {Systems},
volume = {13},
number = {4},
pages = {294},
abstract = {Hybrid Reality (HyR) is the place where human beings and artificial entities interact. HyR modelling relies simultaneously on the cognitive power of humans and artificial entities. In addition, HyR is an evolving paradigm where natural and artificial intelligence can intervene in processes that demand proper control. This work aims to lay the foundation for a systematic approach to understanding and modeling present and future human–machine symbiosis under a systems engineering perspective. It introduces a novel cyber-systemic methodology for managing the engineering of purposeful regulation for HyR phenomena by integrating the Blockchain technology framework and principled methods of cybernetics. This formalized interdisciplinary methodology integrates system dynamics, agent-based computation, artificial intelligence, and Blockchain-powered security and safety layers. The Blockchain framework, seen under a new cyber-systemic perspective, provides new opportunities and tools for the organization and control of HyR. A Cybersystemic Security Kit is here defined as a major component of the methodology, representing a candidate to offer viable breakthroughs in the field with respect to the best practices of Industry 5.0 when a systemically augmented perspective is adopted. Ongoing research and experimentation in the real field of sustainable supply chains is used as a motivating use case to support the proposed position. The industrial target is the primary one in its multi-dimensional and multi-faceted sustainability impacts, but this study will also reveal other potential societal areas of intervention.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hybrid Reality (HyR) is the place where human beings and artificial entities interact. HyR modelling relies simultaneously on the cognitive power of humans and artificial entities. In addition, HyR is an evolving paradigm where natural and artificial intelligence can intervene in processes that demand proper control. This work aims to lay the foundation for a systematic approach to understanding and modeling present and future human–machine symbiosis under a systems engineering perspective. It introduces a novel cyber-systemic methodology for managing the engineering of purposeful regulation for HyR phenomena by integrating the Blockchain technology framework and principled methods of cybernetics. This formalized interdisciplinary methodology integrates system dynamics, agent-based computation, artificial intelligence, and Blockchain-powered security and safety layers. The Blockchain framework, seen under a new cyber-systemic perspective, provides new opportunities and tools for the organization and control of HyR. A Cybersystemic Security Kit is here defined as a major component of the methodology, representing a candidate to offer viable breakthroughs in the field with respect to the best practices of Industry 5.0 when a systemically augmented perspective is adopted. Ongoing research and experimentation in the real field of sustainable supply chains is used as a motivating use case to support the proposed position. The industrial target is the primary one in its multi-dimensional and multi-faceted sustainability impacts, but this study will also reveal other potential societal areas of intervention.
Moreschini, Sergio; Pour, Shahrzad; Lanese, Ivan; Balouek, Daniel; Bogner, Justus; Li, Xiaozhou; Pecorelli, Fabiano; Soldani, Jacopo; Truyen, Eddy; Taibi, Davide
AI Techniques in the Microservices Life-Cycle: a Systematic Mapping Study Journal Article
In: Computing, vol. 107, no. 4, pp. 100, 2025, ISSN: 0010-485X, 1436-5057.
Abstract | Links | BibTeX | Tags:
@article{moreschini_ai_2025,
title = {AI Techniques in the Microservices Life-Cycle: a Systematic Mapping Study},
author = {Sergio Moreschini and Shahrzad Pour and Ivan Lanese and Daniel Balouek and Justus Bogner and Xiaozhou Li and Fabiano Pecorelli and Jacopo Soldani and Eddy Truyen and Davide Taibi},
url = {https://link.springer.com/10.1007/s00607-025-01432-z},
doi = {10.1007/s00607-025-01432-z},
issn = {0010-485X, 1436-5057},
year = {2025},
date = {2025-04-01},
urldate = {2025-09-30},
journal = {Computing},
volume = {107},
number = {4},
pages = {100},
abstract = {Abstract
The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases. Our results include 16 research themes that connect to the intersection of particular QAs, AI domains and DevOps phases. Moreover by mapping identified future research challenges and relevant industry domains, we can show that many studies aim to deliver prototypes to be automated at a later stage, aiming at providing exploitable products in a number of key industry domains.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases. Our results include 16 research themes that connect to the intersection of particular QAs, AI domains and DevOps phases. Moreover by mapping identified future research challenges and relevant industry domains, we can show that many studies aim to deliver prototypes to be automated at a later stage, aiming at providing exploitable products in a number of key industry domains.
The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases. Our results include 16 research themes that connect to the intersection of particular QAs, AI domains and DevOps phases. Moreover by mapping identified future research challenges and relevant industry domains, we can show that many studies aim to deliver prototypes to be automated at a later stage, aiming at providing exploitable products in a number of key industry domains.
Vergallo, Roberto; Cruz, Luís; Errico, Alessio; Mainetti, Luca
On the Effectiveness of the 'Follow-the-Sun' Strategy in Mitigating the Carbon Footprint of AI in Cloud Instances Miscellaneous
2025, (arXiv:2506.10990 [cs]).
Abstract | Links | BibTeX | Tags: and Cluster Computing, and Science, Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Computer Science - Distributed, Computer Science - Software Engineering, Finance, Parallel
@misc{vergallo_effectiveness_2025,
title = {On the Effectiveness of the 'Follow-the-Sun' Strategy in Mitigating the Carbon Footprint of AI in Cloud Instances},
author = {Roberto Vergallo and Luís Cruz and Alessio Errico and Luca Mainetti},
url = {http://arxiv.org/abs/2506.10990},
doi = {10.48550/arXiv.2506.10990},
year = {2025},
date = {2025-03-01},
urldate = {2025-09-30},
publisher = {arXiv},
abstract = {'Follow-the-Sun' (FtS) is a theoretical computational model aimed at minimizing the carbon footprint of computer workloads. It involves dynamically moving workloads to regions with cleaner energy sources as demand increases and energy production relies more on fossil fuels. With the significant power consumption of Artificial Intelligence (AI) being a subject of extensive debate, FtS is proposed as a strategy to mitigate the carbon footprint of training AI models. However, the literature lacks scientific evidence on the advantages of FtS to mitigate the carbon footprint of AI workloads. In this paper, we present the results of an experiment conducted in a partial synthetic scenario to address this research gap. We benchmarked four AI algorithms in the anomaly detection domain and measured the differences in carbon emissions in four cases: no strategy, FtS, and two strategies previously introduced in the state of the art, namely Flexible Start and Pause and Resume. To conduct our experiment, we utilized historical carbon intensity data from the year 2021 for seven European cities. Our results demonstrate that the FtS strategy not only achieves average reductions of up to 14.6% in carbon emissions (with peaks of 16.3%) but also helps in preserving the time needed for training.},
note = {arXiv:2506.10990 [cs]},
keywords = {and Cluster Computing, and Science, Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Computer Science - Distributed, Computer Science - Software Engineering, Finance, Parallel},
pubstate = {published},
tppubtype = {misc}
}
'Follow-the-Sun' (FtS) is a theoretical computational model aimed at minimizing the carbon footprint of computer workloads. It involves dynamically moving workloads to regions with cleaner energy sources as demand increases and energy production relies more on fossil fuels. With the significant power consumption of Artificial Intelligence (AI) being a subject of extensive debate, FtS is proposed as a strategy to mitigate the carbon footprint of training AI models. However, the literature lacks scientific evidence on the advantages of FtS to mitigate the carbon footprint of AI workloads. In this paper, we present the results of an experiment conducted in a partial synthetic scenario to address this research gap. We benchmarked four AI algorithms in the anomaly detection domain and measured the differences in carbon emissions in four cases: no strategy, FtS, and two strategies previously introduced in the state of the art, namely Flexible Start and Pause and Resume. To conduct our experiment, we utilized historical carbon intensity data from the year 2021 for seven European cities. Our results demonstrate that the FtS strategy not only achieves average reductions of up to 14.6% in carbon emissions (with peaks of 16.3%) but also helps in preserving the time needed for training.
Gatto, Carola; Barba, Maria Cristina; Chiarello, Sofia; Corchia, Laura; Faggiano, Federica; Nuzzo, Benito Luigi; Sumerano, Giada; Luca, Valerio De; Paolis, Lucio Tommaso De
Breaking the barriers: Extended reality and innovative technologies for enhanced accessibility of the Ceramics Museum of Cutrofiano Journal Article
In: Digital Applications in Archaeology and Cultural Heritage, vol. 36, pp. e00400, 2025, ISSN: 22120548.
Links | BibTeX | Tags: Cultural Heritage, Extended reality, Human Computer Interaction, User experience
@article{gatto_breaking_2025,
title = {Breaking the barriers: Extended reality and innovative technologies for enhanced accessibility of the Ceramics Museum of Cutrofiano},
author = {Carola Gatto and Maria Cristina Barba and Sofia Chiarello and Laura Corchia and Federica Faggiano and Benito Luigi Nuzzo and Giada Sumerano and Valerio De Luca and Lucio Tommaso De Paolis},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2212054825000025},
doi = {10.1016/j.daach.2025.e00400},
issn = {22120548},
year = {2025},
date = {2025-03-01},
urldate = {2025-09-30},
journal = {Digital Applications in Archaeology and Cultural Heritage},
volume = {36},
pages = {e00400},
keywords = {Cultural Heritage, Extended reality, Human Computer Interaction, User experience},
pubstate = {published},
tppubtype = {article}
}
Intini, Paolo; Blasi, Gianni; Fracella, Francesco; Francone, Antonio; Vergallo, Roberto; Perrone, Daniele
Predicting traffic volumes on road infrastructures in the context of multi-risk assessment frameworks Journal Article
In: International Journal of Disaster Risk Reduction, vol. 117, pp. 105139, 2025, ISSN: 22124209.
Abstract | Links | BibTeX | Tags:
@article{intini_predicting_2025,
title = {Predicting traffic volumes on road infrastructures in the context of multi-risk assessment frameworks},
author = {Paolo Intini and Gianni Blasi and Francesco Fracella and Antonio Francone and Roberto Vergallo and Daniele Perrone},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2212420924009014},
doi = {10.1016/j.ijdrr.2024.105139},
issn = {22124209},
year = {2025},
date = {2025-02-01},
urldate = {2025-01-20},
journal = {International Journal of Disaster Risk Reduction},
volume = {117},
pages = {105139},
abstract = {In multi-risk assessment frameworks involving road infrastructures, measures of exposure to natural hazards include traffic volumes. However, traffic counts are usually collected through traffic counter/radar stations which only cover a small part of the road network. In this study, country-wide Annual Average Daily Traffic (AADT) prediction models based on Italian data were developed to provide direct risk exposure measures both in terms of traffic volumes (continuous variable) and traffic volume discrete classes, using province-/municipality-related geographic, socio-economic and road-related variables as predictors. To ease transferability and applicability of the models, only publicly available predictors were selected. Traditional statistical techniques (generalized linear models for predicting traffic values and ordered logistic models for traffic classes) and Machine Learning (ML) approaches (XGBoost for both regression and classification problems) were used. Both the direct estimation of traffic volumes and the classification into traffic ranges provided satisfactory results in terms of goodness-of-fit and predictive accuracy metrics. Results show that population, occupation, tourism, density, number of lanes, urban environment, complex intersections and ring roads were generally related to an increase in traffic volumes. Distance from large cities and accessibility metrics are inversely related to traffic instead. The application of the XGBoost ML approach proved to be more accurate than traditional approaches only for heavy vehicles. It was discussed how the obtained models can be used as input modules for overall multi-risk assessment frameworks involving road infrastructures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In multi-risk assessment frameworks involving road infrastructures, measures of exposure to natural hazards include traffic volumes. However, traffic counts are usually collected through traffic counter/radar stations which only cover a small part of the road network. In this study, country-wide Annual Average Daily Traffic (AADT) prediction models based on Italian data were developed to provide direct risk exposure measures both in terms of traffic volumes (continuous variable) and traffic volume discrete classes, using province-/municipality-related geographic, socio-economic and road-related variables as predictors. To ease transferability and applicability of the models, only publicly available predictors were selected. Traditional statistical techniques (generalized linear models for predicting traffic values and ordered logistic models for traffic classes) and Machine Learning (ML) approaches (XGBoost for both regression and classification problems) were used. Both the direct estimation of traffic volumes and the classification into traffic ranges provided satisfactory results in terms of goodness-of-fit and predictive accuracy metrics. Results show that population, occupation, tourism, density, number of lanes, urban environment, complex intersections and ring roads were generally related to an increase in traffic volumes. Distance from large cities and accessibility metrics are inversely related to traffic instead. The application of the XGBoost ML approach proved to be more accurate than traditional approaches only for heavy vehicles. It was discussed how the obtained models can be used as input modules for overall multi-risk assessment frameworks involving road infrastructures.