Publications by Christian Riccio
Published:
Publications by “Christian Riccio”
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. - DetailsNapoli, F., Castaldo, M., Marrone, S., & Campanile, L. (2026). Comparing Emerging Technologies in Image Classification: From Quantum to Kolmogorov [Conference paper]. Lecture Notes in Computer Science, 15886 LNCS, 260–273. https://doi.org/10.1007/978-3-031-97576-9_17
Abstract
The rapid evolution of Artificial Intelligence has led to significant advancements in image classification, with novel approaches emerging beyond traditional deep learning paradigms. This paper presents a comparative analysis of three distinct methodologies for image classification: classical Convolutional Neural Networks (CNNs), Kolmogorov-Arnold Networks (KANs) and KAN-based CNNs and Quantum Machine Learning using Quantum Convolutional Neural Networks. The study evaluates these models on the Labeled Faces in the Wild dataset, implementing the different classifiers with existing, well-assessed technologies. Given the fundamental differences in computational paradigms, performance assessment extends beyond traditional accuracy metrics to include computational efficiency, interpretability, and, for quantum models, gate depth and noise. As a summary of the results, the proposed Quantum Convolutional Neural Network (QCNN) model achieves an accuracy of 75% on the target images classification task, indicating promising performance within current quantum computational limits. All the experiments strongly suggest that Convolutional Kolmogorov-Arnold Networks (CKANs) exhibit increased accuracy as image resolution decreases, QCNN performance meaningfully changes in relation to noise level, while CNNs still keeping strong discriminative capabilities. © 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
- DetailsCampanile, L., de Biase, M. S., & Marulli, F. (2025). Edge-Cloud Distributed Approaches to Text Authorship Analysis: A Feasibility Study [Book chapter]. Lecture Notes on Data Engineering and Communications Technologies, 250, 284–293. https://doi.org/10.1007/978-3-031-87778-0_28
Abstract
Automatic authorship analysis, often referred to as stylometry, is a captivating yet contentious field that employs computational techniques to determine the authorship of textual artefacts. In recent years, the importance of author profiling has grown significantly due to the proliferation of automatic text generation systems. These include both early-generation bots and the latest generative AI-based models, which have heightened concerns about misinformation and content authenticity. This study proposes a novel approach to evaluate the feasibility and effectiveness of contemporary distributed learning methods. The approach leverages the computational advantages of distributed systems while preserving the privacy of human contributors, enabling the collection and analysis of extensive datasets of “human-written” texts in contrast to those generated by bots. More specifically, the proposed method adopts a Federated Learning (FL) framework, integrating readability and stylometric metrics to deliver a privacy-preserving solution for Authorship Attribution (AA). The primary objective is to enhance the accuracy of AA processes, thus achieving a more robust “authorial fingerprint”. Experimental results reveal that while FL effectively protects privacy and mitigates data exposure risks, the combined use of readability and stylometric features significantly increases the accuracy of AA. This approach demonstrates promise for secure and scalable AA applications, particularly in privacy-sensitive contexts and real-time edge computing scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 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. - DetailsMarulli, F., Campanile, L., Ragucci, G., Carbone, S., & Bifulco, M. (2025). Data Generation and Cybersecurity: A Major Opportunity or the Next Nightmare? [Conference paper]. Proceedings of the 2025 IEEE International Conference on Cyber Security and Resilience, CSR 2025, 969–974. https://doi.org/10.1109/CSR64739.2025.11130069
Abstract
In recent years, the proliferation of synthetic data generation techniques-driven by advances in artificial intelli-gence-has opened new possibilities across a wide range of fields, from healthcare to autonomous systems, by addressing critical data scarcity issues. However, this technological progress also brings with it a growing concern: the dual-use nature of synthetic data. While it offers powerful tools for innovation, it simultaneously introduces significant risks related to information disorder and cybersecurity. As AI systems become increasingly capable of producing highly realistic yet entirely fabricated content, the boundaries between authentic and artificial information blur, making it more difficult to detect manipulation, protect digital infrastructures, and maintain public trust. This work undertakes a preliminary exploration of the evolving nexus between Generative AI, Information Disorder, and Cybersecurity: it aims to investigate the complex interplay among these three and to map their dynamic interactions and reciprocal influences, highlighting both the potential benefits and the looming challenges posed by this evolving landscape. Moreover, it seeks to propose a conceptual framework for assessing these interdependencies through a set of indicative metrics, offering a foundation for future empirical evaluation and strategic response. © 2025 IEEE. - DetailsCampanile, L., Iacono, M., Mastroianni, M., & Riccio, C. (2025). Performance Evaluation of an Edge-Blockchain Architecture for Smart City [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2025-June, 620–627. https://doi.org/10.7148/2025-0620
Abstract
This paper presents a simulation-based methodology to evaluate the performance of a privacy-compliant edge-blockchain architecture for smart city environments. The proposed model combines edge computing with a private, permissioned blockchain to ensure low-latency processing, secure data management, and verifiable transactions. Using a discrete-event simulation framework, we analyze the behavior of the system under realistic workloads and time-varying traffic conditions. The model captures edge operations, including preprocessing and cryptographic tasks, as well as blockchain validation using Proof of Stake consensus. Several experiments explore saturation thresholds, resource utilization, and latency dynamics, under both synthetic and realistic traffic profiles. Results reveal how architectural bottlenecks shift depending on resource allocation and input rate, and demonstrate the importance of balanced dimensioning between edge and blockchain layers. © ECMS Marco Scarpa, Salvatore Cavalieri, Salvatore Serrano, Fabrizio De Vita (Editors) 2025. - DetailsDi Bonito, L. P., Campanile, L., Iacono, M., & Di Natale, F. (2025). An eXplainable Artificial Intelligence framework to predict marine scrubbers performances [Article]. Engineering Applications of Artificial Intelligence, 160. https://doi.org/10.1016/j.engappai.2025.111860
Abstract
This study presents an eXplainable Artificial Intelligence (XAI) framework to predict the performance of marine scrubbers used for sulfur dioxide (SO2) removal from marine diesel engine flue gases. Using an aggregated dataset from a roll-on/roll-off (Ro-Ro) cargo ship equipped with an open-loop scrubber, combined with satellite data, the study constructs and evaluates multiple artificial intelligence models, including ensemble models, which were benchmarked against each other using standard regression metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). Results achieve high accuracy R2>0.92 and offer insights for optimizing scrubber operations. Nevertheless, artificial intelligence models lack transparency. To overcome this problem, this research integrates post-hoc explainability techniques to elucidate the contributions of various features to model predictions, thereby enhancing interpretability and reliability. The integration of SHapley Additive exPlanations (SHAP) and Explain Like I’m 5 (ELI5) not only confirmed the consistency of feature importance rankings (e.g. seawater acidity level, SO2 inlet concentration, outlet temperature) but also aligned with the physical-chemical principles of SO2 absorption. Quantitative comparisons with theoretical expectations demonstrated the reliability of the XAI insights, enhancing both model transparency and interpretability. This can improve the current capability of designing scrubber units by defining more efficient and less expensive options for environmental regulation compliance. © 2025 The Authors - DetailsConference Quantum Convolutional Neural Networks for Image Classification: Perspectives and ChallengesNapoli, F., Campanile, L., De Gregorio, G., & Marrone, S. (2025). Quantum Convolutional Neural Networks for Image Classification: Perspectives and Challenges [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 509–516. https://doi.org/10.5220/0013521500003944
Abstract
Quantum Computing is becoming a central point of discussion in both academic and industrial communities. Quantum Machine Learning is one of the most promising subfields of this technology, in particular for image classification. In this paper, the model of Quantum Convolutional Neural Networks and some related implementations are explored in their potential for a non-trivial task of image classification. The paper presents some experimentations and discusses the limitations and the strengths of these approaches when compared with classical Convolutional Neural Networks. Furthermore, an analysis of the impact of the noise level on the quality of the classification task has been performed. This paper reports a substantial equivalence of the perfomance of the model with respect the level of noise. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda. - DetailsCampanile, L., Zona, R., Perfetti, A., & Rosatelli, F. (2025). An AI-Driven Methodology for Patent Evaluation in the IoT Sector: Assessing Relevance and Future Impact [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 501–508. https://doi.org/10.5220/0013519700003944
Abstract
The rapid expansion of the Internet of Things has led to a surge in patent filings, creating challenges in evaluating their relevance and potential impact. Traditional patent assessment methods, relying on manual review and keyword-based searches, are increasingly inadequate for analyzing the complexity of emerging IoT technologies. In this paper, we propose an AI-driven methodology for patent evaluation that leverages Large Language Models and machine learning techniques to assess patent relevance and estimate future impact. Our framework integrates advanced Natural Language Processing techniques with structured patent metadata to establish a systematic approach to patent analysis. The methodology consists of three key components: (1) feature extraction from patent text using LLM embeddings and conventional NLP methods, (2) relevance classification and clustering to identify emerging technological trends, and (3) an initial formulation of impact estimation based on semantic similarity and citation patterns. While this study focuses primarily on defining the methodology, we include a minimal validation on a sample dataset to illustrate its feasibility and potential. The proposed approach lays the groundwork for a scalable, automated patent evaluation system, with future research directions aimed at refining impact prediction models and expanding empirical validation. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
2024
- DetailsBarzegar, A., Campanile, L., Marrone, S., Marulli, F., Verde, L., & Mastroianni, M. (2024). Fuzzy-based Severity Evaluation in Privacy Problems: An Application to Healthcare [Conference paper]. Proceedings - 2024 19th European Dependable Computing Conference, EDCC 2024, 147–154. https://doi.org/10.1109/EDCC61798.2024.00037
Abstract
The growing diffusion of smart pervasive applications is starting to mine personal privacy: from Internet of Things to Machine Learning, the opportunities for privacy loss are many. As for other concerns involving people and goods as financial, safety and security, researchers and practitioners have defined in time different risk assessment procedures to have repeatable and accurate ways of detecting, quantifying and managing the (possible) source of privacy loss. This paper defines a methodology to deal with privacy risk assessment, overcoming the traditional dichotomy between qualitative (easy to apply) and quantitative (accurate) approaches. The present paper introduces an approach based on fuzzy logic, able to conjugate the benefits of both techniques. The feasibility of the proposed methodology is demonstrated using a healthcare case study. © 2024 IEEE. - DetailsBook Chapter Combining Federated and Ensemble Learning in Distributed and Cloud Environments: An Exploratory StudyMarulli, F., Campanile, L., Marrone, S., & Verde, L. (2024). Combining Federated and Ensemble Learning in Distributed and Cloud Environments: An Exploratory Study [Book chapter]. Lecture Notes on Data Engineering and Communications Technologies, 203, 297–306. https://doi.org/10.1007/978-3-031-57931-8_29
Abstract
Conventional modern Machine Learning (ML) applications involve training models in the cloud and then transferring them back to the edge, especially in an Internet of Things (IoT) enabled environment. However, privacy-related limitations on data transfer from the edge to the cloud raise challenges: among various solutions, Federated Learning (FL) could satisfy privacy related concerns and accommodate power and energy issues of edge devices. This paper proposes a novel approach that combines FL and Ensemble Learning (EL) to improve both security and privacy challenges. The presented methodology introduces an extra layer, the Federation Layer, to enhance security. It uses Bayesian Networks (BNs) to dynamically filter untrusted/unsecure federation clients. This approach presents a solution for increasing the security and robustness of FL systems, considering also privacy and performance aspects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. - DetailsMarulli, F., Campanile, L., de Biase, M. S., Marrone, S., Verde, L., & Bifulco, M. (2024). Understanding Readability of Large Language Models Output: An Empirical Analysis [Conference paper]. Procedia Computer Science, 246(C), 5273–5282. https://doi.org/10.1016/j.procs.2024.09.636
Abstract
Recently, Large Language Models (LLMs) have seen some impressive leaps, achieving the ability to accomplish several tasks, from text completion to powerful chatbots. The great variety of available LLMs and the fast pace of technological innovations in this field, is making LLM assessment a hard task to accomplish: understanding not only what such a kind of systems generate but also which is the quality of their results is of a paramount importance. Generally, the quality of a synthetically generated object could refer to the reliability of the content, to the lexical variety or coherence of the text. Regarding the quality of text generation, an aspect that up to now has not been adequately discussed is concerning the readability of textual artefacts. This work focuses on the latter aspect, proposing a set of experiments aiming to better understanding and evaluating the degree of readability of texts automatically generated by an LLM. The analysis is performed through an empirical study based on: considering a subset of five pre-trained LLMs; considering a pool of English text generation tasks, with increasing difficulty, assigned to each of the models; and, computing a set of the most popular readability indexes available from the computational linguistics literature. Readability indexes will be computed for each model to provide a first perspective of the readability of textual contents artificially generated can vary among different models and under different requirements of the users. The results obtained by evaluating and comparing different models provide interesting insights, especially into the responsible use of these tools by both beginners and not overly experienced practitioners. © 2024 The Authors. - 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. - DetailsOther eXplainable Artificial Intelligence in Process Engineering: Promises, Facts, and Current LimitationsDi Bonito, L. P., Campanile, L., Di Natale, F., Mastroianni, M., & Iacono, M. (2024). eXplainable Artificial Intelligence in Process Engineering: Promises, Facts, and Current Limitations [Review]. Applied System Innovation, 7(6). https://doi.org/10.3390/asi7060121
Abstract
Artificial Intelligence (AI) has been swiftly incorporated into the industry to become a part of both customer services and manufacturing operations. To effectively address the ethical issues now being examined by the government, AI models must be explainable in order to be used in both scientific and societal contexts. The current state of eXplainable artificial intelligence (XAI) in process engineering is examined in this study through a systematic literature review (SLR), with particular attention paid to the technology’s effect, degree of adoption, and potential to improve process and product quality. Due to restricted access to sizable, reliable datasets, XAI research in process engineering is still primarily exploratory or propositional, despite noteworthy applicability in well-known case studies. According to our research, XAI is becoming more and more positioned as a tool for decision support, with a focus on robustness and dependability in process optimization, maintenance, and quality assurance. This study, however, emphasizes that the use of XAI in process engineering is still in its early stages, and there is significant potential for methodological development and wider use across technical domains. © 2024 by the authors.
2023
- DetailsCampanile, L., de Fazio, R., Di Giovanni, M., Marrone, S., Marulli, F., & Verde, L. (2023). Inferring Emotional Models from Human-Machine Speech Interactions [Conference paper]. Procedia Computer Science, 225, 1241–1250. https://doi.org/10.1016/j.procs.2023.10.112
Abstract
Human-Machine Interfaces (HMIs) are getting more and more important in a hyper-connected society. Traditional HMIs are built considering cognitive features while emotional ones are often neglected, bringing sometimes such interfaces to misuse. As a part of a long run research, oriented to the definition of an HMI engineering approach, this paper concretely proposes a method to build an emotional-aware explicit model of the user starting from the behaviour of the human with a virtual agent. The paper also proposes an instance of this model inference process in voice assistants in an automatic depression context, which can constitute the core phase to realize a Human Digital Twin of a patient. The case study generated a model composed of Fluid Stochastic Petri Net sub-models, achieved after the data analysis by a Support Vector Machine. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) - DetailsMarrone, S., Campanile, L., De Fazio, R., Di Giovanni, M., Gentile, U., Marulli, F., & Verde, L. (2023). A Petri net oriented approach for advanced building energy management systems [Article]. Journal of Ambient Intelligence and Smart Environments, 15(3), 211–233. https://doi.org/10.3233/AIS-230065
Abstract
Sustainability is one of the main goals to pursue in several aspects of everyday life; the recent energy shortage and the price raise worsen this problem, especially in the management of energy in buildings. As the Internet of Things (IoT) is an assessed computing paradigm able to capture meaningful data from the field and send them to cloud infrastructures, other approaches are also enabled, namely model-based approaches. These methods can be used to predict functional and non-functional properties of Building Energy Management Systems (BEMS) before setting up them. This paper aims at bridging the gap between model-based approaches and physical realizations of sensing and small computing devices. Through an integrated approach, able to exploit the power of different dialects of Petri Nets, this paper proposes a methodology for the early evaluation of BEMS properties as well as the automatic generation of IoT controllers. © 2023 - IOS Press. All rights reserved. - DetailsCampanile, L., Di Bonito, L. P., Gribaudo, M., & Iacono, M. (2023). A Domain Specific Language for the Design of Artificial Intelligence Applications for Process Engineering [Conference paper]. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 482 LNICST, 133–146. https://doi.org/10.1007/978-3-031-31234-2_8
Abstract
Processes in chemical engineering are frequently enacted by one-of-a-kind devices that implement dynamic processes with feedback regulations designed according to experimental studies and empirical tuning of new devices after the experience obtained on similar setups. While application of artificial intelligence based solutions is largely advocated by researchers in several fields of chemical engineering to face the problems deriving from these practices, few actual cases exist in literature and in industrial plants that leverage currently available tools as much as other application fields suggest. One of the factors that is limiting the spread of AI-based solutions in the field is the lack of tools that support the evaluation of the needs of plants, be those existing or to-be settlements. In this paper we provide a Domain Specific Language based approach for the evaluation of the basic performance requirements for cloud-based setups capable of supporting chemical engineering plants, with a metaphor that attempts to bridge the two worlds. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. - DetailsBobbio, A., Campanile, L., Gribaudo, M., Iacono, M., Marulli, F., & Mastroianni, M. (2023). A cyber warfare perspective on risks related to health IoT devices and contact tracing [Article]. Neural Computing and Applications, 35(19), 13823–13837. https://doi.org/10.1007/s00521-021-06720-1
Abstract
The wide use of IT resources to assess and manage the recent COVID-19 pandemic allows to increase the effectiveness of the countermeasures and the pervasiveness of monitoring and prevention. Unfortunately, the literature reports that IoT devices, a widely adopted technology for these applications, are characterized by security vulnerabilities that are difficult to manage at the state level. Comparable problems exist for related technologies that leverage smartphones, such as contact tracing applications, and non-medical health monitoring devices. In analogous situations, these vulnerabilities may be exploited in the cyber domain to overload the crisis management systems with false alarms and to interfere with the interests of target countries, with consequences on their economy and their political equilibria. In this paper we analyze the potential threat to an example subsystem to show how these influences may impact it and evaluate a possible consequence. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. - DetailsDi Giovanni, M., Campanile, L., D’Onofrio, A., Marrone, S., Marulli, F., Romoli, M., Sabbarese, C., & Verde, L. (2023). Supporting the Development of Digital Twins in Nuclear Waste Monitoring Systems [Conference paper]. Procedia Computer Science, 225, 3133–3142. https://doi.org/10.1016/j.procs.2023.10.307
Abstract
In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) - DetailsConference Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins DesignCampanile, L., De Biase, M. S., De Fazio, R., Di Giovanni, M., Marulli, F., & Verde, L. (2023). Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins Design [Conference paper]. Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023, 301–306. https://doi.org/10.1109/CSR57506.2023.10224945
Abstract
Nowadays, the problem of system robustness, es-pecially in critical infrastructures, is a challenging open question. Some systems provide crucial services continuously failing, threatening the availability of the provided services. By designing a robust architecture, this criticality could be overcome or limited, ensuring service continuity. The definition of a resilient system involves not only its architecture but also the methodology implemented for the calculation and analysis of some indices, quantifying system performance. This study provides an innovative architecture for Digital Twins implementation based on a hybrid methodology for improving the control system in realtime. The introduced approach brings together different techniques. In particular, the work combines the point of strengths of Model-based methods and Data-driven ones, aiming to improve system performances. © 2023 IEEE. - DetailsDi Bonito, L. P., Campanile, L., Napolitano, E., Iacono, M., Portolano, A., & Di Natale, F. (2023). Analysis of a marine scrubber operation with a combined analytical/AI-based method [Article]. Chemical Engineering Research and Design, 195, 613–623. https://doi.org/10.1016/j.cherd.2023.06.006
Abstract
This paper describes the performances of a marine SO2 absorption scrubber installed onboard a large Ro-Ro cargo ship. The study is based on the reconstruction of an extensive dataset from one-year continuous monitoring of the scrubber’s performances and operating conditions. The dataset has been interpreted with a conventional analytical, physical-mathematical, model for absorbers’ rating and its combination with an Artificial Intelligence (AI) one. First, the analytical model has been used to provide a deterministic mathematical framework for the interpretation and the prediction of the scrubber’s performances in terms of absorbed SO2 molar flow and SO2 concentration at the scrubber exit. Then, data mining and AI techniques have been applied to develop an Artificial Neural Network able to predict the error between the actual SO2 concentration at the scrubber exit and the corresponding analytical model predictions. The final result is a combined model providing superior robustness and accuracy in the prediction of the scrubber performance while preserving a rationale for process design and operation. This interesting outcome suggests that the development of combined, or hybrid, Analytical/AI models can be a reliable and cost-effective way to improve chemical engineers’ ability to design and control marine scrubbers, as well as other chemical equipment. © 2023 Institution of Chemical Engineers - DetailsCampanile, L., Di Bonito, L. P., Iacono, M., & Di Natale, F. (2023). Prediction of chemical plants operating performances: a machine learning approach [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2023-June, 575–581. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163436467&partnerID=40&md5=2e96d04affd9bb4a126b224d7cc8d75a
Abstract
Modern environmental regulations require rigorous optimization of operations in process engineering to reduce waste, pollution, and risks while maximizing efficiency. However, the nature of chemical plants, which include components with non-linear behavior, challenges the use of consolidated tuning and control techniques. Instead, ad-hoc, self-adapting, and time-variant controls, with a balanced tuning of parameters at both the subsystem and system level, may be necessary. Needed computing processes may require significant resources and high performance systems, if managed by means of traditional approaches and with exact solution methods. In this regard, domain experts suggest instead the use of integrated techniques based on Artificial Intelligence (AI), which include Explainable AI (XAI) and Trustworthy AI (TAI), which are unique in this industry and still in the early stages of development. To pave the way for a real-time, cost-effective solution for this problem, this paper proposes an AI-based approach to model the performance of a real chemical plant, i.e. a marine scrubber installed on a Ro-Ro ship. The study aims to investigate Machine Learning (ML) techniques which can be used to model such processes. Notably, this analysis is the first of its kind, at the best of the authors’ knowledge. Overall, the study highlights the potential of using ML-based techniques, to optimize environmental compliance in the shipping industry. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.
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. - DetailsCampanile, L., Iacono, M., & Mastroianni, M. (2022). Towards privacy-aware software design in small and medium enterprises. 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/Cy55231.2022.9927958
Abstract
The legal definition of privacy regulations, like GDPR in the European Union, significantly impacted on the way in which software, systems and organizations should be designed or maintained to be compliant to rules. While the privacy community stated proper risk assessment and mitigation approaches to be applied, literature seems to suggest that the software engineering community, with special reference to companies, did actually concentrate on the specification phase, with less attention for the test phase of products. In coherence with the privacy-by-design approach, we believe that a bigger methodological effort must be put in the systematic adaptation of software development cycles to privacy regulations, and that this effort might be promoted in the industrial community by focusing on the relation between organizational costs vs technical features, also leveraging the benefits of targeted testing as a mean to lower operational privacy enforcement costs. © 2022 IEEE. - DetailsCampanile, L., Forgione, F., Mastroianni, M., Palmiero, G., & Sanghez, C. (2022). Evaluating the Impact of Data Anonymization in a Machine Learning Application [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13380 LNCS, 389–400. https://doi.org/10.1007/978-3-031-10542-5_27
Abstract
The data protection impact assessment is used to verify the necessity, proportionality and risks of data processing. Our work is based on the data processed by the technical support of a Wireless Service Provider. The team of WISP tech support uses a machine learning system to predict failures. The goal of our the experiments was to evaluate the DPIA with personal data and without personal data. In fact, in a first scenario, the experiments were conducted using a machine learning application powered by non-anonymous personal data. Instead in the second scenario, the data was anonymized before feeding the machine learning system. In this article we evaluate how much the Data Protection Impact Assessment changes when moving from a scenario with raw data to a scenario with anonymized data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. - DetailsCampanile, L., Iacono, M., Marulli, F., Gribaudo, M., & Mastroianni, M. (2022). A DSL-based modeling approach for energy harvesting IoT/WSN [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2022-May, 317–323. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130645195&partnerID=40&md5=f2d475b445f76d3b5f49752171c0fada
Abstract
The diffusion of intelligent services and the push for the integration of computing systems and services in the environment in which they operate require a constant sensing activity and the acquisition of different information from the environment and the users. Health monitoring, domotics, Industry 4.0 and environmental challenges leverage the availability of cost-effective sensing solutions that allow both the creation of knowledge bases and the automatic process of them, be it with algorithmic approaches or artificial intelligence solutions. The foundation of these solutions is given by the Internet of Things (IoT), and the substanding Wireless Sensor Networks (WSN) technology stack. Of course, design approaches are needed that enable defining efficient and effective sensing infrastructures, including energy related aspects. In this paper we present a Domain Specific Language for the design of energy aware WSN IoT solutions, that allows domain experts to define sensor network models that may be then analyzed by simulation-based or analytic techniques to evaluate the effect of task allocation and offioading and energy harvesting and utilization in the network. The language has been designed to leverage the SIMTHESys modeling framework and its multiformalism modeling evaluation features. ©ECMS Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat (Editors) 2022 - 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., Marrone, S., Marulli, F., & Verde, L. (2022). Challenges and Trends in Federated Learning for Well-being and Healthcare [Conference paper]. Procedia Computer Science, 207, 1144–1153. https://doi.org/10.1016/j.procs.2022.09.170
Abstract
Currently, research in Artificial Intelligence, both in Machine Learning and Deep Learning, paves the way for promising innovations in several areas. In healthcare, especially, where large amounts of quantitative and qualitative data are transferred to support studies and early diagnosis and monitoring of any diseases, potential security and privacy issues cannot be underestimated. Federated learning is an approach where privacy issues related to sensitive data management can be significantly reduced, due to the possibility to train algorithms without exchanging data. The main idea behind this approach is that learning models can be trained in a distributed way, where multiple devices or servers with decentralized data samples can provide their contributions without having to exchange their local data. Recent studies provided evidence that prototypes trained by adopting Federated Learning strategies are able to achieve reliable performance, thus by generating robust models without sharing data and, consequently, limiting the impact on security and privacy. This work propose a literature overview of Federated Learning approaches and systems, focusing on its application for healthcare. The main challenges, implications, issues and potentials of this approach in the healthcare are outlined. © 2022 The Authors. Published by Elsevier B.V. - DetailsConference Sensitive Information Detection Adopting Named Entity Recognition: A Proposed MethodologyCampanile, L., de Biase, M. S., Marrone, S., Marulli, F., Raimondo, M., & Verde, L. (2022). Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13380 LNCS, 377–388. https://doi.org/10.1007/978-3-031-10542-5_26
Abstract
Protecting and safeguarding privacy has become increasingly important, especially in recent years. The increasing possibilities of acquiring and sharing personal information and data through digital devices and platforms, such as apps or social networks, have increased the risks of privacy breaches. In order to effectively respect and guarantee the privacy and protection of sensitive information, it is necessary to develop mechanisms capable of providing such guarantees automatically and reliably. In this paper we propose a methodology able to automatically recognize sensitive data. A Named Entity Recognition was used to identify appropriate entities. An improvement in the recognition of these entities is achieved by evaluating the words contained in an appropriate context window by assessing their similarity to words in a domain taxonomy. This, in fact, makes it possible to refine the labels of the recognized categories using a generic Named Entity Recognition. A preliminary evaluation of the reliability of the proposed approach was performed. In detail, texts of juridical documents written in Italian were analyzed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. - DetailsConference A Federated Consensus-Based Model for Enhancing Fake News and Misleading Information DebunkingMarulli, F., Verde, L., Marrore, S., & Campanile, L. (2022). A Federated Consensus-Based Model for Enhancing Fake News and Misleading Information Debunking [Conference paper]. Smart Innovation, Systems and Technologies, 309, 587–596. https://doi.org/10.1007/978-981-19-3444-5_50
Abstract
Misinformation and Fake News are hard to dislodge. According to experts on this phenomenon, to fight disinformation a less credulous public is needed; so, current AI techniques can support misleading information debunking, given the human tendency to believe “facts” that confirm biases. Much effort has been recently spent by the research community on this plague: several AI-based approaches for automatic detection and classification of Fake News have been proposed; unfortunately, Fake News producers have refined their ability in eluding automatic ML and DL-based detection systems. So, debunking false news represents an effective weapon to contrast the users’ reliance on false information. In this work, we propose a preliminary study aiming to approach the design of effective fake news debunking systems, harnessing two complementary federated approaches. We propose, firstly, a federation of independent classification systems to accomplish a debunking process, by applying a distributed consensus mechanism. Secondly, a federated learning task, involving several cooperating nodes, is accomplished, to obtain a unique merged model, including features of single participants models, trained on different and independent data fragments. This study is a preliminary work aiming to to point out the feasibility and the comparability of these proposed approaches, thus paving the way to an experimental campaign that will be performed on effective real data, thus providing an evidence for an effective and feasible model for detecting potential heterogeneous fake news. Debunking misleading information is mission critical to increase the awareness of facts on the part of news consumers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. - 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., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Designing a GDPR compliant blockchain-based IoV distributed information tracking system [Article]. Information Processing and Management, 58(3). https://doi.org/10.1016/j.ipm.2021.102511
Abstract
Blockchain technologies and distributed ledgers enable the design and implementation of trustable data logging systems that can be used by multiple parties to produce a non-repudiable database. The case of Internet of Vehicles may greatly benefit of such a possibility to track the chain of responsibility in case of accidents or damages due to bad or omitted maintenance, improving the safety of circulation and helping granting a correct handling of related legal issues. However, there are privacy issues that have to be considered, as tracked information potentially include data about private persons (position, personal habits), commercially relevant information (state of the fleet of a company, freight movement and related planning, logistic strategies), or even more critical knowledge (e.g., considering vehicles belonging to police, public authorities, governments or officers in sensible positions). In the European Union, all this information is covered by the General Data Protection Regulation (GDPR). In this paper we propose a reference model for a system that manages relevant information to show how blockchain can support GDPR compliant solutions for Internet of Vehicles, taking as a reference an integrated scenario based on Italy, and analyze a subset of its use cases to show its viability with reference to privacy issues. © 2021 Elsevier Ltd - 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. - DetailsJournal Privacy regulations, smart roads, blockchain, and liability insurance: Putting technologies to workCampanile, L., Iacono, M., Levis, A. H., Marulli, F., & Mastroianni, M. (2021). Privacy regulations, smart roads, blockchain, and liability insurance: Putting technologies to work [Article]. IEEE Security and Privacy, 19(1), 34–43. https://doi.org/10.1109/MSEC.2020.3012059
Abstract
Smart streets promise widely available traffic information to help improve people’s safety. Unfortunately, gathering that data may threaten privacy. We describe an architecture that exploits a blockchain and the Internet of Vehicles and show its compliance with the General Data Protection Regulation. © 2003-2012 IEEE. - DetailsCampanile, L., Gribaudo, M., Iacono, M., & Mastroianni, M. (2021). Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13104 LNCS, 345–359. https://doi.org/10.1007/978-3-030-91825-5_21
Abstract
Internet of Things (IoT) is a well established approach used for the implementation of surveillance systems that are suitable for monitoring large portions of territory. Current developments allow the design of battery powered IoT nodes that can communicate over the network with low energy requirements and locally perform some computing and coordination task, besides running sensing and related processing: it is thus possible to implement edge computing oriented solutions on IoT, if the design encompasses both hardware and software elements in terms of sensing, processing, computing, communications and routing energy costs as one of the quality indices of the system. In this paper we propose a modeling approach for edge computing IoT-based monitoring systems energy related characteristics, suitable for the analysis of energy levels of large battery powered monitoring systems with dynamic and reactive computing workloads. © 2021, Springer Nature Switzerland AG. - DetailsCampanile, L., Forgione, F., Marulli, F., Palmiero, G., & Sanghez, C. (2021). Dataset Anonimyzation for Machine Learning: An ISP Case Study [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12950 LNCS, 589–597. https://doi.org/10.1007/978-3-030-86960-1_42
Abstract
Internet Service Providers technical support needs personal data to predict potential anomalies. In this paper, we performed a comparative study of forecasting performance using raw data and anonymized data, in order to assess how much performance may vary, when plain personal data are replaced by anonymized personal data. © 2021, Springer Nature Switzerland AG. - DetailsCampanile, L., Cantiello, P., Iacono, M., Lotito, R., Marulli, F., & Mastroianni, M. (2021). Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 354–363. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135227609&partnerID=40&md5=5a7c117fa01d0ba8d779b0e092bc0f63
Abstract
Local pollution is a problem that affects urban areas and has effects on the quality of life and on health conditions. In order to not develop strict measures and to better manage territories, the national authorities have applied a vast range of predictive models. Actually, the application of machine learning has been studied in the last decades in various cases with various declination to simplify this problem. In this paper, we apply a regression-based analysis technique to a dataset containing official historical local pollution and weather data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome by the use of a maximum inclusion strategy as performing the joining action with”outer” mode. Among the four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R2 (Coefficient of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean Squared Error), equal to 0.12 and 0.04 respectively. © 2021 by SCITEPRESS - Science and Technology Publications, Lda. - DetailsMarulli, F., Verde, L., & Campanile, L. (2021). Exploring data and model poisoning attacks to deep learning-based NLP systems [Conference paper]. Procedia Computer Science, 192, 3570–3579. https://doi.org/10.1016/j.procs.2021.09.130
Abstract
Natural Language Processing (NLP) is being recently explored also to its application in supporting malicious activities and objects detection. Furthermore, NLP and Deep Learning have become targets of malicious attacks too. Very recent researches evidenced that adversarial attacks are able to affect also NLP tasks, in addition to the more popular adversarial attacks on deep learning systems for image processing tasks. More precisely, while small perturbations applied to the data set adopted for training typical NLP tasks (e.g., Part-of-Speech Tagging, Named Entity Recognition, etc..) could be easily recognized, models poisoning, performed by the means of altered data models, typically provided in the transfer learning phase to a deep neural networks (e.g., poisoning attacks by word embeddings), are harder to be detected. In this work, we preliminary explore the effectiveness of a poisoned word embeddings attack aimed at a deep neural network trained to accomplish a Named Entity Recognition (NER) task. By adopting the NER case study, we aimed to analyze the severity of such a kind of attack to accuracy in recognizing the right classes for the given entities. Finally, this study represents a preliminary step to assess the impact and the vulnerabilities of some NLP systems we adopt in our research activities, and further investigating some potential mitigation strategies, in order to make these systems more resilient to data and models poisoning attacks. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International. - DetailsCampanile, L., Marulli, F., Mastroianni, M., Palmiero, G., & Sanghez, C. (2021). Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 343–353. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137959400&partnerID=40&md5=eb78330cb4d585e500b77cd906edfbc7
Abstract
In this paper, we introduce a solution aiming to improve the accuracy of the surface temperature detection in an outdoor environment. The temperature sensing subsystem relies on Mobotix thermal camera without the black body, the automatic compensation subsystem relies on Raspberry Pi with Node-RED and TensorFlow 2.x. The final results showed that it is possible to automatically calibrate the camera using machine learning and that it is possible to use thermal imaging cameras even in critical conditions such as outdoors. Future development is to improve performance using computer vision techniques to rule out irrelevant measurements. © 2021 by SCITEPRESS - Science and Technology Publications, Lda. - DetailsMarulli, F., Balzanella, A., Campanile, L., Iacono, M., & Mastroianni, M. (2021). Exploring a Federated Learning Approach to Enhance Authorship Attribution of Misleading Information from Heterogeneous Sources [Conference paper]. Proceedings of the International Joint Conference on Neural Networks, 2021-July. https://doi.org/10.1109/IJCNN52387.2021.9534377
Abstract
Authorship Attribution (AA) is currently applied in several applications, among which fraud detection and anti-plagiarism checks: this task can leverage stylometry and Natural Language Processing techniques. In this work, we explored some strategies to enhance the performance of an AA task for the automatic detection of false and misleading information (e.g., fake news). We set up a text classification model for AA based on stylometry exploiting recurrent deep neural networks and implemented two learning tasks trained on the same collection of fake and real news, comparing their performances: one is based on Federated Learning architecture, the other on a centralized architecture. The goal was to discriminate potential fake information from true ones when the fake news comes from heterogeneous sources, with different styles. Preliminary experiments show that a distributed approach significantly improves recall with respect to the centralized model. As expected, precision was lower in the distributed model. This aspect, coupled with the statistical heterogeneity of data, represents some open issues that will be further investigated in future work. © 2021 IEEE.
2020
- DetailsCampanile, L., Iacono, M., Marrone, S., & Mastroianni, M. (2020). On Performance Evaluation of Security Monitoring in Multitenant Cloud Applications [Article]. Electronic Notes in Theoretical Computer Science, 353, 107–127. https://doi.org/10.1016/j.entcs.2020.09.020
Abstract
In this paper we present a modeling approach suitable for practical evaluation of the delays that may affect security monitoring systems in (multitenant) cloud based architecture, and in general to support professionals in planning and evaluating relevant parameters in dealing with new designs or migration projects. The approach is based on modularity and multiformalism techniques to manage complexity and guide designers in an incremental process, to help transferring technical knowledge into modeling practice and to help easing the use of simulation. We present a case study based on a real experience, triggered by a new legal requirement that Italian Public Administration should comply about their datacenters. © 2020 The Author(s) - DetailsCampanile, L., Gribaudo, M., Iacono, M., & Mastroianni, M. (2020). Modelling performances of an autonomic router running under attack [Conference paper]. International Journal of Embedded Systems, 12(4), 458–466. https://doi.org/10.1504/IJES.2020.107645
Abstract
Modern warehouse-scale computing facilities, seamlessly enabled by virtualisation technologies, are based on thousands of independent computing nodes that are administered according to efficiency criteria that depend on workload. Networks play a pivotal role in these systems, as they are likely to be the performance bottleneck, and because of the high variability of data and management traffic. Because of the scale of the system, the prevalent network management model is based on autonomic networking, a paradigm based on self-regulation of the networking subsystem, that requires routers capable of adapting their policies to traffic by a local or global strategy. In this paper we focus on performance modelling of autonomic routers, to provide a simple, yet representative elementary performance model to provide a starting point for a comprehensive autonomic network modelling approach. The proposed model is used to evaluate the behaviour of a router under attack under realistic workload and parameters assumptions. Copyright © 2020 Inderscience Enterprises Ltd. - DetailsCampanile, L., Iacono, M., Lotito, R., & Mastroianni, M. (2020). A WSN Energy-Aware approach for air pollution monitoring in waste treatment facility site: A case study for landfill monitoring odour [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 526–532. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089477488&partnerID=40&md5=13ea9ca38f15c5b885ef7e501067010c
Abstract
The gaseous emissions derived from industrial plants are generally subject to a strictly program of monitoring, both continuous or one-spot, in order to comply with the limits imposed by the permitting license. Nowadays the problem of odour emission, and the consequently nuisance generated to the nearest receptors, has acquired importance so that is frequently asked a specific implementation of the air pollution monitoring program. In this paper we studied the case study of a generic landfill for the implementation of the odour monitoring system and time-specific use of air pollution control technology. The off-site monitoring is based on the deployment of electronic nose as part of a specifically built WSN system. The nodes outside the landfill boundary do not act as a continuously monitoring stations but as sensors activated when specific conditions, inside and outside the landfill, are achieved. The WSN is then organized on an energy-aware approach so to prolong the lifetime of the entire system, with significant cost-benefit advancement, and produce a monitoring-structure that can answer to specific input like threshold overshooting. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. - 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. - DetailsConference A flexible simulation-based framework for model-based/data-driven dependability evaluationAbate, C., Campanile, L., & Marrone, S. (2020). A flexible simulation-based framework for model-based/data-driven dependability evaluation [Conference paper]. Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020, 261–266. https://doi.org/10.1109/ISSREW51248.2020.00083
Abstract
Modern predictive maintenance is the convergence of several technological trends: developing new techniques and algorithms can be very costly due to the need for a physical prototype. This research has the final aim to build a simulation-based software framework for modeling and analysing complex systems and for defining predictive maintenance algorithms. By the usage of simulation, quantitative evaluation of the dependability of such systems will be possible. The ERTMS/ETCS dependability case study is presented to prove the applicability of the software. © 2020 IEEE. - DetailsCampanile, L., Iacono, M., Marulli, F., Mastroianni, M., & Mazzocca, N. (2020). Toward a fuzzy-based approach for computational load offloading of IoT devices [Article]. Journal of Universal Computer Science, 26(11), 1455–1474. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100774625&partnerID=40&md5=1d88148124172a6b7ac374387f21199a
Abstract
Technological development and market expansion offer an increased availability of resources and computing power on IoT nodes at affordable cost. The edge computing paradigm allows keeping locally on the edge of the network a part of computing, while keeping all advantages of the cloud and adding support for privacy, real-time and network resilience. This can be further improved in IoT applications by flexibly harvesting resources on IoT nodes, by moving part of the computing tasks related to data from the edge server to the nodes, raising the abstraction level of the data aspects of the architecture and potentially enabling larger IoT networks to be efficiently deployed and managed, in a stand-alone logic or as a component of edge architecture. Anyway, an efficient energy management mechanism is needed for battery powered IoT networks, the most flexible implementations, that dynamically balances task allocation and execution in order to In this paper we present a fuzzy logic based power management strategy for IoT subsystem that aims at maximizing the duration of the network by locally migrating part of the computing tasks between nodes. As our goal is to enable the deployment of semi-autonomic large IoT networks, our proposal does not rely on external resources for migration control and operates on a local basis to ensure scalability: at the best of our knowledge, this diferentiates our proposal with respect to similar solutions available in literature. © 2020, IICM. All rights reserved. - DetailsMainenti, G., Campanile, L., Marulli, F., Ricciardi, C., & Valente, A. S. (2020). Machine learning approaches for diabetes classification: Perspectives to artificial intelligence methods updating [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 533–540. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089519717&partnerID=40&md5=bf7cc36e86c1988dd85e04c2fce06de1
Abstract
In recent years the application of Machine Learning (ML) and Artificial Intelligence (AI) techniques in healthcare helped clinicians to improve the management of chronic patients. Diabetes is among the most common chronic illness in the world for which often is still challenging do an early detection and a correct classification of type of diabetes to an individual. In fact it often depends on the circumstances present at the time of diagnosis, and many diabetic individuals do not easily fit into a single class. The aim is this paper is the application of ML techniques in order to classify the occurrence of different mellitus diabetes on the base of clinical data obtained from diabetic patients during the daily hospitals activities. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. - DetailsCampanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2020). A simulation study on a WSN for emergency management [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 34(1), 384–392. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094937629&partnerID=40&md5=69ee7b771d76c72bd5012883b86e67ca
Abstract
Wireless Sensors Networks (WSN) are one of the ways to provide the communication infrastructure for advanced applications based on the Internet of Things (IoT) paradigm. IoT supports high level applications over WSN to provide services in a number of fields. WSN are also suitable to support critical applications, as the supporting technologies are consolidated and standard network services can be used on top of the specific layers. Furthermore, generic distributed or network-enabled software can be run over the nodes of a WSN. In this paper we evaluate and compare performances of IEEE 802.llg and 802.1 In, two implementations of the popular Wi-Fi technology, to support the deployment and utilization of an energy management support system, used to monitor the field by a team of firefighters during a mission. Evaluation on an example scenario is done by using ns-3, an open network simulator characterized by its realistic details, to understand the actual limitations of the two standards besides theoretical limits. © ECMS Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther. - DetailsCampanile, L., Iacono, M., Martinelli, F., Marulli, F., Mastroianni, M., Mercaldo, F., & Santone, A. (2020). Towards the Use of Generative Adversarial Neural Networks to Attack Online Resources [Conference paper]. Advances in Intelligent Systems and Computing, 1150 AISC, 890–901. https://doi.org/10.1007/978-3-030-44038-1_81
Abstract
The role of remote resources, such as the ones provided by Cloud infrastructures, is of paramount importance for the implementation of cost effective, yet reliable software systems to provide services to third parties. Cost effectiveness is a direct consequence of a correct estimation of resource usage, to be able to define a budget and estimate the right price to put own services on the market. Attacks that overload resources with non legitimate requests, being them explicit attacks or just malicious, non harmful resource engagements, may push the use of Cloud resources beyond estimation, causing additional costs, or unexpected energy usage, or a lower overall quality of services, so intrusion detection devices or firewalls are set to avoid undesired accesses. We propose the use of Generative Adversarial Neural Networks (GANs) to setup a method for shaping request based attacks capable of reaching resources beyond defenses. The approach is studied by using a publicly available traffic data set, to test the concept and demonstrate its potential applications. © 2020, Springer Nature Switzerland AG. - DetailsCampanile, L., Gribaudo, M., Iacono, M., & Mastroianni, M. (2020). Performance evaluation of a fog WSN infrastructure for emergency management [Article]. Simulation Modelling Practice and Theory, 104. https://doi.org/10.1016/j.simpat.2020.102120
Abstract
Advances in technology and the rise of new computing paradigms, such as Fog computing, may boost the definition of a new generation of advanced support services in critical applications. In this paper we explore the possibilities of a Wireless Sensor Network support (WSN) for a Fog computing system in an emergency management architecture that has been previously presented. Disposable intelligent wireless sensors, capable of processing tasks locally, are deployed and used to support and protect the intervention of a squad of firemen equipped with augmented reality and life monitoring devices to provide an environmental monitoring system and communication infrastructure,in the framework of a next-generation, cloud-supported emergency management system. Simulation is used to explore the design parameter space and dimension the workloads and the extension of the WSN, according to an adaptive behavior of the resulting Fog computing system that varies workloads to save the integrity of the WSN. © 2020 Elsevier B.V. - DetailsCampanile, L., Gribaudo, M., Iacono, M., Marulli, F., & Mastroianni, M. (2020). Computer network simulation with ns-3: A systematic literature review [Article]. Electronics (Switzerland), 9(2). https://doi.org/10.3390/electronics9020272
Abstract
Complexity of current computer networks, including e.g., local networks, large structured networks, wireless sensor networks, datacenter backbones, requires a thorough study to perform analysis and support design. Simulation is a tool of paramount importance to encompass all the different aspects that contribute to design quality and network performance (including as well energy issues, security management overheads, dependability), due to the fact that such complexity produces several interactions at all network layers that is not easily modellable with analytic approaches. In this systematic literature review we aim to analyze, basing our investigation on available literature, the adoption of a popular network simulator, namely ns-3, and its use in the scientific community. More in detail, we are interested in understanding what are the impacted application domains in which authors prefer ns-3 to other similar tools and how extensible it is in practice according to the experience of authors. The results of our analysis, which has been conducted by especially focusing on 128 papers published between 2009 to 2019, reveals that 10% of the evaluated papers were discarded because they represented informal literature; most of the studies presented comparisons among different network simulators, beyond ns-3 and conceptual studies related to performance assessment and validation and routing protocols. Only about 30% of considered studies present extensions of ns-3 in terms of new modules and only about 10% present effective case studies demonstrating the effectiveness of employing network simulator in real application, except conceptual and modeling studies. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
2019
- DetailsCampanile, L., Iacono, M., Gribaudo, M., & Mastroianni, M. (2019). Quantitative modeling of the behaviour of an autonomic router [Conference paper]. ACM International Conference Proceeding Series, 193–194. https://doi.org/10.1145/3306309.3306344
Abstract
Autonomic routers are the main component on which autonomic networking is founded. Our goal is to provide a first approach performance modeling method that can be usable by networking professionals that are not part of the Performance Evaluation community. © 2019 Copyright held by the owner/author(s). - DetailsGribaudo, M., Campanile, L., Iacono, M., & Mastroianni, M. (2019). Performance modeling and analysis of an autonomic router [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 33(1), 441–447. https://doi.org/10.7148/2019-0441
Abstract
Modern networking is moving towards exploitation of autonomic features into networks to reduce management effort and compensate the increasing complexity of network infrastructures, e.g. in large computing facilities such the data centers that support cloud services delivery. Autonomicity provides the possibility of reacting to anomalies in network traffic by recognizing them and applying administrator defined reactions without the need for human intervention, obtaining a quicker response and easier adaptation to network dynamics, and letting administrators focus on general system-wide policies, rather than on each component of the infrastructure. The process of defining proper policies may benefit from adopting model-based design cycles, to get an estimation of their effects. In this paper we propose a model-based analysis approach of a simple autonomic router, using Stochastic Petri Nets, to evaluate the behavior of given policies designed to react to traffic workloads. The approach allows a detailed analysis of the dynamics of the policy and is suitable to be used in the preliminary phases of the design cycle for a Software Defined Networks compliant router control plane. ©ECMS Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco (Editors).