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
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.
2024
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.
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.
Tomazzoli, Claudio; Quaglia, Davide; Migliorini, Sara
Planning the Greenhouse Climatic Mapping Using an Agricultural Robot and Recurrent-Neural- Network-Based Virtual Sensors Journal Article
In: IEEE Transactions on AgriFood Electronics, vol. 2, no. 2, pp. 617–626, 2024.
Abstract | Links | BibTeX | Tags: Agricultural robot, Deep Learning, Genetic algorithm, Predictive Models, Rnn, Virtual sensor
@article{tomazzoli_planning_2024,
title = {Planning the Greenhouse Climatic Mapping Using an Agricultural Robot and Recurrent-Neural- Network-Based Virtual Sensors},
author = {Claudio Tomazzoli and Davide Quaglia and Sara Migliorini},
url = {https://ieeexplore.ieee.org/document/10701545},
doi = {10.1109/TAFE.2024.3460970},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on AgriFood Electronics},
volume = {2},
number = {2},
pages = {617–626},
abstract = {Assuming climatic homogeneity is no longer acceptable in greenhouse farming since it can result in less-than-ideal agronomic decisions. Indeed, several approaches have been proposed based on installing sensors in predefined points of interest (PoIs) to obtain a better mapping of climatic conditions. However, these approaches suffer from two main problems, i.e., identifying the most significant PoIs inside the greenhouse and placing a sensor at each PoI, which may be costly and incompatible with field operations. As regards the first problem, we propose a genetic algorithm to identify the best sensing places based on the agronomic definition of zones of interest. As regards the second problem, we exploit agricultural robots to collect climatic information to train a set of virtual sensors based on recurrent neural networks. The proposed solution has been tested on a real-world dataset regarding a greenhouse in Verona (Italy).},
keywords = {Agricultural robot, Deep Learning, Genetic algorithm, Predictive Models, Rnn, Virtual sensor},
pubstate = {published},
tppubtype = {article}
}
Assuming climatic homogeneity is no longer acceptable in greenhouse farming since it can result in less-than-ideal agronomic decisions. Indeed, several approaches have been proposed based on installing sensors in predefined points of interest (PoIs) to obtain a better mapping of climatic conditions. However, these approaches suffer from two main problems, i.e., identifying the most significant PoIs inside the greenhouse and placing a sensor at each PoI, which may be costly and incompatible with field operations. As regards the first problem, we propose a genetic algorithm to identify the best sensing places based on the agronomic definition of zones of interest. As regards the second problem, we exploit agricultural robots to collect climatic information to train a set of virtual sensors based on recurrent neural networks. The proposed solution has been tested on a real-world dataset regarding a greenhouse in Verona (Italy).
Tomazzoli, Claudio; Ponza, Andrea; Cristani, Matteo; Olivieri, Francesco; Scannapieco, Simone
A Cobot in the Vineyard: Computer Vision for Smart Chemicals Spraying Journal Article
In: Applied Sciences, vol. 14, no. 9, 2024, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Collaborative robotic, Computer vision, Cyber-Physical Systems, Deep Learning, Machine Learning, Precision agriculture
@article{tomazzoli_cobot_2024,
title = {A Cobot in the Vineyard: Computer Vision for Smart Chemicals Spraying},
author = {Claudio Tomazzoli and Andrea Ponza and Matteo Cristani and Francesco Olivieri and Simone Scannapieco},
url = {https://www.mdpi.com/2076-3417/14/9/3777},
doi = {10.3390/app14093777},
issn = {2076-3417},
year = {2024},
date = {2024-01-01},
journal = {Applied Sciences},
volume = {14},
number = {9},
abstract = {Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field.},
keywords = {Artificial Intelligence, Collaborative robotic, Computer vision, Cyber-Physical Systems, Deep Learning, Machine Learning, Precision agriculture},
pubstate = {published},
tppubtype = {article}
}
Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field.
Scannapieco, Simone; Tomazzoli, Claudio
Cnosso, a Novel Method for Business Document Automation Based on Open Information Extraction Journal Article
In: Expert Systems with Applications, vol. 245, pp. 123038, 2024, ISSN: 0957-4174.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Document automation, Information Extraction, Natural language analysis, Natural Language Processing, Semantic Role Labeling
@article{scannapieco_cnosso_2024,
title = {Cnosso, a Novel Method for Business Document Automation Based on Open Information Extraction},
author = {Simone Scannapieco and Claudio Tomazzoli},
url = {https://www.sciencedirect.com/science/article/pii/S0957417423035406},
doi = {10.1016/j.eswa.2023.123038},
issn = {0957-4174},
year = {2024},
date = {2024-01-01},
journal = {Expert Systems with Applications},
volume = {245},
pages = {123038},
abstract = {The state-of-the-art in automated processing of unstructured business documents has evolved from manual labor to advanced AI systems in the span of mere decades. Such systems involve learning techniques, rule or clause sets, neural models – either used alone or in combination – for the extraction to work. As an example, rule-based processes operate on a perceived layout or positioning of the information, whereas model-based frameworks adopt a semantic, and often uninspectable, approach. Verb-Based Semantic Role Labeling (VBSRL) is a novel system presented in a former paper that uses a hybrid foundation to inform the extraction phase via a set of rules modeling natural language. We propose a new VBSRL-based document processing method, aided by valuable and innovative architectural choices, which has been implemented for the Italian language and experimented upon with promising results. Even in its infancy, in fact, the first implementation of this system shows better results than comparable IE solutions, obtaining an aggregate, average F-measure of nearly 79%.},
keywords = {Artificial Intelligence, Document automation, Information Extraction, Natural language analysis, Natural Language Processing, Semantic Role Labeling},
pubstate = {published},
tppubtype = {article}
}
The state-of-the-art in automated processing of unstructured business documents has evolved from manual labor to advanced AI systems in the span of mere decades. Such systems involve learning techniques, rule or clause sets, neural models – either used alone or in combination – for the extraction to work. As an example, rule-based processes operate on a perceived layout or positioning of the information, whereas model-based frameworks adopt a semantic, and often uninspectable, approach. Verb-Based Semantic Role Labeling (VBSRL) is a novel system presented in a former paper that uses a hybrid foundation to inform the extraction phase via a set of rules modeling natural language. We propose a new VBSRL-based document processing method, aided by valuable and innovative architectural choices, which has been implemented for the Italian language and experimented upon with promising results. Even in its infancy, in fact, the first implementation of this system shows better results than comparable IE solutions, obtaining an aggregate, average F-measure of nearly 79%.
Tomazzoli, Claudio; Brentarolli, Elia; Quaglia, Davide; Migliorini, Sara
Estimating Greenhouse Climate through Context-Aware Recurrent Neural Networks over an Embedded System Journal Article
In: IEEE Transactions on AgriFood Electronics, vol. 2, no. 2, pp. 554–562, 2024.
Abstract | Links | BibTeX | Tags: Deep Learning, Edge computing, Predictive Models, Rnn, Time series, Virtual sensor
@article{tomazzoli_estimating_2024,
title = {Estimating Greenhouse Climate through Context-Aware Recurrent Neural Networks over an Embedded System},
author = {Claudio Tomazzoli and Elia Brentarolli and Davide Quaglia and Sara Migliorini},
url = {https://ieeexplore.ieee.org/document/10663269},
doi = {10.1109/TAFE.2024.3441470},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on AgriFood Electronics},
volume = {2},
number = {2},
pages = {554–562},
abstract = {The assumption of climate homogeneity is no longer acceptable in greenhouse farming since it can result in less-than-ideal decisions. At the same time, installing a sensor in each area of interest is costly and unsuitable for field operations. In this article, we address this problem by putting forth the idea of virtual sensors; their behavior is modeled by a context-aware recurrent neural network trained through the contextual relationships between a small set of permanent monitoring stations and a set of temporary sensors placed in specific points of interest for a short period. More precisely, we consider not only space location but also temporal features and distance with respect to the permanent sensors. This article shows the complete pipeline to configure the recurrent neural network, perform training, and deploy the resulting model into an embedded system for on-site application execution.},
keywords = {Deep Learning, Edge computing, Predictive Models, Rnn, Time series, Virtual sensor},
pubstate = {published},
tppubtype = {article}
}
The assumption of climate homogeneity is no longer acceptable in greenhouse farming since it can result in less-than-ideal decisions. At the same time, installing a sensor in each area of interest is costly and unsuitable for field operations. In this article, we address this problem by putting forth the idea of virtual sensors; their behavior is modeled by a context-aware recurrent neural network trained through the contextual relationships between a small set of permanent monitoring stations and a set of temporary sensors placed in specific points of interest for a short period. More precisely, we consider not only space location but also temporal features and distance with respect to the permanent sensors. This article shows the complete pipeline to configure the recurrent neural network, perform training, and deploy the resulting model into an embedded system for on-site application execution.
Workneh, Tewabe Chekole; Cristani, Matteo; Tomazzoli, Claudio
Assessing the Impact of Climate Change on Mineral-Associated Organic Carbon (MAOC) Using Machine Learning Models Proceedings Article
In: pp. 35–47, 2024.
Abstract | Links | BibTeX | Tags: Machine Learning, Mineral-associated organic carbon, Predictive Models
@inproceedings{workneh_assessing_2024,
title = {Assessing the Impact of Climate Change on Mineral-Associated Organic Carbon (MAOC) Using Machine Learning Models},
author = {Tewabe Chekole Workneh and Matteo Cristani and Claudio Tomazzoli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214791497&partnerID=40&md5=5a2e249e0da0fd4f9571af2bc00696ca},
year = {2024},
date = {2024-01-01},
volume = {3883},
pages = {35–47},
series = {CEUR Workshop Proceedings},
abstract = {This study examines the impact of climate change on Soil Organic Carbon (SOC) stocks, with a particular focus on Mineral-Associated Organic Carbon (MAOC)—a stable fraction of soil organic matter critical for long-term carbon sequestration. This study aims to develop a predictive tool for estimating MAOC at a finer spatial resolution, addressing gaps in current models and enabling cost-effective climate change mitigation strategies. Using an extensive dataset from the Zenodo repository, augmented with detailed meteorological data, machine learning techniques were employed—specifically, the Random Forest (RF) Regressor and Support Vector Machine (SVM) Regressor. The RF model not only outperformed the SVM in predictive accuracy but also identified key factors influencing MAOC content under various climate change scenarios. These findings deepen our understanding of soil carbon sequestration potential in future climate conditions, offering actionable insights for sustainable soil management and cost-effective climate change mitigation strategies. textbackslashcopyright 2024 Copyright for this paper by its authors.},
keywords = {Machine Learning, Mineral-associated organic carbon, Predictive Models},
pubstate = {published},
tppubtype = {inproceedings}
}
This study examines the impact of climate change on Soil Organic Carbon (SOC) stocks, with a particular focus on Mineral-Associated Organic Carbon (MAOC)—a stable fraction of soil organic matter critical for long-term carbon sequestration. This study aims to develop a predictive tool for estimating MAOC at a finer spatial resolution, addressing gaps in current models and enabling cost-effective climate change mitigation strategies. Using an extensive dataset from the Zenodo repository, augmented with detailed meteorological data, machine learning techniques were employed—specifically, the Random Forest (RF) Regressor and Support Vector Machine (SVM) Regressor. The RF model not only outperformed the SVM in predictive accuracy but also identified key factors influencing MAOC content under various climate change scenarios. These findings deepen our understanding of soil carbon sequestration potential in future climate conditions, offering actionable insights for sustainable soil management and cost-effective climate change mitigation strategies. textbackslashcopyright 2024 Copyright for this paper by its authors.
Tomazzoli, Claudio; Cristani, Matteo; Workneh, Tewabe Chekole; Olivieri, Francesco; Scannapieco, Simone
AI-driven Quasi-Optimal Security Camera Positioning for Harbor Control Proceedings Article
In: Procedia Computer Science, pp. 4269–4277, 2024.
Abstract | Links | BibTeX | Tags: Cyber-Physical Systems, Digital Twin, Genetic algorithm
@inproceedings{tomazzoli_ai-driven_2024,
title = {AI-driven Quasi-Optimal Security Camera Positioning for Harbor Control},
author = {Claudio Tomazzoli and Matteo Cristani and Tewabe Chekole Workneh and Francesco Olivieri and Simone Scannapieco},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1877050924022956},
doi = {10.1016/j.procs.2024.09.276},
year = {2024},
date = {2024-01-01},
urldate = {2025-03-28},
booktitle = {Procedia Computer Science},
volume = {246},
pages = {4269–4277},
abstract = {The problem of positioning cameras for optimal monitoring of a given surface, while given a budget, is quite evidently hard. Moreover, it is very difficult to collect information that could reduce the span of admissible solutions and therefore downsize the problem itself. In this paper, as referred to a specific case in a critical infrastructure, the harbour of the Ventotene Island in southern Italy, how the above mentioned problem can be solved in an effective way by employing a Genetic algorithm. The approach is also generalised in order to solve similar problems.},
keywords = {Cyber-Physical Systems, Digital Twin, Genetic algorithm},
pubstate = {published},
tppubtype = {inproceedings}
}
The problem of positioning cameras for optimal monitoring of a given surface, while given a budget, is quite evidently hard. Moreover, it is very difficult to collect information that could reduce the span of admissible solutions and therefore downsize the problem itself. In this paper, as referred to a specific case in a critical infrastructure, the harbour of the Ventotene Island in southern Italy, how the above mentioned problem can be solved in an effective way by employing a Genetic algorithm. The approach is also generalised in order to solve similar problems.
2023
Brentarolli, Elia; Migliorini, Sara; Quaglia, Davide; Tomazzoli, Claudio
Greenhouse Climatic Sensing through Agricultural Robots and Recurrent Neural Networks Proceedings Article
In: pp. 108–113, 2023, ISBN: 979-8-3503-1272-0.
Abstract | Links | BibTeX | Tags: Agricultural robot, Deep Learning, Precision agriculture, Rnn, Time series, Virtual sensor
@inproceedings{brentarolli_greenhouse_2023,
title = {Greenhouse Climatic Sensing through Agricultural Robots and Recurrent Neural Networks},
author = {Elia Brentarolli and Sara Migliorini and Davide Quaglia and Claudio Tomazzoli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186525655&doi=10.1109%2fMetroAgriFor58484.2023.10424311&partnerID=40&md5=d69ac9e8cebeed66b1a3b7c8d5b1e8d4},
doi = {10.1109/MetroAgriFor58484.2023.10424311},
isbn = {979-8-3503-1272-0},
year = {2023},
date = {2023-01-01},
pages = {108–113},
series = {2023 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2023 - Proceedings},
abstract = {In today's greenhouse farming the assumption of climatic uniformity is no more acceptable since it may lead to sub-optimal decisions; however, the deployment of a sensor for each point of interest is expensive and incompatible with field operations. Agricultural robots are quickly moving from the research field to real scenarios for weed removal, harvesting and other agronomic operations. In this paper, we show the possibility, enabled by Recurrent Neural Networks, to exploit successive samplings of the mobile robot in its wandering in the greenhouse to infer the value of the desired climatic variable in specific points of interest. More specifically, we use recurrent neural networks to model the dependency of the climate conditions in each point of interest as a function of the distance with respect to various points on robot's trajectory. The proposed technique has been applied to a real dataset coming from a greenhouse located near Verona and compared with traditional approaches such as considering a single sensor located at the center of the greenhouse and compressed sensing.},
keywords = {Agricultural robot, Deep Learning, Precision agriculture, Rnn, Time series, Virtual sensor},
pubstate = {published},
tppubtype = {inproceedings}
}
In today's greenhouse farming the assumption of climatic uniformity is no more acceptable since it may lead to sub-optimal decisions; however, the deployment of a sensor for each point of interest is expensive and incompatible with field operations. Agricultural robots are quickly moving from the research field to real scenarios for weed removal, harvesting and other agronomic operations. In this paper, we show the possibility, enabled by Recurrent Neural Networks, to exploit successive samplings of the mobile robot in its wandering in the greenhouse to infer the value of the desired climatic variable in specific points of interest. More specifically, we use recurrent neural networks to model the dependency of the climate conditions in each point of interest as a function of the distance with respect to various points on robot's trajectory. The proposed technique has been applied to a real dataset coming from a greenhouse located near Verona and compared with traditional approaches such as considering a single sensor located at the center of the greenhouse and compressed sensing.
Brentarolli, Elia; Migliorini, Sara; Quaglia, Davide; Tomazzoli, Claudio
Mapping Micro-Climate in a Greenhouse through a Context-Aware Recurrent Neural Network Proceedings Article
In: pp. 113–117, 2023.
Abstract | Links | BibTeX | Tags: Deep Learning, Precision agriculture, Rnn, Time series, Virtual sensor
@inproceedings{brentarolli_mapping_2023,
title = {Mapping Micro-Climate in a Greenhouse through a Context-Aware Recurrent Neural Network},
author = {Elia Brentarolli and Sara Migliorini and Davide Quaglia and Claudio Tomazzoli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178561996&doi=10.1109%2fCAFE58535.2023.10291595&partnerID=40&md5=766900019096c3eede15c6c6479ee54a},
doi = {10.1109/CAFE58535.2023.10291595},
year = {2023},
date = {2023-01-01},
pages = {113–117},
series = {2023 IEEE Conference on AgriFood Electronics, CAFE 2023 - Proceedings},
abstract = {Existing greenhouse climatic models usually rely on the assumption that climate conditions inside a greenhouse are uniform. This allows to maintain the model as simple as possible at the expense of less accuracy in the final predictions. However, in real-world applications this uniformity assumption is not satisfactory from the agronomic perspective, since it may lead to wrong decisions and irreversible damages to crops. Conversely, more sophisticated techniques need to collect data from a great number of sensors. In this paper, we try to overcome this situation by proposing the concept of virtual sensor whose behaviour is modeled by a context-aware recurrent neural network trained by the relationship between temporary sensors placed in specific point of interests, and a set of sensors placed in permanent positions in the greenhouse. More specifically, we try to model the dependency of climate conditions as time series where the variability does not depend only on temporal aspects, but also on richer contextual dimensions, like space locations and distance relationships with a predefined and small set of permanent sensors. The proposed technique has been applied to a real-world dataset coming from a greenhouse located in Verona in which one permanent sensor has been placed at the center of the greenhouse and seven temporary sensors have been maintained for a limited period of time.},
keywords = {Deep Learning, Precision agriculture, Rnn, Time series, Virtual sensor},
pubstate = {published},
tppubtype = {inproceedings}
}
Existing greenhouse climatic models usually rely on the assumption that climate conditions inside a greenhouse are uniform. This allows to maintain the model as simple as possible at the expense of less accuracy in the final predictions. However, in real-world applications this uniformity assumption is not satisfactory from the agronomic perspective, since it may lead to wrong decisions and irreversible damages to crops. Conversely, more sophisticated techniques need to collect data from a great number of sensors. In this paper, we try to overcome this situation by proposing the concept of virtual sensor whose behaviour is modeled by a context-aware recurrent neural network trained by the relationship between temporary sensors placed in specific point of interests, and a set of sensors placed in permanent positions in the greenhouse. More specifically, we try to model the dependency of climate conditions as time series where the variability does not depend only on temporal aspects, but also on richer contextual dimensions, like space locations and distance relationships with a predefined and small set of permanent sensors. The proposed technique has been applied to a real-world dataset coming from a greenhouse located in Verona in which one permanent sensor has been placed at the center of the greenhouse and seven temporary sensors have been maintained for a limited period of time.
Cesari, Paola; Cristani, Matteo; Demrozi, Florenc; Pascucci, Francesco; Picotti, Pietro Maria; Pravadelli, Graziano; Tomazzoli, Claudio; Turetta, Cristian; Workneh, Tewabe Chekole; Zenti, Luca
Towards Posture and Gait Evaluation through Wearable-Based Biofeedback Technologies Journal Article
In: Electronics, vol. 12, no. 3, 2023, ISSN: 2079-9292.
Abstract | Links | BibTeX | Tags: Biofeedback, Machine Learning, Wearable sensors
@article{cesari_towards_2023,
title = {Towards Posture and Gait Evaluation through Wearable-Based Biofeedback Technologies},
author = {Paola Cesari and Matteo Cristani and Florenc Demrozi and Francesco Pascucci and Pietro Maria Picotti and Graziano Pravadelli and Claudio Tomazzoli and Cristian Turetta and Tewabe Chekole Workneh and Luca Zenti},
url = {https://www.mdpi.com/2079-9292/12/3/644},
doi = {10.3390/electronics12030644},
issn = {2079-9292},
year = {2023},
date = {2023-01-01},
journal = {Electronics},
volume = {12},
number = {3},
abstract = {In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system's efficiency and determining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assembled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimental protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides algorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups.},
keywords = {Biofeedback, Machine Learning, Wearable sensors},
pubstate = {published},
tppubtype = {article}
}
In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system's efficiency and determining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assembled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimental protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides algorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups.
Konovalova, Svetlana Aleksandrovna; Burenina, Valentina Igorevna; Tomazzoli, Claudio
Project Activities as Means in Students' Creative Self-Realization during University Education Proceedings Article
In: 2023, (Type: Conference Paper).
Abstract | Links | BibTeX | Tags: Education, Elearning
@inproceedings{konovalova_project_2023,
title = {Project Activities as Means in Students' Creative Self-Realization during University Education},
author = {Svetlana Aleksandrovna Konovalova and Valentina Igorevna Burenina and Claudio Tomazzoli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176783046&doi=10.1063%2f5.0111185&partnerID=40&md5=d2fc8e78893a947e2ebac574863b8176},
doi = {10.1063/5.0111185},
year = {2023},
date = {2023-01-01},
volume = {2549},
series = {AIP Conference Proceedings},
abstract = {The article presents historical aspect of the project activities problem. Analysis of features and specifics in the project activities implementation in university education practices is provided. Characteristic features of the project competence generation and the development of project culture in a personality are considered. Arguments are advanced that project activities in the context of university education could act as a means in creative self-realization of the student personality. The article analyzes the concept of self-realization, and it proves that university environment is an important precondition for the students' creative self-realization. The article presents results of a study, in which university students from Moscow and Yekaterinburg participated. Project method appeared to be the main research method. The article presents projects, in which students took part ranging from global international to department and educational. Results of the study prove the efficiency of including project activities in the university education practices. In the context of university education, project activities appear to be the means of students' creative self-realization and contribute to personal development and professional growth. Besides, conditions for implementation of creative potential in higher education are indicated. The purpose of training using creativity development is demonstrated. Correlation between the concepts of creativity and curiosity is provided. Several events are indicated in implementing the students' creative successes.},
note = {Type: Conference Paper},
keywords = {Education, Elearning},
pubstate = {published},
tppubtype = {inproceedings}
}
The article presents historical aspect of the project activities problem. Analysis of features and specifics in the project activities implementation in university education practices is provided. Characteristic features of the project competence generation and the development of project culture in a personality are considered. Arguments are advanced that project activities in the context of university education could act as a means in creative self-realization of the student personality. The article analyzes the concept of self-realization, and it proves that university environment is an important precondition for the students' creative self-realization. The article presents results of a study, in which university students from Moscow and Yekaterinburg participated. Project method appeared to be the main research method. The article presents projects, in which students took part ranging from global international to department and educational. Results of the study prove the efficiency of including project activities in the university education practices. In the context of university education, project activities appear to be the means of students' creative self-realization and contribute to personal development and professional growth. Besides, conditions for implementation of creative potential in higher education are indicated. The purpose of training using creativity development is demonstrated. Correlation between the concepts of creativity and curiosity is provided. Several events are indicated in implementing the students' creative successes.
Konovalova, Svetlana Aleksandrovna; Tomazzoli, Claudio; Bykov, Sergey; Zemlyakova, Galina; Panov, Igor
Visual Culture and Ways of Its Development among Students in the System of Higher Education Proceedings Article
In: Studies in Critical Social Sciences, pp. 32–45, 2023.
Abstract | Links | BibTeX | Tags: Education, Elearning
@inproceedings{konovalova_visual_2023,
title = {Visual Culture and Ways of Its Development among Students in the System of Higher Education},
author = {Svetlana Aleksandrovna Konovalova and Claudio Tomazzoli and Sergey Bykov and Galina Zemlyakova and Igor Panov},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174494504&doi=10.1163%2f9789004540019_005&partnerID=40&md5=bb3acff1c4d2b5ec22b9f91215e207d3},
doi = {10.1163/9789004540019_005},
year = {2023},
date = {2023-01-01},
booktitle = {Studies in Critical Social Sciences},
volume = {254},
pages = {32–45},
abstract = {Purpose: The paper aims to search for means and methods of developing a visual culture in university students in the discipline of “History of Fine Art.” The research objects are the pedagogical mechanisms that influence the development of the visual culture of young people. Methodology: The study is of world outlook, culturological, and aesthetic nature. It represents a sociological analysis of several issues: what factors influence the development of visual culture and what objective and subjective conditions are necessary for this; whether art is a contemporary means of communication, information, and cognitive sphere, which forms social activity and spiritual and aesthetic positions; whether young people have structural and functional methods and skills of critical analysis or their ways of knowing the world are prejudicial. The paper analyzes visual culture as an important component of social, behavioral, and cognitive activity and emotional and aesthetic experience of evaluation, in which the individual can master new possibilities and methods of analysis, perception, and interpretation of information, texts, and images. Results: On the one hand, the authors identify shortcomings and cause-effect relationships that lead to a change in the vision and understanding of information by students. On the other hand, these changes serve as a promising model for developing visual activity, designing and implementing search, creative, and interactive methods in the educational process of higher education. These methods allow the individual to interact with the environment and contribute to a personal understanding of the information and knowledge received, playing a significant role in developing emotional, behavioral, cognitive, and motivational spheres. The obtained results indicate a change in the forms of cultural inclusion and the individual's visual abilities, as well as in the development of a new visual paradigm based on the integration of human-computer interaction. Practical relevance: During the experiment, indicators of the development of visual culture were determined. Various research activities were carried out, including questionnaires, tests, discussions, practical conferences, master classes, and virtual tours, which prove the relevance of visual culture. Additionally, the authors reviewed scientific articles by Russian and foreign authors relating to the spiritual and aesthetic demands of society, current trends in art, and means of creative development of the individual.},
keywords = {Education, Elearning},
pubstate = {published},
tppubtype = {inproceedings}
}
Purpose: The paper aims to search for means and methods of developing a visual culture in university students in the discipline of “History of Fine Art.” The research objects are the pedagogical mechanisms that influence the development of the visual culture of young people. Methodology: The study is of world outlook, culturological, and aesthetic nature. It represents a sociological analysis of several issues: what factors influence the development of visual culture and what objective and subjective conditions are necessary for this; whether art is a contemporary means of communication, information, and cognitive sphere, which forms social activity and spiritual and aesthetic positions; whether young people have structural and functional methods and skills of critical analysis or their ways of knowing the world are prejudicial. The paper analyzes visual culture as an important component of social, behavioral, and cognitive activity and emotional and aesthetic experience of evaluation, in which the individual can master new possibilities and methods of analysis, perception, and interpretation of information, texts, and images. Results: On the one hand, the authors identify shortcomings and cause-effect relationships that lead to a change in the vision and understanding of information by students. On the other hand, these changes serve as a promising model for developing visual activity, designing and implementing search, creative, and interactive methods in the educational process of higher education. These methods allow the individual to interact with the environment and contribute to a personal understanding of the information and knowledge received, playing a significant role in developing emotional, behavioral, cognitive, and motivational spheres. The obtained results indicate a change in the forms of cultural inclusion and the individual's visual abilities, as well as in the development of a new visual paradigm based on the integration of human-computer interaction. Practical relevance: During the experiment, indicators of the development of visual culture were determined. Various research activities were carried out, including questionnaires, tests, discussions, practical conferences, master classes, and virtual tours, which prove the relevance of visual culture. Additionally, the authors reviewed scientific articles by Russian and foreign authors relating to the spiritual and aesthetic demands of society, current trends in art, and means of creative development of the individual.
2022
Cristani, Matteo; Olivieri, Francesco; Pasetto, Luca; Tomazzoli, Claudio; Workneh, Tewabe Chekole
Impact Logic: Reasoning with Resources and Losses Proceedings Article
In: pp. 3856–3864, 2022, (Type: Conference Paper).
Abstract | Links | BibTeX | Tags: Automatic reasoning, Business Processes, Defeasible logic, Non monotonic logic
@inproceedings{cristani_impact_2022,
title = {Impact Logic: Reasoning with Resources and Losses},
author = {Matteo Cristani and Francesco Olivieri and Luca Pasetto and Claudio Tomazzoli and Tewabe Chekole Workneh},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143331932&doi=10.1016%2fj.procs.2022.09.447&partnerID=40&md5=e7d2668152fad3e3ef087973f1fcd537},
doi = {10.1016/j.procs.2022.09.447},
year = {2022},
date = {2022-01-01},
volume = {207},
pages = {3856–3864},
series = {Procedia Computer Science},
abstract = {The notion of Business Process Compliance has been widely discussed as one of the most important issues to be solved when comparing the description of a business process against a normative background. Many changes have been provided to the basic notion above. The introduction of changes to the normative background has been considered, as well as the idea of Compliance by Design. In this paper, we discuss how to devise a business process that is compliant to an impact constraint set. We shall show that the technical problems determined by this concept are all within the horizon of adding to the logical framework the notions of Resource and Product.},
note = {Type: Conference Paper},
keywords = {Automatic reasoning, Business Processes, Defeasible logic, Non monotonic logic},
pubstate = {published},
tppubtype = {inproceedings}
}
The notion of Business Process Compliance has been widely discussed as one of the most important issues to be solved when comparing the description of a business process against a normative background. Many changes have been provided to the basic notion above. The introduction of changes to the normative background has been considered, as well as the idea of Compliance by Design. In this paper, we discuss how to devise a business process that is compliant to an impact constraint set. We shall show that the technical problems determined by this concept are all within the horizon of adding to the logical framework the notions of Resource and Product.
Cristani, Matteo; Olvieri, Francesco; Workneh, Tewabe Chekole; Pasetto, Luca; Tomazzoli, Claudio
Classification Rules Explain Machine Learning Proceedings Article
In: pp. 897–904, 2022, (Type: Conference Paper).
Abstract | Links | BibTeX | Tags: Automatic reasoning, Deep Learning, Defeasible logic, Non monotonic logic
@inproceedings{cristani_classification_2022,
title = {Classification Rules Explain Machine Learning},
author = {Matteo Cristani and Francesco Olvieri and Tewabe Chekole Workneh and Luca Pasetto and Claudio Tomazzoli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182762567&doi=10.5220%2f0010927300003116&partnerID=40&md5=ee28830dd8408c9a8e40c7caceb00fe1},
doi = {10.5220/0010927300003116},
year = {2022},
date = {2022-01-01},
volume = {3},
pages = {897–904},
series = {International Conference on Agents and Artificial Intelligence},
abstract = {We introduce a general model for explainable Artificial Intelligence that identifies an explanation of a Machine Learning method by classification rules. We define a notion of distance between two Machine Learning methods, and provide a method that computes a set of classification rules that, in turn, approximates another black box method to a given extent. We further build upon this method an anytime algorithm that returns the best approximation it can compute within a given interval of time. This anytime method returns the minimum and maximum difference in terms of approximation provided by the algorithm and uses it to determine whether the obtained approximation is acceptable. We then illustrate the results of a few experiments on three different datasets that show certain properties of the approximations that should be considered while modelling such systems. On top of this, we design a methodology for constructing approximations for ML, that we compare to the no-methods approach typically used in current studies on the explainable artificial intelligence topic.},
note = {Type: Conference Paper},
keywords = {Automatic reasoning, Deep Learning, Defeasible logic, Non monotonic logic},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce a general model for explainable Artificial Intelligence that identifies an explanation of a Machine Learning method by classification rules. We define a notion of distance between two Machine Learning methods, and provide a method that computes a set of classification rules that, in turn, approximates another black box method to a given extent. We further build upon this method an anytime algorithm that returns the best approximation it can compute within a given interval of time. This anytime method returns the minimum and maximum difference in terms of approximation provided by the algorithm and uses it to determine whether the obtained approximation is acceptable. We then illustrate the results of a few experiments on three different datasets that show certain properties of the approximations that should be considered while modelling such systems. On top of this, we design a methodology for constructing approximations for ML, that we compare to the no-methods approach typically used in current studies on the explainable artificial intelligence topic.
Tomazzoli, Claudio; Scannapieco, Simone; Cristani, Matteo
Forensic Analysis of Text and Messages in Smartphones by a Unification Rosetta Stone Procedure Proceedings Article
In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 315–326, 2022, (Type: Conference Paper).
Abstract | Links | BibTeX | Tags: Deep Learning, Digital Forensics, Machine Learning
@inproceedings{tomazzoli_forensic_2022,
title = {Forensic Analysis of Text and Messages in Smartphones by a Unification Rosetta Stone Procedure},
author = {Claudio Tomazzoli and Simone Scannapieco and Matteo Cristani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137981279&doi=10.1007%2f978-3-031-08530-7_26&partnerID=40&md5=8da6d7ae3341a8b9837e3f837863733b},
doi = {10.1007/978-3-031-08530-7_26},
year = {2022},
date = {2022-01-01},
booktitle = {Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13343 LNAI},
pages = {315–326},
abstract = {In this paper we introduce an innovative application of translation techniques applied to the problem of forensics analysis of smartphones. This analysis has the specific objective of determining which messages (either text or vocal), transmitted from and received by a specific device, seized for forensic analysis, may contain data that are relevant in a criminal investigation. The problems that make this analysis difficult are three: (1) the content could be written in a language that is not spoken by the analyst, (2) the number of messages actually containing pertinent and relevant traits is a small percentage on a potentially quite large space and (3) texts could be rather noisy in terms of content, for they could contain emoticons, language loans, and slang terms (beyond the fact that they could also be written in obscure languages such as specific dialects or languages spoken by small communities). We adopt a machine translation approach by providing an algorithm that takes messages of a smartphone as input, and processes them to a target language in an innovative way. We then show that the application is effective when applied to a set of real world cases, demonstrating a performance increase in terms of accuracy that could exceed 30 % when compared to traditional approaches.},
note = {Type: Conference Paper},
keywords = {Deep Learning, Digital Forensics, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we introduce an innovative application of translation techniques applied to the problem of forensics analysis of smartphones. This analysis has the specific objective of determining which messages (either text or vocal), transmitted from and received by a specific device, seized for forensic analysis, may contain data that are relevant in a criminal investigation. The problems that make this analysis difficult are three: (1) the content could be written in a language that is not spoken by the analyst, (2) the number of messages actually containing pertinent and relevant traits is a small percentage on a potentially quite large space and (3) texts could be rather noisy in terms of content, for they could contain emoticons, language loans, and slang terms (beyond the fact that they could also be written in obscure languages such as specific dialects or languages spoken by small communities). We adopt a machine translation approach by providing an algorithm that takes messages of a smartphone as input, and processes them to a target language in an innovative way. We then show that the application is effective when applied to a set of real world cases, demonstrating a performance increase in terms of accuracy that could exceed 30 % when compared to traditional approaches.
2021
Scannapieco, Simone; Ponza, Andrea; Tomazzoli, Claudio
Unified Semantic Space for a Novel Multimodal Approach to Document Similarity Proceedings Article
In: IEEE Xplore ( for IEEE RTSI 2021 Conference Proceedings ), pp. 457–462, 2021, ISBN: 978-1-6654-4135-3.
Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Semantic Role Labeling, Sentiment analysis
@inproceedings{scannapieco_unified_2021,
title = {Unified Semantic Space for a Novel Multimodal Approach to Document Similarity},
author = {Simone Scannapieco and Andrea Ponza and Claudio Tomazzoli},
doi = {10.1109/RTSI50628.2021.9597240},
isbn = {978-1-6654-4135-3},
year = {2021},
date = {2021-09-01},
booktitle = {IEEE Xplore ( for IEEE RTSI 2021 Conference Proceedings )},
pages = {457–462},
abstract = {The paper explores a new method to represent digital documents - containing several sources of information (text, images, videos, multimedia galleries) - in a semantic space able to elevate the importance of each informative channel in giving a deeper meaning to the document. The proposed approach finds several applications such as Web Content Mining, semantic classification and document topic extraction.},
keywords = {Machine Learning, Natural Language Processing, Semantic Role Labeling, Sentiment analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper explores a new method to represent digital documents - containing several sources of information (text, images, videos, multimedia galleries) - in a semantic space able to elevate the importance of each informative channel in giving a deeper meaning to the document. The proposed approach finds several applications such as Web Content Mining, semantic classification and document topic extraction.
Scannapieco, Simone; Ponza, Andrea; Tomazzoli, Claudio
VBSRL: A Semantic Frame-Based Approach for Data Extraction from Unstructured Business Documents Proceedings Article
In: Lecture Notes in Networks and Systems, (Computing Conference), pp. 1030–1044, Springer, 2021, ISBN: 978-1-7281-7453-2.
Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Semantic Role Labeling
@inproceedings{scannapieco_vbsrl_2021,
title = {VBSRL: A Semantic Frame-Based Approach for Data Extraction from Unstructured Business Documents},
author = {Simone Scannapieco and Andrea Ponza and Claudio Tomazzoli},
doi = {10.1007/978-3-030-80119-9_68},
isbn = {978-1-7281-7453-2},
year = {2021},
date = {2021-01-01},
booktitle = {Lecture Notes in Networks and Systems, (Computing Conference)},
volume = {283},
pages = {1030–1044},
publisher = {Springer},
abstract = {The definition of alternative processing techniques as applied to business documents is inevitably at odds with long-standing issues derived by the unstructured nature of most business-related information. In particular, more and more refined methods for automated data extraction have been investigated over the years. The last frontier in this sense is Semantic Role Labeling (SRL), which extracts relevant information purely based on the overall meaning of sentences. This is carried out by mapping specific situations described in the text into more general scenarios (semantic frames). FrameNet originated as a semantic frame repository by applying SRL techniques to large textual corpora, but its adaptation to languages other than English has been proven a difficult task. In this paper, we introduce a new implementation of SRL called Verb-Based SRL (VBSRL) for information extraction. VBSRL relies on a different conceptual theory used in the context of natural language understanding, which is language-independent and dramatically elevates the importance of verbs to abstract from real-life situations.},
keywords = {Machine Learning, Natural Language Processing, Semantic Role Labeling},
pubstate = {published},
tppubtype = {inproceedings}
}
The definition of alternative processing techniques as applied to business documents is inevitably at odds with long-standing issues derived by the unstructured nature of most business-related information. In particular, more and more refined methods for automated data extraction have been investigated over the years. The last frontier in this sense is Semantic Role Labeling (SRL), which extracts relevant information purely based on the overall meaning of sentences. This is carried out by mapping specific situations described in the text into more general scenarios (semantic frames). FrameNet originated as a semantic frame repository by applying SRL techniques to large textual corpora, but its adaptation to languages other than English has been proven a difficult task. In this paper, we introduce a new implementation of SRL called Verb-Based SRL (VBSRL) for information extraction. VBSRL relies on a different conceptual theory used in the context of natural language understanding, which is language-independent and dramatically elevates the importance of verbs to abstract from real-life situations.
Cristani, Matteo; Maso, Serena Dal; Piccinin, Sabrina; Tomazzoli, Claudio; Vedovato, Marco; Vender, Maria
A Technology for Assisting Literacy Development in Adults with Dyslexia and Illiterate Second Language Learners Proceedings Article
In: Smart Innovation, Systems and Technologies (KES-SEEL 2021), pp. 475–485, 2021.
Abstract | Links | BibTeX | Tags: Deep Learning, Machine Learning, Natural Language Processing
@inproceedings{cristani_technology_2021,
title = {A Technology for Assisting Literacy Development in Adults with Dyslexia and Illiterate Second Language Learners},
author = {Matteo Cristani and Serena Dal Maso and Sabrina Piccinin and Claudio Tomazzoli and Marco Vedovato and Maria Vender},
doi = {10.1007/978-981-16-2834-4_40},
year = {2021},
date = {2021-01-01},
booktitle = {Smart Innovation, Systems and Technologies (KES-SEEL 2021)},
volume = {240},
pages = {475–485},
abstract = {Developing good literacy skills is a domain in which two distinct populations of adults display marked difficulties: individuals suffering from developmental dyslexia and second language learners. These weaknesses in literacy can have a negative impact on their quality of life and in particular on their employability and professional carrier. These subjects share, however, advanced digital skills, in particular those related to the usage of mobile devices. Leveraging on this skills we developed a platform that allows several different subjects (teachers, project managers, learners) in interacting with each other for assisting the development of the above mentioned literacy proficiency.},
keywords = {Deep Learning, Machine Learning, Natural Language Processing},
pubstate = {published},
tppubtype = {inproceedings}
}
Developing good literacy skills is a domain in which two distinct populations of adults display marked difficulties: individuals suffering from developmental dyslexia and second language learners. These weaknesses in literacy can have a negative impact on their quality of life and in particular on their employability and professional carrier. These subjects share, however, advanced digital skills, in particular those related to the usage of mobile devices. Leveraging on this skills we developed a platform that allows several different subjects (teachers, project managers, learners) in interacting with each other for assisting the development of the above mentioned literacy proficiency.
Agostinelli, Sofia; Cumo, Fabrizio; Guidi, Giambattista; Tomazzoli, Claudio
Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence Journal Article
In: ENERGIES, vol. 14, pp. 1–27, 2021, ISSN: 1996-1073.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Digital Twin, Edge computing, Energy Efficiency, Energy management
@article{agostinelli_cyber-physical_2021,
title = {Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence},
author = {Sofia Agostinelli and Fabrizio Cumo and Giambattista Guidi and Claudio Tomazzoli},
url = {https://www.mdpi.com/1996-1073/14/8/2338},
doi = {10.3390/en14082338},
issn = {1996-1073},
year = {2021},
date = {2021-01-01},
journal = {ENERGIES},
volume = {14},
pages = {1–27},
abstract = {The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.},
keywords = {Artificial Intelligence, Digital Twin, Edge computing, Energy Efficiency, Energy management},
pubstate = {published},
tppubtype = {article}
}
The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.
Assolini, Nicola; Baronchelli, Adelaide; Cristani, Matteo; Pasetto, Luca; Olivieri, Francesco; Ricciuti, Roberto; Tomazzoli, Claudio
Text Analytics Can Predict Contract Fairness, Transparency and Applicability Proceedings Article
In: Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST,, pp. 316–323, SciTePress / INSTICC, 2021, ISBN: 978-989-758-536-4.
Abstract | Links | BibTeX | Tags: Machine Learning, Natural Language Processing, Sentiment analysis
@inproceedings{assolini_text_2021,
title = {Text Analytics Can Predict Contract Fairness, Transparency and Applicability},
author = {Nicola Assolini and Adelaide Baronchelli and Matteo Cristani and Luca Pasetto and Francesco Olivieri and Roberto Ricciuti and Claudio Tomazzoli},
doi = {10.5220/0010660700003058},
isbn = {978-989-758-536-4},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST,},
pages = {316–323},
publisher = {SciTePress / INSTICC},
abstract = {There is a growing attention, in the research communities of political economics, onto the potential of text analytics in classifying documents with economic content. This interest extends the data analytics approach that has been the traditional base for economic theory with scientific perspective. To devise a general method for prediction applicability, we identify some phases of a methodology and perform tests on a large well-structured repository of resource contracts containing documents related to resources. The majority of these contracts involve mining resources. In this paper we prove that, by the usage of text analytics measures, we can cluster these documents on three indicators: fairness of the contract content, transparency of the document themselves, and applicability of the clauses of the contract intended to guarantee execution on an international basis. We achieve these results, consistent with a gold-standard test obtained with human experts, using text similarity b ased on the basic notions of bag of words, the index tf-idf, and three distinct cut-off measures},
keywords = {Machine Learning, Natural Language Processing, Sentiment analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
There is a growing attention, in the research communities of political economics, onto the potential of text analytics in classifying documents with economic content. This interest extends the data analytics approach that has been the traditional base for economic theory with scientific perspective. To devise a general method for prediction applicability, we identify some phases of a methodology and perform tests on a large well-structured repository of resource contracts containing documents related to resources. The majority of these contracts involve mining resources. In this paper we prove that, by the usage of text analytics measures, we can cluster these documents on three indicators: fairness of the contract content, transparency of the document themselves, and applicability of the clauses of the contract intended to guarantee execution on an international basis. We achieve these results, consistent with a gold-standard test obtained with human experts, using text similarity b ased on the basic notions of bag of words, the index tf-idf, and three distinct cut-off measures
2020
Ponza, Andrea; Scannapieco, Simone; Simone, Anna; Tomazzoli, Claudio
Envisioning the Digital Transformation of Financial Documents: A Blockchain-Based Bill of Exchange Proceedings Article
In: Advances in Intelligent Systems and Computing, Vol 1238, pp. 81–90, 2020, ISBN: 978-3-030-52534-7.
Abstract | Links | BibTeX | Tags: Blockchain, Business documents, Business Processes
@inproceedings{ponza_envisioning_2020,
title = {Envisioning the Digital Transformation of Financial Documents: A Blockchain-Based Bill of Exchange},
author = {Andrea Ponza and Simone Scannapieco and Anna Simone and Claudio Tomazzoli},
doi = {10.1007/978-3-030-52535-4_9},
isbn = {978-3-030-52534-7},
year = {2020},
date = {2020-06-01},
booktitle = {Advances in Intelligent Systems and Computing, Vol 1238},
volume = {1238 AISC},
pages = {81–90},
abstract = {A Bill of Exchange (BoE) is a paper-written contract involving three parties A, B and C where A is economically in debt with B and in credit with C. Once the parties approve a BoE, C is legally bound to pay B on behalf of A within a set deadline, so that the debt of A towards B is extinguished. Although regarded as an elegant and powerful variant of a promissory note, over time the BoE has become unpractical to use in a global market where suppliers and customers aren't next-door companies anymore. On the other hand, the blockchain distributed ledger, AES authentication, and digital archiving with suitable long-period standards (e.g., PDF/A) may encourage the revival of such an instrument, while ensuring legal validity, strength and a non-tampering warranty. This paper exploits said state-of-the-art technologies to bring the paper-based BoE into the digital era as the DigiBoE. Its envisioned applications are B2B, C2C and B2C secure and legally acknowledged transactions for debt resolution no longer requiring financial intermediaries.},
keywords = {Blockchain, Business documents, Business Processes},
pubstate = {published},
tppubtype = {inproceedings}
}
A Bill of Exchange (BoE) is a paper-written contract involving three parties A, B and C where A is economically in debt with B and in credit with C. Once the parties approve a BoE, C is legally bound to pay B on behalf of A within a set deadline, so that the debt of A towards B is extinguished. Although regarded as an elegant and powerful variant of a promissory note, over time the BoE has become unpractical to use in a global market where suppliers and customers aren't next-door companies anymore. On the other hand, the blockchain distributed ledger, AES authentication, and digital archiving with suitable long-period standards (e.g., PDF/A) may encourage the revival of such an instrument, while ensuring legal validity, strength and a non-tampering warranty. This paper exploits said state-of-the-art technologies to bring the paper-based BoE into the digital era as the DigiBoE. Its envisioned applications are B2B, C2C and B2C secure and legally acknowledged transactions for debt resolution no longer requiring financial intermediaries.
Cristani, Matteo; Pasetto, Luca; Tomazzoli, Claudio
Protecting the Environment: A Multi-Agent Approach to Environmental Monitoring Proceedings Article
In: pp. 3636–3644, 2020, ISSN: 1877-0509, (ISSN: 1877-0509).
Abstract | Links | BibTeX | Tags: Environmental monitoring, Intelligent Systems, Scada System
@inproceedings{cristani_protecting_2020,
title = {Protecting the Environment: A Multi-Agent Approach to Environmental Monitoring},
author = {Matteo Cristani and Luca Pasetto and Claudio Tomazzoli},
url = {https://www.sciencedirect.com/science/article/pii/S1877050920322547},
doi = {10.1016/j.procs.2020.09.336},
issn = {1877-0509},
year = {2020},
date = {2020-01-01},
volume = {176},
pages = {3636–3644},
series = {Procedia Computer Science},
abstract = {In this paper we discuss a transition model from commonly adopted models of data gathering, transfer and management for environmental monitoring towards more sophisticated ones based on Artificial Intelligence and IoT. The transition model is based on the paradigm of multiple agent systems. The adoption of this transition model is motivated by the need to improve effectiveness, efficiency and interoperability of environmental monitoring by simultaneously guaranteeing its sustainability in economic terms.},
note = {ISSN: 1877-0509},
keywords = {Environmental monitoring, Intelligent Systems, Scada System},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we discuss a transition model from commonly adopted models of data gathering, transfer and management for environmental monitoring towards more sophisticated ones based on Artificial Intelligence and IoT. The transition model is based on the paradigm of multiple agent systems. The adoption of this transition model is motivated by the need to improve effectiveness, efficiency and interoperability of environmental monitoring by simultaneously guaranteeing its sustainability in economic terms.