Publications​

@article{ippolitoHowInteractionTechnological2026,

title = {How the interaction between technological innovations and management control affects health performance accountability},

author = {Adelaide Ippolito and Raffaella Montera and Carmela Di Guida and Francesca Landolfi and Marco Sorrentino},

url = {https://www.emerald.com/qram/article-abstract/23/3/346/1335160/How-the-interaction-between-technological?redirectedFrom=fulltext},

doi = {10.1108/qram-11-2024-0255},

issn = {1176-6093, 1758-7654},

year  = {2026},

date = {2026-01-01},

journal = {Qualitative Research in Accounting & Management},

volume = {23},

number = {3},

pages = {246–269},

abstract = {Purpose – The purpose of this paper is to analyse how the effective interaction between management control and innovative information technologies gives rise to effective accountability of performance in the public sector, with particular reference to a public health organization. Design/methodology/approach – This paper applied a retrospective longitudinal case study method for understanding how performance accountability is influenced by interaction between management control and innovative information technology. The research considered the Chronic Care system of the Local Health Authority (LHA) of Caserta (Italy). Findings – The retrospective longitudinal case study highlights how the effective interaction between management control and innovative information technologies allows an effective accountability of performance in the LHA Caserta analysed, although such technological innovations derive from a package of management control systems that has been stratified over time. Research limitations/implications – The limitation of this paper is that only one case study is analysed, albeit in depth, while it would be interesting to consider more public health organizations. Originality/value – This research contributes to the literature confirming that, although management control flows are the result of a management control package that has been formed over time, it is possible to promote a profitable integration between management control and IT innovations for fostering an effective performance accountability.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{barbareschi_reliability_2026,

title = {Reliability analysis of hardware accelerators for decision tree-based classifier systems},

author = {Mario Barbareschi and Salvatore Barone and Alberto Bosio and Antonio Emmanuele},

url = {https://www.sciencedirect.com/science/article/pii/S0167739X26000129},

doi = {10.1016/j.future.2026.108378},

issn = {0167-739X},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-21},

journal = {Future Generation Computer Systems},

pages = {108378},

abstract = {The increasing adoption of AI models has driven applications toward the use of hardware accelerators to meet high computational demands and strict performance requirements. Beyond consideration of performance and energy efficiency, explainability and reliability have emerged as pivotal requirements, particularly for critical applications such as automotive, medical, and aerospace systems. Among the various AI models, Decision Tree Ensembles (DTEs) are particularly notable for their high accuracy and explainability. Moreover, they are particularly well-suited for hardware implementations, enabling high-performance and improved energy efficiency. However, a frequently overlooked aspect of DTEs is their reliability in the presence of hardware malfunctions. While DTEs are generally regarded as robust by design, due to their redundancy and voting mechanisms, hardware faults can still have catastrophic consequences. To address this gap, we present an in-depth reliability analysis of two types of DTE hardware accelerators: classical and approximate implementations. Specifically, we conduct a comprehensive fault injection campaign, varying the number of trees involved in the classification task, the approximation technique used, and the tolerated accuracy loss, while evaluating several benchmark datasets. The results of this study demonstrate that approximation techniques have to be carefully designed, as they can significantly impact resilience. However, techniques that target the representation of features and thresholds appear to be better suited for fault tolerance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{agostinelliGeometryEnoughEvaluation2026,

title = {Is Geometry Enough? An Evaluation of Landmark-Based Gaze Estimation},

author = {Daniele Agostinelli and Thomas Agostinelli and Andrea Generosi and Maura Mengoni},

url = {https://ieeexplore.ieee.org/document/11535044/},

doi = {10.1109/ACCESS.2026.3696778},

issn = {2169-3536},

year  = {2026},

date = {2026-01-01},

urldate = {2026-06-17},

journal = {IEEE Access},

volume = {14},

pages = {87241–87251},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{agostinelliNovelApproachMonocular2026,

title = {A novel approach for monocular RGB-based ergonomics monitoring in industrial workspaces employing synthetic datasets to train a deep learning model},

author = {Thomas Agostinelli and Andrea Generosi and Maura Mengoni},

url = {https://link.springer.com/10.1007/s00170-025-17168-1},

doi = {10.1007/s00170-025-17168-1},

issn = {0268-3768, 1433-3015},

year  = {2026},

date = {2026-01-01},

urldate = {2026-06-17},

journal = {The International Journal of Advanced Manufacturing Technology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aversanoTimeSeriesForecasting2025,

title = {Time series forecasting for bug resolution using machine learning and deep learning models},

author = {Lerina Aversano and Martina Iammarino and Antonella Madau and Fabiano Pecorelli},

url = {https://www.frontiersin.org/articles/10.3389/fdata.2025.1745751/full},

doi = {10.3389/fdata.2025.1745751},

issn = {2624-909X},

year  = {2025},

date = {2025-12-01},

urldate = {2026-06-22},

journal = {Frontiers in Big Data},

volume = {8},

pages = {1745751},

abstract = {Predicting bug fix times is a key objective for improving software maintenance and supporting planning in open source projects. In this study, we evaluate the effectiveness of different time series forecasting models applied to real-world data from multiple repositories, comparing local (one model per project) and global (a single model trained across multiple projects) approaches. We considered classical models (Naive, Linear Regression, Random Forest) and neural networks (MLP, LSTM, GRU), with global extensions including Random Forest and LSTM with project embeddings. The results highlight that, at the local level, Random Forest achieves lower errors and better classification metrics than deep learning models in several cases. However, global models show greater robustness and generalizability: in particular, the global Random Forest significantly reduces the mean error and maintains high performance in terms of accuracy and F1 score, while the global LSTM captures temporal dependencies and provides additional insights into cross-project dynamics. The explainable AI techniques adopted (permutation importance, saliency maps, and embedding analysis) allow us to interpret the main drivers of forecasts, confirming the role of process variables and temporal characteristics. Overall, the study demonstrates that an integrated approach, combining classical models and deep learning in a global perspective, offers more reliable and interpretable forecasts to support software maintenance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mondal_logic_2025-1,

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 = {2025-11-06},

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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mondalLogicTensorNetworkBased2025,

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 = {2026-06-22},

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.},

keywords = {Explainability, first-order logic, logic tensor network (LTN), neurosymbolic AI, symbolic reasoning},

pubstate = {published},

tppubtype = {article}

}

@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.},

keywords = {Explainability, first-order logic, logic tensor network (LTN), neurosymbolic AI, symbolic reasoning},

pubstate = {published},

tppubtype = {article}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {Artificial Intelligence, Deep Learning, Deep Neural Network, Parkinson Disease},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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},

pages = {110550},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}