Publications
OUR RESEARCH
Scientific Publications
Here you can find the comprehensive list of publications from the members of the Research Center on Computer Vision and eXtended Reality (xRAI).
Use the tag cloud to filter papers based on specific research topics, or use the menus to filter by year, type of publication, or authors.
For each paper, you have the option to view additional details such as the Abstract, Links, and BibTex record.
Research is formalized curiosity. It is poking and prying with a purpose
Zora Neale Hurston
2025
Intini, Paolo; Blasi, Gianni; Fracella, Francesco; Francone, Antonio; Vergallo, Roberto; Perrone, Daniele
Predicting traffic volumes on road infrastructures in the context of multi-risk assessment frameworks Journal Article
In: International Journal of Disaster Risk Reduction, vol. 117, pp. 105139, 2025, ISSN: 22124209.
Abstract | Links | BibTeX | Tags:
@article{intini_predicting_2025,
title = {Predicting traffic volumes on road infrastructures in the context of multi-risk assessment frameworks},
author = {Paolo Intini and Gianni Blasi and Francesco Fracella and Antonio Francone and Roberto Vergallo and Daniele Perrone},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2212420924009014},
doi = {10.1016/j.ijdrr.2024.105139},
issn = {22124209},
year = {2025},
date = {2025-02-01},
urldate = {2025-01-20},
journal = {International Journal of Disaster Risk Reduction},
volume = {117},
pages = {105139},
abstract = {In multi-risk assessment frameworks involving road infrastructures, measures of exposure to natural hazards include traffic volumes. However, traffic counts are usually collected through traffic counter/radar stations which only cover a small part of the road network. In this study, country-wide Annual Average Daily Traffic (AADT) prediction models based on Italian data were developed to provide direct risk exposure measures both in terms of traffic volumes (continuous variable) and traffic volume discrete classes, using province-/municipality-related geographic, socio-economic and road-related variables as predictors. To ease transferability and applicability of the models, only publicly available predictors were selected. Traditional statistical techniques (generalized linear models for predicting traffic values and ordered logistic models for traffic classes) and Machine Learning (ML) approaches (XGBoost for both regression and classification problems) were used. Both the direct estimation of traffic volumes and the classification into traffic ranges provided satisfactory results in terms of goodness-of-fit and predictive accuracy metrics. Results show that population, occupation, tourism, density, number of lanes, urban environment, complex intersections and ring roads were generally related to an increase in traffic volumes. Distance from large cities and accessibility metrics are inversely related to traffic instead. The application of the XGBoost ML approach proved to be more accurate than traditional approaches only for heavy vehicles. It was discussed how the obtained models can be used as input modules for overall multi-risk assessment frameworks involving road infrastructures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In multi-risk assessment frameworks involving road infrastructures, measures of exposure to natural hazards include traffic volumes. However, traffic counts are usually collected through traffic counter/radar stations which only cover a small part of the road network. In this study, country-wide Annual Average Daily Traffic (AADT) prediction models based on Italian data were developed to provide direct risk exposure measures both in terms of traffic volumes (continuous variable) and traffic volume discrete classes, using province-/municipality-related geographic, socio-economic and road-related variables as predictors. To ease transferability and applicability of the models, only publicly available predictors were selected. Traditional statistical techniques (generalized linear models for predicting traffic values and ordered logistic models for traffic classes) and Machine Learning (ML) approaches (XGBoost for both regression and classification problems) were used. Both the direct estimation of traffic volumes and the classification into traffic ranges provided satisfactory results in terms of goodness-of-fit and predictive accuracy metrics. Results show that population, occupation, tourism, density, number of lanes, urban environment, complex intersections and ring roads were generally related to an increase in traffic volumes. Distance from large cities and accessibility metrics are inversely related to traffic instead. The application of the XGBoost ML approach proved to be more accurate than traditional approaches only for heavy vehicles. It was discussed how the obtained models can be used as input modules for overall multi-risk assessment frameworks involving road infrastructures.
Neroni, Pietro; Caggianese, Giuseppe; Esposito, Ciro; Gallo, Luigi
Real-Time 3D Posture Tracking for Surgeons in Pediatric Minimally Invasive Surgery: Proceedings Article
In: Proceedings of the 17th International Conference on Computer Supported Education, pp. 921–928, SCITEPRESS - Science and Technology Publications, Porto, Portugal, 2025, ISBN: 978-989-758-746-7.
Abstract | Links | BibTeX | Tags: Healthcare, Human Computer Interaction, Surgery, Tracking
@inproceedings{neroni_real-time_2025,
title = {Real-Time 3D Posture Tracking for Surgeons in Pediatric Minimally Invasive Surgery:},
author = {Pietro Neroni and Giuseppe Caggianese and Ciro Esposito and Luigi Gallo},
url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0013504200003932},
doi = {10.5220/0013504200003932},
isbn = {978-989-758-746-7},
year = {2025},
date = {2025-01-01},
urldate = {2025-05-06},
booktitle = {Proceedings of the 17th International Conference on Computer Supported Education},
pages = {921–928},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Porto, Portugal},
abstract = {Minimally invasive pediatric surgery presents ergonomic challenges that significantly increase the risk of musculoskeletal disorders (MSDs) among surgeons due to prolonged periods of suboptimal posture. This study introduces a real-time posture monitoring and correction system designed to address this issue. The system utilizes depth camera technology, interactive feedback mechanisms, advanced skeletal tracking, and ergonomic assessment algorithms to continuously monitor and evaluate surgeons’ posture. Through rapid data processing, the system provides real-time feedback, enabling immediate posture adjustments during surgical procedures. It delivers non-intrusive alerts to inform medical staff when incorrect postures are detected, thereby promoting ergonomic well-being and reducing the incidence of MSDs. Designed for seamless integration into the perioperative environment, the system meets strict requirements for privacy, sterility, and operational efficiency. Beyond its application in surgical practice, the system can also enhance surgical education and training by providing real-time feedback, enabling personalized learning pathways, and gamified simulation exercises. It provides detailed analyses of trainee performance, enabling instructors to deliver targeted feedback and develop adaptive training strategies based on detected posture deviations.},
keywords = {Healthcare, Human Computer Interaction, Surgery, Tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
Minimally invasive pediatric surgery presents ergonomic challenges that significantly increase the risk of musculoskeletal disorders (MSDs) among surgeons due to prolonged periods of suboptimal posture. This study introduces a real-time posture monitoring and correction system designed to address this issue. The system utilizes depth camera technology, interactive feedback mechanisms, advanced skeletal tracking, and ergonomic assessment algorithms to continuously monitor and evaluate surgeons’ posture. Through rapid data processing, the system provides real-time feedback, enabling immediate posture adjustments during surgical procedures. It delivers non-intrusive alerts to inform medical staff when incorrect postures are detected, thereby promoting ergonomic well-being and reducing the incidence of MSDs. Designed for seamless integration into the perioperative environment, the system meets strict requirements for privacy, sterility, and operational efficiency. Beyond its application in surgical practice, the system can also enhance surgical education and training by providing real-time feedback, enabling personalized learning pathways, and gamified simulation exercises. It provides detailed analyses of trainee performance, enabling instructors to deliver targeted feedback and develop adaptive training strategies based on detected posture deviations.
Cristani, Matteo; Zorzan, Mattia; Workneh, Tewabe Chekole; Tomazzoli, Claudio
Data Augmentation for Business Process Alignment: Proof of Concept and Experimental Design Proceedings Article
In: Agents and Multi-agent Systems: Technologies and Applications 2024, pp. 87–97, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-97-6468-6 978-981-97-6469-3.
Abstract | Links | BibTeX | Tags: BPM, Business Processes, Knowledge Engineering
@inproceedings{cristani_data_2025,
title = {Data Augmentation for Business Process Alignment: Proof of Concept and Experimental Design},
author = {Matteo Cristani and Mattia Zorzan and Tewabe Chekole Workneh and Claudio Tomazzoli},
url = {https://link.springer.com/10.1007/978-981-97-6469-3_8},
doi = {10.1007/978-981-97-6469-3_8},
isbn = {978-981-97-6468-6 978-981-97-6469-3},
year = {2025},
date = {2025-01-01},
booktitle = {Agents and Multi-agent Systems: Technologies and Applications 2024},
volume = {406},
pages = {87–97},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {In the dynamic landscape of contemporary corporate operations, extracting knowledge from business processes has emerged as a pivotal factor influencing the success and sustainability of companies. This paper delves into the growing significance of using knowledge extracted from business processes to achieve organizational goals and objectives, shedding light on how it has become central to a company's overall functioning. As businesses increasingly rely on streamlined processes to gain a competitive edge, the impact of insufficient data quantity on these processes must be balanced. Many companies need help with the challenges posed by inadequate data, impeding their ability to make informed decisions and hindering operational efficiency. This research explores the intricate relationship between process mining and data quantity, unraveling the repercussions of suboptimal data practices on organizational performance in business intelligence. It aims to provide a novel approach involving data augmentation to mitigate this problem, allowing companies that rely on poorly logged processes to benefit from process mining and business process alignment.},
keywords = {BPM, Business Processes, Knowledge Engineering},
pubstate = {published},
tppubtype = {inproceedings}
}
In the dynamic landscape of contemporary corporate operations, extracting knowledge from business processes has emerged as a pivotal factor influencing the success and sustainability of companies. This paper delves into the growing significance of using knowledge extracted from business processes to achieve organizational goals and objectives, shedding light on how it has become central to a company's overall functioning. As businesses increasingly rely on streamlined processes to gain a competitive edge, the impact of insufficient data quantity on these processes must be balanced. Many companies need help with the challenges posed by inadequate data, impeding their ability to make informed decisions and hindering operational efficiency. This research explores the intricate relationship between process mining and data quantity, unraveling the repercussions of suboptimal data practices on organizational performance in business intelligence. It aims to provide a novel approach involving data augmentation to mitigate this problem, allowing companies that rely on poorly logged processes to benefit from process mining and business process alignment.
Yimer, Hailemicael Lulseged; Cristani, Matteo; Workneh, Tewabe Chekole; Tomazzoli, Claudio
AI-Driven Nitrogen Stress Management in Cereal Crops via Drone Technology Proceedings Article
In: Agents and Multi-agent Systems: Technologies and Applications 2024, pp. 53–62, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-97-6468-6 978-981-97-6469-3.
Abstract | Links | BibTeX | Tags: Computer vision, Deep Learning, Precision agriculture, Predictive Models
@inproceedings{lulseged_yimer_ai-driven_2025,
title = {AI-Driven Nitrogen Stress Management in Cereal Crops via Drone Technology},
author = {Hailemicael Lulseged Yimer and Matteo Cristani and Tewabe Chekole Workneh and Claudio Tomazzoli},
url = {https://link.springer.com/10.1007/978-981-97-6469-3_5},
doi = {10.1007/978-981-97-6469-3_5},
isbn = {978-981-97-6468-6 978-981-97-6469-3},
year = {2025},
date = {2025-01-01},
booktitle = {Agents and Multi-agent Systems: Technologies and Applications 2024},
volume = {406},
pages = {53–62},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {This paper addresses the global challenge of food production losses caused by plant diseases, pests, and nitrogen stress, focusing on the specific context of Ethiopia where cereal crop yields face a significant annual decline of 20–30%, i.e. losses of 420000 tons per year. Traditional fertilization methods have proven imprecise and inefficient. To tackle this issue, the study proposes an artificial intelligence-based system for early detection, analysis, and treatment of nitrogen stress in cereal crops, particularly corn. The integrated system combines Android applications and drone technology. The system demonstrates robust performance metrics, achieving a mean average precision exceeding 60. The model is described as secure, user-friendly, and reliable, making it suitable for testing in diverse African scenarios.},
keywords = {Computer vision, Deep Learning, Precision agriculture, Predictive Models},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper addresses the global challenge of food production losses caused by plant diseases, pests, and nitrogen stress, focusing on the specific context of Ethiopia where cereal crop yields face a significant annual decline of 20–30%, i.e. losses of 420000 tons per year. Traditional fertilization methods have proven imprecise and inefficient. To tackle this issue, the study proposes an artificial intelligence-based system for early detection, analysis, and treatment of nitrogen stress in cereal crops, particularly corn. The integrated system combines Android applications and drone technology. The system demonstrates robust performance metrics, achieving a mean average precision exceeding 60. The model is described as secure, user-friendly, and reliable, making it suitable for testing in diverse African scenarios.
Maggioli, Filippo; Baieri, Daniele; Rodolà, Emanuele; Melzi, Simone
ReMatching: Low-Resolution Representations for Scalable Shape Correspondence Proceedings Article
In: Leonardis, Aleš; Ricci, Elisa; Roth, Stefan; Russakovsky, Olga; Sattler, Torsten; Varol, Gül (Ed.): Computer Vision – ECCV 2024, pp. 183–200, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-72912-6 978-3-031-72913-3.
Abstract | Links | BibTeX | Tags: Computer graphics, Computer Vision and Pattern Recognition, Geometry Processing, Shape Analysis, Shape Matching, Spectral Geometry
@inproceedings{maggioli_rematching_2025,
title = {ReMatching: Low-Resolution Representations for Scalable Shape Correspondence},
author = {Filippo Maggioli and Daniele Baieri and Emanuele Rodolà and Simone Melzi},
editor = {Aleš Leonardis and Elisa Ricci and Stefan Roth and Olga Russakovsky and Torsten Sattler and Gül Varol},
url = {https://link.springer.com/10.1007/978-3-031-72913-3_11},
doi = {10.1007/978-3-031-72913-3_11},
isbn = {978-3-031-72912-6 978-3-031-72913-3},
year = {2025},
date = {2025-01-01},
urldate = {2025-03-30},
booktitle = {Computer Vision – ECCV 2024},
volume = {15095},
pages = {183–200},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {We introduce ReMatching, a novel shape correspondence solution based on the functional maps framework. Our method, by exploiting a new and appropriate re-meshing paradigm, can target shape-matching tasks even on meshes counting millions of vertices, where the original functional maps does not apply or requires a massive computational cost. The core of our procedure is a time-efficient remeshing algorithm which constructs a low-resolution geometry while acting conservatively on the original topology and metric. These properties allow translating the functional maps optimization problem on the resulting low-resolution representation, thus enabling efficient computation of correspondences with functional map approaches. Finally, we propose an efficient technique for extending the estimated correspondence to the original meshes. We show that our method is more efficient and effective through quantitative and qualitative comparisons, outperforming state-of-the-art pipelines in quality and computational cost.},
keywords = {Computer graphics, Computer Vision and Pattern Recognition, Geometry Processing, Shape Analysis, Shape Matching, Spectral Geometry},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce ReMatching, a novel shape correspondence solution based on the functional maps framework. Our method, by exploiting a new and appropriate re-meshing paradigm, can target shape-matching tasks even on meshes counting millions of vertices, where the original functional maps does not apply or requires a massive computational cost. The core of our procedure is a time-efficient remeshing algorithm which constructs a low-resolution geometry while acting conservatively on the original topology and metric. These properties allow translating the functional maps optimization problem on the resulting low-resolution representation, thus enabling efficient computation of correspondences with functional map approaches. Finally, we propose an efficient technique for extending the estimated correspondence to the original meshes. We show that our method is more efficient and effective through quantitative and qualitative comparisons, outperforming state-of-the-art pipelines in quality and computational cost.
Krilavičius, Tomas; Paolis, Lucio Tommaso De; Luca, Valerio De; Spjut, Josef
eXtended Reality and Artificial Intelligence in Medicine and Rehabilitation Journal Article
In: Information Systems Frontiers, 2025, ISSN: 13873326.
Abstract | Links | BibTeX | Tags: 3D modeling, Artificial Intelligence, Augmented Reality, Extended reality, Minimally-invasive surgery, Personalized medicine, Pre-operative planning, Surgery
@article{krilavicius_extended_2025,
title = {eXtended Reality and Artificial Intelligence in Medicine and Rehabilitation},
author = {Tomas Krilavičius and Lucio Tommaso De Paolis and Valerio De Luca and Josef Spjut},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217159420&doi=10.1007%2fs10796-025-10580-8&partnerID=40&md5=3bc0bae0925d3f2f6d1a6e1e659b9aae},
doi = {10.1007/s10796-025-10580-8},
issn = {13873326},
year = {2025},
date = {2025-01-01},
journal = {Information Systems Frontiers},
abstract = {This special issue focuses on the application of eXtended Reality (XR) technologies—comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—and Artificial Intelligence (AI) in the fields of medicine and rehabilitation. AR provides support in minimally invasive surgery, where it visualises internal anatomical structures on the patient’s body and provides real-time feedback to improve accuracy, keep the surgeon’s attention and reduce the risk of errors. Furthermore, XR technologies can be used to develop applications for pre-operative planning or for training surgeons through serious games. AI finds applications both in medical image processing, for the recognition of anatomical structures and the reconstruction of 3D models, and in the analysis of biological data for patient monitoring and disease diagnosis. In rehabilitation, XR and AI can enable personalised therapy plans, increase patient engagement through immersive environments and provide real-time feedback to improve recovery outcomes. The papers in this special issue deal with rehabilitation through serious games, AI-enhanced XR applications for healthcare, digital twins and the analysis of bio/neuro-adaptive signals. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.},
keywords = {3D modeling, Artificial Intelligence, Augmented Reality, Extended reality, Minimally-invasive surgery, Personalized medicine, Pre-operative planning, Surgery},
pubstate = {published},
tppubtype = {article}
}
This special issue focuses on the application of eXtended Reality (XR) technologies—comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—and Artificial Intelligence (AI) in the fields of medicine and rehabilitation. AR provides support in minimally invasive surgery, where it visualises internal anatomical structures on the patient’s body and provides real-time feedback to improve accuracy, keep the surgeon’s attention and reduce the risk of errors. Furthermore, XR technologies can be used to develop applications for pre-operative planning or for training surgeons through serious games. AI finds applications both in medical image processing, for the recognition of anatomical structures and the reconstruction of 3D models, and in the analysis of biological data for patient monitoring and disease diagnosis. In rehabilitation, XR and AI can enable personalised therapy plans, increase patient engagement through immersive environments and provide real-time feedback to improve recovery outcomes. The papers in this special issue deal with rehabilitation through serious games, AI-enhanced XR applications for healthcare, digital twins and the analysis of bio/neuro-adaptive signals. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
2024
Agostinelli, Thomas; Generosi, Andrea; Ceccacci, Silvia; Mengoni, Maura
Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments Journal Article
In: Scientific Reports, vol. 14, no. 1, pp. 27785, 2024, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Centered Design, Industry 4.0
@article{agostinelli_validation_2024,
title = {Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments},
author = {Thomas Agostinelli and Andrea Generosi and Silvia Ceccacci and Maura Mengoni},
url = {https://www.nature.com/articles/s41598-024-79373-4},
doi = {10.1038/s41598-024-79373-4},
issn = {2045-2322},
year = {2024},
date = {2024-11-01},
urldate = {2024-12-28},
journal = {Scientific Reports},
volume = {14},
number = {1},
pages = {27785},
abstract = {This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Centered Design, Industry 4.0},
pubstate = {published},
tppubtype = {article}
}
This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers’ postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers’ postures. By conducting experiments across varied manufacturing environments, this research evaluates the system’s effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.
Vergallo, Roberto; Cagnazzo, Alberto; Mele, Emanuele; Casciaro, Simone
In: Sensors, vol. 24, no. 22, pp. 7246, 2024, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags:
@article{vergallo_measuring_2024,
title = {Measuring the Effectiveness of the ‘Batch Operations’ Energy Design Pattern to Mitigate the Carbon Footprint of Communication Peripherals on Mobile Devices},
author = {Roberto Vergallo and Alberto Cagnazzo and Emanuele Mele and Simone Casciaro},
url = {https://www.mdpi.com/1424-8220/24/22/7246},
doi = {10.3390/s24227246},
issn = {1424-8220},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-20},
journal = {Sensors},
volume = {24},
number = {22},
pages = {7246},
abstract = {The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named ‘Batch Operations’ (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in CO2 emissions. (2) no advantages for the GPS interface.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named ‘Batch Operations’ (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in CO2 emissions. (2) no advantages for the GPS interface.
Tomazzoli, Claudio; Migliorini, Sara; Pastres, Roberto
Forecasting Dissolved Oxygen Level in Land-Based Fish Farms using a Context-Aware Recurrent Neural Network Proceedings Article
In: 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 429–433, IEEE, Padua, Italy, 2024, ISBN: 979-8-3503-5544-4.
Abstract | Links | BibTeX | Tags: Deep Learning, Precision agriculture, Predictive Models, Time series
@inproceedings{tomazzoli_forecasting_2024,
title = {Forecasting Dissolved Oxygen Level in Land-Based Fish Farms using a Context-Aware Recurrent Neural Network},
author = {Claudio Tomazzoli and Sara Migliorini and Roberto Pastres},
url = {https://ieeexplore.ieee.org/document/10948763/},
doi = {10.1109/MetroAgriFor63043.2024.10948763},
isbn = {979-8-3503-5544-4},
year = {2024},
date = {2024-10-01},
urldate = {2025-04-13},
booktitle = {2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
pages = {429–433},
publisher = {IEEE},
address = {Padua, Italy},
abstract = {Predicting Dissolved Oxygen (DO) levels in precision fish farming is crucial as it directly impacts the well-being and growth of fishes. In this paper, we propose a sensing method that is suitable to be used in edge-computing and which makes use of deep learning to estimate dissolved oxygen in fish farms based on a context-aware recurrent neural network trained by the relationship between the inlet dissolved oxygen, the estimated biomass, the period and time of measurement, and the food given to the fish. The proposed technique has been applied to a real-world dataset coming from a trout fish farm located in Trentino, a region in Northern Italy.},
keywords = {Deep Learning, Precision agriculture, Predictive Models, Time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Predicting Dissolved Oxygen (DO) levels in precision fish farming is crucial as it directly impacts the well-being and growth of fishes. In this paper, we propose a sensing method that is suitable to be used in edge-computing and which makes use of deep learning to estimate dissolved oxygen in fish farms based on a context-aware recurrent neural network trained by the relationship between the inlet dissolved oxygen, the estimated biomass, the period and time of measurement, and the food given to the fish. The proposed technique has been applied to a real-world dataset coming from a trout fish farm located in Trentino, a region in Northern Italy.
Rausa, Maria; Gaglio, Salvatore; Augello, Agnese; Caggianese, Giuseppe; Franchini, Silvia; Gallo, Luigi; Sabatucci, Luca
Enriching Metaverse with Memories Through Generative AI: A Case Study Proceedings Article
In: 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 371–376, 2024.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Metaverse, Modeling, Virtual Reality
@inproceedings{rausa_enriching_2024,
title = {Enriching Metaverse with Memories Through Generative AI: A Case Study},
author = {Maria Rausa and Salvatore Gaglio and Agnese Augello and Giuseppe Caggianese and Silvia Franchini and Luigi Gallo and Luca Sabatucci},
url = {https://ieeexplore.ieee.org/abstract/document/10796338},
doi = {10.1109/MetroXRAINE62247.2024.10796338},
year = {2024},
date = {2024-10-01},
urldate = {2025-01-08},
booktitle = {2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {371–376},
abstract = {The paper introduces MetaMemory, an approach to generate 3D models from either textual descriptions or photographs of objects, offering dual input modes for enhanced representation. MetaMemory's architecture is discussed presenting the tools employed in extracting the object from the image, generating the 3D mesh from texts or images, and visualizing the object reconstruction in an immersive scenario. Afterwards, a case study in which we experienced reconstructing memories of ancient crafts is examined together with the achieved results, by highlighting current limitations and potential applications.},
keywords = {Artificial Intelligence, Metaverse, Modeling, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper introduces MetaMemory, an approach to generate 3D models from either textual descriptions or photographs of objects, offering dual input modes for enhanced representation. MetaMemory's architecture is discussed presenting the tools employed in extracting the object from the image, generating the 3D mesh from texts or images, and visualizing the object reconstruction in an immersive scenario. Afterwards, a case study in which we experienced reconstructing memories of ancient crafts is examined together with the achieved results, by highlighting current limitations and potential applications.
Recupito, Gilberto; Pecorelli, Fabiano; Catolino, Gemma; Lenarduzzi, Valentina; Taibi, Davide; Nucci, Dario Di; Palomba, Fabio
Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture Journal Article
In: Journal of Systems and Software, vol. 216, pp. 112151, 2024, ISSN: 0164-1212.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Software Engineering, Technical Debt Management
@article{recupitoTechnicalDebtAIenabled2024,
title = {Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture},
author = {Gilberto Recupito and Fabiano Pecorelli and Gemma Catolino and Valentina Lenarduzzi and Davide Taibi and Dario Di Nucci and Fabio Palomba},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121224001961},
doi = {10.1016/j.jss.2024.112151},
issn = {0164-1212},
year = {2024},
date = {2024-10-01},
urldate = {2024-07-07},
journal = {Journal of Systems and Software},
volume = {216},
pages = {112151},
abstract = {Context: Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks related to the systems' quality. Such development alone is insufficient to fully address socio-technical consequences and the need for rapid adaptation to evolutionary changes. Recent work proposed the concept of AI technical debt, a potential liability concerned with developing AI-enabled systems whose impact can affect the overall systems' quality. While the problem of AI technical debt is rapidly gaining the attention of the software engineering research community, scientific knowledge that contributes to understanding and managing the matter is still limited. Objective: In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers' awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject. Method: We develop a survey study featuring 53 AI practitioners, in which we collect information on the practical prevalence, severity, and impact of AI technical debt issues affecting the code and the architecture other than the strategies applied by practitioners to identify and mitigate them. Results: The key findings of the study reveal the multiple impacts that AI technical debt issues may have on the quality of AI-enabled systems (e.g., the high negative impact that Undeclared consumers has on security, whereas Jumbled Model Architecture can induce the code to be hard to maintain) and the little support practitioners have to deal with them, limited to apply manual effort for identification and refactoring. Conclusion: We conclude the article by distilling lessons learned and actionable insights for researchers.},
keywords = {Artificial Intelligence, Software Engineering, Technical Debt Management},
pubstate = {published},
tppubtype = {article}
}
Context: Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks related to the systems' quality. Such development alone is insufficient to fully address socio-technical consequences and the need for rapid adaptation to evolutionary changes. Recent work proposed the concept of AI technical debt, a potential liability concerned with developing AI-enabled systems whose impact can affect the overall systems' quality. While the problem of AI technical debt is rapidly gaining the attention of the software engineering research community, scientific knowledge that contributes to understanding and managing the matter is still limited. Objective: In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers' awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject. Method: We develop a survey study featuring 53 AI practitioners, in which we collect information on the practical prevalence, severity, and impact of AI technical debt issues affecting the code and the architecture other than the strategies applied by practitioners to identify and mitigate them. Results: The key findings of the study reveal the multiple impacts that AI technical debt issues may have on the quality of AI-enabled systems (e.g., the high negative impact that Undeclared consumers has on security, whereas Jumbled Model Architecture can induce the code to be hard to maintain) and the little support practitioners have to deal with them, limited to apply manual effort for identification and refactoring. Conclusion: We conclude the article by distilling lessons learned and actionable insights for researchers.
Aversano, Lerina; Bernardi, Mario Luca; Calgano, Vincenzo; Cimitile, Marta; Esposito, Concetta; Iammarino, Martina; Pisco, Marco; Spaziani, Sara; Verdone, Chiara
Using Machine Learning for Classification of Cancer Cells from Raman Spectroscopy Proceedings Article
In: pp. 15–24, 2024, ISBN: 978-989-758-584-5.
Abstract | Links | BibTeX | Tags: Classification, Healthcare, Machine Learning
@inproceedings{aversano_using_2024,
title = {Using Machine Learning for Classification of Cancer Cells from Raman Spectroscopy},
author = {Lerina Aversano and Mario Luca Bernardi and Vincenzo Calgano and Marta Cimitile and Concetta Esposito and Martina Iammarino and Marco Pisco and Sara Spaziani and Chiara Verdone},
url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0011142600003277},
doi = {10.5220/0011142600003277},
isbn = {978-989-758-584-5},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-02},
pages = {15–24},
abstract = {Since cancer represents one of the leading causes of death worldwide, the development of approaches capable of discerning healthy from diseased cells would be of fundamental importance to support diagnostic and screening techniques. Raman spectroscopy is the most effective molecular analysis technique currently available and provides information on the molecular composition, bonds, chemical environment, phase, and crystalline structure of the samples under examination. This work exploits a combination of Raman spectroscopy and machine learning models to discriminate patients’ liver cells between tumor and non-tumor. The research uses real patient data, provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient with liver cancer. Specifically, the dataset has been built through a long data collection process, which first involved the analysis of the cells with Raman spectroscopy and then the training of two classifiers, Decision Tree and Random Forest. The results show good performance for the trained classifiers, especially those relating to the Random Forest, which reaches an accuracy of 90%.},
keywords = {Classification, Healthcare, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Since cancer represents one of the leading causes of death worldwide, the development of approaches capable of discerning healthy from diseased cells would be of fundamental importance to support diagnostic and screening techniques. Raman spectroscopy is the most effective molecular analysis technique currently available and provides information on the molecular composition, bonds, chemical environment, phase, and crystalline structure of the samples under examination. This work exploits a combination of Raman spectroscopy and machine learning models to discriminate patients’ liver cells between tumor and non-tumor. The research uses real patient data, provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient with liver cancer. Specifically, the dataset has been built through a long data collection process, which first involved the analysis of the cells with Raman spectroscopy and then the training of two classifiers, Decision Tree and Random Forest. The results show good performance for the trained classifiers, especially those relating to the Random Forest, which reaches an accuracy of 90%.
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina; Romanyuk, Kateryna
Investigating on the Relationships between Design Smells Removals and Refactorings Proceedings Article
In: pp. 212–219, 2024, ISBN: 978-989-758-443-5.
Abstract | Links | BibTeX | Tags: Design Smells, Software Evolution, Software Maintenance
@inproceedings{aversano_investigating_2024,
title = {Investigating on the Relationships between Design Smells Removals and Refactorings},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino and Kateryna Romanyuk},
url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0009887102120219},
doi = {10.5220/0009887102120219},
isbn = {978-989-758-443-5},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-02},
pages = {212–219},
abstract = {Software systems continually evolve and this conducts to its architectural degradation due to the existence of numerous design problems. The presence of Design Smells is the main indicator of such problems, it points out the use of constructs that generally hurt system evolution. In this work, an investigation on Design Smells removals has been performed, focusing specifically on the co-occurrence of refactoring and related changes performed on a software system. An empirical study has been conducted considering the evolution history of 5 software systems. The detection of instances of multiple Design Smell types has been performed, along with all the history of the systems, along with, the detection of refactoring activities. The empirical study shows that Design Smells removals are not correlated to the presence of refactoring. The analysis provides useful indications about the percentage of activities conducted on smelly classes, including refactoring (even if these activities in few cases lead to effective smell removals).},
keywords = {Design Smells, Software Evolution, Software Maintenance},
pubstate = {published},
tppubtype = {inproceedings}
}
Software systems continually evolve and this conducts to its architectural degradation due to the existence of numerous design problems. The presence of Design Smells is the main indicator of such problems, it points out the use of constructs that generally hurt system evolution. In this work, an investigation on Design Smells removals has been performed, focusing specifically on the co-occurrence of refactoring and related changes performed on a software system. An empirical study has been conducted considering the evolution history of 5 software systems. The detection of instances of multiple Design Smell types has been performed, along with all the history of the systems, along with, the detection of refactoring activities. The empirical study shows that Design Smells removals are not correlated to the presence of refactoring. The analysis provides useful indications about the percentage of activities conducted on smelly classes, including refactoring (even if these activities in few cases lead to effective smell removals).
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina; Montano, Debora
Is There Any Correlation between Refactoring and Design Smell Occurrence? Proceedings Article
In: pp. 129–136, 2024, ISBN: 978-989-758-588-3.
Abstract | Links | BibTeX | Tags: Design Smells, Refactoring, Software Evolution, Software Quality
@inproceedings{aversano_is_2024,
title = {Is There Any Correlation between Refactoring and Design Smell Occurrence?},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino and Debora Montano},
url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0011139400003266},
doi = {10.5220/0011139400003266},
isbn = {978-989-758-588-3},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-02},
pages = {129–136},
abstract = {Software systems are constantly evolving making their architecture vulnerable to decay and the emergence of numerous design problems. This paper focuses on the occurrence of design smells in software systems and their elimination through the use of refactoring activities. To do this, the data relating to the presence of Design Smell, the use of refactoring, and the result of this use are analyzed in detail. In particular, the history of five open-source Java software systems and of 17 different types of design smells is studied. Overall, the results show that the removal of Design Smells is not correlated with the use of refactoring techniques. The analysis also provides useful insights about the developers’ use of refactoring activities, the likelihood of refactoring on affected commits and clean commits, and removing and/or adding Design Smells both during refactoring and manual code cleaning operations.},
keywords = {Design Smells, Refactoring, Software Evolution, Software Quality},
pubstate = {published},
tppubtype = {inproceedings}
}
Software systems are constantly evolving making their architecture vulnerable to decay and the emergence of numerous design problems. This paper focuses on the occurrence of design smells in software systems and their elimination through the use of refactoring activities. To do this, the data relating to the presence of Design Smell, the use of refactoring, and the result of this use are analyzed in detail. In particular, the history of five open-source Java software systems and of 17 different types of design smells is studied. Overall, the results show that the removal of Design Smells is not correlated with the use of refactoring techniques. The analysis also provides useful insights about the developers’ use of refactoring activities, the likelihood of refactoring on affected commits and clean commits, and removing and/or adding Design Smells both during refactoring and manual code cleaning operations.
Aversano, Lerina; Bernardi, Mario; Cimitile, Marta; Iammarino, Martina; Montano, Debora
An Empirical Study on the Relationship Between the Co-Occurrence of Design Smell and Refactoring Activities Proceedings Article
In: pp. 742–749, 2024, ISBN: 978-989-758-647-7.
Abstract | Links | BibTeX | Tags: Design Smells, Refactoring, Software Evolution, Software Quality
@inproceedings{aversano_empirical_2024,
title = {An Empirical Study on the Relationship Between the Co-Occurrence of Design Smell and Refactoring Activities},
author = {Lerina Aversano and Mario Bernardi and Marta Cimitile and Martina Iammarino and Debora Montano},
url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0012006600003464},
doi = {10.5220/0012006600003464},
isbn = {978-989-758-647-7},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-02},
pages = {742–749},
abstract = {Due to the continuous evolution of software systems, their architecture is subject to damage and the formation of numerous design issues. This empirical study focuses on the co-occurrence of design smells in software systems and refactoring activities. To this end, a detailed analysis is carried out of the data relating to the presence of Design Smells, the use of refactoring, and the consequences of such use. Specifically, the evolution of 17 different types of design odors in five open-source Java software projects has been examined. Overall, the results indicate that the application of refactoring is not used by developers on design smells. This work also offers new and interesting insights for future research methods in this field.},
keywords = {Design Smells, Refactoring, Software Evolution, Software Quality},
pubstate = {published},
tppubtype = {inproceedings}
}
Due to the continuous evolution of software systems, their architecture is subject to damage and the formation of numerous design issues. This empirical study focuses on the co-occurrence of design smells in software systems and refactoring activities. To this end, a detailed analysis is carried out of the data relating to the presence of Design Smells, the use of refactoring, and the consequences of such use. Specifically, the evolution of 17 different types of design odors in five open-source Java software projects has been examined. Overall, the results indicate that the application of refactoring is not used by developers on design smells. This work also offers new and interesting insights for future research methods in this field.
Ardimento, Pasquale; Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Iammarino, Martina
Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks Proceedings Article
In: pp. 310–317, 2024, ISBN: 978-989-758-523-4.
Abstract | Links | BibTeX | Tags: Deep Learning, Design Smells, Software Quality, Transfer learning
@inproceedings{ardimento_transfer_2024,
title = {Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks},
author = {Pasquale Ardimento and Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Martina Iammarino},
url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010602203100317},
doi = {10.5220/0010602203100317},
isbn = {978-989-758-523-4},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-02},
pages = {310–317},
abstract = {This paper investigates whether the adoption of a transfer learning approach can be effective for just-in-time design smells prediction. The approach uses a variant of Temporal Convolutional Networks to predict design smells and a carefully selected fine-grained process and product metrics. The validation is performed on a dataset composed of three open-source systems and includes a comparison between transfer and direct learning. The hypothesis, which we want to verify, is that the proposed transfer learning approach is feasible to transfer the knowledge gained on mature systems to the system of interest to make reliable predictions even at the beginning of development when the available historical data is limited. The obtained results show that, when the class imbalance is high, the transfer learning provides F1-scores very close to the ones obtained by direct learning.},
keywords = {Deep Learning, Design Smells, Software Quality, Transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper investigates whether the adoption of a transfer learning approach can be effective for just-in-time design smells prediction. The approach uses a variant of Temporal Convolutional Networks to predict design smells and a carefully selected fine-grained process and product metrics. The validation is performed on a dataset composed of three open-source systems and includes a comparison between transfer and direct learning. The hypothesis, which we want to verify, is that the proposed transfer learning approach is feasible to transfer the knowledge gained on mature systems to the system of interest to make reliable predictions even at the beginning of development when the available historical data is limited. The obtained results show that, when the class imbalance is high, the transfer learning provides F1-scores very close to the ones obtained by direct learning.
Olivieri, Francesco; Cristani, Matteo; Governatori, Guido; Pasetto, Luca; Rotolo, Antonino; Scannapieco, Simone; Tomazzoli, Claudio; Workneh, Tewabe Chekole
Revising Non-Monotonic Theories with Sufficient and Necessary Conditions: The Case of Defeasible Logic Journal Article
In: Journal of Logic and Computation, pp. 1–28, 2024, ISSN: 0955-792X.
Abstract | Links | BibTeX | Tags: Automatic reasoning, Defeasible logic
@article{olivieri_revising_2024,
title = {Revising Non-Monotonic Theories with Sufficient and Necessary Conditions: The Case of Defeasible Logic},
author = {Francesco Olivieri and Matteo Cristani and Guido Governatori and Luca Pasetto and Antonino Rotolo and Simone Scannapieco and Claudio Tomazzoli and Tewabe Chekole Workneh},
url = {https://doi.org/10.1093/logcom/exae044},
doi = {10.1093/logcom/exae044},
issn = {0955-792X},
year = {2024},
date = {2024-09-01},
journal = {Journal of Logic and Computation},
pages = {1–28},
abstract = {In the setting of Defeasible Logic, we deal with the problem of revising and contracting a non-monotonic theory while minimizing the number of rules to be removed from the theory itself. The process is based on the notions of a set of rules being necessary and sufficient in order to prove a claim. The substantial difference among classical and non-monotonic reasoning processes makes this issue significant in order to achieve the correct revision processes. We show that the process is however computationally hard, and can be solved in polynomial time on non-deterministic machines.},
keywords = {Automatic reasoning, Defeasible logic},
pubstate = {published},
tppubtype = {article}
}
In the setting of Defeasible Logic, we deal with the problem of revising and contracting a non-monotonic theory while minimizing the number of rules to be removed from the theory itself. The process is based on the notions of a set of rules being necessary and sufficient in order to prove a claim. The substantial difference among classical and non-monotonic reasoning processes makes this issue significant in order to achieve the correct revision processes. We show that the process is however computationally hard, and can be solved in polynomial time on non-deterministic machines.
Ruvio, Alessandro; Lamedica, Regina; Geri, Alberto; Maccioni, Marco; Carere, Federico; Alati, Francesca Romana; Carones, Nicola; Buffarini, Guido Guidi
Integrated procedure to design optimal hybrid renewable power plant for railways’ traction power substation Journal Article
In: Sustainable Energy, Grids and Networks, vol. 39, pp. 101446, 2024, ISSN: 23524677.
@article{ruvio_integrated_2024,
title = {Integrated procedure to design optimal hybrid renewable power plant for railways’ traction power substation},
author = {Alessandro Ruvio and Regina Lamedica and Alberto Geri and Marco Maccioni and Federico Carere and Francesca Romana Alati and Nicola Carones and Guido Guidi Buffarini},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2352467724001759},
doi = {10.1016/j.segan.2024.101446},
issn = {23524677},
year = {2024},
date = {2024-09-01},
urldate = {2025-02-18},
journal = {Sustainable Energy, Grids and Networks},
volume = {39},
pages = {101446},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mennella, Ciro; Esposito, Massimo; Pietro, Giuseppe De; Maniscalco, Umberto
Promoting fairness in activity recognition algorithms for patient’s monitoring and evaluation systems in healthcare Journal Article
In: Computers in Biology and Medicine, vol. 179, pp. 108826, 2024, ISSN: 00104825.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Bias, Deep Learning, Motion analysis, Rehabilitation, Time-series
@article{mennella_promoting_2024,
title = {Promoting fairness in activity recognition algorithms for patient’s monitoring and evaluation systems in healthcare},
author = {Ciro Mennella and Massimo Esposito and Giuseppe De Pietro and Umberto Maniscalco},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482524009119},
doi = {10.1016/j.compbiomed.2024.108826},
issn = {00104825},
year = {2024},
date = {2024-09-01},
urldate = {2024-07-21},
journal = {Computers in Biology and Medicine},
volume = {179},
pages = {108826},
abstract = {Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.},
keywords = {Artificial Intelligence, Bias, Deep Learning, Motion analysis, Rehabilitation, Time-series},
pubstate = {published},
tppubtype = {article}
}
Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.
The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis.
Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects’ characteristics on activity recognition performance, providing valuable insights into the algorithm’s robustness across diverse populations.
This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.
Falco, Salvatore Esposito De; Montera, Raffaella; Leo, Sabrina; Laviola, Francesco; Vito, Pietro; Sardanelli, Domenico; Basile, Gianpaolo; Nevi, Giulia; Alaia, Raffaele
Trends and patterns in ESG research: A bibliometric odyssey and research agenda Journal Article
In: Corporate Social Responsibility and Environmental Management, vol. 31, no. 5, pp. 3703–3723, 2024, ISSN: 1535-3958, 1535-3966.
Abstract | Links | BibTeX | Tags: ESG, Strategy and Management, Sustainability
@article{de_falco_trends_2024,
title = {Trends and patterns in ESG research: A bibliometric odyssey and research agenda},
author = {Salvatore Esposito De Falco and Raffaella Montera and Sabrina Leo and Francesco Laviola and Pietro Vito and Domenico Sardanelli and Gianpaolo Basile and Giulia Nevi and Raffaele Alaia},
url = {https://onlinelibrary.wiley.com/doi/10.1002/csr.2744},
doi = {10.1002/csr.2744},
issn = {1535-3958, 1535-3966},
year = {2024},
date = {2024-09-01},
urldate = {2024-10-07},
journal = {Corporate Social Responsibility and Environmental Management},
volume = {31},
number = {5},
pages = {3703–3723},
abstract = {The paper provides a detailed analysis of the ESG literature with the aim of bringing clarity to this area of research and proposing a research agenda. An overview of the development of the literature on ESG pillars is offered through a review of 903 peer‐reviewed articles. The paper identifies four thematic clusters: impacts of ESG disclosure and practices, sustainability, accounting, and responsible investments. The main research streams and sub‐streams for each cluster are discussed, highlighting the most frequent theoretical perspectives and methodologies. Furthermore, the evolution of ESG research across time is delineated. The paper also identifies future research directions within each cluster to advance knowledge, and proposes an integrative framework based on focal themes and their reciprocal connections.},
keywords = {ESG, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {article}
}
The paper provides a detailed analysis of the ESG literature with the aim of bringing clarity to this area of research and proposing a research agenda. An overview of the development of the literature on ESG pillars is offered through a review of 903 peer‐reviewed articles. The paper identifies four thematic clusters: impacts of ESG disclosure and practices, sustainability, accounting, and responsible investments. The main research streams and sub‐streams for each cluster are discussed, highlighting the most frequent theoretical perspectives and methodologies. Furthermore, the evolution of ESG research across time is delineated. The paper also identifies future research directions within each cluster to advance knowledge, and proposes an integrative framework based on focal themes and their reciprocal connections.
Vergallo, Roberto; D’Alò, Teodoro; Mainetti, Luca; Paiano, Roberto; Matino, Sara
Evaluating Sustainable Digitalization: A Carbon-Aware Framework for Enhancing Eco-Friendly Business Process Reengineering Journal Article
In: Sustainability, vol. 16, no. 17, pp. 7789, 2024, ISSN: 2071-1050.
Abstract | Links | BibTeX | Tags: Business Process Management Systems, Carbon Awareness, Digitalization, Sustainability
@article{vergallo_evaluating_2024,
title = {Evaluating Sustainable Digitalization: A Carbon-Aware Framework for Enhancing Eco-Friendly Business Process Reengineering},
author = {Roberto Vergallo and Teodoro D’Alò and Luca Mainetti and Roberto Paiano and Sara Matino},
url = {https://www.mdpi.com/2071-1050/16/17/7789},
doi = {10.3390/su16177789},
issn = {2071-1050},
year = {2024},
date = {2024-09-01},
urldate = {2024-10-02},
journal = {Sustainability},
volume = {16},
number = {17},
pages = {7789},
abstract = {In an era where sustainability is paramount, understanding the environmental impact of digitalizing business processes is critical. Despite the growing emphasis on sustainable practices, there is a lack of comprehensive methodologies to evaluate how digitalization impacts environmental sustainability compared to traditional processes. This paper introduces a carbon-aware methodological framework specifically designed to assess the sustainability of business process reengineering through digitalization. The Digital Green framework quantitatively analyzes the environmental costs associated with digital transformation, ensuring that truly sustainable digitalization results in lower resource consumption relative to the complexity of the process being digitalized. To demonstrate its effectiveness, the framework was applied to a case study involving the reengineering of an administrative process at a small university in southern Italy. The case study highlighted the framework’s ability to quantify the environmental benefits or detriments of digital transformation, thus guiding organizations toward more sustainable digital practices. This research contributes to the field by offering a concrete tool for aligning digitalization efforts with ecological sustainability, and by paving the way for integration with initiatives such as the Green Software Foundation’s Software Carbon Intensity (SCI) specifications.},
keywords = {Business Process Management Systems, Carbon Awareness, Digitalization, Sustainability},
pubstate = {published},
tppubtype = {article}
}
In an era where sustainability is paramount, understanding the environmental impact of digitalizing business processes is critical. Despite the growing emphasis on sustainable practices, there is a lack of comprehensive methodologies to evaluate how digitalization impacts environmental sustainability compared to traditional processes. This paper introduces a carbon-aware methodological framework specifically designed to assess the sustainability of business process reengineering through digitalization. The Digital Green framework quantitatively analyzes the environmental costs associated with digital transformation, ensuring that truly sustainable digitalization results in lower resource consumption relative to the complexity of the process being digitalized. To demonstrate its effectiveness, the framework was applied to a case study involving the reengineering of an administrative process at a small university in southern Italy. The case study highlighted the framework’s ability to quantify the environmental benefits or detriments of digital transformation, thus guiding organizations toward more sustainable digital practices. This research contributes to the field by offering a concrete tool for aligning digitalization efforts with ecological sustainability, and by paving the way for integration with initiatives such as the Green Software Foundation’s Software Carbon Intensity (SCI) specifications.
Marin, Diana; Maggioli, Filippo; Melzi, Simone; Ohrhallinger, Stefan; Wimmer, Michael
Reconstructing Curves from Sparse Samples on Riemannian Manifolds Journal Article
In: Computer Graphics Forum, vol. 43, no. 5, pp. e15136, 2024, ISSN: 0167-7055, 1467-8659.
Abstract | Links | BibTeX | Tags: Computational Geometry, Computer graphics, Geometry Processing, Geometry Reconstruction
@article{marin_reconstructing_2024,
title = {Reconstructing Curves from Sparse Samples on Riemannian Manifolds},
author = {Diana Marin and Filippo Maggioli and Simone Melzi and Stefan Ohrhallinger and Michael Wimmer},
url = {https://onlinelibrary.wiley.com/doi/10.1111/cgf.15136},
doi = {10.1111/cgf.15136},
issn = {0167-7055, 1467-8659},
year = {2024},
date = {2024-08-01},
urldate = {2025-03-30},
journal = {Computer Graphics Forum},
volume = {43},
number = {5},
pages = {e15136},
abstract = {Reconstructing 2D curves from sample points has long been a critical challenge in computer graphics, finding essential applications in vector graphics. The design and editing of curves on surfaces has only recently begun to receive attention, primarily relying on human assistance, and where not, limited by very strict sampling conditions. In this work, we formally improve on the state-of-the-art requirements and introduce an innovative algorithm capable of reconstructing closed curves directly on surfaces from a given sparse set of sample points. We extend and adapt a state-of-the-art planar curve reconstruction method to the realm of surfaces while dealing with the challenges arising from working on non-Euclidean domains. We demonstrate the robustness of our method by reconstructing multiple curves on various surface meshes. We explore novel potential applications of our approach, allowing for automated reconstruction of curves on Riemannian manifolds.},
keywords = {Computational Geometry, Computer graphics, Geometry Processing, Geometry Reconstruction},
pubstate = {published},
tppubtype = {article}
}
Reconstructing 2D curves from sample points has long been a critical challenge in computer graphics, finding essential applications in vector graphics. The design and editing of curves on surfaces has only recently begun to receive attention, primarily relying on human assistance, and where not, limited by very strict sampling conditions. In this work, we formally improve on the state-of-the-art requirements and introduce an innovative algorithm capable of reconstructing closed curves directly on surfaces from a given sparse set of sample points. We extend and adapt a state-of-the-art planar curve reconstruction method to the realm of surfaces while dealing with the challenges arising from working on non-Euclidean domains. We demonstrate the robustness of our method by reconstructing multiple curves on various surface meshes. We explore novel potential applications of our approach, allowing for automated reconstruction of curves on Riemannian manifolds.
Falco, Salvatore Esposito De; Montera, Raffaella; Cucari, Nicola
Deconstructing Corporate Purpose: A Conceptual Framework in an Evolutionary Perspective Journal Article
In: Academy of Management Proceedings, vol. 2024, no. 1, pp. 17207, 2024, ISSN: 0065-0668, 2151-6561.
Abstract | Links | BibTeX | Tags: Entrepreneurship, Strategy and Management, Sustainability
@article{esposito_de_falco_deconstructing_2024,
title = {Deconstructing Corporate Purpose: A Conceptual Framework in an Evolutionary Perspective},
author = {Salvatore Esposito De Falco and Raffaella Montera and Nicola Cucari},
url = {http://journals.aom.org/doi/full/10.5465/AMPROC.2024.17207abstract},
doi = {10.5465/AMPROC.2024.17207abstract},
issn = {0065-0668, 2151-6561},
year = {2024},
date = {2024-08-01},
urldate = {2024-10-07},
journal = {Academy of Management Proceedings},
volume = {2024},
number = {1},
pages = {17207},
abstract = {This paper introduces a novel multi-dimensional co-evolutionary framework for understanding and analysing corporate purpose, addressing its under-conceptualized nature and diverse interpretations in contemporary business studies. Grounded in the principles of coevolution and integrating Esposito De Falco’s (2012) framework on the genesis and evolution of firms, our work advances a unique theoretical typology of corporate purpose. This typology elucidates the evolutionary pathway of corporate purpose through the "3S" dimensions (structural, systemic, and strategic), offering organizations a guide to align their strategies with both their identity and public image. By examining corporate purpose through these dimensions, we highlight the interconnectedness of an organization’s intrinsic identity (structural), its engagement within its ecosystem (systemic), and the alignment of purpose with actionable strategies (strategic). This multi-dimensional approach reveals how corporate purpose guides firms through a transformative journey from their genesis to their interactions with stakeholders and broader market dynamics. To do this, the paper delineates four distinct typologies of corporate purpose: purpose washing, formal purpose, promising purpose, and deep purpose. These typologies are systematically organized within a synthesis matrix, providing a nuanced and detailed understanding of how corporate purpose manifests and evolves within different organizational contexts. Central to the study are two key propositions. The first proposition positions corporate purpose as the “metabolism of firms”, drawing a parallel with biological metabolism to underscore its crucial role in ensuring business survival, prosperity, and evolutionary adaptation. The second proposition emphasizes the importance of a sequential progression across the genetic, relational, and phenotypic stages of corporate purpose, which is critical for the design of genuinely purposeful organizations. By moving beyond a purely definitional approach, this paper contributes significantly to the ontological understanding of corporate purpose. It sheds light on the dynamics of purpose in organizations, highlighting its strategic importance and the need for alignment between a firm’s identity and actions. Our paper provides a valuable resource for academics and practitioners alike, seeking to navigate the complexities of corporate purpose in a dynamic business landscape. Keywords: Corporate purpose; evolutionary perspective; genotype; relational dimension; phenotype; corporate purpose typologies},
keywords = {Entrepreneurship, Strategy and Management, Sustainability},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a novel multi-dimensional co-evolutionary framework for understanding and analysing corporate purpose, addressing its under-conceptualized nature and diverse interpretations in contemporary business studies. Grounded in the principles of coevolution and integrating Esposito De Falco’s (2012) framework on the genesis and evolution of firms, our work advances a unique theoretical typology of corporate purpose. This typology elucidates the evolutionary pathway of corporate purpose through the "3S" dimensions (structural, systemic, and strategic), offering organizations a guide to align their strategies with both their identity and public image. By examining corporate purpose through these dimensions, we highlight the interconnectedness of an organization’s intrinsic identity (structural), its engagement within its ecosystem (systemic), and the alignment of purpose with actionable strategies (strategic). This multi-dimensional approach reveals how corporate purpose guides firms through a transformative journey from their genesis to their interactions with stakeholders and broader market dynamics. To do this, the paper delineates four distinct typologies of corporate purpose: purpose washing, formal purpose, promising purpose, and deep purpose. These typologies are systematically organized within a synthesis matrix, providing a nuanced and detailed understanding of how corporate purpose manifests and evolves within different organizational contexts. Central to the study are two key propositions. The first proposition positions corporate purpose as the “metabolism of firms”, drawing a parallel with biological metabolism to underscore its crucial role in ensuring business survival, prosperity, and evolutionary adaptation. The second proposition emphasizes the importance of a sequential progression across the genetic, relational, and phenotypic stages of corporate purpose, which is critical for the design of genuinely purposeful organizations. By moving beyond a purely definitional approach, this paper contributes significantly to the ontological understanding of corporate purpose. It sheds light on the dynamics of purpose in organizations, highlighting its strategic importance and the need for alignment between a firm’s identity and actions. Our paper provides a valuable resource for academics and practitioners alike, seeking to navigate the complexities of corporate purpose in a dynamic business landscape. Keywords: Corporate purpose; evolutionary perspective; genotype; relational dimension; phenotype; corporate purpose typologies
Vaccarini, Massimo; Spegni, Francesco; Giretti, Alberto; Pirani, Massimiliano; Carbonari, Alessandro
Interoperable mixed reality for facility management: a cyber-physical perspective Journal Article
In: Journal of Information Technology in Construction (ITcon), vol. 29, no. 26, pp. 573–595, 2024.
Abstract | Links | BibTeX | Tags: Building Commissioning, Cyber-Physical Systems, Interoperability, Mixed Reality
@article{vaccariniInteroperableMixedReality2024,
title = {Interoperable mixed reality for facility management: a cyber-physical perspective},
author = {Massimo Vaccarini and Francesco Spegni and Alberto Giretti and Massimiliano Pirani and Alessandro Carbonari},
url = {http://www.itcon.org/paper/2024/26},
doi = {10.36680/j.itcon.2024.026},
year = {2024},
date = {2024-08-01},
urldate = {2024-10-05},
journal = {Journal of Information Technology in Construction (ITcon)},
volume = {29},
number = {26},
pages = {573–595},
abstract = {The management of building commissioning requires specialists from different organizations and with different skills. Collaboration processes involves several actors and decision-making at different levels. As building commissioning has already been described as systems-of-systems (SoS), the research reported in this paper claims that this definition can be extended into cyber-physical system-of-systems (CPSoS), requiring identification and support of both human-machine and machine-machine interactions in a hybrid environment. These requirements give rise to several challenges, such as capturing information about the existing facility, visualizing, comparing, and validating the compliance of alternative commissioning projects. The study presented in this paper reports methodological and technological solutions that are built on the integration between BIM and mixed reality, to actualize a CPSoS paradigm and to implement human-machine interaction for situated cognition towards an immersive collaborative working environment. The results of the experimental platform have been showcased in a full-scale real-life demonstrator.},
keywords = {Building Commissioning, Cyber-Physical Systems, Interoperability, Mixed Reality},
pubstate = {published},
tppubtype = {article}
}
The management of building commissioning requires specialists from different organizations and with different skills. Collaboration processes involves several actors and decision-making at different levels. As building commissioning has already been described as systems-of-systems (SoS), the research reported in this paper claims that this definition can be extended into cyber-physical system-of-systems (CPSoS), requiring identification and support of both human-machine and machine-machine interactions in a hybrid environment. These requirements give rise to several challenges, such as capturing information about the existing facility, visualizing, comparing, and validating the compliance of alternative commissioning projects. The study presented in this paper reports methodological and technological solutions that are built on the integration between BIM and mixed reality, to actualize a CPSoS paradigm and to implement human-machine interaction for situated cognition towards an immersive collaborative working environment. The results of the experimental platform have been showcased in a full-scale real-life demonstrator.
Ippolito, Adelaide; Barberà-Mariné, Maria Gloria; Zollo, Giuseppe; Cannavacciuolo, Lorella
How organisational factors and clinical decision support system affect nurses' knowledge for decisions in triage Journal Article
In: Knowledge Management Research & Practice, vol. 53, pp. 1–14, 2024, ISSN: 1477-8238, 1477-8246.
Abstract | Links | BibTeX | Tags: Clinical Decision Support Systems, Cognitive heuristic, Organizational factors, Triage
@article{ippolitoHowOrganisationalFactors2024,
title = {How organisational factors and clinical decision support system affect nurses' knowledge for decisions in triage},
author = {Adelaide Ippolito and Maria Gloria Barberà-Mariné and Giuseppe Zollo and Lorella Cannavacciuolo},
url = {https://www.tandfonline.com/doi/full/10.1080/14778238.2024.2377973},
doi = {10.1080/14778238.2024.2377973},
issn = {1477-8238, 1477-8246},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-16},
journal = {Knowledge Management Research & Practice},
volume = {53},
pages = {1–14},
abstract = {This paper delves into heuristic decision-making by nurses during the triage process, aiming to elucidate how organisational factors influence nurses' decision-making regarding the assignment of priority codes to patients, and to assess the effectiveness of Clinical Decision Support Systems (CDSS) in this context. Drawing on an experimental dataset of 25 triage cases evaluated by 35 nurses via interviews, the study was conducted in two Spanish Emergency Departments using CDSS. Findings indicate that organisational factors predominantly influence decisions in cases with complete and coherent information. However, in cases where information is incoherent or missing, individual nurse characteristics guide decision-making. Furthermore, it suggests that CDSS should be tailored to nurses' clinical reasoning to serve as effective support for individual decision-making processes.},
keywords = {Clinical Decision Support Systems, Cognitive heuristic, Organizational factors, Triage},
pubstate = {published},
tppubtype = {article}
}
This paper delves into heuristic decision-making by nurses during the triage process, aiming to elucidate how organisational factors influence nurses' decision-making regarding the assignment of priority codes to patients, and to assess the effectiveness of Clinical Decision Support Systems (CDSS) in this context. Drawing on an experimental dataset of 25 triage cases evaluated by 35 nurses via interviews, the study was conducted in two Spanish Emergency Departments using CDSS. Findings indicate that organisational factors predominantly influence decisions in cases with complete and coherent information. However, in cases where information is incoherent or missing, individual nurse characteristics guide decision-making. Furthermore, it suggests that CDSS should be tailored to nurses' clinical reasoning to serve as effective support for individual decision-making processes.