Publications​

@article{mondal_concept_2026,

title = {Concept and rule guided neural network for early crop leaf nutrient deficiency diagnosis},

author = {Semanto Mondal and Antonino Ferraro and Fabiano Pecorelli and Martina Iammarino and Giuseppe De Pietro},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0168169926003303},

doi = {10.1016/j.compag.2026.111735},

issn = {01681699},

year  = {2026},

date = {2026-07-01},

urldate = {2026-06-16},

journal = {Computers and Electronics in Agriculture},

volume = {248},

pages = {111735},

abstract = {Crop nutrition deficiency poses a major challenge to achieving optimal yield, particularly in smallholder farming systems where timely expert diagnosis is limited. Early detection is crucial to minimize losses and reduce unnecessary fertilizer or pesticide usage. While deep learning offers potential for automated visual diagnosis, most existing approaches operate as black boxes and lack interpretability, explainability, or actionable recommendations. In this work, we present a neurosymbolic framework for early nutrient deficiency detection in ash gourd leaves using the EarlyNSD dataset. Our approach integrates a ResNet-50 backbone with a dual-head design: a classification head for deficiency prediction and a concept-prediction head that quantifies physiologically meaningful visual patterns such as yellowing, edge discoloration, spots, and vein greenness. These concept scores are combined with predefined domain rules to guide the learning of the neural component and to generate transparent, human-aligned explanations for each diagnosis. Building on the model outputs, we incorporate a Retrieval Augmented Generation (RAG)-based pipeline along with an agricultural knowledge base to generate targeted recommendations. This approach overcomes key shortcomings of pure neural models by incorporating domain knowledge in the form of differentiable fuzzy logic rules. The study demonstrates that the proposed framework improves both classification performance and interpretability compared to standard ResNet baselines. Grad-CAM analysis demonstrates that concept-guided attention aligns with symptom-specific regions, such as yellowed areas for Nitrogen deficiency or marginal discoloration for Potassium deficiency, providing visual validation of the reasoning process. Since EarlyNSD is limited in scale and visual diversity, the results are not directly comparable to large open-field datasets. Overall, our results establish a proof of concept for integrating neural detection with symbolic reasoning, enabling interpretable, actionable, and domain-informed nutrient management for practical applications.},

keywords = {Artificial Intelligence, Deep Learning, Neurosymbolic},

pubstate = {published},

tppubtype = {article}

}

@article{pappalardo_analyzing_2026,

title = {Analyzing the impact of functional approximation on the resilience of Deep Neural Networks},

author = {Salvatore Pappalardo and Salvatore Barone and Bastien Deveautour and Annachiara Ruospo and Ernesto Sanchez and Marcello Traiola and Alberto Bosio},

url = {https://www.sciencedirect.com/science/article/pii/S0141933126000165},

doi = {10.1016/j.micpro.2026.105259},

issn = {0141-9331},

year  = {2026},

date = {2026-06-01},

urldate = {2026-03-06},

journal = {Microprocessors and Microsystems},

volume = {122},

pages = {105259},

abstract = {This paper investigates the use of Approximate Computing (AxC), specifically functional approximation, to enhance the resilience of Deep Neural Networks (DNNs) against hardware faults in various applications, including safety-critical systems such as autonomous vehicles. As deploying DNNs requires balancing performance, energy efficiency, and reliability, traditional methods often achieve reliability through redundancy, which can increase area, power consumption, and latency. Our work shows preliminary results that leveraging approximate multipliers can, under some conditions, lead to energy reductions without compromising DNN resilience, under the right conditions. We evaluate the impact of approximation on DNN performance and robustness, exploring the interplay between energy efficiency and fault tolerance. Through comprehensive benchmarking, we highlight the potential of AxC to enable more efficient and reliable DNN implementations, paving the way for advanced applications in real-time and edge computing environments. Results obtained on four different DNNs show that it is possible to achieve up to a 3× reduction in power consumption without any negative impact on resilience.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nenna_impact_2026,

title = {Impact of digital technologies on pediatric asthma care},

author = {Raffaella Nenna and Sara Manti and Giuliana Ferrante and Velia Malizia and Pietro Alfano and Giuseppe Fabio Parisi and Domenico Paolo La Regina and Luigi Gallo and Alessandra Pandolfo and Amelia Licari and Stefania La Grutta},

url = {https://www.pediatric-respiratory-journal.com/impact-of-digital-technologies-on-pediatric-asthma-care/},

doi = {10.56164/PediatrRespirJ.2026.01},

issn = {3035-2134},

year  = {2026},

date = {2026-04-01},

urldate = {2026-04-03},

journal = {Pediatric Respiratory Journal},

volume = {04},

number = {01},

pages = {2–15},

publisher = {Edra Media S.r.l.},

abstract = {Asthma is one of the most common chronic diseases in children, significantly impacting their health, quality of life, and healthcare systems globally. Pediatric asthma accounts for substantial morbidity, including frequent exacerbations, emergency department visits, and missed school days. Despite the availability of effective treatments and clear management guidelines, achieving optimal asthma control remains a challenge. In recent years, digital technologies have emerged as transformative tools in asthma care, offering new ways to monitor, educate, and treat pediatric patients. A systematic review was conducted to examine the impact of digital technologies on pediatric asthma care, synthesizing evidence on their effectiveness, challenges, and future directions. Covering studies from January 2020 to December 2024, the review analyzed 59 primary studies that involved mobile health (mHealth) applications, electronic medication monitoring systems, wearable devices, artificial intelligence (AI)-powered solutions, and school-based telemedicine programs. Findings reveal that mHealth applications and serious games promote self-management, improve medication adherence, and support patient education. Telemedicine, including school-based and remote patient monitoring, enhances care accessibility, reduces emergency visits, and promotes continuity of care, particularly in underserved populations. Wearable devices and electronic monitoring tools enhance symptom tracking and evaluation of inhaler technique. AI-driven interventions, such as digital twin systems, show promise in personalizing treatment and predicting exacerbations. Despite encouraging outcomes, challenges remain, including digital literacy gaps, limited access to devices and the internet, and difficulties integrating digital tools into clinical workflows. Usability and sustainability vary widely depending on design approaches, caregiver engagement, and infrastructure readiness.},

keywords = {Artificial Intelligence, Asthma, Healthcare, Mobile Application, Serious games},

pubstate = {published},

tppubtype = {article}

}

@article{gallo_editorial_2026,

title = {Editorial: AI innovations in education: adaptive learning and beyond},

author = {Luigi Gallo and Maria Concetta Carruba and Antonino Ferraro and Henrik Hautop Lund and Angelo Rega and Stefano Triberti},

url = {https://www.frontiersin.org/articles/10.3389/fcomp.2026.1822456/full},

doi = {10.3389/fcomp.2026.1822456},

issn = {2624-9898},

year  = {2026},

date = {2026-03-01},

urldate = {2026-04-03},

journal = {Frontiers in Computer Science},

volume = {8},

pages = {1822456},

publisher = {Frontiers Media SA},

abstract = {Artificial Intelligence (AI) is gradually transforming educational practices. AI-powered teaching assistants, large language models, and multimodal analytics platforms are reshaping how learning experiences are designed and assessed. However, AI integration is not merely a technological matter: it is also heavily influenced by pedagogical, psychological, and sociocultural factors. Beyond technical implementation, AI systems can be framed within a human augmentation perspective, where technologies enhance sensory, motor, and cognitive processes in hybrid environments (Augello et al., 2022), including immersive contexts in which presence and cognition dynamically interact (Palombi et al., 2023). At the same time, advances in adaptive and data-driven AI, including explainable and diversity-aware approaches, highlight the role of algorithmic choices in shaping users' experiences and behaviors (Ferraro et al., 2025). Recent studies further show that, while educators are using AI tools for instructional purposes (e.g., creating teaching materials), concerns remain about the risk of unfair AI use and the difficulty of detecting it (Amato et al., 2023; Carruba et al., 2025). Accordingly, research on AI in education is becoming increasingly focused on factors that support implementation and adoption in real-life contexts, beyond mere improvement of algorithms from a computer science perspective (Triberti et al., 2024; Acosta-Enriquez et al., 2025; Galindo-Domĺnguez et al., 2024). 

Against this backdrop, this Research Topic (RT), which spans four Frontiers journals, focuses on empirical and theoretical aspects of personalized and adaptive learning. More specifically, it examines the role of AI in fostering inclusive, data-driven, and emotionally responsive educational ecosystems, with particular attention to motivation, beliefs, creativity, self-regulation, and ethics. 

For analytical clarity, the contributions are organized into six interrelated thematic areas: AI Adoption, Acceptance, and Self-Regulation; Teacher AI Literacy and Sustainable Integration; Adaptive, Immersive, and AI-Enhanced Learning Environments; Multimodal Analytics, Assessment, and Predictive AI; Learner Psychological, Cognitive, and Sociocultural Factors; Conceptual, Ethical, and Human-AI Synergy Perspectives. These areas reflect three broader dimensions of current research: the adoption of AI by learners and teachers, the design of intelligent learning environments, and the broader psychological and ethical implications of AI-supported education.},

keywords = {Artificial Intelligence, Education},

pubstate = {published},

tppubtype = {article}

}

@article{augello_expertbased_2026,

title = {Expert‐Based Usability Evaluation of an mHealth Application for Older Adults: A Mixed‐Method Approach},

author = {Agnese Augello and Giuseppe Caggianese and Sotiria Antaranian and George Zissis and Giuseppe De Pietro and Luigi Gallo},

editor = {Puspa Setia Pratiwi},

url = {https://onlinelibrary.wiley.com/doi/10.1155/hbe2/6622167},

doi = {10.1155/hbe2/6622167},

issn = {2578-1863, 2578-1863},

year  = {2026},

date = {2026-01-01},

urldate = {2026-05-14},

journal = {Human Behavior and Emerging Technologies},

volume = {2026},

number = {1},

pages = {6622167},

publisher = {Wiley Online Library},

abstract = {Demographic aging presents new challenges and highlights the growing need for smart healthcare applications tailored to older adults. This paper presents the assessment of a mobile health application designed to facilitate the communication between healthcare providers and older adults, in the context of the Smart Bear Horizon 2020 project. The study describes a task‐oriented usability assessment, applying a mixed‐method approach through the combination of quantitative metrics of performance, standardized usability assessment questionnaires (ASQ and PSSUQ), and interaction logs, along with qualitative user comments. For this purpose, 50 ICT specialists in digital health were engaged who conducted a structured, expert‐based diagnostic evaluation aimed at identifying usability barriers relevant to older adults prior to end‐user testing. Usability was evaluated in terms of effectiveness, efficiency, and satisfaction, following the ISO 9241‐11: 2018 standard. Results show an overall task completion rate of 83.5% and a PSSUQ score of 3.16 on a 7‐point scale (lower scores indicating higher perceived usability). The findings reveal some critical usability issues, particularly in navigation efficiency and discoverability of specific system features. The study underlines the importance of an expert‐based, mixed‐methods usability evaluation as an initial step in the design of mHealth applications for older adults. This process is crucial in identifying and resolving key usability challenges before engaging vulnerable user populations. The proposed method provides actionable guidance for developing and evaluating more inclusive digital health technologies, supporting expert‐first usability testing as a best practice in the eHealth domain.},

keywords = {Elderly, Human Computer Interaction, Mobile Application, Usability},

pubstate = {published},

tppubtype = {article}

}

@article{ippolito_how_2026,

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/doi/10.1108/QRAM-11-2024-0255/1335160/How-the-interaction-between-technological},

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

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

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-17},

journal = {Qualitative Research in Accounting & Management},

pages = {1–24},

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 = {Chronic care systems, Management control, Performance accountability, Public health organisation, Technology innovations},

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{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{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 = {},

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}

}