Publications​

@article{espositoAssessmentPrimaryHuman2023,

title = {Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy},

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

url = {https://www.mdpi.com/2073-4409/12/22/2645},

doi = {10.3390/cells12222645},

issn = {2073-4409},

year  = {2023},

date = {2023-11-01},

urldate = {2024-10-02},

journal = {Cells},

volume = {12},

number = {22},

pages = {2645},

abstract = {We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.},

keywords = {Machine Learning, Neural networks, Raman Spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{esposito_assessment_2023,

title = {Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy},

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

url = {https://www.mdpi.com/2073-4409/12/22/2645},

doi = {10.3390/cells12222645},

issn = {2073-4409},

year  = {2023},

date = {2023-11-01},

urldate = {2024-10-02},

journal = {Cells},

volume = {12},

number = {22},

pages = {2645},

abstract = {We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.},

keywords = {Machine Learning, Neural networks, Raman Spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{esposito_assessment_2023-1,

title = {Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy},

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

url = {https://www.mdpi.com/2073-4409/12/22/2645},

doi = {10.3390/cells12222645},

issn = {2073-4409},

year  = {2023},

date = {2023-11-01},

urldate = {2024-10-02},

journal = {Cells},

volume = {12},

number = {22},

pages = {2645},

abstract = {We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.},

keywords = {Machine Learning, Neural networks, Raman Spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{ferraro_benchmarking_2023,

title = {Benchmarking open source and paid services for speech to text: an analysis of quality and input variety},

author = {Antonino Ferraro and Antonio Galli and Valerio La Gatta and Marco Postiglione},

url = {https://www.frontiersin.org/articles/10.3389/fdata.2023.1210559/full},

doi = {10.3389/fdata.2023.1210559},

issn = {2624-909X},

year  = {2023},

date = {2023-09-01},

urldate = {2024-07-12},

journal = {Frontiers in Big Data},

volume = {6},

pages = {1210559},

abstract = {Introduction 

Speech to text (STT) technology has seen increased usage in recent years for automating transcription of spoken language. To choose the most suitable tool for a given task, it is essential to evaluate the performance and quality of both open source and paid STT services. 

Methods 

In this paper, we conduct a benchmarking study of open source and paid STT services, with a specific focus on assessing their performance concerning the variety of input text. We utilizes ix datasets obtained from diverse sources, including interviews, lectures, and speeches, as input for the STT tools. The evaluation of the instruments employs the Word Error Rate (WER), a standard metric for STT evaluation. 

Results 

Our analysis of the results demonstrates significant variations in the performance of the STT tools based on the input text. Certain tools exhibit superior performance on specific types of audio samples compared to others. Our study provides insights into STT tool performance when handling substantial data volumes, as well as the challenges and opportunities posed by the multimedia nature of the data. 

Discussion 

Although paid services generally demonstrate better accuracy and speed compared to open source alternatives, their performance remains dependent on the input text. The study highlights the need for considering specific requirements and characteristics of the audio samples when selecting an appropriate STT tool.},

keywords = {Artificial Intelligence, ASR, Benchmark, Multimedia, Speech Recognition, Speech to text},

pubstate = {published},

tppubtype = {article}

}

@article{ardimento_evo-gunet3_2023,

title = {Evo-GUNet3++: Using evolutionary algorithms to train UNet-based architectures for efficient 3D lung cancer detection},

author = {Pasquale Ardimento and Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino and Chiara Verdone},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1568494623004830},

doi = {10.1016/j.asoc.2023.110465},

issn = {15684946},

year  = {2023},

date = {2023-09-01},

urldate = {2025-10-22},

journal = {Applied Soft Computing},

volume = {144},

pages = {110465},

abstract = {The early detection of malignant lung nodules can strongly increase the chances of life in lung cancer patients. A computer tomography scan represents an effective way to identify and locate malignant nodules in the body and monitor their growth. However, the reading and interpretation of tomography scans are subject to errors that can be reduced with a second reader. The adoption of image processing systems can reduce the possibility of errors and can support radiologists in ensuring multiple readings of tomography scans. This study proposes a new approach for accurate 3D lung nodule detection starting from computer tomography scans. This work exploits an evolutionary algorithm to build variants of a UNet-based architecture, called GUNet3++, to detect patients affected by lung cancer, from the analysis of CT-scan images of lungs. The approach is validated on the LIDC-IDRI real dataset and results show that it improves segmentation quality metrics (IoU and Dice) over baselines, leading to better 3D models reconstruction of lesions.},

keywords = {Artificial Intelligence, Deep Learning, Deep Neural Network, Healthcare},

pubstate = {published},

tppubtype = {article}

}

@article{defalcoClassificationCovid19Chest2023,

title = {Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach},

author = {Ivanoe De Falco and Giuseppe De Pietro and Giovanna Sannino},

url = {https://link.springer.com/10.1007/s00521-021-06806-w},

doi = {10.1007/s00521-021-06806-w},

issn = {0941-0643, 1433-3058},

year  = {2023},

date = {2023-08-01},

urldate = {2024-07-21},

journal = {Neural Computing and Applications},

volume = {35},

number = {22},

pages = {16061–16071},

abstract = {In medical practice, all decisions, as for example the diagnosis based on the classification of images, must be made reliably and effectively. The possibility of having automatic tools helping doctors in performing these important decisions is highly welcome. Artificial Intelligence techniques, and in particular Deep Learning methods, have proven very effective on these tasks, with excellent performance in terms of classification accuracy. The problem with such methods is that they represent black boxes, so they do not provide users with an explanation of the reasons for their decisions. Confidence from medical experts in clinical decisions can increase if they receive from Artificial Intelligence tools interpretable output under the form of, e.g., explanations in natural language or visualized information. This way, the system outcome can be critically assessed by them, and they can evaluate the trustworthiness of the results. In this paper, we propose a new general-purpose method that relies on interpretability ideas. The approach is based on two successive steps, the former being a filtering scheme typically used in Content-Based Image Retrieval, whereas the latter is an evolutionary algorithm able to classify and, at the same time, automatically extract explicit knowledge under the form of a set of IF-THEN rules. This approach is tested on a set of chest X-ray images aiming at assessing the presence of COVID-19.},

keywords = {Artificial Intelligence, Chest X-ray images, Classification, COVID-19, Evolutionary algorithms},

pubstate = {published},

tppubtype = {article}

}

@article{palombiRoleSensePresence2023,

title = {The role of sense of presence in expressing cognitive abilities in a virtual reality task: an initial validation study},

author = {Tommaso Palombi and Federica Galli and Francesco Giancamilli and Monica D’Amico and Fabio Alivernini and Luigi Gallo and Pietro Neroni and Marco Predazzi and Giuseppe De Pietro and Fabio Lucidi and Antonio Giordano and Andrea Chirico},

url = {https://www.nature.com/articles/s41598-023-40510-0},

doi = {10.1038/s41598-023-40510-0},

issn = {2045-2322},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-24},

journal = {Scientific Reports},

volume = {13},

number = {1},

pages = {13396},

publisher = {Nature Publishing Group},

abstract = {There is a raised interest in literature to use Virtual Reality (VR) technology as an assessment tool for cognitive domains. One of the essential advantages of transforming tests in an immersive virtual environment is the possibility of automatically calculating the test’s score, a time-consuming process under natural conditions. Although the characteristics of VR can deliver different degrees of immersion in a virtual environment, the sense of presence could jeopardize the evolution of these practices. The sense of presence results from a complex interaction between human, contextual factors, and the VR environment. The present study has two aims: firstly, it contributes to the validation of a virtual version of the naturalistic action test (i.e., virtual reality action test); second, it aims to evaluate the role of sense of presence as a critical booster of the expression of cognitive abilities during virtual reality tasks. The study relies on healthy adults tested in virtual and real conditions in a cross-over research design. The study’s results support the validity of the virtual reality action test. Furthermore, two structural equation models are tested to comprehend the role of sense of presence as a moderator in the relationship between cognitive abilities and virtual task performance.},

note = {Number: 1},

keywords = {Artificial Intelligence, Healthcare, Psychology, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{verdeArtificialIntelligenceTechniques2023,

title = {Artificial Intelligence Techniques for the Non-invasive Detection of COVID-19 Through the Analysis of Voice Signals},

author = {Laura Verde and Giuseppe De Pietro and Giovanna Sannino},

url = {https://link.springer.com/10.1007/s13369-021-06041-4},

doi = {10.1007/s13369-021-06041-4},

issn = {2193-567X, 2191-4281},

year  = {2023},

date = {2023-08-01},

urldate = {2024-07-21},

journal = {Arabian Journal for Science and Engineering},

volume = {48},

number = {8},

pages = {11143–11153},

abstract = {Healthcare sensors represent a valid and non-invasive instrument to capture and analyse physiological data. Several vital signals, such as voice signals, can be acquired anytime and anywhere, achieved with the least possible discomfort to the patient thanks to the development of increasingly advanced devices. The integration of sensors with artificial intelligence techniques contributes to the realization of faster and easier solutions aimed at improving early diagnosis, personalized treatment, remote patient monitoring and better decision making, all tasks vital in a critical situation such as that of the COVID-19 pandemic. This paper presents a study about the possibility to support the early and non-invasive detection of COVID-19 through the analysis of voice signals by means of the main machine learning algorithms. If demonstrated, this detection capacity could be embedded in a powerful mobile screening application. To perform this important study, the Coswara dataset is considered. The aim of this investigation is not only to evaluate which machine learning technique best distinguishes a healthy voice from a pathological one, but also to identify which vowel sound is most seriously affected by COVID-19 and is, therefore, most reliable in detecting the pathology. The results show that Random Forest is the technique that classifies most accurately healthy and pathological voices. Moreover, the evaluation of the vowel /e/ allows the detection of the effects of COVID-19 on voice quality with a better accuracy than the other vowels.},

keywords = {Artificial Intelligence, COVID-19, Healthcare, Voice analysis, Vowel sounds},

pubstate = {published},

tppubtype = {article}

}

@article{palombi_role_2023,

title = {The role of sense of presence in expressing cognitive abilities in a virtual reality task: an initial validation study},

author = {Tommaso Palombi and Federica Galli and Francesco Giancamilli and Monica D’Amico and Fabio Alivernini and Luigi Gallo and Pietro Neroni and Marco Predazzi and Giuseppe De Pietro and Fabio Lucidi and Antonio Giordano and Andrea Chirico},

url = {https://www.nature.com/articles/s41598-023-40510-0},

doi = {10.1038/s41598-023-40510-0},

issn = {2045-2322},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-24},

journal = {Scientific Reports},

volume = {13},

number = {1},

pages = {13396},

publisher = {Nature Publishing Group},

abstract = {There is a raised interest in literature to use Virtual Reality (VR) technology as an assessment tool for cognitive domains. One of the essential advantages of transforming tests in an immersive virtual environment is the possibility of automatically calculating the test’s score, a time-consuming process under natural conditions. Although the characteristics of VR can deliver different degrees of immersion in a virtual environment, the sense of presence could jeopardize the evolution of these practices. The sense of presence results from a complex interaction between human, contextual factors, and the VR environment. The present study has two aims: firstly, it contributes to the validation of a virtual version of the naturalistic action test (i.e., virtual reality action test); second, it aims to evaluate the role of sense of presence as a critical booster of the expression of cognitive abilities during virtual reality tasks. The study relies on healthy adults tested in virtual and real conditions in a cross-over research design. The study’s results support the validity of the virtual reality action test. Furthermore, two structural equation models are tested to comprehend the role of sense of presence as a moderator in the relationship between cognitive abilities and virtual task performance.},

note = {Number: 1},

keywords = {Artificial Intelligence, Healthcare, Psychology, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{verde_artificial_2023,

title = {Artificial Intelligence Techniques for the Non-invasive Detection of COVID-19 Through the Analysis of Voice Signals},

author = {Laura Verde and Giuseppe De Pietro and Giovanna Sannino},

url = {https://link.springer.com/10.1007/s13369-021-06041-4},

doi = {10.1007/s13369-021-06041-4},

issn = {2193-567X, 2191-4281},

year  = {2023},

date = {2023-08-01},

urldate = {2024-07-21},

journal = {Arabian Journal for Science and Engineering},

volume = {48},

number = {8},

pages = {11143–11153},

abstract = {Healthcare sensors represent a valid and non-invasive instrument to capture and analyse physiological data. Several vital signals, such as voice signals, can be acquired anytime and anywhere, achieved with the least possible discomfort to the patient thanks to the development of increasingly advanced devices. The integration of sensors with artificial intelligence techniques contributes to the realization of faster and easier solutions aimed at improving early diagnosis, personalized treatment, remote patient monitoring and better decision making, all tasks vital in a critical situation such as that of the COVID-19 pandemic. This paper presents a study about the possibility to support the early and non-invasive detection of COVID-19 through the analysis of voice signals by means of the main machine learning algorithms. If demonstrated, this detection capacity could be embedded in a powerful mobile screening application. To perform this important study, the Coswara dataset is considered. The aim of this investigation is not only to evaluate which machine learning technique best distinguishes a healthy voice from a pathological one, but also to identify which vowel sound is most seriously affected by COVID-19 and is, therefore, most reliable in detecting the pathology. The results show that Random Forest is the technique that classifies most accurately healthy and pathological voices. Moreover, the evaluation of the vowel /e/ allows the detection of the effects of COVID-19 on voice quality with a better accuracy than the other vowels.},

keywords = {Artificial Intelligence, COVID-19, Healthcare, Voice analysis, Vowel sounds},

pubstate = {published},

tppubtype = {article}

}

@article{de_falco_classification_2023,

title = {Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach},

author = {Ivanoe De Falco and Giuseppe De Pietro and Giovanna Sannino},

url = {https://link.springer.com/10.1007/s00521-021-06806-w},

doi = {10.1007/s00521-021-06806-w},

issn = {0941-0643, 1433-3058},

year  = {2023},

date = {2023-08-01},

urldate = {2024-07-21},

journal = {Neural Computing and Applications},

volume = {35},

number = {22},

pages = {16061–16071},

abstract = {In medical practice, all decisions, as for example the diagnosis based on the classification of images, must be made reliably and effectively. The possibility of having automatic tools helping doctors in performing these important decisions is highly welcome. Artificial Intelligence techniques, and in particular Deep Learning methods, have proven very effective on these tasks, with excellent performance in terms of classification accuracy. The problem with such methods is that they represent black boxes, so they do not provide users with an explanation of the reasons for their decisions. Confidence from medical experts in clinical decisions can increase if they receive from Artificial Intelligence tools interpretable output under the form of, e.g., explanations in natural language or visualized information. This way, the system outcome can be critically assessed by them, and they can evaluate the trustworthiness of the results. In this paper, we propose a new general-purpose method that relies on interpretability ideas. The approach is based on two successive steps, the former being a filtering scheme typically used in Content-Based Image Retrieval, whereas the latter is an evolutionary algorithm able to classify and, at the same time, automatically extract explicit knowledge under the form of a set of IF-THEN rules. This approach is tested on a set of chest X-ray images aiming at assessing the presence of COVID-19.},

keywords = {Artificial Intelligence, Chest X-ray images, Classification, COVID-19, Evolutionary algorithms},

pubstate = {published},

tppubtype = {article}

}

@article{ferraroEvaluatingEXplainableArtificial2023,

title = {Evaluating eXplainable artificial intelligence tools for hard disk drive predictive maintenance},

author = {Antonino Ferraro and Antonio Galli and Vincenzo Moscato and Giancarlo Sperlì},

url = {https://link.springer.com/10.1007/s10462-022-10354-7},

doi = {10.1007/s10462-022-10354-7},

issn = {0269-2821, 1573-7462},

year  = {2023},

date = {2023-07-01},

urldate = {2024-07-12},

journal = {Artificial Intelligence Review},

volume = {56},

number = {7},

pages = {7279–7314},

abstract = {In the last years, one of the main challenges in Industry 4.0 concerns maintenance operations optimization, which has been widely dealt with several predictive maintenance frameworks aiming to jointly reduce maintenance costs and downtime intervals. Nevertheless, the most recent and effective frameworks mainly rely on deep learning models, but their internal representations (black box) are too complex for human understanding making difficult explain their predictions. This issue can be challenged by using eXplainable artificial intelligence (XAI) methodologies, the aim of which is to explain the decisions of data-driven AI models, characterizing the strengths and weaknesses of the decision-making process by making results more understandable by humans. In this paper, we focus on explanation of the predictions made by a recurrent neural networks based model, which requires a tree-dimensional dataset because it exploits spatial and temporal features for estimating remaining useful life (RUL) of hard disk drives (HDDs). In particular, we have analyzed in depth as explanations about RUL prediction provided by different XAI tools, compared using different metrics and showing the generated dashboards, can be really useful for supporting predictive maintenance task by means of both global and local explanations. For this aim, we have realized an explanation framework able to investigate local interpretable model-agnostic explanations (LIME) and SHapley Additive exPlanations (SHAP) tools w.r.t. to the Backblaze Dataset and a long short-term memory (LSTM) prediction model. The achieved results show how SHAP outperforms LIME in almost all the considered metrics, resulting a suitable and effective solution for HDD predictive maintenance applications.},

keywords = {Artificial Intelligence, Explainable AI, Industry 4.0},

pubstate = {published},

tppubtype = {article}

}

@article{ferraro_evaluating_2023,

title = {Evaluating eXplainable artificial intelligence tools for hard disk drive predictive maintenance},

author = {Antonino Ferraro and Antonio Galli and Vincenzo Moscato and Giancarlo Sperlì},

url = {https://link.springer.com/10.1007/s10462-022-10354-7},

doi = {10.1007/s10462-022-10354-7},

issn = {0269-2821, 1573-7462},

year  = {2023},

date = {2023-07-01},

urldate = {2024-07-12},

journal = {Artificial Intelligence Review},

volume = {56},

number = {7},

pages = {7279–7314},

abstract = {In the last years, one of the main challenges in Industry 4.0 concerns maintenance operations optimization, which has been widely dealt with several predictive maintenance frameworks aiming to jointly reduce maintenance costs and downtime intervals. Nevertheless, the most recent and effective frameworks mainly rely on deep learning models, but their internal representations (black box) are too complex for human understanding making difficult explain their predictions. This issue can be challenged by using eXplainable artificial intelligence (XAI) methodologies, the aim of which is to explain the decisions of data-driven AI models, characterizing the strengths and weaknesses of the decision-making process by making results more understandable by humans. In this paper, we focus on explanation of the predictions made by a recurrent neural networks based model, which requires a tree-dimensional dataset because it exploits spatial and temporal features for estimating remaining useful life (RUL) of hard disk drives (HDDs). In particular, we have analyzed in depth as explanations about RUL prediction provided by different XAI tools, compared using different metrics and showing the generated dashboards, can be really useful for supporting predictive maintenance task by means of both global and local explanations. For this aim, we have realized an explanation framework able to investigate local interpretable model-agnostic explanations (LIME) and SHapley Additive exPlanations (SHAP) tools w.r.t. to the Backblaze Dataset and a long short-term memory (LSTM) prediction model. The achieved results show how SHAP outperforms LIME in almost all the considered metrics, resulting a suitable and effective solution for HDD predictive maintenance applications.},

keywords = {Artificial Intelligence, Explainable AI, Industry 4.0},

pubstate = {published},

tppubtype = {article}

}

@article{shah_projection_2023,

title = {Projection based inverse reinforcement learning for the analysis of dynamic treatment regimes},

author = {Syed Ihtesham Hussain Shah and Giuseppe De Pietro and Giovanni Paragliola and Antonio Coronato},

url = {https://link.springer.com/10.1007/s10489-022-04173-0},

doi = {10.1007/s10489-022-04173-0},

issn = {0924-669X, 1573-7497},

year  = {2023},

date = {2023-06-01},

urldate = {2024-07-21},

journal = {Applied Intelligence},

volume = {53},

number = {11},

pages = {14072–14084},

abstract = {Abstract 

 Dynamic Treatment Regimes (DTRs) are adaptive treatment strategies that allow clinicians to personalize dynamically the treatment for each patient based on their step-by-step response to their treatment. There are a series of predefined alternative treatments for each disease and any patient may associate with one of these treatments according to his/her demographics. DTRs for a certain disease are studied and evaluated by means of statistical approaches where patients are randomized at each step of the treatment and their responses are observed. Recently, the Reinforcement Learning (RL) paradigm has also been applied to determine DTRs. However, such approaches may be limited by the need to design a true reward function, which may be difficult to formalize when the expert knowledge is not well assessed, as when the DTR is in the design phase. To address this limitation, an extension of the RL paradigm, namely Inverse Reinforcement Learning (IRL), has been adopted to learn the reward function from data, such as those derived from DTR trials. In this paper, we define a Projection Based Inverse Reinforcement Learning (PB-IRL) approach to learn the true underlying reward function for given demonstrations (DTR trials). Such a reward function can be used both to evaluate the set of DTRs determined for a certain disease, as well as to enable an RL-based intelligent agent to self-learn the best way and then act as a decision support system for the clinician.},

keywords = {Artificial Intelligence, Decision Support Systems, Dynamic Treatment Regime, Inverse Reinforcement Learning, Reinforcement Learning},

pubstate = {published},

tppubtype = {article}

}

@article{aversano_forecasting_2023,

title = {Forecasting technical debt evolution in software systems: an empirical study},

author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino and Debora Montano},

url = {https://link.springer.com/10.1007/s11704-022-1541-7},

doi = {10.1007/s11704-022-1541-7},

issn = {2095-2228, 2095-2236},

year  = {2023},

date = {2023-06-01},

urldate = {2024-10-02},

journal = {Frontiers of Computer Science},

volume = {17},

number = {3},

pages = {173210},

abstract = {Technical debt is considered detrimental to the long-term success of software development, but despite the numerous studies in the literature, there are still many aspects that need to be investigated for a better understanding of it. In particular, the main problems that hinder its complete understanding are the absence of a clear definition and a model for its identification, management, and forecasting. Focusing on forecasting technical debt, there is a growing notion that preventing technical debt build-up allows you to identify and address the riskiest debt items for the project before they can permanently compromise it. However, despite this high relevance, the forecast of technical debt is still little explored. To this end, this study aims to evaluate whether the quality metrics of a software system can be useful for the correct prediction of the technical debt. Therefore, the data related to the quality metrics of 8 different open-source software systems were analyzed and supplied as input to multiple machine learning algorithms to perform the prediction of the technical debt. In addition, several partitions of the initial dataset were evaluated to assess whether prediction performance could be improved by performing a data selection. The results obtained show good forecasting performance and the proposed document provides a useful approach to understanding the overall phenomenon of technical debt for practical purposes.},

keywords = {Artificial Intelligence, Empirical Study, Machine Learning, Software Quality, Technical Debt},

pubstate = {published},

tppubtype = {article}

}

@article{desanto_actionreaction_2023,

title = {An action–reaction influence model relying on OSN user-generated content},

author = {Aniello De Santo and Antonino Ferraro and Vincenzo Moscato and Giancarlo Sperlí},

url = {https://link.springer.com/10.1007/s10115-023-01833-6},

doi = {10.1007/s10115-023-01833-6},

issn = {0219-1377, 0219-3116},

year  = {2023},

date = {2023-05-01},

urldate = {2024-07-12},

journal = {Knowledge and Information Systems},

volume = {65},

number = {5},

pages = {2251–2280},

abstract = {Due to the sustained popularization of Online Social Networks (OSNs), it has become of interest for a variety of domains of applications to correctly characterize how the behavior of an individual user can be influenced by the actions of other users in a network. Additionally, the richness of available features in modern OSNs highlights the growing importance of user-generated data in establishing user relations. In this paper, we follow a data-driven methodology and propose a diffusion algorithm designed around user-to-content relationships and an action–reaction paradigm. Crucially, we design our approach by integrating different cross-disciplinary theories of how users influence each other. Thus, we enrich the influence maximization task with a psychological dimension and define a model that ties influence diffusion to recurrent users’ behavior from OSN logs, considering relationships between users mediated by user-generated content. We evaluate our approach over the Yahoo Flickr Creative Commons 100 Million real-world dataset. We measure efficiency and effectiveness by analyzing scalability and spread efficacy and show how our model outperforms existing state-of-the-art methods.},

keywords = {Artificial Intelligence, Diffusion model, Heterogeneous online social network, Influence analysis, Influence maximization, Social network analysis, Social Networks},

pubstate = {published},

tppubtype = {article}

}

@article{de_luca_deep_2023,

title = {A deep attention based approach for predictive maintenance applications in IoT scenarios},

author = {Roberto De Luca and Antonino Ferraro and Antonio Galli and Mosè Gallo and Vincenzo Moscato and Giancarlo Sperlì},

url = {https://www.emerald.com/insight/content/doi/10.1108/JMTM-02-2022-0093/full/html},

doi = {10.1108/JMTM-02-2022-0093},

issn = {1741-038X},

year  = {2023},

date = {2023-05-01},

urldate = {2024-07-12},

journal = {Journal of Manufacturing Technology Management},

volume = {34},

number = {4},

pages = {535–556},

abstract = {Purpose 

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware. 

Design/methodology/approach 

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware. 

Findings 

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques. 

Research limitations/implications 

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time. 

Practical implications 

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature. 

Originality/value 

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.},

keywords = {Artificial Intelligence, Decision Making, Decision Support Systems, Deep Learning, Deep neural networks, Industry 4.0, Predictive Maintenance},

pubstate = {published},

tppubtype = {article}

}

@article{ceccacci_emotional_2023,

title = {Emotional Valence from Facial Expression as an Experience Audit Tool: An Empirical Study in the Context of Opera Performance},

author = {Silvia Ceccacci and Andrea Generosi and Luca Giraldi and Maura Mengoni},

url = {https://www.mdpi.com/1424-8220/23/5/2688},

doi = {10.3390/s23052688},

issn = {1424-8220},

year  = {2023},

date = {2023-03-01},

urldate = {2024-12-28},

journal = {Sensors},

volume = {23},

number = {5},

pages = {2688},

abstract = {This paper aims to explore the potential offered by emotion recognition systems to provide a feasible response to the growing need for audience understanding and development in the field of arts organizations. Through an empirical study, it was investigated whether the emotional valence measured on the audience through an emotion recognition system based on facial expression analysis can be used with an experience audit to: (1) support the understanding of the emotional responses of customers toward any clue that characterizes a staged performance; and (2) systematically investigate the customer’s overall experience in terms of their overall satisfaction. The study was carried out in the context of opera live shows in the open-air neoclassical theater Arena Sferisterio in Macerata, during 11 opera performances. A total of 132 spectators were involved. Both the emotional valence provided by the considered emotion recognition system and the quantitative data related to customers’ satisfaction, collected through a survey, were considered. Results suggest how collected data can be useful for the artistic director to estimate the audience’s overall level of satisfaction and make choices about the specific characteristics of the performance, and that emotional valence measured on the audience during the show can be useful to predict overall customer satisfaction, as measured using traditional self-report methods.},

keywords = {Artificial Intelligence, Emotion Recognition, Tourism, User experience},

pubstate = {published},

tppubtype = {article}

}

@article{generosi_smart_2023,

title = {Smart retrofitting for human factors: a face recognition-based system proposal},

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

url = {https://doi.org/10.1007/s12008-022-01035-4},

doi = {10.1007/s12008-022-01035-4},

issn = {1955-2505},

year  = {2023},

date = {2023-02-01},

urldate = {2024-12-28},

journal = {International Journal on Interactive Design and Manufacturing (IJIDeM)},

volume = {17},

number = {1},

pages = {421–433},

abstract = {Industry nowadays must deal with the so called “fourth industrial revolution”, i.e. Industry 4.0. This revolution is based on the introduction of new paradigms in the manufacturing industry such as flexibility, efficiency, safety, digitization, big data analysis and interconnection. However, human factors’ integration is usually not considered, although included as one of the paradigms. Some of these human factors’ most overlooked aspects are the customization of the worker’s user experience and on-board safety. Moreover, the issue of integrating state of the art technologies on legacy machines is also of utmost importance, as it can make a considerable difference on the economic and environmental aspects of their management, by extending the machine’s life cycle. In response to this issue, the Retrofitting paradigm, the addition of new technologies to legacy machines, has been considered. In this paper we propose a novel modular system architecture for secure authentication and worker’s log-in/log-out traceability based on face recognition and on state-of-the-art Deep Learning and Computer Vision techniques, as Convolutional Neural Networks. Starting from the proposed architecture, we developed and tested a device designed to retrofit legacy machines with such capabilities, keeping particular attention to the interface usability in the design phase, little considered in retrofitting applications along with other Human Factors, despite being one of the pillars of Industry 4.0. This research work’s results showed a dramatic improvement regarding machines on-board access safety.},

keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Centered Design, Industry 4.0, Machine Learning},

pubstate = {published},

tppubtype = {article}

}