Topic: Fair use
Published:
2026
- DetailsConference Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern ItalyCampanile, L., Di Bonito, L. P., Marulli, F., Balzanella, A., & Verde, R. (2026). Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern Italy [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 317–333. https://doi.org/10.1007/978-3-031-97645-2_21
Abstract
The increasing levels of CO2 and air pollutants represent a major challenge to environmental sustainability and public health, particularly in regions characterized by complex geographic and socio-economic dynamics. This work proposes a study focused on the Southern Italy regions, where environmental vulnerabilities are displayed, along with a limited availability of high-granularity data. The main aim of this work is to build and provide a comprehensive and detailed dataset tailored to the region’s unique needs, by leveraging datasets from EDGAR for greenhouse gases and air pollutants, integrated with demographic and territorial morphology data from ISTAT. The creation of composite indicators to monitor trends in emissions and pollution on a fine spatial scale is supported by the data set. These indicators enable initial insight into spatial disparities in pollutant concentrations, offering valuable data to inform targeted policy interventions. The work provided a foundation for next analytical studies, integrating different datasets and highlighting the potential for complex spatiotemporal analysis. The study provides a robust dataset and preliminary insights, enhancing the understanding of environmental dynamics in Southern Italy. Subsequent efforts will focus on extending this methodology to more extensive geographic contexts and incorporating real-time data for adaptive monitoring. The proposed framework also lays the groundwork for privacy-aware environmental monitoring solutions, enabling future integration with edge and IoT-based architectures while addressing privacy and data protection concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. - DetailsCampanile, L., Iacono, M., Mastroianni, M., Riccio, C., & Viscardi, B. (2026). A TOPSIS-Based Approach to Evaluate Alternative Solutions for GDPR-Compliant Smart-City Services Implementation [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 303–316. https://doi.org/10.1007/978-3-031-97645-2_20
Abstract
Adapting or designing a system which operates on personal data in EU is impacted by the privacy-by-design and privacy-by-default principles because of the prescriptions of the GDPR. In this paper we propose an approach to decision making which is based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The approach is applied to a GDPR system compliance design process, based on a case study about system performance evaluation by means of queuing networks, but is absolutely general with respect to analogous problems, in which cost issues should be balanced with technical performances and risk exposure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2025
- DetailsDi Giovanni, M., Verde, L., Campanile, L., Romoli, M., Sabbarese, C., & Marrone, S. (2025). Assessing Safety and Sustainability of a Monitoring System for Nuclear Waste Management [Article]. IEEE Access, 13, 120486–120505. https://doi.org/10.1109/ACCESS.2025.3586735
Abstract
Nowadays, nuclear technologies are increasingly being integrated into industry, healthcare and manufacturing. As a side effect, waste materials are produced according to standard processes which are subject to international regulations. One of the most critical phases is the pre-disposal, due to the uncertainty related to the evolution of the materials and their potential impact on environmental protection. This paper introduces the architecture of a monitoring system able to accomplish safety goals and to guarantee energetic sustainability. The possibility of defining different system configurations (e. g., sensor scheduling policies, geometry of the sites, trustworthiness of the sensors) fosters a high adaptability to several monitoring scenarios, being characterised by different safety and sustainability levels. A methodology, integrating a model-based approach with data collection and processing, is proposed to quantitatively evaluate system configurations. This methodology is based on the definition of two metrics — one for safety and one for sustainability — and an assessment model. The model computes the metrics considering geometry of the place, scheduling and trustworthiness of monitoring sensors. This is a first step in the construction of a Decision Support System able to aid human operators in assessing system configurations and finding possible safety/sustainability trade-offs. A case study is used to show the feasibility of the approach: some configurations are evaluated on the real plant, placed at Řež in the Czech Republic, assessing them on the base of the defined metrics. © 2025 The Authors.
2024
- DetailsVerde, L., Marulli, F., De Fazio, R., Campanile, L., & Marrone, S. (2024). HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis [Article]. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109021
Abstract
Background: Voice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. Method: In this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. Results: The reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. Conclusions: The comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders. © 2024 The Author(s) - DetailsCampanile, L., De Fazio, R., Di Giovanni, M., & Marulli, F. (2024). Beyond the Hype: Toward a Concrete Adoption of the Fair and Responsible Use of AI [Conference paper]. CEUR Workshop Proceedings, 3762, 60–65. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205601768&partnerID=40&md5=99140624de79e37b370ed4cf816c24e7
Abstract
Artificial Intelligence (AI) is a fast-changing technology that is having a profound impact on our society, from education to industry. Its applications cover a wide range of areas, such as medicine, military, engineering and research. The emergence of AI and Generative AI have significant potential to transform society, but they also raise concerns about transparency, privacy, ownership, fair use, reliability, and ethical considerations. The Generative AI adds complexity to the existing problems of AI due to its ability to create machine-generated data that is barely distinguishable from human-generated data. Bringing to the forefront the issue of responsible and fair use of AI. The security, safety and privacy implications are enormous, and the risks associated with inappropriate use of these technologies are real. Although some governments, such as the European Union and the United States, have begun to address the problem with recommendations and proposed regulations, it is probably not enough. Regulatory compliance should be seen as a starting point in a continuous process of improving the ethical procedures and privacy risk assessment of AI systems. The need to have a baseline to manage the process of creating an AI system even from an ethics and privacy perspective becomes progressively more important In this study, we discuss the ethical implications of these advances and propose a conceptual framework for the responsible, fair, and safe use of AI. © 2024 Copyright for this paper by its authors. - DetailsCampanile, L., Di Bonito, L. P., Natale, F. D., & Iacono, M. (2024). Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 38(1), 558–564. https://doi.org/10.7148/2024-0558
Abstract
Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.
2022
- DetailsCampanile, L., Cesarano, M., Palmiero, G., & Sanghez, C. (2022). Break the Fake: A Technical Report on Browsing Behavior During the Pandemic [Conference paper]. Smart Innovation, Systems and Technologies, 309, 573–586. https://doi.org/10.1007/978-981-19-3444-5_49
Abstract
The widespread use of the internet as the main source of information for many users has led to the spread of fake news and misleading information as a side effect. The pandemic that in the last 2 years has forced us to change our lifestyle and to increase the time spent at home, has further increased the time spent surfing the Internet. In this work we analyze the navigation logs of a sample of users, in compliance with the current privacy regulation, comparing and dividing between the different categories of target sites, also identifying some well-known sites that spread fake news. The results of the report show that during the most acute periods of the pandemic there was an increase in surfing on untrusted sites. The report also shows the tendency to use such sites in the evening and night hours and highlights the differences between the different years considered. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. - DetailsVerde, L., Campanile, L., Marulli, F., & Marrone, S. (2022). Speech-based Evaluation of Emotions-Depression Correlation. Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022. https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927758
Abstract
Early detection of depression symptoms is fundamental to limit the onset of further associated behavioural disorders, such as psychomotor or social withdrawal. The combination of Artificial Intelligence and speech analysis revealed the existence of objectively measurable physical manifestations for early detection of depressive symptoms, constituting a valid support to evaluate these signals. To push forward the research state-of-art, this aim of this paper is to understand quantitative correlations between emotional states and depression by proposing a study across different datasets containing speech of both depressed/non-depressed people and emotional-related samples. The relationship between affective measures and depression can, in fact, a support to evaluate the presence of depression state. This work constitutes a preliminary step of a study whose final aim is to pursue AI-powered personalized medicine by building sophisticated Clinical Decision Support Systems for depression, as well as other psychological disorders. © 2022 IEEE. - DetailsCampanile, L., Biase, M. S. de, Marrone, S., Raimondo, M., & Verde, L. (2022). On the Evaluation of BDD Requirements with Text-based Metrics: The ETCS-L3 Case Study [Conference paper]. Smart Innovation, Systems and Technologies, 309, 561–571. https://doi.org/10.1007/978-981-19-3444-5_48
Abstract
A proper requirement definition phase is of a paramount importance in software engineering. It is the first and prime mean to realize efficient and reliable systems. System requirements are usually formulated and expressed in natural language, given its universality and ease of communication and writing. Unfortunately, natural language can be a source of ambiguity, complexity and omissions, which may cause system failures. Among the different approaches proposed by the software engineering community, Behavioural-Driven Development (BDD) is affirming as a valid, practical method to structure effective and non-ambiguous requirement specifications. The paper tackles with the problem of measuring requirements in BDD by assessing some traditional Natural Language Processing-related metrics with respect to a sample excerpt of requirement specification rewritten according to the BDD criteria. This preliminary assessment is made on the ERTMS-ETCS Level 3 case study whose specification, up to this date, is not managed by a standardisation body. The paper demonstrates the necessity of novel metrics able to cope with the BDD specification paradigms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
2021
- DetailsBarbierato, E., Campanile, L., Gribaudo, M., Iacono, M., Mastroianni, M., & Nacchia, S. (2021). Performance evaluation for the design of a hybrid cloud based distance synchronous and asynchronous learning architecture [Article]. Simulation Modelling Practice and Theory, 109. https://doi.org/10.1016/j.simpat.2021.102303
Abstract
The COVID-19 emergency suddenly obliged schools and universities around the world to deliver on-line lectures and services. While the urgency of response resulted in a fast and massive adoption of standard, public on-line platforms, generally owned by big players in the digital services market, this does not sufficiently take into account privacy-related and security-related issues and potential legal problems about the legitimate exploitation of the intellectual rights about contents. However, the experience brought to attention a vast set of issues, which have been addressed by implementing these services by means of private platforms. This work presents a modeling and evaluation framework, defined on a set of high-level, management-oriented parameters and based on a Vectorial Auto Regressive Fractional (Integrated) Moving Average based approach, to support the design of distance learning architectures. The purpose of this framework is to help decision makers to evaluate the requirements and the costs of hybrid cloud technology solutions. Furthermore, it aims at providing a coarse grain reference organization integrating low-cost, long-term storage management services to implement a viable and accessible history feature for all materials. The proposed solution has been designed bearing in mind the ecosystem of Italian universities. A realistic case study has been shaped on the needs of an important, generalist, polycentric Italian university, where some of the authors of this paper work. © 2021 Elsevier B.V. - DetailsCampanile, L., Cantiello, P., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12956 LNCS, 3–14. https://doi.org/10.1007/978-3-030-87010-2_1
Abstract
Blockchains provide a valid and profitable support for the implementation of trustable and secure distributed ledgers, in support to groups of subjects that are potentially competitors in conflict of interest but need to share progressive information recording processes. Blockchains prevent data stored in blocks from being altered or deleted, but there are situations in which stored information must be deleted or made inaccessible on request or periodically, such as the ones in which GDPR is applicable. In this paper we present literature solutions and design an implementation in the context of a traffic management system for the Internet of Vehicles based on the Pseudonymization/Cryptography solution, evaluating its viability, its GDPR compliance and its level of risk. © 2021, Springer Nature Switzerland AG.
2020
- DetailsConference Privacy regulations challenges on data-centric and iot systems: A case study for smart vehiclesCampanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2020). Privacy regulations challenges on data-centric and iot systems: A case study for smart vehicles [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 507–520. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089476036&partnerID=40&md5=c18dd73c221ec312a330521bf03d332e
Abstract
Internet of Things (IoTs) services and data-centric systems allow smart and efficient information exchanging. Anyway, even if existing IoTs and cyber security architectures are enforcing, they are still vulnerable to security issues, as unauthorized access, data breaches, intrusions. They can’t provide yet sufficiently robust and secure solutions to be applied in a straightforward way, both for ensuring privacy preservation and trustworthiness of transmitted data, evenly preventing from its fraudulent and unauthorized usage. Such data potentially include critical information about persons’ privacy (locations, visited places, behaviors, goods, anagraphic data and health conditions). So, novel approaches for IoTs and data-centric security are needed. In this work, we address IoTs systems security problem focusing on the privacy preserving issue. Indeed, after the European Union introduced the General Data Protection Regulation (GDPR), privacy data protection is a mandatory requirement for systems producing and managing sensible users’ data. Starting from a case study for the Internet of Vehicles (IoVs), we performed a pilot study and DPIA assessment to analyze possible mitigation strategies for improving the compliance of IoTs based systems to GDPR requirements. Our preliminary results evidenced that the introduction of blockchains in IoTs systems architectures can improve significantly the compliance to privacy regulations. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
2026
- DetailsConference Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern ItalyCampanile, L., Di Bonito, L. P., Marulli, F., Balzanella, A., & Verde, R. (2026). Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern Italy [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 317–333. https://doi.org/10.1007/978-3-031-97645-2_21
Abstract
The increasing levels of CO2 and air pollutants represent a major challenge to environmental sustainability and public health, particularly in regions characterized by complex geographic and socio-economic dynamics. This work proposes a study focused on the Southern Italy regions, where environmental vulnerabilities are displayed, along with a limited availability of high-granularity data. The main aim of this work is to build and provide a comprehensive and detailed dataset tailored to the region’s unique needs, by leveraging datasets from EDGAR for greenhouse gases and air pollutants, integrated with demographic and territorial morphology data from ISTAT. The creation of composite indicators to monitor trends in emissions and pollution on a fine spatial scale is supported by the data set. These indicators enable initial insight into spatial disparities in pollutant concentrations, offering valuable data to inform targeted policy interventions. The work provided a foundation for next analytical studies, integrating different datasets and highlighting the potential for complex spatiotemporal analysis. The study provides a robust dataset and preliminary insights, enhancing the understanding of environmental dynamics in Southern Italy. Subsequent efforts will focus on extending this methodology to more extensive geographic contexts and incorporating real-time data for adaptive monitoring. The proposed framework also lays the groundwork for privacy-aware environmental monitoring solutions, enabling future integration with edge and IoT-based architectures while addressing privacy and data protection concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. - DetailsCampanile, L., Iacono, M., Mastroianni, M., Riccio, C., & Viscardi, B. (2026). A TOPSIS-Based Approach to Evaluate Alternative Solutions for GDPR-Compliant Smart-City Services Implementation [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 303–316. https://doi.org/10.1007/978-3-031-97645-2_20
Abstract
Adapting or designing a system which operates on personal data in EU is impacted by the privacy-by-design and privacy-by-default principles because of the prescriptions of the GDPR. In this paper we propose an approach to decision making which is based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The approach is applied to a GDPR system compliance design process, based on a case study about system performance evaluation by means of queuing networks, but is absolutely general with respect to analogous problems, in which cost issues should be balanced with technical performances and risk exposure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2025
- DetailsDi Giovanni, M., Verde, L., Campanile, L., Romoli, M., Sabbarese, C., & Marrone, S. (2025). Assessing Safety and Sustainability of a Monitoring System for Nuclear Waste Management [Article]. IEEE Access, 13, 120486–120505. https://doi.org/10.1109/ACCESS.2025.3586735
Abstract
Nowadays, nuclear technologies are increasingly being integrated into industry, healthcare and manufacturing. As a side effect, waste materials are produced according to standard processes which are subject to international regulations. One of the most critical phases is the pre-disposal, due to the uncertainty related to the evolution of the materials and their potential impact on environmental protection. This paper introduces the architecture of a monitoring system able to accomplish safety goals and to guarantee energetic sustainability. The possibility of defining different system configurations (e. g., sensor scheduling policies, geometry of the sites, trustworthiness of the sensors) fosters a high adaptability to several monitoring scenarios, being characterised by different safety and sustainability levels. A methodology, integrating a model-based approach with data collection and processing, is proposed to quantitatively evaluate system configurations. This methodology is based on the definition of two metrics — one for safety and one for sustainability — and an assessment model. The model computes the metrics considering geometry of the place, scheduling and trustworthiness of monitoring sensors. This is a first step in the construction of a Decision Support System able to aid human operators in assessing system configurations and finding possible safety/sustainability trade-offs. A case study is used to show the feasibility of the approach: some configurations are evaluated on the real plant, placed at Řež in the Czech Republic, assessing them on the base of the defined metrics. © 2025 The Authors.
2024
- DetailsVerde, L., Marulli, F., De Fazio, R., Campanile, L., & Marrone, S. (2024). HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis [Article]. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109021
Abstract
Background: Voice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. Method: In this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. Results: The reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. Conclusions: The comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders. © 2024 The Author(s) - DetailsCampanile, L., De Fazio, R., Di Giovanni, M., & Marulli, F. (2024). Beyond the Hype: Toward a Concrete Adoption of the Fair and Responsible Use of AI [Conference paper]. CEUR Workshop Proceedings, 3762, 60–65. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205601768&partnerID=40&md5=99140624de79e37b370ed4cf816c24e7
Abstract
Artificial Intelligence (AI) is a fast-changing technology that is having a profound impact on our society, from education to industry. Its applications cover a wide range of areas, such as medicine, military, engineering and research. The emergence of AI and Generative AI have significant potential to transform society, but they also raise concerns about transparency, privacy, ownership, fair use, reliability, and ethical considerations. The Generative AI adds complexity to the existing problems of AI due to its ability to create machine-generated data that is barely distinguishable from human-generated data. Bringing to the forefront the issue of responsible and fair use of AI. The security, safety and privacy implications are enormous, and the risks associated with inappropriate use of these technologies are real. Although some governments, such as the European Union and the United States, have begun to address the problem with recommendations and proposed regulations, it is probably not enough. Regulatory compliance should be seen as a starting point in a continuous process of improving the ethical procedures and privacy risk assessment of AI systems. The need to have a baseline to manage the process of creating an AI system even from an ethics and privacy perspective becomes progressively more important In this study, we discuss the ethical implications of these advances and propose a conceptual framework for the responsible, fair, and safe use of AI. © 2024 Copyright for this paper by its authors. - DetailsCampanile, L., Di Bonito, L. P., Natale, F. D., & Iacono, M. (2024). Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 38(1), 558–564. https://doi.org/10.7148/2024-0558
Abstract
Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.
2022
- DetailsCampanile, L., Cesarano, M., Palmiero, G., & Sanghez, C. (2022). Break the Fake: A Technical Report on Browsing Behavior During the Pandemic [Conference paper]. Smart Innovation, Systems and Technologies, 309, 573–586. https://doi.org/10.1007/978-981-19-3444-5_49
Abstract
The widespread use of the internet as the main source of information for many users has led to the spread of fake news and misleading information as a side effect. The pandemic that in the last 2 years has forced us to change our lifestyle and to increase the time spent at home, has further increased the time spent surfing the Internet. In this work we analyze the navigation logs of a sample of users, in compliance with the current privacy regulation, comparing and dividing between the different categories of target sites, also identifying some well-known sites that spread fake news. The results of the report show that during the most acute periods of the pandemic there was an increase in surfing on untrusted sites. The report also shows the tendency to use such sites in the evening and night hours and highlights the differences between the different years considered. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. - DetailsVerde, L., Campanile, L., Marulli, F., & Marrone, S. (2022). Speech-based Evaluation of Emotions-Depression Correlation. Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022. https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927758
Abstract
Early detection of depression symptoms is fundamental to limit the onset of further associated behavioural disorders, such as psychomotor or social withdrawal. The combination of Artificial Intelligence and speech analysis revealed the existence of objectively measurable physical manifestations for early detection of depressive symptoms, constituting a valid support to evaluate these signals. To push forward the research state-of-art, this aim of this paper is to understand quantitative correlations between emotional states and depression by proposing a study across different datasets containing speech of both depressed/non-depressed people and emotional-related samples. The relationship between affective measures and depression can, in fact, a support to evaluate the presence of depression state. This work constitutes a preliminary step of a study whose final aim is to pursue AI-powered personalized medicine by building sophisticated Clinical Decision Support Systems for depression, as well as other psychological disorders. © 2022 IEEE. - DetailsCampanile, L., Biase, M. S. de, Marrone, S., Raimondo, M., & Verde, L. (2022). On the Evaluation of BDD Requirements with Text-based Metrics: The ETCS-L3 Case Study [Conference paper]. Smart Innovation, Systems and Technologies, 309, 561–571. https://doi.org/10.1007/978-981-19-3444-5_48
Abstract
A proper requirement definition phase is of a paramount importance in software engineering. It is the first and prime mean to realize efficient and reliable systems. System requirements are usually formulated and expressed in natural language, given its universality and ease of communication and writing. Unfortunately, natural language can be a source of ambiguity, complexity and omissions, which may cause system failures. Among the different approaches proposed by the software engineering community, Behavioural-Driven Development (BDD) is affirming as a valid, practical method to structure effective and non-ambiguous requirement specifications. The paper tackles with the problem of measuring requirements in BDD by assessing some traditional Natural Language Processing-related metrics with respect to a sample excerpt of requirement specification rewritten according to the BDD criteria. This preliminary assessment is made on the ERTMS-ETCS Level 3 case study whose specification, up to this date, is not managed by a standardisation body. The paper demonstrates the necessity of novel metrics able to cope with the BDD specification paradigms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
2021
- DetailsBarbierato, E., Campanile, L., Gribaudo, M., Iacono, M., Mastroianni, M., & Nacchia, S. (2021). Performance evaluation for the design of a hybrid cloud based distance synchronous and asynchronous learning architecture [Article]. Simulation Modelling Practice and Theory, 109. https://doi.org/10.1016/j.simpat.2021.102303
Abstract
The COVID-19 emergency suddenly obliged schools and universities around the world to deliver on-line lectures and services. While the urgency of response resulted in a fast and massive adoption of standard, public on-line platforms, generally owned by big players in the digital services market, this does not sufficiently take into account privacy-related and security-related issues and potential legal problems about the legitimate exploitation of the intellectual rights about contents. However, the experience brought to attention a vast set of issues, which have been addressed by implementing these services by means of private platforms. This work presents a modeling and evaluation framework, defined on a set of high-level, management-oriented parameters and based on a Vectorial Auto Regressive Fractional (Integrated) Moving Average based approach, to support the design of distance learning architectures. The purpose of this framework is to help decision makers to evaluate the requirements and the costs of hybrid cloud technology solutions. Furthermore, it aims at providing a coarse grain reference organization integrating low-cost, long-term storage management services to implement a viable and accessible history feature for all materials. The proposed solution has been designed bearing in mind the ecosystem of Italian universities. A realistic case study has been shaped on the needs of an important, generalist, polycentric Italian university, where some of the authors of this paper work. © 2021 Elsevier B.V. - DetailsCampanile, L., Cantiello, P., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12956 LNCS, 3–14. https://doi.org/10.1007/978-3-030-87010-2_1
Abstract
Blockchains provide a valid and profitable support for the implementation of trustable and secure distributed ledgers, in support to groups of subjects that are potentially competitors in conflict of interest but need to share progressive information recording processes. Blockchains prevent data stored in blocks from being altered or deleted, but there are situations in which stored information must be deleted or made inaccessible on request or periodically, such as the ones in which GDPR is applicable. In this paper we present literature solutions and design an implementation in the context of a traffic management system for the Internet of Vehicles based on the Pseudonymization/Cryptography solution, evaluating its viability, its GDPR compliance and its level of risk. © 2021, Springer Nature Switzerland AG.
2020
- DetailsConference Privacy regulations challenges on data-centric and iot systems: A case study for smart vehiclesCampanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2020). Privacy regulations challenges on data-centric and iot systems: A case study for smart vehicles [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 507–520. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089476036&partnerID=40&md5=c18dd73c221ec312a330521bf03d332e
Abstract
Internet of Things (IoTs) services and data-centric systems allow smart and efficient information exchanging. Anyway, even if existing IoTs and cyber security architectures are enforcing, they are still vulnerable to security issues, as unauthorized access, data breaches, intrusions. They can’t provide yet sufficiently robust and secure solutions to be applied in a straightforward way, both for ensuring privacy preservation and trustworthiness of transmitted data, evenly preventing from its fraudulent and unauthorized usage. Such data potentially include critical information about persons’ privacy (locations, visited places, behaviors, goods, anagraphic data and health conditions). So, novel approaches for IoTs and data-centric security are needed. In this work, we address IoTs systems security problem focusing on the privacy preserving issue. Indeed, after the European Union introduced the General Data Protection Regulation (GDPR), privacy data protection is a mandatory requirement for systems producing and managing sensible users’ data. Starting from a case study for the Internet of Vehicles (IoVs), we performed a pilot study and DPIA assessment to analyze possible mitigation strategies for improving the compliance of IoTs based systems to GDPR requirements. Our preliminary results evidenced that the introduction of blockchains in IoTs systems architectures can improve significantly the compliance to privacy regulations. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.