Publications
OUR RESEARCH
Scientific Publications
Here you can find the comprehensive list of publications from the members of the Research Center on Computer Vision and eXtended Reality (xRAI).
Use the tag cloud to filter papers based on specific research topics, or use the menus to filter by year, type of publication, or authors.
For each paper, you have the option to view additional details such as the Abstract, Links, and BibTex record.
Research is formalized curiosity. It is poking and prying with a purpose
Zora Neale Hurston
2025
Mondal, Semanto; Ferraro, Antonino; Pecorelli, Fabiano; Pietro, Giuseppe De
A Logic Tensor Network-Based Neurosymbolic Framework for Explainable Diabetes Prediction Journal Article
In: Applied Sciences, vol. 15, no. 21, pp. 11806, 2025, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags:
@article{mondal_logic_2025-1,
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}
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Neurosymbolic AI is an emerging paradigm that combines neural network learning capabilities with the structured reasoning capacity of symbolic systems. Although machine learning has achieved cutting-edge outcomes in diverse fields, including healthcare, agriculture, and environmental science, it has potential limitations. Machine learning and neural models excel at identifying intricate data patterns, yet they often lack transparency, depend on large labelled datasets, and face challenges with logical reasoning and tasks that require explainability. These challenges reduce their reliability in high-stakes applications such as healthcare. To address these limitations, we propose a hybrid framework that integrates symbolic knowledge expressed in First-Order Logic into neural learning via a Logic Tensor Network (LTN). In this framework, expert-defined medical rules are embedded as logical axioms with learnable thresholds. As a result, the model gains predictive power, interpretability, and explainability through reasoning over the logical rules. We have utilized this neurosymbolic method for predicting diabetes by employing the Pima Indians Diabetes Dataset. Our experimental setup evaluates the LTN-based model against several conventional methods, including Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (K-NN), Random Forest Classifiers (RF), Naive Bayes (NB), and a Standalone Neural Network (NN). The findings demonstrate that the neurosymbolic framework not only surpasses traditional models in predictive accuracy but also offers improved explainability and robustness. Notably, the LTN-based neurosymbolic framework achieves an excellent balance between recall and precision, along with a higher AUC-ROC score. These results underscore its potential for trustworthy medical diagnostics. This work highlights how integrating symbolic reasoning with data-driven models can bridge the gap between explainability, interpretability, and performance, offering a promising direction for AI systems in domains where both accuracy and explainability are critical.
Mondal, Semanto; Ferraro, Antonino; Pecorelli, Fabiano; Pietro, Giuseppe De
A Logic Tensor Network-Based Neurosymbolic Framework for Explainable Diabetes Prediction Journal Article
In: Applied Sciences, vol. 15, no. 21, pp. 11806, 2025, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags:
@article{mondal_logic_2025,
title = {A Logic Tensor Network-Based Neurosymbolic Framework for Explainable Diabetes Prediction},
author = {Semanto Mondal and Antonino Ferraro and Fabiano Pecorelli and Giuseppe De Pietro},
url = {https://www.mdpi.com/2076-3417/15/21/11806},
doi = {10.3390/app152111806},
issn = {2076-3417},
year = {2025},
date = {2025-11-01},
urldate = {2025-11-25},
journal = {Applied Sciences},
volume = {15},
number = {21},
pages = {11806},
abstract = {Neurosymbolic AI is an emerging paradigm that combines neural network learning capabilities with the structured reasoning capacity of symbolic systems. Although machine learning has achieved cutting-edge outcomes in diverse fields, including healthcare, agriculture, and environmental science, it has potential limitations. Machine learning and neural models excel at identifying intricate data patterns, yet they often lack transparency, depend on large labelled datasets, and face challenges with logical reasoning and tasks that require explainability. These challenges reduce their reliability in high-stakes applications such as healthcare. To address these limitations, we propose a hybrid framework that integrates symbolic knowledge expressed in First-Order Logic into neural learning via a Logic Tensor Network (LTN). In this framework, expert-defined medical rules are embedded as logical axioms with learnable thresholds. As a result, the model gains predictive power, interpretability, and explainability through reasoning over the logical rules. We have utilized this neurosymbolic method for predicting diabetes by employing the Pima Indians Diabetes Dataset. Our experimental setup evaluates the LTN-based model against several conventional methods, including Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (K-NN), Random Forest Classifiers (RF), Naive Bayes (NB), and a Standalone Neural Network (NN). The findings demonstrate that the neurosymbolic framework not only surpasses traditional models in predictive accuracy but also offers improved explainability and robustness. Notably, the LTN-based neurosymbolic framework achieves an excellent balance between recall and precision, along with a higher AUC-ROC score. These results underscore its potential for trustworthy medical diagnostics. This work highlights how integrating symbolic reasoning with data-driven models can bridge the gap between explainability, interpretability, and performance, offering a promising direction for AI systems in domains where both accuracy and explainability are critical.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
Santoriello, Vittorio; Ponsiglione, Alfonso Maria; Giugliano, Carmine; Buonaguro, Carmen; Gallo, Luigi; Caggianese, Giuseppe; Cascella, Marco; Pietro, Giuseppe De; Chirico, Andrea; Giordano, Antonio; Amato, Francesco; Romano, Maria; Guida, Maurizio
Virtual Reality and Biosignals for Labor Pain Relief: A Pilot Study Proceedings Article
In: 2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 565–569, 2025.
Abstract | Links | BibTeX | Tags: Biosignals, ECG, Heart rate variability, Neural engineering, Pain, Pregnancy, Skin, Visualization, Wearable devices
@inproceedings{santoriello_virtual_2025,
title = {Virtual Reality and Biosignals for Labor Pain Relief: A Pilot Study},
author = {Vittorio Santoriello and Alfonso Maria Ponsiglione and Carmine Giugliano and Carmen Buonaguro and Luigi Gallo and Giuseppe Caggianese and Marco Cascella and Giuseppe De Pietro and Andrea Chirico and Antonio Giordano and Francesco Amato and Maria Romano and Maurizio Guida},
url = {https://ieeexplore.ieee.org/abstract/document/11340443},
doi = {10.1109/MetroXRAINE66377.2025.11340443},
year = {2025},
date = {2025-10-01},
urldate = {2026-02-06},
booktitle = {2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {565–569},
abstract = {Labor pain is intense and multifaceted, requiring effective management to ensure both maternal and neonatal well-being. This study explores the use of virtual reality (VR) as a distraction tool, combined with biosignal monitoring. Electro-dermal activity and heart rate variability (HRV) were recorded using wearable devices on four pregnant women. The acquisition protocol was divided into three phases: before, during, and after VR exposure. To complement the physiological data, the Visual Analog Scale was administered before and after each session. Participants were also asked to evaluate how effective they found the experimental treatment in helping them relax during labor, using a 1 to 10 scale. Results showed reduced sympathetic activity during VR, indicated by lower skin conductance and HRV features (heart rate and low-frequency/high-frequency ratio), suggesting a calming effect. In addition, participants manifested a 55.72% reduction in perceived anxiety and expressed positive appreciation for the VR treatment. Ongoing data collection will allow for deeper investigation of these trends, enabling more detailed analyses during individual contractions and facilitating correlation with subjective questionnaire responses. These findings highlight the potential of VR as a non-invasive, personalized approach to managing labor pain.},
keywords = {Biosignals, ECG, Heart rate variability, Neural engineering, Pain, Pregnancy, Skin, Visualization, Wearable devices},
pubstate = {published},
tppubtype = {inproceedings}
}
Labor pain is intense and multifaceted, requiring effective management to ensure both maternal and neonatal well-being. This study explores the use of virtual reality (VR) as a distraction tool, combined with biosignal monitoring. Electro-dermal activity and heart rate variability (HRV) were recorded using wearable devices on four pregnant women. The acquisition protocol was divided into three phases: before, during, and after VR exposure. To complement the physiological data, the Visual Analog Scale was administered before and after each session. Participants were also asked to evaluate how effective they found the experimental treatment in helping them relax during labor, using a 1 to 10 scale. Results showed reduced sympathetic activity during VR, indicated by lower skin conductance and HRV features (heart rate and low-frequency/high-frequency ratio), suggesting a calming effect. In addition, participants manifested a 55.72% reduction in perceived anxiety and expressed positive appreciation for the VR treatment. Ongoing data collection will allow for deeper investigation of these trends, enabling more detailed analyses during individual contractions and facilitating correlation with subjective questionnaire responses. These findings highlight the potential of VR as a non-invasive, personalized approach to managing labor pain.
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
Advancing AI-driven surveillance systems in hospital: A fine-grained instance segmentation dataset for accurate in-bed patient monitoring Journal Article
In: Computers in Biology and Medicine, vol. 195, pp. 110550, 2025, ISSN: 00104825.
@article{mennella_advancing_2025,
title = {Advancing AI-driven surveillance systems in hospital: A fine-grained instance segmentation dataset for accurate in-bed patient monitoring},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482525009011},
doi = {10.1016/j.compbiomed.2025.110550},
issn = {00104825},
year = {2025},
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journal = {Computers in Biology and Medicine},
volume = {195},
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Mennella, Ciro; Esposito, Massimo; Pietro, Giuseppe De; Maniscalco, Umberto
Multiscale activity recognition algorithms to improve cross-subjects performance resilience in rehabilitation monitoring systems Journal Article
In: Computer Methods and Programs in Biomedicine, vol. 267, pp. 108792, 2025, ISSN: 01692607.
@article{mennella_multiscale_2025,
title = {Multiscale activity recognition algorithms to improve cross-subjects performance resilience in rehabilitation monitoring systems},
author = {Ciro Mennella and Massimo Esposito and Giuseppe De Pietro and Umberto Maniscalco},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0169260725002093},
doi = {10.1016/j.cmpb.2025.108792},
issn = {01692607},
year = {2025},
date = {2025-07-01},
urldate = {2025-09-30},
journal = {Computer Methods and Programs in Biomedicine},
volume = {267},
pages = {108792},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Buonaiuto, Giuseppe; Guarasci, Raffaele; Pietro, Giuseppe De; Esposito, Massimo
Multilingual multi-task quantum transfer learning Journal Article
In: Quantum Machine Intelligence, vol. 7, no. 1, pp. 46, 2025, ISSN: 2524-4906, 2524-4914.
Abstract | Links | BibTeX | Tags:
@article{buonaiuto_multilingual_2025,
title = {Multilingual multi-task quantum transfer learning},
author = {Giuseppe Buonaiuto and Raffaele Guarasci and Giuseppe De Pietro and Massimo Esposito},
url = {https://link.springer.com/10.1007/s42484-025-00260-w},
doi = {10.1007/s42484-025-00260-w},
issn = {2524-4906, 2524-4914},
year = {2025},
date = {2025-06-01},
urldate = {2025-09-30},
journal = {Quantum Machine Intelligence},
volume = {7},
number = {1},
pages = {46},
abstract = {Abstract
Hybrid quantum-classical algorithms have emerged as promising candidates for overcoming current limitations of deep learning techniques and recently have attracted a lot of attention for their application in natural language processing (NLP). Among the potential applications of quantum computing in this field, quantum transfer learning—using quantum circuits for fine-tuning pre-trained classical models specific to a task—is regarded as a potential avenue to exploit the potentiality of quantum computers. This study validates, both experimentally and with domain knowledge analysis, the efficacy of quantum transfer learning for two distinct NLP tasks—semantic and syntactic—and employ multilingual data encompassing both English and Italian. In particular is hereby demonstrated that embedded knowledge coming from pre-trained deep learning models can be effectively transferred into a quantum classifier, which shows good performances, either comparable or potentially better than their classical counterparts, with a further reduction of parameters compared to a purely classical classifier. Furthermore, a qualitative linguistic analysis of the results is presented, that elucidates two points: the lack of language dependence in the quantum models and the ability to discriminate with higher precision than standard classifiers, sub-types of linguistic structures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
Hybrid quantum-classical algorithms have emerged as promising candidates for overcoming current limitations of deep learning techniques and recently have attracted a lot of attention for their application in natural language processing (NLP). Among the potential applications of quantum computing in this field, quantum transfer learning—using quantum circuits for fine-tuning pre-trained classical models specific to a task—is regarded as a potential avenue to exploit the potentiality of quantum computers. This study validates, both experimentally and with domain knowledge analysis, the efficacy of quantum transfer learning for two distinct NLP tasks—semantic and syntactic—and employ multilingual data encompassing both English and Italian. In particular is hereby demonstrated that embedded knowledge coming from pre-trained deep learning models can be effectively transferred into a quantum classifier, which shows good performances, either comparable or potentially better than their classical counterparts, with a further reduction of parameters compared to a purely classical classifier. Furthermore, a qualitative linguistic analysis of the results is presented, that elucidates two points: the lack of language dependence in the quantum models and the ability to discriminate with higher precision than standard classifiers, sub-types of linguistic structures.
Hybrid quantum-classical algorithms have emerged as promising candidates for overcoming current limitations of deep learning techniques and recently have attracted a lot of attention for their application in natural language processing (NLP). Among the potential applications of quantum computing in this field, quantum transfer learning—using quantum circuits for fine-tuning pre-trained classical models specific to a task—is regarded as a potential avenue to exploit the potentiality of quantum computers. This study validates, both experimentally and with domain knowledge analysis, the efficacy of quantum transfer learning for two distinct NLP tasks—semantic and syntactic—and employ multilingual data encompassing both English and Italian. In particular is hereby demonstrated that embedded knowledge coming from pre-trained deep learning models can be effectively transferred into a quantum classifier, which shows good performances, either comparable or potentially better than their classical counterparts, with a further reduction of parameters compared to a purely classical classifier. Furthermore, a qualitative linguistic analysis of the results is presented, that elucidates two points: the lack of language dependence in the quantum models and the ability to discriminate with higher precision than standard classifiers, sub-types of linguistic structures.
2024
Mennella, Ciro; Esposito, Massimo; Pietro, Giuseppe De; Maniscalco, Umberto
Promoting fairness in activity recognition algorithms for patient’s monitoring and evaluation systems in healthcare Journal Article
In: Computers in Biology and Medicine, vol. 179, pp. 108826, 2024, ISSN: 00104825.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Bias, Deep Learning, Motion analysis, Rehabilitation, Time-series
@article{mennellaPromotingFairnessActivity2024,
title = {Promoting fairness in activity recognition algorithms for patient’s monitoring and evaluation systems in healthcare},
author = {Ciro Mennella and Massimo Esposito and Giuseppe De Pietro and Umberto Maniscalco},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482524009119},
doi = {10.1016/j.compbiomed.2024.108826},
issn = {00104825},
year = {2024},
date = {2024-09-01},
urldate = {2024-07-21},
journal = {Computers in Biology and Medicine},
volume = {179},
pages = {108826},
abstract = {Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.},
keywords = {Artificial Intelligence, Bias, Deep Learning, Motion analysis, Rehabilitation, Time-series},
pubstate = {published},
tppubtype = {article}
}
Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.
Mennella, Ciro; Esposito, Massimo; Pietro, Giuseppe De; Maniscalco, Umberto
Promoting fairness in activity recognition algorithms for patient’s monitoring and evaluation systems in healthcare Journal Article
In: Computers in Biology and Medicine, vol. 179, pp. 108826, 2024, ISSN: 00104825.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Bias, Deep Learning, Motion analysis, Rehabilitation, Time-series
@article{mennella_promoting_2024,
title = {Promoting fairness in activity recognition algorithms for patient’s monitoring and evaluation systems in healthcare},
author = {Ciro Mennella and Massimo Esposito and Giuseppe De Pietro and Umberto Maniscalco},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482524009119},
doi = {10.1016/j.compbiomed.2024.108826},
issn = {00104825},
year = {2024},
date = {2024-09-01},
urldate = {2024-07-21},
journal = {Computers in Biology and Medicine},
volume = {179},
pages = {108826},
abstract = {Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.},
keywords = {Artificial Intelligence, Bias, Deep Learning, Motion analysis, Rehabilitation, Time-series},
pubstate = {published},
tppubtype = {article}
}
Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.
Dubbioso, Raffaele; Spisto, Myriam; Verde, Laura; Iuzzolino, Valentina Virginia; Senerchia, Gianmaria; Salvatore, Elena; Pietro, Giuseppe De; Falco, Ivanoe De; Sannino, Giovanna
Voice signals database of ALS patients with different dysarthria severity and healthy controls Journal Article
In: Scientific Data, vol. 11, no. 1, pp. 800, 2024, ISSN: 2052-4463, (Publisher: Nature Publishing Group UK London).
Abstract | Links | BibTeX | Tags:
@article{dubbioso_voice_2024,
title = {Voice signals database of ALS patients with different dysarthria severity and healthy controls},
author = {Raffaele Dubbioso and Myriam Spisto and Laura Verde and Valentina Virginia Iuzzolino and Gianmaria Senerchia and Elena Salvatore and Giuseppe De Pietro and Ivanoe De Falco and Giovanna Sannino},
url = {https://www.nature.com/articles/s41597-024-03597-2},
doi = {10.1038/s41597-024-03597-2},
issn = {2052-4463},
year = {2024},
date = {2024-07-01},
urldate = {2025-09-30},
journal = {Scientific Data},
volume = {11},
number = {1},
pages = {800},
publisher = {Nature Publishing Group UK London},
abstract = {Abstract
This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject’s voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F
0
), jitter and shimmer were calculated. The F
0
standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.},
note = {Publisher: Nature Publishing Group UK London},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject’s voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F
0
), jitter and shimmer were calculated. The F
0
standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.
This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject’s voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F
0
), jitter and shimmer were calculated. The F
0
standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.
Dubbioso, Raffaele; Spisto, Myriam; Verde, Laura; Iuzzolino, Valentina Virginia; Senerchia, Gianmaria; Salvatore, Elena; Pietro, Giuseppe De; Falco, Ivanoe De; Sannino, Giovanna
Voice signals database of ALS patients with different dysarthria severity and healthy controls Journal Article
In: Scientific Data, vol. 11, no. 1, pp. 800, 2024, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{dubbioso_voice_2024-1,
title = {Voice signals database of ALS patients with different dysarthria severity and healthy controls},
author = {Raffaele Dubbioso and Myriam Spisto and Laura Verde and Valentina Virginia Iuzzolino and Gianmaria Senerchia and Elena Salvatore and Giuseppe De Pietro and Ivanoe De Falco and Giovanna Sannino},
url = {https://www.nature.com/articles/s41597-024-03597-2},
doi = {10.1038/s41597-024-03597-2},
issn = {2052-4463},
year = {2024},
date = {2024-07-01},
urldate = {2025-09-30},
journal = {Scientific Data},
volume = {11},
number = {1},
pages = {800},
abstract = {Abstract
This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject’s voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F
0
), jitter and shimmer were calculated. The F
0
standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject’s voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F
0
), jitter and shimmer were calculated. The F
0
standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.
This paper describes a new publicly-available database of VOiCe signals acquired in Amyotrophic Lateral Sclerosis (ALS) patients (VOC-ALS) and healthy controls performing different speech tasks. This dataset consists of 1224 voice signals recorded from 153 participants: 51 healthy controls (32 males and 19 females) and 102 ALS patients (65 males and 37 females) with different severity of dysarthria. Each subject’s voice was recorded using a smartphone application (Vox4Health) while performing several vocal tasks, including a sustained phonation of the vowels /a/, /e/, /i/, /o/, /u/ and /pa/, /ta/, /ka/ syllable repetition. Basic derived speech metrics such as harmonics-to-noise ratio, mean and standard deviation of fundamental frequency (F
0
), jitter and shimmer were calculated. The F
0
standard deviation of vowels and syllables showed an excellent ability to identify people with ALS and to discriminate the different severity of dysarthria. These data represent the most comprehensive database of voice signals in ALS and form a solid basis for research on the recognition of voice impairment in ALS patients for use in clinical applications.
Melillo, Antonio; Rachedi, Sarah; Caggianese, Giuseppe; Gallo, Luigi; Maiorano, Patrizia; Gimigliano, Francesca; Lucidi, Fabio; Pietro, Giuseppe De; Guida, Maurizio; Giordano, Antonio; Chirico, Andrea
In: Games for Health Journal, pp. g4h.2023.0202, 2024, ISSN: 2161-783X, 2161-7856.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Labor, Pain, User study, Virtual Reality
@article{melilloSynchronizationVirtualReality2024,
title = {Synchronization of a Virtual Reality Scenario to Uterine Contractions for Labor Pain Management: Development Study and Randomized Controlled Trial},
author = {Antonio Melillo and Sarah Rachedi and Giuseppe Caggianese and Luigi Gallo and Patrizia Maiorano and Francesca Gimigliano and Fabio Lucidi and Giuseppe De Pietro and Maurizio Guida and Antonio Giordano and Andrea Chirico},
url = {https://www.liebertpub.com/doi/10.1089/g4h.2023.0202},
doi = {10.1089/g4h.2023.0202},
issn = {2161-783X, 2161-7856},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-21},
journal = {Games for Health Journal},
pages = {g4h.2023.0202},
abstract = {Background: Labor is described as one of the most painful events women can experience through their lives, and labor pain shows unique features and rhythmic fluctuations.
Purpose: The present study aims to evaluate virtual reality (VR) analgesic interventions for active labor with biofeedback-based VR technologies synchronized to uterine activity.
Materials and Methods: We developed a VR system modeled on uterine contractions by connecting it to cardiotocographic equipment. We conducted a randomized controlled trial on a sample of 74 cases and 80 controls during active labor.
Results: Results of the study showed a significant reduction of pain scores compared with both preintervention scores and to control group scores; a significant reduction of anxiety levels both compared with preintervention assessment and to control group and significant reduction in fear of labor experience compared with controls.
Conclusion: VR may be considered as an effective nonpharmacological analgesic technique for the treatment of pain and anxiety and fear of childbirth experience during labor. The developed system could improve personalization of care, modulating the multisensory stimulation tailored to labor progression. Further studies are needed to compare the synchronized VR system to uterine activity and unsynchronized VR interventions.},
keywords = {Artificial Intelligence, Labor, Pain, User study, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Background: Labor is described as one of the most painful events women can experience through their lives, and labor pain shows unique features and rhythmic fluctuations.
Purpose: The present study aims to evaluate virtual reality (VR) analgesic interventions for active labor with biofeedback-based VR technologies synchronized to uterine activity.
Materials and Methods: We developed a VR system modeled on uterine contractions by connecting it to cardiotocographic equipment. We conducted a randomized controlled trial on a sample of 74 cases and 80 controls during active labor.
Results: Results of the study showed a significant reduction of pain scores compared with both preintervention scores and to control group scores; a significant reduction of anxiety levels both compared with preintervention assessment and to control group and significant reduction in fear of labor experience compared with controls.
Conclusion: VR may be considered as an effective nonpharmacological analgesic technique for the treatment of pain and anxiety and fear of childbirth experience during labor. The developed system could improve personalization of care, modulating the multisensory stimulation tailored to labor progression. Further studies are needed to compare the synchronized VR system to uterine activity and unsynchronized VR interventions.
Purpose: The present study aims to evaluate virtual reality (VR) analgesic interventions for active labor with biofeedback-based VR technologies synchronized to uterine activity.
Materials and Methods: We developed a VR system modeled on uterine contractions by connecting it to cardiotocographic equipment. We conducted a randomized controlled trial on a sample of 74 cases and 80 controls during active labor.
Results: Results of the study showed a significant reduction of pain scores compared with both preintervention scores and to control group scores; a significant reduction of anxiety levels both compared with preintervention assessment and to control group and significant reduction in fear of labor experience compared with controls.
Conclusion: VR may be considered as an effective nonpharmacological analgesic technique for the treatment of pain and anxiety and fear of childbirth experience during labor. The developed system could improve personalization of care, modulating the multisensory stimulation tailored to labor progression. Further studies are needed to compare the synchronized VR system to uterine activity and unsynchronized VR interventions.
Guarasci, Raffaele; Minutolo, Aniello; Buonaiuto, Giuseppe; Pietro, Giuseppe De; Esposito, Massimo
Raising the Bar on Acceptability Judgments Classification: An Experiment on ItaCoLA Using ELECTRA Journal Article
In: Electronics, vol. 13, no. 13, pp. 2500, 2024, ISSN: 2079-9292.
Abstract | Links | BibTeX | Tags: BERT, ELECTRA, Low-resource languages, Natural Language Processing, Sentence classification
@article{guarasci_raising_2024,
title = {Raising the Bar on Acceptability Judgments Classification: An Experiment on ItaCoLA Using ELECTRA},
author = {Raffaele Guarasci and Aniello Minutolo and Giuseppe Buonaiuto and Giuseppe De Pietro and Massimo Esposito},
url = {https://www.mdpi.com/2079-9292/13/13/2500},
doi = {10.3390/electronics13132500},
issn = {2079-9292},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-21},
journal = {Electronics},
volume = {13},
number = {13},
pages = {2500},
abstract = {The task of automatically evaluating acceptability judgments has relished increasing success in Natural Language Processing, starting from including the Corpus of Linguistic Acceptability (CoLa) in the GLUE benchmark dataset. CoLa spawned a thread that led to the development of several similar datasets in different languages, broadening the investigation possibilities to many languages other than English. In this study, leveraging the Italian Corpus of Linguistic Acceptability (ItaCoLA), comprising nearly 10,000 sentences with acceptability judgments, we propose a new methodology that utilizes the neural language model ELECTRA. This approach exceeds the scores obtained from current baselines and demonstrates that it can overcome language-specific limitations in dealing with specific phenomena.},
keywords = {BERT, ELECTRA, Low-resource languages, Natural Language Processing, Sentence classification},
pubstate = {published},
tppubtype = {article}
}
The task of automatically evaluating acceptability judgments has relished increasing success in Natural Language Processing, starting from including the Corpus of Linguistic Acceptability (CoLa) in the GLUE benchmark dataset. CoLa spawned a thread that led to the development of several similar datasets in different languages, broadening the investigation possibilities to many languages other than English. In this study, leveraging the Italian Corpus of Linguistic Acceptability (ItaCoLA), comprising nearly 10,000 sentences with acceptability judgments, we propose a new methodology that utilizes the neural language model ELECTRA. This approach exceeds the scores obtained from current baselines and demonstrates that it can overcome language-specific limitations in dealing with specific phenomena.
Buonaiuto, Giuseppe; Gargiulo, Francesco; Pietro, Giuseppe De; Esposito, Massimo; Pota, Marco
The effects of quantum hardware properties on the performances of variational quantum learning algorithms Journal Article
In: Quantum Machine Intelligence, vol. 6, no. 1, pp. 9, 2024, ISSN: 2524-4906, 2524-4914.
Abstract | Links | BibTeX | Tags: Optimization, Quantum Computing, Quantum Machine Learning
@article{buonaiutoEffectsQuantumHardware2024,
title = {The effects of quantum hardware properties on the performances of variational quantum learning algorithms},
author = {Giuseppe Buonaiuto and Francesco Gargiulo and Giuseppe De Pietro and Massimo Esposito and Marco Pota},
url = {https://link.springer.com/10.1007/s42484-024-00144-5},
doi = {10.1007/s42484-024-00144-5},
issn = {2524-4906, 2524-4914},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-21},
journal = {Quantum Machine Intelligence},
volume = {6},
number = {1},
pages = {9},
abstract = {Abstract
In-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.},
keywords = {Optimization, Quantum Computing, Quantum Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Abstract
In-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.
In-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.
Buonaiuto, Giuseppe; Guarasci, Raffaele; Minutolo, Aniello; Pietro, Giuseppe De; Esposito, Massimo
Quantum transfer learning for acceptability judgements Journal Article
In: Quantum Machine Intelligence, vol. 6, no. 1, pp. 13, 2024, ISSN: 2524-4906, 2524-4914.
Abstract | Links | BibTeX | Tags: Quantum Computing, Quantum Machine Learning, Quantum Natural Language Processing, Variational Quantum Classifier
@article{buonaiutoQuantumTransferLearning2024,
title = {Quantum transfer learning for acceptability judgements},
author = {Giuseppe Buonaiuto and Raffaele Guarasci and Aniello Minutolo and Giuseppe De Pietro and Massimo Esposito},
url = {https://link.springer.com/10.1007/s42484-024-00141-8},
doi = {10.1007/s42484-024-00141-8},
issn = {2524-4906, 2524-4914},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-21},
journal = {Quantum Machine Intelligence},
volume = {6},
number = {1},
pages = {13},
abstract = {Abstract
Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage. This work shows potential advantages, in terms of both performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a large language model to perform classification on a classical linguistics task—acceptability judgements. Acceptability judgement is the ability to determine whether a sentence is considered natural and well-formed by a native speaker. The approach has been tested on sentences extracted from ItaCoLa, a corpus that collects Italian sentences labeled with their acceptability judgement. The evaluation phase shows results for the quantum transfer learning pipeline comparable to state-of-the-art classical transfer learning algorithms, proving current quantum computers’ capabilities to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative linguistic analysis, aided by explainable AI methods, reveals the capabilities of quantum transfer learning algorithms to correctly classify complex and more structured sentences, compared to their classical counterpart. This finding sets the ground for a quantifiable quantum advantage in NLP in the near future.},
keywords = {Quantum Computing, Quantum Machine Learning, Quantum Natural Language Processing, Variational Quantum Classifier},
pubstate = {published},
tppubtype = {article}
}
Abstract
Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage. This work shows potential advantages, in terms of both performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a large language model to perform classification on a classical linguistics task—acceptability judgements. Acceptability judgement is the ability to determine whether a sentence is considered natural and well-formed by a native speaker. The approach has been tested on sentences extracted from ItaCoLa, a corpus that collects Italian sentences labeled with their acceptability judgement. The evaluation phase shows results for the quantum transfer learning pipeline comparable to state-of-the-art classical transfer learning algorithms, proving current quantum computers’ capabilities to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative linguistic analysis, aided by explainable AI methods, reveals the capabilities of quantum transfer learning algorithms to correctly classify complex and more structured sentences, compared to their classical counterpart. This finding sets the ground for a quantifiable quantum advantage in NLP in the near future.
Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage. This work shows potential advantages, in terms of both performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a large language model to perform classification on a classical linguistics task—acceptability judgements. Acceptability judgement is the ability to determine whether a sentence is considered natural and well-formed by a native speaker. The approach has been tested on sentences extracted from ItaCoLa, a corpus that collects Italian sentences labeled with their acceptability judgement. The evaluation phase shows results for the quantum transfer learning pipeline comparable to state-of-the-art classical transfer learning algorithms, proving current quantum computers’ capabilities to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative linguistic analysis, aided by explainable AI methods, reveals the capabilities of quantum transfer learning algorithms to correctly classify complex and more structured sentences, compared to their classical counterpart. This finding sets the ground for a quantifiable quantum advantage in NLP in the near future.
Buonaiuto, Giuseppe; Guarasci, Raffaele; Minutolo, Aniello; Pietro, Giuseppe De; Esposito, Massimo
Quantum transfer learning for acceptability judgements Journal Article
In: Quantum Machine Intelligence, vol. 6, no. 1, pp. 13, 2024, ISSN: 2524-4906, 2524-4914.
Abstract | Links | BibTeX | Tags: Quantum Computing, Quantum Machine Learning, Quantum Natural Language Processing, Variational Quantum Classifier
@article{buonaiuto_quantum_2024,
title = {Quantum transfer learning for acceptability judgements},
author = {Giuseppe Buonaiuto and Raffaele Guarasci and Aniello Minutolo and Giuseppe De Pietro and Massimo Esposito},
url = {https://link.springer.com/10.1007/s42484-024-00141-8},
doi = {10.1007/s42484-024-00141-8},
issn = {2524-4906, 2524-4914},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-21},
journal = {Quantum Machine Intelligence},
volume = {6},
number = {1},
pages = {13},
abstract = {Abstract
Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage. This work shows potential advantages, in terms of both performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a large language model to perform classification on a classical linguistics task—acceptability judgements. Acceptability judgement is the ability to determine whether a sentence is considered natural and well-formed by a native speaker. The approach has been tested on sentences extracted from ItaCoLa, a corpus that collects Italian sentences labeled with their acceptability judgement. The evaluation phase shows results for the quantum transfer learning pipeline comparable to state-of-the-art classical transfer learning algorithms, proving current quantum computers’ capabilities to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative linguistic analysis, aided by explainable AI methods, reveals the capabilities of quantum transfer learning algorithms to correctly classify complex and more structured sentences, compared to their classical counterpart. This finding sets the ground for a quantifiable quantum advantage in NLP in the near future.},
keywords = {Quantum Computing, Quantum Machine Learning, Quantum Natural Language Processing, Variational Quantum Classifier},
pubstate = {published},
tppubtype = {article}
}
Abstract
Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage. This work shows potential advantages, in terms of both performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a large language model to perform classification on a classical linguistics task—acceptability judgements. Acceptability judgement is the ability to determine whether a sentence is considered natural and well-formed by a native speaker. The approach has been tested on sentences extracted from ItaCoLa, a corpus that collects Italian sentences labeled with their acceptability judgement. The evaluation phase shows results for the quantum transfer learning pipeline comparable to state-of-the-art classical transfer learning algorithms, proving current quantum computers’ capabilities to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative linguistic analysis, aided by explainable AI methods, reveals the capabilities of quantum transfer learning algorithms to correctly classify complex and more structured sentences, compared to their classical counterpart. This finding sets the ground for a quantifiable quantum advantage in NLP in the near future.
Hybrid quantum-classical classifiers promise to positively impact critical aspects of natural language processing tasks, particularly classification-related ones. Among the possibilities currently investigated, quantum transfer learning, i.e., using a quantum circuit for fine-tuning pre-trained classical models for a specific task, is attracting significant attention as a potential platform for proving quantum advantage. This work shows potential advantages, in terms of both performance and expressiveness, of quantum transfer learning algorithms trained on embedding vectors extracted from a large language model to perform classification on a classical linguistics task—acceptability judgements. Acceptability judgement is the ability to determine whether a sentence is considered natural and well-formed by a native speaker. The approach has been tested on sentences extracted from ItaCoLa, a corpus that collects Italian sentences labeled with their acceptability judgement. The evaluation phase shows results for the quantum transfer learning pipeline comparable to state-of-the-art classical transfer learning algorithms, proving current quantum computers’ capabilities to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative linguistic analysis, aided by explainable AI methods, reveals the capabilities of quantum transfer learning algorithms to correctly classify complex and more structured sentences, compared to their classical counterpart. This finding sets the ground for a quantifiable quantum advantage in NLP in the near future.
Buonaiuto, Giuseppe; Gargiulo, Francesco; Pietro, Giuseppe De; Esposito, Massimo; Pota, Marco
The effects of quantum hardware properties on the performances of variational quantum learning algorithms Journal Article
In: Quantum Machine Intelligence, vol. 6, no. 1, pp. 9, 2024, ISSN: 2524-4906, 2524-4914.
Abstract | Links | BibTeX | Tags: Optimization, Quantum Computing, Quantum Machine Learning
@article{buonaiuto_effects_2024,
title = {The effects of quantum hardware properties on the performances of variational quantum learning algorithms},
author = {Giuseppe Buonaiuto and Francesco Gargiulo and Giuseppe De Pietro and Massimo Esposito and Marco Pota},
url = {https://link.springer.com/10.1007/s42484-024-00144-5},
doi = {10.1007/s42484-024-00144-5},
issn = {2524-4906, 2524-4914},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-21},
journal = {Quantum Machine Intelligence},
volume = {6},
number = {1},
pages = {9},
abstract = {Abstract
In-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.},
keywords = {Optimization, Quantum Computing, Quantum Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Abstract
In-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.
In-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.
Melillo, Antonio; Rachedi, Sarah; Caggianese, Giuseppe; Gallo, Luigi; Maiorano, Patrizia; Gimigliano, Francesca; Lucidi, Fabio; Pietro, Giuseppe De; Guida, Maurizio; Giordano, Antonio; Chirico, Andrea
In: Games for Health Journal, pp. g4h.2023.0202, 2024, ISSN: 2161-783X, 2161-7856.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Labor, Pain, User study, Virtual Reality
@article{melillo_synchronization_2024,
title = {Synchronization of a Virtual Reality Scenario to Uterine Contractions for Labor Pain Management: Development Study and Randomized Controlled Trial},
author = {Antonio Melillo and Sarah Rachedi and Giuseppe Caggianese and Luigi Gallo and Patrizia Maiorano and Francesca Gimigliano and Fabio Lucidi and Giuseppe De Pietro and Maurizio Guida and Antonio Giordano and Andrea Chirico},
url = {https://www.liebertpub.com/doi/10.1089/g4h.2023.0202},
doi = {10.1089/g4h.2023.0202},
issn = {2161-783X, 2161-7856},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-21},
journal = {Games for Health Journal},
pages = {g4h.2023.0202},
abstract = {Background: Labor is described as one of the most painful events women can experience through their lives, and labor pain shows unique features and rhythmic fluctuations.
Purpose: The present study aims to evaluate virtual reality (VR) analgesic interventions for active labor with biofeedback-based VR technologies synchronized to uterine activity.
Materials and Methods: We developed a VR system modeled on uterine contractions by connecting it to cardiotocographic equipment. We conducted a randomized controlled trial on a sample of 74 cases and 80 controls during active labor.
Results: Results of the study showed a significant reduction of pain scores compared with both preintervention scores and to control group scores; a significant reduction of anxiety levels both compared with preintervention assessment and to control group and significant reduction in fear of labor experience compared with controls.
Conclusion: VR may be considered as an effective nonpharmacological analgesic technique for the treatment of pain and anxiety and fear of childbirth experience during labor. The developed system could improve personalization of care, modulating the multisensory stimulation tailored to labor progression. Further studies are needed to compare the synchronized VR system to uterine activity and unsynchronized VR interventions.},
keywords = {Artificial Intelligence, Labor, Pain, User study, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Background: Labor is described as one of the most painful events women can experience through their lives, and labor pain shows unique features and rhythmic fluctuations.
Purpose: The present study aims to evaluate virtual reality (VR) analgesic interventions for active labor with biofeedback-based VR technologies synchronized to uterine activity.
Materials and Methods: We developed a VR system modeled on uterine contractions by connecting it to cardiotocographic equipment. We conducted a randomized controlled trial on a sample of 74 cases and 80 controls during active labor.
Results: Results of the study showed a significant reduction of pain scores compared with both preintervention scores and to control group scores; a significant reduction of anxiety levels both compared with preintervention assessment and to control group and significant reduction in fear of labor experience compared with controls.
Conclusion: VR may be considered as an effective nonpharmacological analgesic technique for the treatment of pain and anxiety and fear of childbirth experience during labor. The developed system could improve personalization of care, modulating the multisensory stimulation tailored to labor progression. Further studies are needed to compare the synchronized VR system to uterine activity and unsynchronized VR interventions.
Purpose: The present study aims to evaluate virtual reality (VR) analgesic interventions for active labor with biofeedback-based VR technologies synchronized to uterine activity.
Materials and Methods: We developed a VR system modeled on uterine contractions by connecting it to cardiotocographic equipment. We conducted a randomized controlled trial on a sample of 74 cases and 80 controls during active labor.
Results: Results of the study showed a significant reduction of pain scores compared with both preintervention scores and to control group scores; a significant reduction of anxiety levels both compared with preintervention assessment and to control group and significant reduction in fear of labor experience compared with controls.
Conclusion: VR may be considered as an effective nonpharmacological analgesic technique for the treatment of pain and anxiety and fear of childbirth experience during labor. The developed system could improve personalization of care, modulating the multisensory stimulation tailored to labor progression. Further studies are needed to compare the synchronized VR system to uterine activity and unsynchronized VR interventions.
Dubbioso, Raffaele; Spisto, Myriam; Verde, Laura; Iuzzolino, Valentina Virginia; Senerchia, Gianmaria; Pietro, Giuseppe De; Falco, Ivanoe De; Sannino, Giovanna
Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment Journal Article
In: Biomedical Signal Processing and Control, vol. 89, pp. 105706, 2024, ISSN: 17468094.
Abstract | Links | BibTeX | Tags: Amyotrophic lateral sclerosis (ALS), Artificial Intelligence, Bulbar functions, Classification, Dysarthria, Precision medicine
@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}
}
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.
Dubbioso, Raffaele; Spisto, Myriam; Verde, Laura; Iuzzolino, Valentina Virginia; Senerchia, Gianmaria; Pietro, Giuseppe De; Falco, Ivanoe De; Sannino, Giovanna
Precision medicine in ALS: Identification of new acoustic markers for dysarthria severity assessment Journal Article
In: Biomedical Signal Processing and Control, vol. 89, pp. 105706, 2024, ISSN: 17468094.
Abstract | Links | BibTeX | Tags: Amyotrophic lateral sclerosis (ALS), Artificial Intelligence, Bulbar functions, Classification, Dysarthria, Precision medicine
@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}
}
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.
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
Ethical and regulatory challenges of AI technologies in healthcare: A narrative review Journal Article
In: Heliyon, vol. 10, no. 4, pp. e26297, 2024, ISSN: 24058440.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Decision-Making, Ethics, Healthcare, Regulatory guidelines, Technologies
@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}
}
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.
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.
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
Ethical and regulatory challenges of AI technologies in healthcare: A narrative review Journal Article
In: Heliyon, vol. 10, no. 4, pp. e26297, 2024, ISSN: 24058440.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Decision-Making, Ethics, Healthcare, Regulatory guidelines, Technologies
@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}
}
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.
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.
Dubbioso, Raffaele; Spisto, Myriam; Verde, Laura; Iuzzolino, Valentina Virginia; Senerchia, Gianmaria; Salvatore, Elena; Pietro, Giuseppe De; Falco, Ivanoe De; Sannino, Giovanna
Voice signals database of ALS patients with different dysarthria severity and healthy controls Journal Article
In: Scientific Data, vol. 11, no. 1, pp. 800, 2024.
BibTeX | Tags:
@article{dubbiosoVoiceSignalsDatabase2024,
title = {Voice signals database of ALS patients with different dysarthria severity and healthy controls},
author = {Raffaele Dubbioso and Myriam Spisto and Laura Verde and Valentina Virginia Iuzzolino and Gianmaria Senerchia and Elena Salvatore and Giuseppe De Pietro and Ivanoe De Falco and Giovanna Sannino},
year = {2024},
date = {2024-01-01},
journal = {Scientific Data},
volume = {11},
number = {1},
pages = {800},
publisher = {Nature Publishing Group UK London},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
DiscoverArtificial Intelligence Journal Article
In: Discover, vol. 4, pp. 43, 2024.
BibTeX | Tags:
@article{mennellaDiscoverArtificialIntelligence2024,
title = {DiscoverArtificial Intelligence},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
year = {2024},
date = {2024-01-01},
journal = {Discover},
volume = {4},
pages = {43},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Costagliola, Concetta; Buonaguro, Carmen; Rachedi, Sarah; Gambardella, Ilenia; Mancusi, Luca; Sorrentino, Emanuele; Mallardo, Giuliana; Turco, Maddalena; Marino, Simona; Chirico, Andrea; Caggianese, Giuseppe; Gallo, Luigi; Pietro, Giuseppe De; Giordano, Antonio; Guida, Maurizio
Applicazione della realtà virtuale in corso di travaglio di parto Miscellaneous
2024.
Links | BibTeX | Tags: Healthcare, Human Computer Interaction, Virtual Reality
@misc{costagliola_applicazione_2024,
title = {Applicazione della realtà virtuale in corso di travaglio di parto},
author = {Concetta Costagliola and Carmen Buonaguro and Sarah Rachedi and Ilenia Gambardella and Luca Mancusi and Emanuele Sorrentino and Giuliana Mallardo and Maddalena Turco and Simona Marino and Andrea Chirico and Giuseppe Caggianese and Luigi Gallo and Giuseppe De Pietro and Antonio Giordano and Maurizio Guida},
url = {https://www.sigo.it/wp-content/uploads/2024/04/SIGO2024_finale_Firenze.pdf},
year = {2024},
date = {2024-01-01},
address = {Firenze, Italy},
keywords = {Healthcare, Human Computer Interaction, Virtual Reality},
pubstate = {published},
tppubtype = {misc}
}
Buonaguro, Carmen; Gambardella, Ilenia; Rachedi, Sarah; Costagliola, Concetta; Muoio, Giuseppina; Forno, Sabatino Del; Paino, Jessica Anna Cinzia; Chirico, Andrea; Caggianese, Giuseppe; Gallo, Luigi; Pietro, Giuseppe De; Giordano, Antonio; Guida, Maurizio
Correlazione tra uso della realtà virtuale in corso di travaglio di parto e fear of childbirth Miscellaneous
2024.
Links | BibTeX | Tags: Healthcare, Human Computer Interaction, Virtual Reality
@misc{buonaguro_correlazione_2024,
title = {Correlazione tra uso della realtà virtuale in corso di travaglio di parto e fear of childbirth},
author = {Carmen Buonaguro and Ilenia Gambardella and Sarah Rachedi and Concetta Costagliola and Giuseppina Muoio and Sabatino Del Forno and Jessica Anna Cinzia Paino and Andrea Chirico and Giuseppe Caggianese and Luigi Gallo and Giuseppe De Pietro and Antonio Giordano and Maurizio Guida},
url = {https://www.sigo.it/wp-content/uploads/2024/04/SIGO2024_finale_Firenze.pdf},
year = {2024},
date = {2024-01-01},
address = {Firenze, Italy},
keywords = {Healthcare, Human Computer Interaction, Virtual Reality},
pubstate = {published},
tppubtype = {misc}
}