Publications​

@article{pontilloMachineLearningbasedTest2024,

title = {Machine learning-based test smell detection},

author = {Valeria Pontillo and Dario Amoroso d'Aragona and Fabiano Pecorelli and Dario Di Nucci and Filomena Ferrucci and Fabio Palomba},

url = {https://link.springer.com/10.1007/s10664-023-10436-2},

doi = {10.1007/s10664-023-10436-2},

issn = {1382-3256, 1573-7616},

year  = {2024},

date = {2024-03-01},

urldate = {2024-07-07},

journal = {Empirical Software Engineering},

volume = {29},

number = {2},

pages = {55},

abstract = {Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and dependent on tunable thresholds. We design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we compare the ML-based approach with state-of-the-art heuristic-based techniques. The key findings of the study report a negative result. The performance of the machine learning-based detector is significantly better than heuristic-based techniques, but none of the learners able to overcome an average F-Measure of 51%. We further elaborate and discuss the reasons behind this negative result through a qualitative investigation into the current issues and challenges that prevent the appropriate detection of test smells, which allowed us to catalog the next steps that the research community may pursue to improve test smell detection techniques.},

keywords = {Artificial Intelligence, Code Smell Detection, Software Engineering, Technical Debt Management},

pubstate = {published},

tppubtype = {article}

}

@article{dubbiosoPrecisionMedicineALS2024,

title = {Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment},

author = {Raffaele Dubbioso and Myriam Spisto and Laura Verde and Valentina Virginia Iuzzolino and Gianmaria Senerchia and Giuseppe De Pietro and Ivanoe De Falco and Giovanna Sannino},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1746809423011394},

doi = {10.1016/j.bspc.2023.105706},

issn = {17468094},

year  = {2024},

date = {2024-03-01},

urldate = {2024-07-21},

journal = {Biomedical Signal Processing and Control},

volume = {89},

pages = {105706},

abstract = {Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motorneurons of the bulbar, cervical, thoracic, or lumbar segments. Bulbar presentation is a devastating characteristic that impairs patients’ ability to communicate and is linked to shorter survival. Early acoustic manifestation of voice symptoms, such as dysarthria, is very variable, making its detection and classification challenging, both by human specialists and automatic systems. In this context, precision medicine, defined as “prevention and treatment strategies that take individual variability into account”, has gained a great interest in the ALS community. Specifically, the use of innovative Artificial Intelligence techniques, such as Machine Learning, plays a pivotal role in finding specific patterns in the data set to help neurologists in clinical decision-making. Therefore, the main objective of this study was to find new markers, and new patterns, to promptly detect the possible presence of dysarthria and to correctly classify its severity. We have performed an acoustic analysis on different voice signals of various degrees of impairment acquired during outpatient visits at the ALS center of the “Federico II” University Hospital. From the collected signals, a new database containing different acoustic parameters was realized, on which several experiments were performed. The study led us to the discovery of markers that helped to develop a decision tree that separated healthy subjects from patients and, among patients, those with different severity of dysarthria. This model achieved good results in terms of dysarthria classification accuracy, 86.6%, which is excellent considering the small number of subjects in the data set.},

keywords = {Amyotrophic lateral sclerosis (ALS), Artificial Intelligence, Bulbar functions, Classification, Dysarthria, Precision medicine},

pubstate = {published},

tppubtype = {article}

}

@article{dubbioso_precision_2024,

title = {Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment},

author = {Raffaele Dubbioso and Myriam Spisto and Laura Verde and Valentina Virginia Iuzzolino and Gianmaria Senerchia and Giuseppe De Pietro and Ivanoe De Falco and Giovanna Sannino},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1746809423011394},

doi = {10.1016/j.bspc.2023.105706},

issn = {17468094},

year  = {2024},

date = {2024-03-01},

urldate = {2024-07-21},

journal = {Biomedical Signal Processing and Control},

volume = {89},

pages = {105706},

abstract = {Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motorneurons of the bulbar, cervical, thoracic, or lumbar segments. Bulbar presentation is a devastating characteristic that impairs patients’ ability to communicate and is linked to shorter survival. Early acoustic manifestation of voice symptoms, such as dysarthria, is very variable, making its detection and classification challenging, both by human specialists and automatic systems. In this context, precision medicine, defined as “prevention and treatment strategies that take individual variability into account”, has gained a great interest in the ALS community. Specifically, the use of innovative Artificial Intelligence techniques, such as Machine Learning, plays a pivotal role in finding specific patterns in the data set to help neurologists in clinical decision-making. Therefore, the main objective of this study was to find new markers, and new patterns, to promptly detect the possible presence of dysarthria and to correctly classify its severity. We have performed an acoustic analysis on different voice signals of various degrees of impairment acquired during outpatient visits at the ALS center of the “Federico II” University Hospital. From the collected signals, a new database containing different acoustic parameters was realized, on which several experiments were performed. The study led us to the discovery of markers that helped to develop a decision tree that separated healthy subjects from patients and, among patients, those with different severity of dysarthria. This model achieved good results in terms of dysarthria classification accuracy, 86.6%, which is excellent considering the small number of subjects in the data set.},

keywords = {Amyotrophic lateral sclerosis (ALS), Artificial Intelligence, Bulbar functions, Classification, Dysarthria, Precision medicine},

pubstate = {published},

tppubtype = {article}

}

@article{mennellaEthicalRegulatoryChallenges2024,

title = {Ethical and regulatory challenges of AI technologies in healthcare: A narrative review},

author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2405844024023284},

doi = {10.1016/j.heliyon.2024.e26297},

issn = {24058440},

year  = {2024},

date = {2024-02-01},

urldate = {2024-07-21},

journal = {Heliyon},

volume = {10},

number = {4},

pages = {e26297},

abstract = {Over the past decade, there has been a notable surge in AI-driven research, specifically geared toward enhancing crucial clinical processes and outcomes. The potential of AI-powered decision support systems to streamline clinical workflows, assist in diagnostics, and enable personalized treatment is increasingly evident. Nevertheless, the introduction of these cutting-edge solutions poses substantial challenges in clinical and care environments, necessitating a thorough exploration of ethical, legal, and regulatory considerations. 

A robust governance framework is imperative to foster the acceptance and successful implementation of AI in healthcare. This article delves deep into the critical ethical and regulatory concerns entangled with the deployment of AI systems in clinical practice. It not only provides a comprehensive overview of the role of AI technologies but also offers an insightful perspective on the ethical and regulatory challenges, making a pioneering contribution to the field. 

This research aims to address the current challenges in digital healthcare by presenting valuable recommendations for all stakeholders eager to advance the development and implementation of innovative AI systems.},

keywords = {Artificial Intelligence, Decision-Making, Ethics, Healthcare, Regulatory guidelines, Technologies},

pubstate = {published},

tppubtype = {article}

}

@article{mennella_ethical_2024,

title = {Ethical and regulatory challenges of AI technologies in healthcare: A narrative review},

author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2405844024023284},

doi = {10.1016/j.heliyon.2024.e26297},

issn = {24058440},

year  = {2024},

date = {2024-02-01},

urldate = {2024-07-21},

journal = {Heliyon},

volume = {10},

number = {4},

pages = {e26297},

abstract = {Over the past decade, there has been a notable surge in AI-driven research, specifically geared toward enhancing crucial clinical processes and outcomes. The potential of AI-powered decision support systems to streamline clinical workflows, assist in diagnostics, and enable personalized treatment is increasingly evident. Nevertheless, the introduction of these cutting-edge solutions poses substantial challenges in clinical and care environments, necessitating a thorough exploration of ethical, legal, and regulatory considerations. 

A robust governance framework is imperative to foster the acceptance and successful implementation of AI in healthcare. This article delves deep into the critical ethical and regulatory concerns entangled with the deployment of AI systems in clinical practice. It not only provides a comprehensive overview of the role of AI technologies but also offers an insightful perspective on the ethical and regulatory challenges, making a pioneering contribution to the field. 

This research aims to address the current challenges in digital healthcare by presenting valuable recommendations for all stakeholders eager to advance the development and implementation of innovative AI systems.},

keywords = {Artificial Intelligence, Decision-Making, Ethics, Healthcare, Regulatory guidelines, Technologies},

pubstate = {published},

tppubtype = {article}

}

@article{barbareschi_investigating_2024,

title = {Investigating the Resilience Source of Classification Systems for Approximate Computing Techniques},

author = {Mario Barbareschi and Salvatore Barone},

url = {https://ieeexplore.ieee.org/document/10542568},

doi = {10.1109/TETC.2024.3403757},

issn = {2168-6750},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Transactions on Emerging Topics in Computing},

pages = {12},

abstract = {During the last decade, classification systems (CSs) received significant research attention, with new learning algorithms achieving high accuracy in various applications. However, their resource-intensive nature, in terms of hardware and computation time, poses new design challenges. 

CSs exhibit inherent error resilience, due to redundancy of training sets, and self-healing properties, making them suitable for Approximate Computing (AxC). 

AxC enables efficient computation by using reduced precision or approximate values, leading to energy, time, and silicon area savings. 

Exploiting AxC involves estimating the introduced error for each approximate variant found during a Design-Space Exploration (DSE). This estimation has to be both rapid and meaningful, considering a substantial number of test samples, which are utterly conflicting demands. 

In this paper, we investigate on sources of error resiliency of CSs, and we propose a technique to haste the DSE that reduces the computational time for error estimation by systematically reducing the test set. In particular, we cherry-pick samples that are likely to be more sensitive to approximation and perform accuracy-loss estimation just by exploiting such a sample subset. 

In order to demonstrate its efficacy, we integrate our technique into two different approaches for generating approximate CSs, showing an average speed-up up to approx18.},

keywords = {Artificial Intelligence, Classification systems, Neural networks, Tree Ensemble},

pubstate = {published},

tppubtype = {article}

}

@article{agnolucciQualityAwareImageTextAlignment2024,

title = {Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment},

author = {Lorenzo Agnolucci and Leonardo Galteri and Marco Bertini},

url = {https://arxiv.org/abs/2403.11176},

doi = {10.48550/ARXIV.2403.11176},

year  = {2024},

date = {2024-01-01},

journal = {arXiv preprint arXiv:2403.11176},

abstract = {No-Reference Image Quality Assessment (NR-IQA) focuses on designing methods to measure image quality in alignment with human perception when a high-quality reference image is unavailable. The reliance on annotated Mean Opinion Scores (MOS) in the majority of state-of-the-art NR-IQA approaches limits their scalability and broader applicability to real-world scenarios. To overcome this limitation, we propose QualiCLIP (Quality-aware CLIP), a CLIP-based self-supervised opinion-unaware method that does not require labeled MOS. In particular, we introduce a quality-aware image-text alignment strategy to make CLIP generate representations that correlate with the inherent quality of the images. Starting from pristine images, we synthetically degrade them with increasing levels of intensity. Then, we train CLIP to rank these degraded images based on their similarity to quality-related antonym text prompts, while guaranteeing consistent representations for images with comparable quality. Our method achieves state-of-the-art performance on several datasets with authentic distortions. Moreover, despite not requiring MOS, QualiCLIP outperforms supervised methods when their training dataset differs from the testing one, thus proving to be more suitable for real-world scenarios. Furthermore, our approach demonstrates greater robustness and improved explainability than competing methods. The code and the model are publicly available at https://github.com/miccunifi/QualiCLIP.},

note = {arXiv: 2403.11176 

tex.copyright: All rights reserved},

keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Image Processing, Quality Assessment, Self-Supervised Learning, Vision and Language},

pubstate = {published},

tppubtype = {article}

}

@article{tomazzoli_cobot_2024,

title = {A Cobot in the Vineyard: Computer Vision for Smart Chemicals Spraying},

author = {Claudio Tomazzoli and Andrea Ponza and Matteo Cristani and Francesco Olivieri and Simone Scannapieco},

url = {https://www.mdpi.com/2076-3417/14/9/3777},

doi = {10.3390/app14093777},

issn = {2076-3417},

year  = {2024},

date = {2024-01-01},

journal = {Applied Sciences},

volume = {14},

number = {9},

abstract = {Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field.},

keywords = {Artificial Intelligence, Collaborative robotic, Computer vision, Cyber-Physical Systems, Deep Learning, Machine Learning, Precision agriculture},

pubstate = {published},

tppubtype = {article}

}

@article{scannapieco_cnosso_2024,

title = {Cnosso, a Novel Method for Business Document Automation Based on Open Information Extraction},

author = {Simone Scannapieco and Claudio Tomazzoli},

url = {https://www.sciencedirect.com/science/article/pii/S0957417423035406},

doi = {10.1016/j.eswa.2023.123038},

issn = {0957-4174},

year  = {2024},

date = {2024-01-01},

journal = {Expert Systems with Applications},

volume = {245},

pages = {123038},

abstract = {The state-of-the-art in automated processing of unstructured business documents has evolved from manual labor to advanced AI systems in the span of mere decades. Such systems involve learning techniques, rule or clause sets, neural models – either used alone or in combination – for the extraction to work. As an example, rule-based processes operate on a perceived layout or positioning of the information, whereas model-based frameworks adopt a semantic, and often uninspectable, approach. Verb-Based Semantic Role Labeling (VBSRL) is a novel system presented in a former paper that uses a hybrid foundation to inform the extraction phase via a set of rules modeling natural language. We propose a new VBSRL-based document processing method, aided by valuable and innovative architectural choices, which has been implemented for the Italian language and experimented upon with promising results. Even in its infancy, in fact, the first implementation of this system shows better results than comparable IE solutions, obtaining an aggregate, average F-measure of nearly 79%.},

keywords = {Artificial Intelligence, Document automation, Information Extraction, Natural language analysis, Natural Language Processing, Semantic Role Labeling},

pubstate = {published},

tppubtype = {article}

}

@article{esposito_artificial_2024,

title = {Artificial Intelligence-assisted Raman Spectroscopy for Liver cancer diagnosis},

author = {Concetta Esposito and Mohammed Janneh and Sara Spaziani and Vincenzo Calcagno and Mario Luca Bernardi and Martina Iammarino and Chiara Verdone and Maria Tagliamonte and Luigi Buonaguro and Marco Pisco and Lerina Aversano and Andrea Cusano},

editor = {L. De Stefano and R. Velotta and E. Descrovi},

url = {https://www.epj-conferences.org/10.1051/epjconf/202430910010},

doi = {10.1051/epjconf/202430910010},

issn = {2100-014X},

year  = {2024},

date = {2024-01-01},

urldate = {2025-10-22},

journal = {EPJ Web of Conferences},

volume = {309},

pages = {10010},

abstract = {Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, represents a global health challenge due to its complexity and the limitations of current diagnostic techniques. By combining Raman spectroscopy and Artificial Intelligence (AI), we have succeeded in classifying tumor cells. In fact, we have performed a first Raman spectral analysis based on the characterization and differentiation between uncultured primary human liver cells derived from resected HCC tumor tissue and the adjacent non-tumor counterpart. Biochemical analysis of the collected Raman spectra revealed that there is more DNA in the nuclei of the tumor cells than in non-tumor cells. We then develop three machine learning approaches, including multivariate models and neural networks, to rapidly automate the recognition and classification of the Raman spectra of both cells. To evaluate the performance of the developed AI models, we prepared and analyzed two additional cell samples with a ratio of 4:1 and 3:1 between tumor and non-tumor cells and compared the obtained results with the nominal percentages (accuracy of 80 and 60%, respectively). These results confirm that the models are able to make classifications at the level of a single spectrum, indicating the possibility of rapidly analysing and classifying a primary HCC cell.},

keywords = {Artificial Intelligence, Diagnosis, Healthcare, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

@article{aversano_machine_2024,

title = {A Machine Learning Approach for the Detection of Thoracic Disease using Chest X-ray reports},

author = {Lerina Aversano and Martina Iammarino and Antonella Madau and Debora Montano and Chiara Verdone},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1877050924025808},

doi = {10.1016/j.procs.2024.09.535},

issn = {18770509},

year  = {2024},

date = {2024-01-01},

urldate = {2025-10-22},

journal = {Procedia Computer Science},

volume = {246},

pages = {1130–1139},

abstract = {Today, several chest diseases are on the rise and these are often diagnosed through the use of chest X-rays, a common and economical clinical test to perform. This work uses a machine learning approach for the detection of thoracic diseases using chest X-ray reports and involves leveraging algorithms and models to analyze medical imaging data for the presence of various conditions affecting the chest area. Our main goal is to create a predictive model based on textual reports released by radiologists, with the use of Natural Language Processing. The proposed approach aims to facilitate the examination of textual reports written by radiologists, to predict the onset of diseases in patients. Specifically, reports generated by radiologists are meticulously processed and reviewed using the GloVe and LSI models. This analysis allows you to identify the presence of diseases and provides insights into the specific thoracic pathology. The results obtained through the implementation of our approach (accuracy above 96% for the best model) underline the good performance and potential of the developed predictive model.},

keywords = {Artificial Intelligence, Machine Learning, Natural Language Processing, Smart Health},

pubstate = {published},

tppubtype = {article}

}

@article{mennellaGeneratingNovelSynthetic2023,

title = {Generating a novel synthetic dataset for rehabilitation exercises using pose-guided conditioned diffusion models: A quantitative and qualitative evaluation},

author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482523011307},

doi = {10.1016/j.compbiomed.2023.107665},

issn = {00104825},

year  = {2023},

date = {2023-12-01},

urldate = {2024-07-21},

journal = {Computers in Biology and Medicine},

volume = {167},

pages = {107665},

abstract = {Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation. 

This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises. 

A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90. 

This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.},

keywords = {Artificial Intelligence, Generative algorithms, Rehabilitation, Synthetic data},

pubstate = {published},

tppubtype = {article}

}

@article{mennella_generating_2023,

title = {Generating a novel synthetic dataset for rehabilitation exercises using pose-guided conditioned diffusion models: A quantitative and qualitative evaluation},

author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482523011307},

doi = {10.1016/j.compbiomed.2023.107665},

issn = {00104825},

year  = {2023},

date = {2023-12-01},

urldate = {2024-07-21},

journal = {Computers in Biology and Medicine},

volume = {167},

pages = {107665},

abstract = {Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation. 

This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises. 

A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90. 

This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.},

keywords = {Artificial Intelligence, Generative algorithms, Rehabilitation, Synthetic data},

pubstate = {published},

tppubtype = {article}

}

@article{buonaiutoBestPracticesPortfolio2023,

title = {Best practices for portfolio optimization by quantum computing, experimented on real quantum devices},

author = {Giuseppe Buonaiuto and Francesco Gargiulo and Giuseppe De Pietro and Massimo Esposito and Marco Pota},

url = {https://www.nature.com/articles/s41598-023-45392-w},

doi = {10.1038/s41598-023-45392-w},

issn = {2045-2322},

year  = {2023},

date = {2023-11-01},

urldate = {2024-07-21},

journal = {Scientific Reports},

volume = {13},

number = {1},

pages = {19434},

abstract = {Abstract 

 In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.},

keywords = {Artificial Intelligence, Computational science, Information technology, Mathematics and computing, Quantum Physics},

pubstate = {published},

tppubtype = {article}

}

@article{mennellaDeepLearningSystem2023,

title = {A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions},

author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482523009502},

doi = {10.1016/j.compbiomed.2023.107485},

issn = {00104825},

year  = {2023},

date = {2023-11-01},

urldate = {2024-07-21},

journal = {Computers in Biology and Medicine},

volume = {166},

pages = {107485},

abstract = {In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality. 

This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired. 

The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns. 

By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.},

keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Movement classification, Pose estimation, Rehabilitation},

pubstate = {published},

tppubtype = {article}

}