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
2023
Vergallo, Roberto; Errico, Alessio; Mainetti, Luca
On the Effectiveness of the 'Follow-the-Sun' Strategy in Mitigating the Carbon Footprint of AI in Cloud Instances Miscellaneous
2023.
Links | BibTeX | Tags: Artificial Intelligence, Carbon Awareness, Energy management, Fintech, Sustainability
@misc{vergallo_effectiveness_2023,
title = {On the Effectiveness of the 'Follow-the-Sun' Strategy in Mitigating the Carbon Footprint of AI in Cloud Instances},
author = {Roberto Vergallo and Alessio Errico and Luca Mainetti},
url = {https://www.ssrn.com/abstract=4566638},
doi = {10.2139/ssrn.4566638},
year = {2023},
date = {2023-01-01},
urldate = {2024-10-02},
keywords = {Artificial Intelligence, Carbon Awareness, Energy management, Fintech, Sustainability},
pubstate = {published},
tppubtype = {misc}
}
Palloni, Lorenzo; Galteri, Leonardo; Bertini, Marco
Optimization Techniques of Deep Learning Models for Visual Quality Improvement Book Section
In: New Trends in Intelligent Software Methodologies, Tools and Techniques, pp. 173–184, IOS Press, 2023, (tex.copyright: All rights reserved).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Compression Artifact Removal, Computer Vision and Pattern Recognition, Generative Adversarial Networks, Machine Learning, Network Quantization, Real-time Video Processing, Super-Resolution, Video Streaming
@incollection{palloniOptimizationTechniquesDeep2023,
title = {Optimization Techniques of Deep Learning Models for Visual Quality Improvement},
author = {Lorenzo Palloni and Leonardo Galteri and Marco Bertini},
url = {https://ebooks.iospress.nl/doi/10.3233/FAIA230233},
year = {2023},
date = {2023-01-01},
booktitle = {New Trends in Intelligent Software Methodologies, Tools and Techniques},
pages = {173–184},
publisher = {IOS Press},
abstract = {Video restoration is a widely studied task in the field of computer vision and image processing. The primary objective of video restoration is to improve the visual quality of degraded videos caused by various factors, such as noise, blur, compression artifacts, and other distortions. In this study, the integration of post-training quantization techniques was investigated to optimize deep learning models for super-resolution inference. The results indicate that reducing the precision of weights and activations in these models substantially decreases the computational complexity and memory requirements without compromising performance, rendering them more practical and cost-effective for real-world applications, where real-time inference is often required. When TensorRT was integrated with PyTorch, the efficiency of the model was further improved taking advantage of the INT8 computational capabilities of recent NVIDIA GPUs.},
note = {tex.copyright: All rights reserved},
keywords = {Artificial Intelligence, Compression Artifact Removal, Computer Vision and Pattern Recognition, Generative Adversarial Networks, Machine Learning, Network Quantization, Real-time Video Processing, Super-Resolution, Video Streaming},
pubstate = {published},
tppubtype = {incollection}
}
Video restoration is a widely studied task in the field of computer vision and image processing. The primary objective of video restoration is to improve the visual quality of degraded videos caused by various factors, such as noise, blur, compression artifacts, and other distortions. In this study, the integration of post-training quantization techniques was investigated to optimize deep learning models for super-resolution inference. The results indicate that reducing the precision of weights and activations in these models substantially decreases the computational complexity and memory requirements without compromising performance, rendering them more practical and cost-effective for real-world applications, where real-time inference is often required. When TensorRT was integrated with PyTorch, the efficiency of the model was further improved taking advantage of the INT8 computational capabilities of recent NVIDIA GPUs.
Ferrari, Claudio; Becattini, Federico; Galteri, Leonardo; Bimbo, Alberto Del
(Compress and restore) N: A robust defense against adversarial attacks on image classification Journal Article
In: ACM Transactions on Multimedia Computing, Communications and Applications, vol. 19, no. 1s, pp. 1–16, 2023, (ISBN: 1551-6857 tex.copyright: All rights reserved).
Abstract | Links | BibTeX | Tags: Adversarial Attacks, Adversarial Defense Mechanisms, Artificial Intelligence, Computer Vision and Pattern Recognition, Gradient Obfuscation, Image Classification, Image Restoration, Robustness in AI Models
@article{ferrariCompressRestoreRobust2023,
title = {(Compress and restore) N: A robust defense against adversarial attacks on image classification},
author = {Claudio Ferrari and Federico Becattini and Leonardo Galteri and Alberto Del Bimbo},
url = {https://dl.acm.org/doi/pdf/10.1145/3524619},
doi = {10.1145/3524619},
year = {2023},
date = {2023-01-01},
journal = {ACM Transactions on Multimedia Computing, Communications and Applications},
volume = {19},
number = {1s},
pages = {1–16},
publisher = {ACM New York, NY},
abstract = {Modern image classification approaches often rely on deep neural networks, which have shown pronounced weakness to adversarial examples: images corrupted with specifically designed yet imperceptible noise that causes the network to misclassify. In this article, we propose a conceptually simple yet robust solution to tackle adversarial attacks on image classification. Our defense works by first applying a JPEG compression with a random quality factor; compression artifacts are subsequently removed by means of a generative model Artifact Restoration GAN. The process can be iterated ensuring the image is not degraded and hence the classification not compromised. We train different AR-GANs for different compression factors, so that we can change its parameters dynamically at each iteration depending on the current compression, making the gradient approximation difficult. We experiment with our defense against three white-box and two blackbox attacks, with a particular focus on the state-of-the-art BPDA attack. Our method does not require any adversarial training, and is independent of both the classifier and the attack. Experiments demonstrate that dynamically changing the AR-GAN parameters is of fundamental importance to obtain significant robustness.},
note = {ISBN: 1551-6857
tex.copyright: All rights reserved},
keywords = {Adversarial Attacks, Adversarial Defense Mechanisms, Artificial Intelligence, Computer Vision and Pattern Recognition, Gradient Obfuscation, Image Classification, Image Restoration, Robustness in AI Models},
pubstate = {published},
tppubtype = {article}
}
Modern image classification approaches often rely on deep neural networks, which have shown pronounced weakness to adversarial examples: images corrupted with specifically designed yet imperceptible noise that causes the network to misclassify. In this article, we propose a conceptually simple yet robust solution to tackle adversarial attacks on image classification. Our defense works by first applying a JPEG compression with a random quality factor; compression artifacts are subsequently removed by means of a generative model Artifact Restoration GAN. The process can be iterated ensuring the image is not degraded and hence the classification not compromised. We train different AR-GANs for different compression factors, so that we can change its parameters dynamically at each iteration depending on the current compression, making the gradient approximation difficult. We experiment with our defense against three white-box and two blackbox attacks, with a particular focus on the state-of-the-art BPDA attack. Our method does not require any adversarial training, and is independent of both the classifier and the attack. Experiments demonstrate that dynamically changing the AR-GAN parameters is of fundamental importance to obtain significant robustness.
Fontanini, Tomaso; Ferrari, Claudio; Lisanti, Giuseppe; Galteri, Leonardo; Berretti, Stefano; Bertozzi, Massimo; Prati, Andrea
FrankenMask: Manipulating semantic masks with transformers for face parts editing Journal Article
In: Pattern Recognition Letters, vol. 176, pp. 14–20, 2023, (ISBN: 0167-8655 tex.copyright: All rights reserved).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Face Editing, Generative Adversarial Networks, Image Processing, Transformer Networks
@article{fontaniniFrankenMaskManipulatingSemantic2023,
title = {FrankenMask: Manipulating semantic masks with transformers for face parts editing},
author = {Tomaso Fontanini and Claudio Ferrari and Giuseppe Lisanti and Leonardo Galteri and Stefano Berretti and Massimo Bertozzi and Andrea Prati},
url = {https://www.sciencedirect.com/science/article/pii/S0167865523002829},
doi = {10.1016/j.patrec.2023.10.010},
year = {2023},
date = {2023-01-01},
journal = {Pattern Recognition Letters},
volume = {176},
pages = {14–20},
publisher = {North-Holland},
abstract = {In this paper, we propose FrankenMask, a novel framework that allows swapping and rearranging face parts in semantic masks for automatic editing of shape-related facial attributes. This is a novel yet challenging task as substituting face parts in a semantic mask requires to account for possible spatial misalignment and the adaptation of surrounding regions. We obtain such a feature by combining a Transformer encoder to learn the spatial relationships of facial parts, with an encoder–decoder architecture, which reconstructs a complete mask from the composition of local parts. Reconstruction and attribute classification results demonstrate the effective synthesis of facial images, while showing the generation of accurate and plausible facial attributes. Code is available at https://github.com/TFonta/FrankenMask_semantic.},
note = {ISBN: 0167-8655
tex.copyright: All rights reserved},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Face Editing, Generative Adversarial Networks, Image Processing, Transformer Networks},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose FrankenMask, a novel framework that allows swapping and rearranging face parts in semantic masks for automatic editing of shape-related facial attributes. This is a novel yet challenging task as substituting face parts in a semantic mask requires to account for possible spatial misalignment and the adaptation of surrounding regions. We obtain such a feature by combining a Transformer encoder to learn the spatial relationships of facial parts, with an encoder–decoder architecture, which reconstructs a complete mask from the composition of local parts. Reconstruction and attribute classification results demonstrate the effective synthesis of facial images, while showing the generation of accurate and plausible facial attributes. Code is available at https://github.com/TFonta/FrankenMask_semantic.
Agnolucci, Lorenzo; Galteri, Leonardo; Bertini, Marco; Bimbo, Alberto Del
Perceptual quality improvement in videoconferencing using keyframes-based gan Journal Article
In: IEEE Transactions on Multimedia, vol. 26, pp. 339–352, 2023, (ISBN: 1520-9210 tex.copyright: All rights reserved).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Compression Artifact Removal, Computer Vision and Pattern Recognition, Face Restoration, Generative Adversarial Networks, Real-time Video Processing, Video Conferencing, Video Streaming
@article{agnolucciPerceptualQualityImprovement2023,
title = {Perceptual quality improvement in videoconferencing using keyframes-based gan},
author = {Lorenzo Agnolucci and Leonardo Galteri and Marco Bertini and Alberto Del Bimbo},
url = {https://ieeexplore.ieee.org/abstract/document/10093128},
doi = {10.1109/TMM.2023.3264882},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Multimedia},
volume = {26},
pages = {339–352},
publisher = {IEEE},
abstract = {In the latest years, videoconferencing has taken a fundamental role in interpersonal relations, both for personal and business purposes. Lossy video compression algorithms are the enabling technology for videoconferencing, as they reduce the bandwidth required for real-time video streaming. However, lossy video compression decreases the perceived visual quality. Thus, many techniques for reducing compression artifacts and improving video visual quality have been proposed in recent years. In this work, we propose a novel GAN-based method for compression artifacts reduction in videoconferencing. Given that, in this context, the speaker is typically in front of the camera and remains the same for the entire duration of the transmission, we can maintain a set of reference keyframes of the person from the higher-quality I-frames that are transmitted within the video stream and exploit them to guide the visual quality improvement; a novel aspect of this approach is the update policy that maintains and updates a compact and effective set of reference keyframes. First, we extract multi-scale features from the compressed and reference frames. Then, our architecture combines these features in a progressive manner according to facial landmarks. This allows the restoration of the high-frequency details lost after the video compression. Experiments show that the proposed approach improves visual quality and generates photo-realistic results even with high compression rates.},
note = {ISBN: 1520-9210
tex.copyright: All rights reserved},
keywords = {Artificial Intelligence, Compression Artifact Removal, Computer Vision and Pattern Recognition, Face Restoration, Generative Adversarial Networks, Real-time Video Processing, Video Conferencing, Video Streaming},
pubstate = {published},
tppubtype = {article}
}
In the latest years, videoconferencing has taken a fundamental role in interpersonal relations, both for personal and business purposes. Lossy video compression algorithms are the enabling technology for videoconferencing, as they reduce the bandwidth required for real-time video streaming. However, lossy video compression decreases the perceived visual quality. Thus, many techniques for reducing compression artifacts and improving video visual quality have been proposed in recent years. In this work, we propose a novel GAN-based method for compression artifacts reduction in videoconferencing. Given that, in this context, the speaker is typically in front of the camera and remains the same for the entire duration of the transmission, we can maintain a set of reference keyframes of the person from the higher-quality I-frames that are transmitted within the video stream and exploit them to guide the visual quality improvement; a novel aspect of this approach is the update policy that maintains and updates a compact and effective set of reference keyframes. First, we extract multi-scale features from the compressed and reference frames. Then, our architecture combines these features in a progressive manner according to facial landmarks. This allows the restoration of the high-frequency details lost after the video compression. Experiments show that the proposed approach improves visual quality and generates photo-realistic results even with high compression rates.
Brancato, Valentina; Brancati, Nadia; Esposito, Giusy; Rosa, Massimo La; Cavaliere, Carlo; Allarà, Ciro; Romeo, Valeria; Pietro, Giuseppe De; Salvatore, Marco; Aiello, Marco; Sangiovanni, Mara
A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer Journal Article
In: Sensors, vol. 23, no. 3, pp. 1552, 2023, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Cancer, Feature selection, Radiomics
@article{brancatoTwoStepFeatureSelection2023,
title = {A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer},
author = {Valentina Brancato and Nadia Brancati and Giusy Esposito and Massimo La Rosa and Carlo Cavaliere and Ciro Allarà and Valeria Romeo and Giuseppe De Pietro and Marco Salvatore and Marco Aiello and Mara Sangiovanni},
url = {https://www.mdpi.com/1424-8220/23/3/1552},
doi = {10.3390/s23031552},
issn = {1424-8220},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-21},
journal = {Sensors},
volume = {23},
number = {3},
pages = {1552},
abstract = {Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.},
keywords = {Artificial Intelligence, Cancer, Feature selection, Radiomics},
pubstate = {published},
tppubtype = {article}
}
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina; Madau, Antonella; Verdone, Chiara
Early Diagnosis of Parkinson's Disease Exploting Motor and Non-Motor Symptoms: Results from the PPMI Cohort Journal Article
In: Procedia Computer Science, vol. 225, pp. 2096–2105, 2023, ISSN: 18770509.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Parkinson Disease
@article{aversanoEarlyDiagnosisParkinsons2023,
title = {Early Diagnosis of Parkinson's Disease Exploting Motor and Non-Motor Symptoms: Results from the PPMI Cohort},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino and Antonella Madau and Chiara Verdone},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1877050923013583},
doi = {10.1016/j.procs.2023.10.200},
issn = {18770509},
year = {2023},
date = {2023-01-01},
urldate = {2024-10-02},
journal = {Procedia Computer Science},
volume = {225},
pages = {2096–2105},
abstract = {Parkinson's is a neurodegenerative disease, with a slow but progressive evolution, which involves some main functions such as the control of movements and balance. Symptoms vary by patient and include motor factors such as tremors and stiffness as well as non-motor symptoms such as cognitive impairment. Its diagnosis is not easy, so it is becoming increasingly necessary to assist doctors in identifying and predicting the disease. Artificial intelligence takes up this challenge and this work proposes a new approach to predict the onset of the disease and monitor patients. The experimentation involved the use of different classification algorithms. The proposed methodology was validated on a large ad hoc data set by compiling data collected by the Parkinson's Progression Markers Initiative (PPMI). Specifically, the study compares the results of the classification taking into consideration only the characteristics belonging to the motor sphere, or those of the non-motor sphere, with the aim of understanding which characteristics are more significant for the identification of the disease. In this regard, a multi-stage feature selection was conducted and SHAP was used to make the model explainable.},
keywords = {Artificial Intelligence, Parkinson Disease},
pubstate = {published},
tppubtype = {article}
}
Parkinson's is a neurodegenerative disease, with a slow but progressive evolution, which involves some main functions such as the control of movements and balance. Symptoms vary by patient and include motor factors such as tremors and stiffness as well as non-motor symptoms such as cognitive impairment. Its diagnosis is not easy, so it is becoming increasingly necessary to assist doctors in identifying and predicting the disease. Artificial intelligence takes up this challenge and this work proposes a new approach to predict the onset of the disease and monitor patients. The experimentation involved the use of different classification algorithms. The proposed methodology was validated on a large ad hoc data set by compiling data collected by the Parkinson's Progression Markers Initiative (PPMI). Specifically, the study compares the results of the classification taking into consideration only the characteristics belonging to the motor sphere, or those of the non-motor sphere, with the aim of understanding which characteristics are more significant for the identification of the disease. In this regard, a multi-stage feature selection was conducted and SHAP was used to make the model explainable.
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review Journal Article
In: IEEE Access, vol. 11, pp. 11024–11043, 2023, ISSN: 2169-3536.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Deep Learning, Healthcare, Rehabilitation
@article{mennellaRoleArtificialIntelligence2023,
title = {The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
url = {https://ieeexplore.ieee.org/document/10015010/},
doi = {10.1109/ACCESS.2023.3236084},
issn = {2169-3536},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-21},
journal = {IEEE Access},
volume = {11},
pages = {11024–11043},
abstract = {Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. Comprehensive state-of-the-art of machine learning methods and applications is presented in this review. Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The main tasks addressed by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings.},
keywords = {Artificial Intelligence, Deep Learning, Healthcare, Rehabilitation},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. Comprehensive state-of-the-art of machine learning methods and applications is presented in this review. Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The main tasks addressed by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings.
Catelli, Rosario; Bevilacqua, Luca; Mariniello, Nicola; Carlo, Vladimiro Scotto Di; Magaldi, Massimo; Fujita, Hamido; Pietro, Giuseppe De; Esposito, Massimo
A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment Journal Article
In: IEEE Access, pp. 1–1, 2023, ISSN: 2169-3536.
Links | BibTeX | Tags: Artificial Intelligence, Biological system modeling, Cultural Heritage, Deep Learning, Sentiment analysis
@article{catelliNewItalianCultural2023,
title = {A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment},
author = {Rosario Catelli and Luca Bevilacqua and Nicola Mariniello and Vladimiro Scotto Di Carlo and Massimo Magaldi and Hamido Fujita and Giuseppe De Pietro and Massimo Esposito},
url = {https://ieeexplore.ieee.org/document/10129178/},
doi = {10.1109/ACCESS.2023.3277490},
issn = {2169-3536},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-21},
journal = {IEEE Access},
pages = {1–1},
keywords = {Artificial Intelligence, Biological system modeling, Cultural Heritage, Deep Learning, Sentiment analysis},
pubstate = {published},
tppubtype = {article}
}
Su, Qiqi; Peretokin, Vadim; Basdekis, Ioannis; Kouris, Ioannis; Maggesi, Jonatan; Sicuranza, Mario; Acebes, Alberto; Bucur, Anca; Mukkala, Vinod Jaswanth Roy; Pozdniakov, Konstantin; Kloukinas, Christos; Koutsouris, Dimitrios D.; Iliadou, Eleftheria; Leontsinis, Ioannis; Gallo, Luigi; Pietro, Giuseppe De; Spanoudakis, George
The SMART BEAR Project: An Overview of Its Infrastructure Proceedings Article
In: Maciaszek, Leszek A.; Mulvenna, Maurice D.; Ziefle, Martina (Ed.): Information and Communication Technologies for Ageing Well and e-Health, pp. 408–425, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-37496-8.
Abstract | Links | BibTeX | Tags: Artificial Intelligence
@inproceedings{suSMARTBEARProject2023,
title = {The SMART BEAR Project: An Overview of Its Infrastructure},
author = {Qiqi Su and Vadim Peretokin and Ioannis Basdekis and Ioannis Kouris and Jonatan Maggesi and Mario Sicuranza and Alberto Acebes and Anca Bucur and Vinod Jaswanth Roy Mukkala and Konstantin Pozdniakov and Christos Kloukinas and Dimitrios D. Koutsouris and Eleftheria Iliadou and Ioannis Leontsinis and Luigi Gallo and Giuseppe De Pietro and George Spanoudakis},
editor = {Leszek A. Maciaszek and Maurice D. Mulvenna and Martina Ziefle},
doi = {10.1007/978-3-031-37496-8_21},
isbn = {978-3-031-37496-8},
year = {2023},
date = {2023-01-01},
booktitle = {Information and Communication Technologies for Ageing Well and e-Health},
pages = {408–425},
publisher = {Springer Nature Switzerland},
address = {Cham},
series = {Communications in Computer and Information Science},
abstract = {The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites.},
keywords = {Artificial Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites.
Ahmadilivani, Mohammad Hasan; Barbareschi, Mario; Barone, Salvatore; Bosio, Alberto; Daneshtalab, Masoud; Torca, Salvatore Della; Gavarini, Gabriele; Jenihhin, Maksim; Raik, Jaan; Ruospo, Annachiara
Special Session: Approximation and Fault Resiliency of DNN Accelerators Proceedings Article
In: 2023 IEEE 41st VLSI Test Symposium (VTS), pp. 1–10, IEEE, 2023.
BibTeX | Tags: Artificial Intelligence, Dependable Systems
@inproceedings{ahmadilivaniSpecialSessionApproximation2023,
title = {Special Session: Approximation and Fault Resiliency of DNN Accelerators},
author = {Mohammad Hasan Ahmadilivani and Mario Barbareschi and Salvatore Barone and Alberto Bosio and Masoud Daneshtalab and Salvatore Della Torca and Gabriele Gavarini and Maksim Jenihhin and Jaan Raik and Annachiara Ruospo},
year = {2023},
date = {2023-01-01},
booktitle = {2023 IEEE 41st VLSI Test Symposium (VTS)},
pages = {1–10},
publisher = {IEEE},
keywords = {Artificial Intelligence, Dependable Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Augello, Agnese; Caggianese, Giuseppe; Gallo, Luigi
VITE I Conference: Contributes in the frame of a Human Augmentation Space Journal Article
In: Journal of the Italian Astronomical Society, vol. 94, no. 1, pp. 97–101, 2023, ISSN: 1824-0178.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Augmented Reality, Enactivism, Human Augmentation, Human Computer Interaction, Virtual Reality
@article{augelloVITEConferenceContributes2023,
title = {VITE I Conference: Contributes in the frame of a Human Augmentation Space},
author = {Agnese Augello and Giuseppe Caggianese and Luigi Gallo},
url = {https://www.memsait.it/volumi/MemSAIT-vol94-n1-2023.php},
doi = {10.36116/MEMSAIT_94n1.2023.97},
issn = {1824-0178},
year = {2023},
date = {2023-01-01},
journal = {Journal of the Italian Astronomical Society},
volume = {94},
number = {1},
pages = {97–101},
abstract = {Our contribution is to examine some of the works presented during the VITE I conference from a perspective of Human Augmentation (HA). In the paper, we provide a definition of HA framed by Enactivism theory, also schematizing our viewpoint in a threedimensional space and into an architecture for designing and implementing HA systems.},
keywords = {Artificial Intelligence, Augmented Reality, Enactivism, Human Augmentation, Human Computer Interaction, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Our contribution is to examine some of the works presented during the VITE I conference from a perspective of Human Augmentation (HA). In the paper, we provide a definition of HA framed by Enactivism theory, also schematizing our viewpoint in a threedimensional space and into an architecture for designing and implementing HA systems.
Barolli, Leonard; Ferraro, Antonino
A Prediction Approach in Health Domain Combining Encoding Strategies and Neural Networks Book Section
In: Barolli, Leonard (Ed.): Advances on P2P, Parallel, Grid, Cloud and Internet Computing, vol. 571, pp. 129–136, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-19944-8 978-3-031-19945-5, (Series Title: Lecture Notes in Networks and Systems).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Deep Learning, Depression, Encoding strategies, Gramian angular field, Healthcare
@incollection{barolli_prediction_2023,
title = {A Prediction Approach in Health Domain Combining Encoding Strategies and Neural Networks},
author = {Leonard Barolli and Antonino Ferraro},
editor = {Leonard Barolli},
url = {https://link.springer.com/10.1007/978-3-031-19945-5_12},
doi = {10.1007/978-3-031-19945-5_12},
isbn = {978-3-031-19944-8 978-3-031-19945-5},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-12},
booktitle = {Advances on P2P, Parallel, Grid, Cloud and Internet Computing},
volume = {571},
pages = {129–136},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Healthcare has always been of paramount importance in the world of scientific research, and the advent of Artificial Intelligence (AI) has contributed to enormous strides in the field of prevention. In particular, research has focused on developing Machine Learning (ML)-based approaches to provide accurate prediction mechanisms to prevent and minimize any health complications [1]. This paper proposes an approach based on Gramian Angular Field (GAF) coding and a convolutional neural network (CNN) to solve a health prediction problem, specifically inherent to a subject’s depressive state.
Specifically, GAF is applied to transform information about a subject’s health condition, modeled as a time series, into images to be used as input for CNN to improve prediction performance. Experiments demonstrate superior performance to the best approach presented to the scientific community.},
note = {Series Title: Lecture Notes in Networks and Systems},
keywords = {Artificial Intelligence, Deep Learning, Depression, Encoding strategies, Gramian angular field, Healthcare},
pubstate = {published},
tppubtype = {incollection}
}
Healthcare has always been of paramount importance in the world of scientific research, and the advent of Artificial Intelligence (AI) has contributed to enormous strides in the field of prevention. In particular, research has focused on developing Machine Learning (ML)-based approaches to provide accurate prediction mechanisms to prevent and minimize any health complications [1]. This paper proposes an approach based on Gramian Angular Field (GAF) coding and a convolutional neural network (CNN) to solve a health prediction problem, specifically inherent to a subject’s depressive state.
Specifically, GAF is applied to transform information about a subject’s health condition, modeled as a time series, into images to be used as input for CNN to improve prediction performance. Experiments demonstrate superior performance to the best approach presented to the scientific community.
Specifically, GAF is applied to transform information about a subject’s health condition, modeled as a time series, into images to be used as input for CNN to improve prediction performance. Experiments demonstrate superior performance to the best approach presented to the scientific community.
Amato, Flora; Barolli, Leonard; Cozzolino, Giovanni; Ferraro, Antonino; Giacalone, Marco
An Intelligent Interface for Human-Computer Interaction in Legal Domain Book Section
In: Barolli, Leonard (Ed.): Advances on P2P, Parallel, Grid, Cloud and Internet Computing, vol. 571, pp. 240–248, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-19944-8 978-3-031-19945-5, (Series Title: Lecture Notes in Networks and Systems).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Chatbot
@incollection{amatoIntelligentInterfaceHumanComputer2023,
title = {An Intelligent Interface for Human-Computer Interaction in Legal Domain},
author = {Flora Amato and Leonard Barolli and Giovanni Cozzolino and Antonino Ferraro and Marco Giacalone},
editor = {Leonard Barolli},
url = {https://link.springer.com/10.1007/978-3-031-19945-5_24},
doi = {10.1007/978-3-031-19945-5_24},
isbn = {978-3-031-19944-8 978-3-031-19945-5},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-12},
booktitle = {Advances on P2P, Parallel, Grid, Cloud and Internet Computing},
volume = {571},
pages = {240–248},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Technological evolution and advances in the field of artificial intelligence have brought about considerable transformations in every area of our lives, also changing our various needs. In particular, the ever-increasing development and use of messaging applications have enabled the growth of services closer to users, as they can be seen as excellent means of advertising, sales and customer service. This is precisely why business models have changed drastically, moving towards new technologies such as ChatBots. The messaging applications nowadays are used daily while ensuring that they can keep up with the pace of this increasingly hectic and demanding world thanks to their 24/7 availability, low costs and customised real-time services. This paper aims to provide a general description and design principle of a ChatBot, designed and developed for the CREA2 (Conflict Resolution with Equitative Algorithms) platform, which includes the management and automatic resolution of disputes concerning the division of assets, trying to avoid costs and bureaucracy.},
note = {Series Title: Lecture Notes in Networks and Systems},
keywords = {Artificial Intelligence, Chatbot},
pubstate = {published},
tppubtype = {incollection}
}
Technological evolution and advances in the field of artificial intelligence have brought about considerable transformations in every area of our lives, also changing our various needs. In particular, the ever-increasing development and use of messaging applications have enabled the growth of services closer to users, as they can be seen as excellent means of advertising, sales and customer service. This is precisely why business models have changed drastically, moving towards new technologies such as ChatBots. The messaging applications nowadays are used daily while ensuring that they can keep up with the pace of this increasingly hectic and demanding world thanks to their 24/7 availability, low costs and customised real-time services. This paper aims to provide a general description and design principle of a ChatBot, designed and developed for the CREA2 (Conflict Resolution with Equitative Algorithms) platform, which includes the management and automatic resolution of disputes concerning the division of assets, trying to avoid costs and bureaucracy.
Generosi, Andrea; Caresana, Flavio; Dourou, Nefeli; Bruschi, Valeria; Cecchi, Stefania; Mengoni, Maura
An Experimentation to Measure the Influence of Music on Emotions Proceedings Article
In: Krömker, Heidi (Ed.): HCI in Mobility, Transport, and Automotive Systems, pp. 142–157, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-35908-8.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Deep Learning, Emotion Recognition
@inproceedings{generosi_experimentation_2023,
title = {An Experimentation to Measure the Influence of Music on Emotions},
author = {Andrea Generosi and Flavio Caresana and Nefeli Dourou and Valeria Bruschi and Stefania Cecchi and Maura Mengoni},
editor = {Heidi Krömker},
doi = {10.1007/978-3-031-35908-8_11},
isbn = {978-3-031-35908-8},
year = {2023},
date = {2023-01-01},
booktitle = {HCI in Mobility, Transport, and Automotive Systems},
pages = {142–157},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Several emotion-adaptive systems frameworks have been proposed to enable listeners’ emotional regulation through music reproduction. However, the majority of these frameworks has been implemented only under in-Lab or in-car conditions, in the second case focusing on improving driving performance. Therefore, to the authors’ best knowledge, no research has been conducted for mobility settings, such as trains, planes, yacht, etc. Focusing on this aspect, the proposed approach reports the results obtained from the study of relationship between listener’s induced emotion and music reproduction exploiting an advanced audio system and an innovative technology for face expressions’ recognition. Starting from an experiment in a university lab scenario, with 15 listeners, and a yacht cabin scenario, with 11 listeners, participants’ emotional variability has been deeply investigated reproducing 4 audio enhanced music tracks, to evaluate the listeners’ emotional “sensitivity” to music stimuli. The experimental results indicated that, during the reproduction in the university lab, listeners’ “happiness” and “anger” states were highly affected by the music stimuli and highlighted a possible relationship between music and listeners’ compound emotions. Furthermore, listeners’ emotional engagement was proven to be more affected by music stimuli in the yacht cabin, rather than the university lab.},
keywords = {Artificial Intelligence, Deep Learning, Emotion Recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
Several emotion-adaptive systems frameworks have been proposed to enable listeners’ emotional regulation through music reproduction. However, the majority of these frameworks has been implemented only under in-Lab or in-car conditions, in the second case focusing on improving driving performance. Therefore, to the authors’ best knowledge, no research has been conducted for mobility settings, such as trains, planes, yacht, etc. Focusing on this aspect, the proposed approach reports the results obtained from the study of relationship between listener’s induced emotion and music reproduction exploiting an advanced audio system and an innovative technology for face expressions’ recognition. Starting from an experiment in a university lab scenario, with 15 listeners, and a yacht cabin scenario, with 11 listeners, participants’ emotional variability has been deeply investigated reproducing 4 audio enhanced music tracks, to evaluate the listeners’ emotional “sensitivity” to music stimuli. The experimental results indicated that, during the reproduction in the university lab, listeners’ “happiness” and “anger” states were highly affected by the music stimuli and highlighted a possible relationship between music and listeners’ compound emotions. Furthermore, listeners’ emotional engagement was proven to be more affected by music stimuli in the yacht cabin, rather than the university lab.
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina; Madau, Antonella; Verdone, Chiara
Early Diagnosis of Parkinson's Disease Exploting Motor and Non-Motor Symptoms: Results from the PPMI Cohort Journal Article
In: Procedia Computer Science, vol. 225, pp. 2096–2105, 2023, ISSN: 18770509.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Parkinson Disease
@article{aversano_early_2023,
title = {Early Diagnosis of Parkinson's Disease Exploting Motor and Non-Motor Symptoms: Results from the PPMI Cohort},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino and Antonella Madau and Chiara Verdone},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1877050923013583},
doi = {10.1016/j.procs.2023.10.200},
issn = {18770509},
year = {2023},
date = {2023-01-01},
urldate = {2024-10-02},
journal = {Procedia Computer Science},
volume = {225},
pages = {2096–2105},
abstract = {Parkinson's is a neurodegenerative disease, with a slow but progressive evolution, which involves some main functions such as the control of movements and balance. Symptoms vary by patient and include motor factors such as tremors and stiffness as well as non-motor symptoms such as cognitive impairment. Its diagnosis is not easy, so it is becoming increasingly necessary to assist doctors in identifying and predicting the disease. Artificial intelligence takes up this challenge and this work proposes a new approach to predict the onset of the disease and monitor patients. The experimentation involved the use of different classification algorithms. The proposed methodology was validated on a large ad hoc data set by compiling data collected by the Parkinson's Progression Markers Initiative (PPMI). Specifically, the study compares the results of the classification taking into consideration only the characteristics belonging to the motor sphere, or those of the non-motor sphere, with the aim of understanding which characteristics are more significant for the identification of the disease. In this regard, a multi-stage feature selection was conducted and SHAP was used to make the model explainable.},
keywords = {Artificial Intelligence, Parkinson Disease},
pubstate = {published},
tppubtype = {article}
}
Parkinson's is a neurodegenerative disease, with a slow but progressive evolution, which involves some main functions such as the control of movements and balance. Symptoms vary by patient and include motor factors such as tremors and stiffness as well as non-motor symptoms such as cognitive impairment. Its diagnosis is not easy, so it is becoming increasingly necessary to assist doctors in identifying and predicting the disease. Artificial intelligence takes up this challenge and this work proposes a new approach to predict the onset of the disease and monitor patients. The experimentation involved the use of different classification algorithms. The proposed methodology was validated on a large ad hoc data set by compiling data collected by the Parkinson's Progression Markers Initiative (PPMI). Specifically, the study compares the results of the classification taking into consideration only the characteristics belonging to the motor sphere, or those of the non-motor sphere, with the aim of understanding which characteristics are more significant for the identification of the disease. In this regard, a multi-stage feature selection was conducted and SHAP was used to make the model explainable.
Amato, Flora; Barolli, Leonard; Cozzolino, Giovanni; Ferraro, Antonino; Giacalone, Marco
An Intelligent Interface for Human-Computer Interaction in Legal Domain Book Section
In: Barolli, Leonard (Ed.): Advances on P2P, Parallel, Grid, Cloud and Internet Computing, vol. 571, pp. 240–248, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-19944-8 978-3-031-19945-5, (Series Title: Lecture Notes in Networks and Systems).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Chatbot
@incollection{barolli_intelligent_2023,
title = {An Intelligent Interface for Human-Computer Interaction in Legal Domain},
author = {Flora Amato and Leonard Barolli and Giovanni Cozzolino and Antonino Ferraro and Marco Giacalone},
editor = {Leonard Barolli},
url = {https://link.springer.com/10.1007/978-3-031-19945-5_24},
doi = {10.1007/978-3-031-19945-5_24},
isbn = {978-3-031-19944-8 978-3-031-19945-5},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-12},
booktitle = {Advances on P2P, Parallel, Grid, Cloud and Internet Computing},
volume = {571},
pages = {240–248},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Technological evolution and advances in the field of artificial intelligence have brought about considerable transformations in every area of our lives, also changing our various needs. In particular, the ever-increasing development and use of messaging applications have enabled the growth of services closer to users, as they can be seen as excellent means of advertising, sales and customer service. This is precisely why business models have changed drastically, moving towards new technologies such as ChatBots. The messaging applications nowadays are used daily while ensuring that they can keep up with the pace of this increasingly hectic and demanding world thanks to their 24/7 availability, low costs and customised real-time services. This paper aims to provide a general description and design principle of a ChatBot, designed and developed for the CREA2 (Conflict Resolution with Equitative Algorithms) platform, which includes the management and automatic resolution of disputes concerning the division of assets, trying to avoid costs and bureaucracy.},
note = {Series Title: Lecture Notes in Networks and Systems},
keywords = {Artificial Intelligence, Chatbot},
pubstate = {published},
tppubtype = {incollection}
}
Technological evolution and advances in the field of artificial intelligence have brought about considerable transformations in every area of our lives, also changing our various needs. In particular, the ever-increasing development and use of messaging applications have enabled the growth of services closer to users, as they can be seen as excellent means of advertising, sales and customer service. This is precisely why business models have changed drastically, moving towards new technologies such as ChatBots. The messaging applications nowadays are used daily while ensuring that they can keep up with the pace of this increasingly hectic and demanding world thanks to their 24/7 availability, low costs and customised real-time services. This paper aims to provide a general description and design principle of a ChatBot, designed and developed for the CREA2 (Conflict Resolution with Equitative Algorithms) platform, which includes the management and automatic resolution of disputes concerning the division of assets, trying to avoid costs and bureaucracy.
Ahmadilivani, Mohammad Hasan; Barbareschi, Mario; Barone, Salvatore; Bosio, Alberto; Daneshtalab, Masoud; Torca, Salvatore Della; Gavarini, Gabriele; Jenihhin, Maksim; Raik, Jaan; Ruospo, Annachiara
Special Session: Approximation and Fault Resiliency of DNN Accelerators Proceedings Article
In: 2023 IEEE 41st VLSI Test Symposium (VTS), pp. 1–10, IEEE, 2023.
BibTeX | Tags: Artificial Intelligence, Dependable Systems
@inproceedings{ahmadilivani_special_2023,
title = {Special Session: Approximation and Fault Resiliency of DNN Accelerators},
author = {Mohammad Hasan Ahmadilivani and Mario Barbareschi and Salvatore Barone and Alberto Bosio and Masoud Daneshtalab and Salvatore Della Torca and Gabriele Gavarini and Maksim Jenihhin and Jaan Raik and Annachiara Ruospo},
year = {2023},
date = {2023-01-01},
booktitle = {2023 IEEE 41st VLSI Test Symposium (VTS)},
pages = {1–10},
publisher = {IEEE},
keywords = {Artificial Intelligence, Dependable Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Augello, Agnese; Caggianese, Giuseppe; Gallo, Luigi
VITE I Conference: Contributes in the frame of a Human Augmentation Space Journal Article
In: Journal of the Italian Astronomical Society, vol. 94, no. 1, pp. 97–101, 2023, ISSN: 1824-0178.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Augmented Reality, Enactivism, Human Augmentation, Human Computer Interaction, Virtual Reality
@article{augello_vite_2023,
title = {VITE I Conference: Contributes in the frame of a Human Augmentation Space},
author = {Agnese Augello and Giuseppe Caggianese and Luigi Gallo},
url = {https://www.memsait.it/volumi/MemSAIT-vol94-n1-2023.php},
doi = {10.36116/MEMSAIT_94n1.2023.97},
issn = {1824-0178},
year = {2023},
date = {2023-01-01},
journal = {Journal of the Italian Astronomical Society},
volume = {94},
number = {1},
pages = {97–101},
abstract = {Our contribution is to examine some of the works presented during the VITE I conference from a perspective of Human Augmentation (HA). In the paper, we provide a definition of HA framed by Enactivism theory, also schematizing our viewpoint in a threedimensional space and into an architecture for designing and implementing HA systems.},
keywords = {Artificial Intelligence, Augmented Reality, Enactivism, Human Augmentation, Human Computer Interaction, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Our contribution is to examine some of the works presented during the VITE I conference from a perspective of Human Augmentation (HA). In the paper, we provide a definition of HA framed by Enactivism theory, also schematizing our viewpoint in a threedimensional space and into an architecture for designing and implementing HA systems.
Su, Qiqi; Peretokin, Vadim; Basdekis, Ioannis; Kouris, Ioannis; Maggesi, Jonatan; Sicuranza, Mario; Acebes, Alberto; Bucur, Anca; Mukkala, Vinod Jaswanth Roy; Pozdniakov, Konstantin; Kloukinas, Christos; Koutsouris, Dimitrios D.; Iliadou, Eleftheria; Leontsinis, Ioannis; Gallo, Luigi; Pietro, Giuseppe De; Spanoudakis, George
The SMART BEAR Project: An Overview of Its Infrastructure Proceedings Article
In: Maciaszek, Leszek A.; Mulvenna, Maurice D.; Ziefle, Martina (Ed.): Information and Communication Technologies for Ageing Well and e-Health, pp. 408–425, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-37496-8.
Abstract | Links | BibTeX | Tags: Artificial Intelligence
@inproceedings{su_smart_2023,
title = {The SMART BEAR Project: An Overview of Its Infrastructure},
author = {Qiqi Su and Vadim Peretokin and Ioannis Basdekis and Ioannis Kouris and Jonatan Maggesi and Mario Sicuranza and Alberto Acebes and Anca Bucur and Vinod Jaswanth Roy Mukkala and Konstantin Pozdniakov and Christos Kloukinas and Dimitrios D. Koutsouris and Eleftheria Iliadou and Ioannis Leontsinis and Luigi Gallo and Giuseppe De Pietro and George Spanoudakis},
editor = {Leszek A. Maciaszek and Maurice D. Mulvenna and Martina Ziefle},
doi = {10.1007/978-3-031-37496-8_21},
isbn = {978-3-031-37496-8},
year = {2023},
date = {2023-01-01},
booktitle = {Information and Communication Technologies for Ageing Well and e-Health},
pages = {408–425},
publisher = {Springer Nature Switzerland},
address = {Cham},
series = {Communications in Computer and Information Science},
abstract = {The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites.},
keywords = {Artificial Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites.
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review Journal Article
In: IEEE Access, vol. 11, pp. 11024–11043, 2023, ISSN: 2169-3536.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Deep Learning, Healthcare, Rehabilitation
@article{mennella_role_2023,
title = {The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
url = {https://ieeexplore.ieee.org/document/10015010/},
doi = {10.1109/ACCESS.2023.3236084},
issn = {2169-3536},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-21},
journal = {IEEE Access},
volume = {11},
pages = {11024–11043},
abstract = {Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. Comprehensive state-of-the-art of machine learning methods and applications is presented in this review. Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The main tasks addressed by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings.},
keywords = {Artificial Intelligence, Deep Learning, Healthcare, Rehabilitation},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. Comprehensive state-of-the-art of machine learning methods and applications is presented in this review. Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The main tasks addressed by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings.
Brancato, Valentina; Brancati, Nadia; Esposito, Giusy; Rosa, Massimo La; Cavaliere, Carlo; Allarà, Ciro; Romeo, Valeria; Pietro, Giuseppe De; Salvatore, Marco; Aiello, Marco; Sangiovanni, Mara
A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer Journal Article
In: Sensors, vol. 23, no. 3, pp. 1552, 2023, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Cancer, Feature selection, Radiomics
@article{brancato_two-step_2023,
title = {A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer},
author = {Valentina Brancato and Nadia Brancati and Giusy Esposito and Massimo La Rosa and Carlo Cavaliere and Ciro Allarà and Valeria Romeo and Giuseppe De Pietro and Marco Salvatore and Marco Aiello and Mara Sangiovanni},
url = {https://www.mdpi.com/1424-8220/23/3/1552},
doi = {10.3390/s23031552},
issn = {1424-8220},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-21},
journal = {Sensors},
volume = {23},
number = {3},
pages = {1552},
abstract = {Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.},
keywords = {Artificial Intelligence, Cancer, Feature selection, Radiomics},
pubstate = {published},
tppubtype = {article}
}
Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER− classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.
Catelli, Rosario; Bevilacqua, Luca; Mariniello, Nicola; Carlo, Vladimiro Scotto Di; Magaldi, Massimo; Fujita, Hamido; Pietro, Giuseppe De; Esposito, Massimo
A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment Journal Article
In: IEEE Access, pp. 1–1, 2023, ISSN: 2169-3536.
Links | BibTeX | Tags: Artificial Intelligence, Biological system modeling, Cultural Heritage, Deep Learning, Sentiment analysis
@article{catelli_new_2023,
title = {A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment},
author = {Rosario Catelli and Luca Bevilacqua and Nicola Mariniello and Vladimiro Scotto Di Carlo and Massimo Magaldi and Hamido Fujita and Giuseppe De Pietro and Massimo Esposito},
url = {https://ieeexplore.ieee.org/document/10129178/},
doi = {10.1109/ACCESS.2023.3277490},
issn = {2169-3536},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-21},
journal = {IEEE Access},
pages = {1–1},
keywords = {Artificial Intelligence, Biological system modeling, Cultural Heritage, Deep Learning, Sentiment analysis},
pubstate = {published},
tppubtype = {article}
}
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina; Madau, Antonella; Verdone, Chiara
Early Diagnosis of Parkinson's Disease Exploting Motor and Non-Motor Symptoms: Results from the PPMI Cohort Journal Article
In: Procedia Computer Science, vol. 225, pp. 2096–2105, 2023, ISSN: 18770509.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Deep Learning, Parkinson Disease
@article{aversano_early_2023-1,
title = {Early Diagnosis of Parkinson's Disease Exploting Motor and Non-Motor Symptoms: Results from the PPMI Cohort},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino and Antonella Madau and Chiara Verdone},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1877050923013583},
doi = {10.1016/j.procs.2023.10.200},
issn = {18770509},
year = {2023},
date = {2023-01-01},
urldate = {2024-10-02},
journal = {Procedia Computer Science},
volume = {225},
pages = {2096–2105},
abstract = {Parkinson's is a neurodegenerative disease, with a slow but progressive evolution, which involves some main functions such as the control of movements and balance. Symptoms vary by patient and include motor factors such as tremors and stiffness as well as non-motor symptoms such as cognitive impairment. Its diagnosis is not easy, so it is becoming increasingly necessary to assist doctors in identifying and predicting the disease. Artificial intelligence takes up this challenge and this work proposes a new approach to predict the onset of the disease and monitor patients. The experimentation involved the use of different classification algorithms. The proposed methodology was validated on a large ad hoc data set by compiling data collected by the Parkinson's Progression Markers Initiative (PPMI). Specifically, the study compares the results of the classification taking into consideration only the characteristics belonging to the motor sphere, or those of the non-motor sphere, with the aim of understanding which characteristics are more significant for the identification of the disease. In this regard, a multi-stage feature selection was conducted and SHAP was used to make the model explainable.},
keywords = {Artificial Intelligence, Deep Learning, Parkinson Disease},
pubstate = {published},
tppubtype = {article}
}
Parkinson's is a neurodegenerative disease, with a slow but progressive evolution, which involves some main functions such as the control of movements and balance. Symptoms vary by patient and include motor factors such as tremors and stiffness as well as non-motor symptoms such as cognitive impairment. Its diagnosis is not easy, so it is becoming increasingly necessary to assist doctors in identifying and predicting the disease. Artificial intelligence takes up this challenge and this work proposes a new approach to predict the onset of the disease and monitor patients. The experimentation involved the use of different classification algorithms. The proposed methodology was validated on a large ad hoc data set by compiling data collected by the Parkinson's Progression Markers Initiative (PPMI). Specifically, the study compares the results of the classification taking into consideration only the characteristics belonging to the motor sphere, or those of the non-motor sphere, with the aim of understanding which characteristics are more significant for the identification of the disease. In this regard, a multi-stage feature selection was conducted and SHAP was used to make the model explainable.
Guarasci, Raffaele; Silvestri, Stefano; Pietro, Giuseppe De; Fujita, Hamido; Esposito, Massimo
Assessing BERT’s ability to learn Italian syntax: a study on null-subject and agreement phenomena Journal Article
In: Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 1, pp. 289–303, 2023, ISSN: 1868-5137, 1868-5145.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Dependency parse tree, Neural language model, Structural probe, Syntactic phenomena
@article{guarasci_assessing_2023,
title = {Assessing BERT’s ability to learn Italian syntax: a study on null-subject and agreement phenomena},
author = {Raffaele Guarasci and Stefano Silvestri and Giuseppe De Pietro and Hamido Fujita and Massimo Esposito},
url = {https://link.springer.com/10.1007/s12652-021-03297-4},
doi = {10.1007/s12652-021-03297-4},
issn = {1868-5137, 1868-5145},
year = {2023},
date = {2023-01-01},
urldate = {2024-07-21},
journal = {Journal of Ambient Intelligence and Humanized Computing},
volume = {14},
number = {1},
pages = {289–303},
abstract = {The work presented in this paper investigates the ability of BERT neural language model pretrained in Italian to embed syntactic dependency relationships into its layers, by approximating a Dependency Parse Tree. To this end, a structural probe, namely a supervised model able to extract linguistic structures from a language model, has been trained leveraging the contextual embeddings from the layers of BERT. An experimental assessment has been performed using an Italian version of BERT-base model and a set of datasets for Italian labelled with Universal Dependencies formalism. The results, achieved using standard metrics of dependency parsers, have shown that a knowledge of the Italian syntax is embedded in central-upper layers of the BERT model, according to what observed in literature for the English case. In addition, the probe has been also used to experimentally evaluate the BERT model behaviour in case of two specific syntactic phenomena in Italian, namely null-subject and subject-verb-agreement, showing better performance than an Italian state-of-the-art parser. These findings can open a path for the development of new hybrid approaches, exploiting the probe to integrate or improve limits or weaknesses in analysing articulated constructions of Italian syntax, traditionally complex to be parsed.},
keywords = {Artificial Intelligence, Dependency parse tree, Neural language model, Structural probe, Syntactic phenomena},
pubstate = {published},
tppubtype = {article}
}
The work presented in this paper investigates the ability of BERT neural language model pretrained in Italian to embed syntactic dependency relationships into its layers, by approximating a Dependency Parse Tree. To this end, a structural probe, namely a supervised model able to extract linguistic structures from a language model, has been trained leveraging the contextual embeddings from the layers of BERT. An experimental assessment has been performed using an Italian version of BERT-base model and a set of datasets for Italian labelled with Universal Dependencies formalism. The results, achieved using standard metrics of dependency parsers, have shown that a knowledge of the Italian syntax is embedded in central-upper layers of the BERT model, according to what observed in literature for the English case. In addition, the probe has been also used to experimentally evaluate the BERT model behaviour in case of two specific syntactic phenomena in Italian, namely null-subject and subject-verb-agreement, showing better performance than an Italian state-of-the-art parser. These findings can open a path for the development of new hybrid approaches, exploiting the probe to integrate or improve limits or weaknesses in analysing articulated constructions of Italian syntax, traditionally complex to be parsed.