2024
Ducange, Pietro; Fazzolari, Michela; Marcelloni, Francesco
Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification Proceedings Article
In: Proceedings of the 57th Hawaii International Conference on System Sciences, 2024.
@inproceedings{ducange2024explainable,
title = {Explainable Intrusion Detection System in IoT Scenarios: A Cross-Device Model Training and Evaluation for Traffic Classification},
author = {Pietro Ducange and Michela Fazzolari and Francesco Marcelloni},
url = {https://hdl.handle.net/10125/106602},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the 57th Hawaii International Conference on System Sciences},
abstract = {The proliferation of IoT devices in our daily lives has raised concerns about the security of transmitted data. Due to their limited resources, IoT devices are vulnerable to malware attacks and cyber threats. Detecting and classifying these attacks is crucial to mitigate their impact. Various intrusion detection techniques have been proposed for IoT, including approaches based on ML and AI. Most of the ML/AI-based intrusion detection techniques, though effective, often lack transparency and trustworthiness. To address these aspects, XAI has emerged as a promising solution, providing insights on AI model decisions. In this work, we describe an explainable IDS in IoT networks which embeds a multi-way FDT as an XAI model for traffic classification. We propose a Cross-Device training and evaluation approach in which we evaluate the generalization capability of the IDS when new devices are connected to the IoT network without retraining the FDT.},
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The proliferation of IoT devices in our daily lives has raised concerns about the security of transmitted data. Due to their limited resources, IoT devices are vulnerable to malware attacks and cyber threats. Detecting and classifying these attacks is crucial to mitigate their impact. Various intrusion detection techniques have been proposed for IoT, including approaches based on ML and AI. Most of the ML/AI-based intrusion detection techniques, though effective, often lack transparency and trustworthiness. To address these aspects, XAI has emerged as a promising solution, providing insights on AI model decisions. In this work, we describe an explainable IDS in IoT networks which embeds a multi-way FDT as an XAI model for traffic classification. We propose a Cross-Device training and evaluation approach in which we evaluate the generalization capability of the IDS when new devices are connected to the IoT network without retraining the FDT.
2023
Merluzzi, Mattia; Borsos, Tamás; Rajatheva, Nandana; Benczúr, András A.; Farhadi, Hamed; Yassine, Taha; Mück, Markus Dominik; Barmpounakis, Sokratis; Strinati, Emilio Calvanese; Dampahalage, Dilin; Demestichas, Panagiotis; Ducange, Pietro; Filippou, Miltiadis C.; Baltar, Leonardo Gomes; Haraldson, Johan; Karaçay, Leyli; Korpi, Dani; Lamprousi, Vasiliki; Marcelloni, Francesco; Mohammadi, Jafar; Rajapaksha, Nuwanthika; Renda, Alessandro; Uusitalo, Mikko A.
The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G Journal Article
In: IEEE Access, pp. 1-1, 2023.
@article{10156818,
title = {The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G},
author = {Mattia Merluzzi and Tamás Borsos and Nandana Rajatheva and András A. Benczúr and Hamed Farhadi and Taha Yassine and Markus Dominik Mück and Sokratis Barmpounakis and Emilio Calvanese Strinati and Dilin Dampahalage and Panagiotis Demestichas and Pietro Ducange and Miltiadis C. Filippou and Leonardo Gomes Baltar and Johan Haraldson and Leyli Karaçay and Dani Korpi and Vasiliki Lamprousi and Francesco Marcelloni and Jafar Mohammadi and Nuwanthika Rajapaksha and Alessandro Renda and Mikko A. Uusitalo},
doi = {10.1109/ACCESS.2023.3287939},
year = {2023},
date = {2023-01-01},
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Cavalieri, A.; Ducange, Pietro; Fabi, S.; Russo, F.; Tonellotto, Nicola
An Intelligent system for the categorization of question time official documents of the Italian Chamber of Deputies Journal Article
In: Journal of Information Technology & Politics, vol. 20, no. 3, pp. 213-234, 2023.
@article{doi:10.1080/19331681.2022.2082622,
title = {An Intelligent system for the categorization of question time official documents of the Italian Chamber of Deputies},
author = {A. Cavalieri and Pietro Ducange and S. Fabi and F. Russo and Nicola Tonellotto},
url = {https://doi.org/10.1080/19331681.2022.2082622},
doi = {10.1080/19331681.2022.2082622},
year = {2023},
date = {2023-01-01},
journal = {Journal of Information Technology & Politics},
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Bechini, Alessio; Bondielli, Alessandro; Dell'Oglio, Pietro; Marcelloni, Francesco
From basic approaches to novel challenges and applications in Sequential Pattern Mining Journal Article
In: Applied Computing and Intelligence, vol. 3, no. 1, pp. 44-78, 2023, ISSN: 2771-392X.
@article{nokey,
title = {From basic approaches to novel challenges and applications in Sequential Pattern Mining},
author = {Alessio Bechini and Alessandro Bondielli and Pietro Dell'Oglio and Francesco Marcelloni},
url = {https://www.aimspress.com/article/doi/10.3934/aci.2023004},
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year = {2023},
date = {2023-01-01},
journal = {Applied Computing and Intelligence},
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Piccoli, Michele; Zoni, Davide; Fornaciari, William; Massari, Giuseppe; Cococcioni, Marco; Rossi, Federico; Saponara, Sergio; Ruffaldi, Emanuele
Dynamic Power Consumption of the Full Posit Processing Unit: Analysis and Experiments Proceedings Article
In: Bispo, João; Charles, Henri-Pierre; Cherubin, Stefano; Massari, Giuseppe (Ed.): 14th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 12th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2023), pp. 6:1–6:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany, 2023, ISSN: 2190-6807.
@inproceedings{piccoli_et_al:OASIcs.PARMA-DITAM.2023.6,
title = {Dynamic Power Consumption of the Full Posit Processing Unit: Analysis and Experiments},
author = {Michele Piccoli and Davide Zoni and William Fornaciari and Giuseppe Massari and Marco Cococcioni and Federico Rossi and Sergio Saponara and Emanuele Ruffaldi},
editor = {João Bispo and Henri-Pierre Charles and Stefano Cherubin and Giuseppe Massari},
url = {https://drops.dagstuhl.de/opus/volltexte/2023/17726},
doi = {10.4230/OASIcs.PARMA-DITAM.2023.6},
issn = {2190-6807},
year = {2023},
date = {2023-01-01},
booktitle = {14th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 12th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2023)},
volume = {107},
pages = {6:1–6:11},
publisher = {Schloss Dagstuhl – Leibniz-Zentrum für Informatik},
address = {Dagstuhl, Germany},
series = {Open Access Series in Informatics (OASIcs)},
keywords = {},
pubstate = {published},
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Nardini, Giovanni; Noferi, Alessandro; Ducange, Pietro; Stea, Giovanni
Exploiting Simu5G for generating datasets for training and testing AI models for 5G/6G network applications Journal Article
In: SoftwareX, vol. 21, pp. 101320, 2023, ISSN: 2352-7110.
@article{NARDINI2023101320,
title = {Exploiting Simu5G for generating datasets for training and testing AI models for 5G/6G network applications},
author = {Giovanni Nardini and Alessandro Noferi and Pietro Ducange and Giovanni Stea},
url = {https://www.sciencedirect.com/science/article/pii/S235271102300016X},
doi = {https://doi.org/10.1016/j.softx.2023.101320},
issn = {2352-7110},
year = {2023},
date = {2023-01-01},
journal = {SoftwareX},
volume = {21},
pages = {101320},
abstract = {Researchers working on Artificial Intelligence (AI) need suitable datasets for training and testing their models. When it comes to applications running through a mobile network, these datasets are difficult to obtain, because network operators are hardly willing to expose their network data or to open their network to experimentation. In this paper we show how Simu5G, a popular 5G network simulator based on OMNeT++, can be used to circumvent this problem: it allows users to log data at arbitrary spatial and temporal resolution, belonging to every network layer — from the application to the physical one.},
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Researchers working on Artificial Intelligence (AI) need suitable datasets for training and testing their models. When it comes to applications running through a mobile network, these datasets are difficult to obtain, because network operators are hardly willing to expose their network data or to open their network to experimentation. In this paper we show how Simu5G, a popular 5G network simulator based on OMNeT++, can be used to circumvent this problem: it allows users to log data at arbitrary spatial and temporal resolution, belonging to every network layer — from the application to the physical one.
Gustafson, John L.; Cococcioni, Marco; Rossi, Federico; Ruffaldi, Emanuele; Saponara, Sergio
Decoding-Free Two-Input Arithmetic for Low-Precision Real Numbers Proceedings Article
In: Gustafson, John; Leong, Siew Hoon; Michalewicz, Marek (Ed.): Next Generation Arithmetic, pp. 61–76, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-32180-1.
@inproceedings{10.1007/978-3-031-32180-1_4,
title = {Decoding-Free Two-Input Arithmetic for Low-Precision Real Numbers},
author = {John L. Gustafson and Marco Cococcioni and Federico Rossi and Emanuele Ruffaldi and Sergio Saponara},
editor = {John Gustafson and Siew Hoon Leong and Marek Michalewicz},
url = {https://dl.acm.org/doi/abs/10.1007/978-3-031-32180-1_4},
isbn = {978-3-031-32180-1},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Next Generation Arithmetic},
pages = {61–76},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {In this work, we present a novel method for directly computing functions of two real numbers using logic circuits without decoding; the real numbers are mapped to a particularly-chosen set of integer numbers. We theoretically prove that this mapping always exists and that we can implement any kind of binary operation between real numbers regardless of the encoding format. While the real numbers in the set can be arbitrary (rational, irrational, transcendental), we find practical applications to low-precision posit™ number arithmetic. We finally provide examples for decoding-free 4-bit Posit arithmetic operations, showing a reduction in gate count up to a factor of $$7.6backslashtimes $$7.6×(and never below $$4.4backslashtimes $$4.4×) compared to a standard two-dimensional tabulation.},
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In this work, we present a novel method for directly computing functions of two real numbers using logic circuits without decoding; the real numbers are mapped to a particularly-chosen set of integer numbers. We theoretically prove that this mapping always exists and that we can implement any kind of binary operation between real numbers regardless of the encoding format. While the real numbers in the set can be arbitrary (rational, irrational, transcendental), we find practical applications to low-precision posit™ number arithmetic. We finally provide examples for decoding-free 4-bit Posit arithmetic operations, showing a reduction in gate count up to a factor of $$7.6backslashtimes $$7.6×(and never below $$4.4backslashtimes $$4.4×) compared to a standard two-dimensional tabulation.
Paolini, Emilio; Marinis, Lorenzo De; Maggiani, Luca; Cococcioni, Marco; Andriolli, Nicola
CHARLES: A C++ fixed-point library for Photonic-Aware Neural Networks Journal Article
In: Neural Networks, vol. 162, pp. 531-540, 2023, ISSN: 0893-6080.
@article{PAOLINI2023531,
title = {CHARLES: A C++ fixed-point library for Photonic-Aware Neural Networks},
author = {Emilio Paolini and Lorenzo De Marinis and Luca Maggiani and Marco Cococcioni and Nicola Andriolli},
url = {https://www.sciencedirect.com/science/article/pii/S0893608023001247},
doi = {https://doi.org/10.1016/j.neunet.2023.03.007},
issn = {0893-6080},
year = {2023},
date = {2023-01-01},
journal = {Neural Networks},
volume = {162},
pages = {531-540},
abstract = {In this paper we present CHARLES (C++ pHotonic Aware neuRaL nEtworkS), a C++ library aimed at providing a flexible tool to simulate the behavior of Photonic-Aware Neural Network (PANN). PANNs are neural network architectures aware of the constraints due to the underlying photonic hardware, mostly in terms of low equivalent precision of the computations. For this reason, CHARLES exploits fixed-point computations for inference, while it supports both floating-point and fixed-point numerical formats for training. In this way, we can compare the effects due to the quantization in the inference phase when the training phase is performed on a classical floating-point model and on a model exploiting high-precision fixed-point numbers. To validate CHARLES and identify the most suited numerical format for PANN training, we report the simulation results obtained considering three datasets: Iris, MNIST, and Fashion-MNIST. Fixed-training is shown to outperform floating-training when executing inference on bitwidths suitable for photonic implementation. Indeed, performing the training phase in the floating-point domain and then quantizing to lower bitwidths results in a very high accuracy loss. Instead, when fixed-point numbers are exploited in the training phase, the accuracy loss due to quantization to lower bitwidths is significantly reduced. In particular, we show that for Iris dataset, fixed-training achieves a performance similar to floating-training. Fixed-training allows to obtain an accuracy of 90.4% and 68.1% with the MNIST and Fashion-MNIST datasets using only 6 bits, while the floating-training reaches an accuracy of just 25.4% and 50.0% when exploiting the same bitwidths.},
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In this paper we present CHARLES (C++ pHotonic Aware neuRaL nEtworkS), a C++ library aimed at providing a flexible tool to simulate the behavior of Photonic-Aware Neural Network (PANN). PANNs are neural network architectures aware of the constraints due to the underlying photonic hardware, mostly in terms of low equivalent precision of the computations. For this reason, CHARLES exploits fixed-point computations for inference, while it supports both floating-point and fixed-point numerical formats for training. In this way, we can compare the effects due to the quantization in the inference phase when the training phase is performed on a classical floating-point model and on a model exploiting high-precision fixed-point numbers. To validate CHARLES and identify the most suited numerical format for PANN training, we report the simulation results obtained considering three datasets: Iris, MNIST, and Fashion-MNIST. Fixed-training is shown to outperform floating-training when executing inference on bitwidths suitable for photonic implementation. Indeed, performing the training phase in the floating-point domain and then quantizing to lower bitwidths results in a very high accuracy loss. Instead, when fixed-point numbers are exploited in the training phase, the accuracy loss due to quantization to lower bitwidths is significantly reduced. In particular, we show that for Iris dataset, fixed-training achieves a performance similar to floating-training. Fixed-training allows to obtain an accuracy of 90.4% and 68.1% with the MNIST and Fashion-MNIST datasets using only 6 bits, while the floating-training reaches an accuracy of just 25.4% and 50.0% when exploiting the same bitwidths.
Dell'Oglio, Pietro; Bondielli, Alessandro; Bechini, Alessio; Marcelloni, Francesco
Leveraging Sequence Mining for Robot Process Automation Proceedings Article
In: Abraham, Ajith; Pllana, Sabri; Casalino, Gabriella; Ma, Kun; Bajaj, Anu (Ed.): Intelligent Systems Design and Applications, pp. 224–233, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-35510-3.
@inproceedings{10.1007/978-3-031-35510-3_22,
title = {Leveraging Sequence Mining for Robot Process Automation},
author = {Pietro Dell'Oglio and Alessandro Bondielli and Alessio Bechini and Francesco Marcelloni},
editor = {Ajith Abraham and Sabri Pllana and Gabriella Casalino and Kun Ma and Anu Bajaj},
isbn = {978-3-031-35510-3},
year = {2023},
date = {2023-01-01},
booktitle = {Intelligent Systems Design and Applications},
pages = {224–233},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The automation of sequences of repetitive actions performed by human operators in interacting with software applications is crucial to prevent work from being perceived as alienating and boring. Robot applications can automatise these sequences once they have been identified. In this paper, we propose a two-step approach to mine sequences of actions that could be automated from log data produced by the interactions of a human operator with specific software applications. Since the number of possible sequences may be very high and not all the sequences are interesting to be automatised, we focus our mining process on sequences that meet precise patterns. First, Frequent Episode Mining algorithms are applied for extracting all the sequences of actions that occur with at least a minimum frequency. Then, we exploit fuzzy string matching based on the Levenshtein distance for filtering out the sequences that do not match established patterns. We evaluate the effectiveness of the approach using a benchmark dataset and present a case study on a real-world dataset of activity logs generated in the context of the AUTOMIA project.},
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The automation of sequences of repetitive actions performed by human operators in interacting with software applications is crucial to prevent work from being perceived as alienating and boring. Robot applications can automatise these sequences once they have been identified. In this paper, we propose a two-step approach to mine sequences of actions that could be automated from log data produced by the interactions of a human operator with specific software applications. Since the number of possible sequences may be very high and not all the sequences are interesting to be automatised, we focus our mining process on sequences that meet precise patterns. First, Frequent Episode Mining algorithms are applied for extracting all the sequences of actions that occur with at least a minimum frequency. Then, we exploit fuzzy string matching based on the Levenshtein distance for filtering out the sequences that do not match established patterns. We evaluate the effectiveness of the approach using a benchmark dataset and present a case study on a real-world dataset of activity logs generated in the context of the AUTOMIA project.
Ferrante, Nicola; Giuffrida, Gianluca; Nannipieri, Pietro; Bechini, Alessio; Fanucci, Luca
Fault Detection Exploiting Artificial Intelligence in Satellite Systems Proceedings Article
In: Ieracitano, Cosimo; Mammone, Nadia; Clemente, Marco Di; Mahmud, Mufti; Furfaro, Roberto; Morabito, Francesco Carlo (Ed.): The Use of Artificial Intelligence for Space Applications, pp. 151–166, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-25755-1.
@inproceedings{10.1007/978-3-031-25755-1_10,
title = {Fault Detection Exploiting Artificial Intelligence in Satellite Systems},
author = {Nicola Ferrante and Gianluca Giuffrida and Pietro Nannipieri and Alessio Bechini and Luca Fanucci},
editor = {Cosimo Ieracitano and Nadia Mammone and Marco Di Clemente and Mufti Mahmud and Roberto Furfaro and Francesco Carlo Morabito},
isbn = {978-3-031-25755-1},
year = {2023},
date = {2023-01-01},
booktitle = {The Use of Artificial Intelligence for Space Applications},
pages = {151–166},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Mission control and fault management are fundamental in safety-critical scenarios such as space applications. To this extent, fault detection techniques are crucial to meet the desired safety and integrity level. This work proposes a fault detection system exploiting an autoregressive model, which is based on a Deep Neural Network (DNN). We trained the aforementioned model on a dataset composed of telemetries acquired from Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS). The training process has been designed as a sequence-to-sequence task, varying the length of input and output time series. Several DNN architectures were proposed, using both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) as basic building blocks. Lastly, we performed fault injection modeling faults of different nature. The results obtained show that the proposed solution detects up to 90% of injected faults. We found that GRU-based models outperform LSTM-based ones in this task. Furthermore, we demonstrated that we can predict signal evolution without any knowledge of the underlying physics of the system, substituting a DNN to the traditional differential equations, reducing expertise and time-to-market concerning existing solutions.},
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Mission control and fault management are fundamental in safety-critical scenarios such as space applications. To this extent, fault detection techniques are crucial to meet the desired safety and integrity level. This work proposes a fault detection system exploiting an autoregressive model, which is based on a Deep Neural Network (DNN). We trained the aforementioned model on a dataset composed of telemetries acquired from Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS). The training process has been designed as a sequence-to-sequence task, varying the length of input and output time series. Several DNN architectures were proposed, using both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) as basic building blocks. Lastly, we performed fault injection modeling faults of different nature. The results obtained show that the proposed solution detects up to 90% of injected faults. We found that GRU-based models outperform LSTM-based ones in this task. Furthermore, we demonstrated that we can predict signal evolution without any knowledge of the underlying physics of the system, substituting a DNN to the traditional differential equations, reducing expertise and time-to-market concerning existing solutions.
Sansone, Giacomo; Cococcioni, Marco
Experiments on Speeding Up the Recursive Fast Fourier Transform by Using AVX-512 SIMD Instructions Proceedings Article
In: Berta, Riccardo; Gloria, Alessandro De (Ed.): Applications in Electronics Pervading Industry, Environment and Society, pp. 255–263, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-30333-3.
@inproceedings{10.1007/978-3-031-30333-3_34,
title = {Experiments on Speeding Up the Recursive Fast Fourier Transform by Using AVX-512 SIMD Instructions},
author = {Giacomo Sansone and Marco Cococcioni},
editor = {Riccardo Berta and Alessandro De Gloria},
isbn = {978-3-031-30333-3},
year = {2023},
date = {2023-01-01},
booktitle = {Applications in Electronics Pervading Industry, Environment and Society},
pages = {255–263},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The Fast Fourier Transform is probably one of the most studied algorithms of all time. New techniques regarding hardware and software are often applied and tested on it, but the interest in FFT is still large because of its applications - signal and image processing, numerical computations, etc. In this paper, we start from a trivial recursive version of the algorithm and we speed it up using AVX-512 Single Instruction Multiple Data (SIMD) instructions on an Intel i7 CPU with native support to AVX-512. In particular, we study the impact of two different storage choices of vector of complex numbers: block interleaving and complex interleaving. Experimental results show that automatic vectorization provides a 10.65% ($$backslashsim 1.12backslashtimes $$∼1.12×) speedup, while with vectorization done by hand the speedup reaches 33.78% ($$backslashsim 1.51backslashtimes $$∼1.51×). We have made our code publicly available, which could be helpful for SIMD instructions teaching purposes.},
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The Fast Fourier Transform is probably one of the most studied algorithms of all time. New techniques regarding hardware and software are often applied and tested on it, but the interest in FFT is still large because of its applications - signal and image processing, numerical computations, etc. In this paper, we start from a trivial recursive version of the algorithm and we speed it up using AVX-512 Single Instruction Multiple Data (SIMD) instructions on an Intel i7 CPU with native support to AVX-512. In particular, we study the impact of two different storage choices of vector of complex numbers: block interleaving and complex interleaving. Experimental results show that automatic vectorization provides a 10.65% ($$backslashsim 1.12backslashtimes $$∼1.12×) speedup, while with vectorization done by hand the speedup reaches 33.78% ($$backslashsim 1.51backslashtimes $$∼1.51×). We have made our code publicly available, which could be helpful for SIMD instructions teaching purposes.
Donati, Massimiliano; Bechini, Alessio; D'Anna, Clelia; Fattori, Bruno; Marini, Marco; Olivelli, Martina; Pelagatti, Susanna; Ricci, Giulia; Schirinzi, Erika; Siciliano, Gabriele; Tavosanis, Mirko; Torri, Francesca; Vanello, Nicola; Fanucci, Luca
A Clinical Tool for Prognosis and Speech Rehabilitation in Dysarthric Patients: The DESIRE Project Proceedings Article
In: Berta, Riccardo; Gloria, Alessandro De (Ed.): Applications in Electronics Pervading Industry, Environment and Society, pp. 380–385, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-30333-3.
@inproceedings{10.1007/978-3-031-30333-3_52,
title = {A Clinical Tool for Prognosis and Speech Rehabilitation in Dysarthric Patients: The DESIRE Project},
author = {Massimiliano Donati and Alessio Bechini and Clelia D'Anna and Bruno Fattori and Marco Marini and Martina Olivelli and Susanna Pelagatti and Giulia Ricci and Erika Schirinzi and Gabriele Siciliano and Mirko Tavosanis and Francesca Torri and Nicola Vanello and Luca Fanucci},
editor = {Riccardo Berta and Alessandro De Gloria},
isbn = {978-3-031-30333-3},
year = {2023},
date = {2023-01-01},
booktitle = {Applications in Electronics Pervading Industry, Environment and Society},
pages = {380–385},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Dysarthria is a motor disorder of speech characterized by alteration of articulation and intelligibility of speech. The goal of dysarthria management is to optimize communication effectiveness for as long as possible. To help clinicians in monitoring disease progression and rehabilitation outcomes, the DESIRE tool analyzes several reading sessions in which the patients pronounce predetermined selected words aloud, elaborating a measure of how much the patient's pronunciation deviates from those of previous sessions and the expected performance. In addition, the electronical record offers a comprehensive view of patient's status, and the web access allows the care team to remotely monitor progresses, so that they can tailor rehabilitation programs over time. Through the possibility to understand the patient difficulty about specific phonemes, word length, consonant clusters, this innovative tool offers a method to assess and monitoring dysarthria, to address therapeutic strategies, and to provide useful requirements for clinical trials readiness.},
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Dysarthria is a motor disorder of speech characterized by alteration of articulation and intelligibility of speech. The goal of dysarthria management is to optimize communication effectiveness for as long as possible. To help clinicians in monitoring disease progression and rehabilitation outcomes, the DESIRE tool analyzes several reading sessions in which the patients pronounce predetermined selected words aloud, elaborating a measure of how much the patient's pronunciation deviates from those of previous sessions and the expected performance. In addition, the electronical record offers a comprehensive view of patient's status, and the web access allows the care team to remotely monitor progresses, so that they can tailor rehabilitation programs over time. Through the possibility to understand the patient difficulty about specific phonemes, word length, consonant clusters, this innovative tool offers a method to assess and monitoring dysarthria, to address therapeutic strategies, and to provide useful requirements for clinical trials readiness.
Rossi, Federico; Fiaschi, Lorenzo; Cococcioni, Marco; Saponara, Sergio
Design and FPGA Synthesis of BAN Processing Unit for Non-Archimedean Number Crunching Proceedings Article
In: Berta, Riccardo; Gloria, Alessandro De (Ed.): Applications in Electronics Pervading Industry, Environment and Society, pp. 320–325, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-30333-3.
@inproceedings{10.1007/978-3-031-30333-3_43,
title = {Design and FPGA Synthesis of BAN Processing Unit for Non-Archimedean Number Crunching},
author = {Federico Rossi and Lorenzo Fiaschi and Marco Cococcioni and Sergio Saponara},
editor = {Riccardo Berta and Alessandro De Gloria},
isbn = {978-3-031-30333-3},
year = {2023},
date = {2023-01-01},
booktitle = {Applications in Electronics Pervading Industry, Environment and Society},
pages = {320–325},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {This work presents the design and synthesis of a processing unit for numbers encoded according to the recently introduced BAN format. Such an encoding allows one to represent numbers which are not only finite (as the reals) but also infinitely large or infinitely small, i.e., non-Archimedean. The motivation behind this study is the significant burst the non-Archimedean numerical computations have received in the last 20 years and the applications that have been found. With a hardware support, this operations would significantly increase in speed, enlarging the spectrum of possible applications to industrial and real-time ones.},
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This work presents the design and synthesis of a processing unit for numbers encoded according to the recently introduced BAN format. Such an encoding allows one to represent numbers which are not only finite (as the reals) but also infinitely large or infinitely small, i.e., non-Archimedean. The motivation behind this study is the significant burst the non-Archimedean numerical computations have received in the last 20 years and the applications that have been found. With a hardware support, this operations would significantly increase in speed, enlarging the spectrum of possible applications to industrial and real-time ones.
Casalino, Gabriella; Ducange, Pietro; Fazzolari, Michela; Pecori, Riccardo
Incremental and Interpretable Learning Analytics Through Fuzzy Hoeffding Decision Trees Proceedings Article
In: Fulantelli, Giovanni; Burgos, Daniel; Casalino, Gabriella; Cimitile, Marta; Bosco, Giosuè Lo; Taibi, Davide (Ed.): Higher Education Learning Methodologies and Technologies Online, pp. 674–690, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-29800-4.
@inproceedings{10.1007/978-3-031-29800-4_51,
title = {Incremental and Interpretable Learning Analytics Through Fuzzy Hoeffding Decision Trees},
author = {Gabriella Casalino and Pietro Ducange and Michela Fazzolari and Riccardo Pecori},
editor = {Giovanni Fulantelli and Daniel Burgos and Gabriella Casalino and Marta Cimitile and Giosuè Lo Bosco and Davide Taibi},
isbn = {978-3-031-29800-4},
year = {2023},
date = {2023-01-01},
booktitle = {Higher Education Learning Methodologies and Technologies Online},
pages = {674–690},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Artificial Intelligence-based methods have been thoroughly applied in various fields over the years and the educational scenario is not an exception. However, the usage of the so-called explainable Artificial Intelligence, even if desirable, is still limited, especially whenever we consider educational datasets. Moreover, the time dimension is not often regarded enough when analyzing such types of data. In this paper, we have applied the fuzzy version of the Hoeffding Decision Tree to an educational dataset, considering separately STEM and Social Sciences subjects, in order to take into consideration both the time evolution of the educational process and the possible interpretability of the final results. The considered models resulted to be successful in discriminating the passing or failing of exams at the end of consecutive semesters on the part of students. Moreover, Fuzzy Hoeffding Decision Tree occurred to be much more compact and interpretable compared to the traditional Hoeffding Decision Tree.},
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Artificial Intelligence-based methods have been thoroughly applied in various fields over the years and the educational scenario is not an exception. However, the usage of the so-called explainable Artificial Intelligence, even if desirable, is still limited, especially whenever we consider educational datasets. Moreover, the time dimension is not often regarded enough when analyzing such types of data. In this paper, we have applied the fuzzy version of the Hoeffding Decision Tree to an educational dataset, considering separately STEM and Social Sciences subjects, in order to take into consideration both the time evolution of the educational process and the possible interpretability of the final results. The considered models resulted to be successful in discriminating the passing or failing of exams at the end of consecutive semesters on the part of students. Moreover, Fuzzy Hoeffding Decision Tree occurred to be much more compact and interpretable compared to the traditional Hoeffding Decision Tree.
Fioriti, Davide; Stevanato, Nicolò; Ducange, Pietro; Marcelloni, Francesco; Colombo, Emanuela; Poli, Davide
In: IEEE Access, pp. 1-1, 2023.
@article{10179910,
title = {Data platform guidelines and prototype for microgrids and energy access: matching demand profiles and socio-economic data to foster project development},
author = {Davide Fioriti and Nicolò Stevanato and Pietro Ducange and Francesco Marcelloni and Emanuela Colombo and Davide Poli},
doi = {10.1109/ACCESS.2023.3294841},
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Daole, Mattia; Schiavo, Alessio; Bárcena, José Luis Corcuera; Ducange, Pietro; Marcelloni, Francesco; Renda, Alessandro
OpenFL-XAI: Federated learning of explainable artificial intelligence models in Python Journal Article
In: SoftwareX, vol. 23, pp. 101505, 2023, ISSN: 2352-7110.
@article{DAOLE2023101505,
title = {OpenFL-XAI: Federated learning of explainable artificial intelligence models in Python},
author = {Mattia Daole and Alessio Schiavo and José Luis Corcuera Bárcena and Pietro Ducange and Francesco Marcelloni and Alessandro Renda},
url = {https://www.sciencedirect.com/science/article/pii/S2352711023002017},
doi = {https://doi.org/10.1016/j.softx.2023.101505},
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year = {2023},
date = {2023-01-01},
journal = {SoftwareX},
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abstract = {Artificial Intelligence (AI) systems play a significant role in manifold decision-making processes in our daily lives, making trustworthiness of AI more and more crucial for its widespread acceptance. Among others, privacy and explainability are considered key requirements for enabling trust in AI. Building on these needs, we propose a software for Federated Learning (FL) of Rule-Based Systems (RBSs): on one hand FL prioritizes user data privacy during collaborative model training. On the other hand, RBSs are deemed as interpretable-by-design models and ensure high transparency in the decision-making process. The proposed software, developed as an extension to the Intel® OpenFL open-source framework, offers a viable solution for developing AI applications balancing accuracy, privacy, and interpretability.},
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Artificial Intelligence (AI) systems play a significant role in manifold decision-making processes in our daily lives, making trustworthiness of AI more and more crucial for its widespread acceptance. Among others, privacy and explainability are considered key requirements for enabling trust in AI. Building on these needs, we propose a software for Federated Learning (FL) of Rule-Based Systems (RBSs): on one hand FL prioritizes user data privacy during collaborative model training. On the other hand, RBSs are deemed as interpretable-by-design models and ensure high transparency in the decision-making process. The proposed software, developed as an extension to the Intel® OpenFL open-source framework, offers a viable solution for developing AI applications balancing accuracy, privacy, and interpretability.
Bárcena, José Luis Corcuera; Ducange, Pietro; Marcelloni, Francesco; Nardini, Giovanni; Noferi, Alessandro; Renda, Alessandro; Ruffini, Fabrizio; Schiavo, Alessio; Stea, Giovanni; Virdis, Antonio
Enabling federated learning of explainable AI models within beyond-5G/6G networks Journal Article
In: Computer Communications, 2023, ISSN: 0140-3664.
@article{BARCENA2023,
title = {Enabling federated learning of explainable AI models within beyond-5G/6G networks},
author = {José Luis Corcuera Bárcena and Pietro Ducange and Francesco Marcelloni and Giovanni Nardini and Alessandro Noferi and Alessandro Renda and Fabrizio Ruffini and Alessio Schiavo and Giovanni Stea and Antonio Virdis},
url = {https://www.sciencedirect.com/science/article/pii/S0140366423002724},
doi = {https://doi.org/10.1016/j.comcom.2023.07.039},
issn = {0140-3664},
year = {2023},
date = {2023-01-01},
journal = {Computer Communications},
abstract = {The quest for trustworthiness in Artificial Intelligence (AI) is increasingly urgent, especially in the field of next-generation wireless networks. Future Beyond 5G (B5G)/6G networks will connect a huge amount of devices and will offer innovative services empowered with AI and Machine Learning tools. Nevertheless, private user data, which are essential for training such services, are not an asset that can be unrestrictedly shared over the network, mainly because of privacy concerns. To overcome this issue, Federated Learning (FL) has recently been proposed as a paradigm to enable collaborative model training among multiple parties, without any disclosure of private raw data. However, the initiative to natively integrate FL services into mobile networks is still far from being accomplished. In this paper we propose a novel FL-as-a-Service framework that provides the B5G/6G network with flexible mechanisms to allow end users to exploit FL services, and we describe its applicability to a Quality of Experience (QoE) forecasting service based on a vehicular networking use case. Specifically, we show how FL of eXplainable AI (XAI) models can be leveraged for the QoE forecasting task, and induces a benefit in terms of both accuracy, compared to local learning, and trustworthiness, thanks to the adoption of inherently interpretable models. Such considerations are supported by an extensive experimental analysis on a publicly available simulated dataset. Finally, we assessed how the learning process is affected by the system deployment and the performance of the underlying communication and computation infrastructure, through system-level simulations, which show the benefits of deploying the proposed framework in edge-based environments.},
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The quest for trustworthiness in Artificial Intelligence (AI) is increasingly urgent, especially in the field of next-generation wireless networks. Future Beyond 5G (B5G)/6G networks will connect a huge amount of devices and will offer innovative services empowered with AI and Machine Learning tools. Nevertheless, private user data, which are essential for training such services, are not an asset that can be unrestrictedly shared over the network, mainly because of privacy concerns. To overcome this issue, Federated Learning (FL) has recently been proposed as a paradigm to enable collaborative model training among multiple parties, without any disclosure of private raw data. However, the initiative to natively integrate FL services into mobile networks is still far from being accomplished. In this paper we propose a novel FL-as-a-Service framework that provides the B5G/6G network with flexible mechanisms to allow end users to exploit FL services, and we describe its applicability to a Quality of Experience (QoE) forecasting service based on a vehicular networking use case. Specifically, we show how FL of eXplainable AI (XAI) models can be leveraged for the QoE forecasting task, and induces a benefit in terms of both accuracy, compared to local learning, and trustworthiness, thanks to the adoption of inherently interpretable models. Such considerations are supported by an extensive experimental analysis on a publicly available simulated dataset. Finally, we assessed how the learning process is affected by the system deployment and the performance of the underlying communication and computation infrastructure, through system-level simulations, which show the benefits of deploying the proposed framework in edge-based environments.
Bondielli, A.; Dell'Oglio, P.; Lenci, A.; Marcelloni, F.; Passaro, L. C.; Sabbatini, M.
EVALITA 2023: 8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, Sep 7-8, Parma, IT, vol. 3473, 2023.
@conference{bondielli2023,
title = {Multi-fake-detective at evalita 2023: Overview of the multimodal fake news detection and verification task},
author = {A. Bondielli and P. Dell'Oglio and A. Lenci and F. Marcelloni and L. C. Passaro and M. Sabbatini},
url = {https://ceur-ws.org/Vol-3473/paper32.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {EVALITA 2023: 8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, Sep 7-8, Parma, IT},
journal = {CEUR Workshop Proceedings},
volume = {3473},
abstract = {This paper introduces the MULTI-Fake-DetectiVE shared task for the EVALITA 2023 campaign. The task was aimed at exploring multimodality within the realm of fake news and intended to address the problem from two perspectives, represented by the two sub-tasks. In sub-task 1, we aimed to evaluate the effectiveness of multimodal fake news detection systems. In sub-task 2, we sought to gain insights into the interplay between text and images, specifically how they mutually influence the interpretation of content in the context of distinguishing between fake and real news. Both perspectives were framed as classification problems. The paper presents an overview of the task. In particular, we detail the key aspects of the task, including the creation of a new dataset for fake news detection in Italian, the evaluation methodology and criteria, the participant systems, and their results. In light of the obtained results, we argue that the problem is still open and propose some future directions.},
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This paper introduces the MULTI-Fake-DetectiVE shared task for the EVALITA 2023 campaign. The task was aimed at exploring multimodality within the realm of fake news and intended to address the problem from two perspectives, represented by the two sub-tasks. In sub-task 1, we aimed to evaluate the effectiveness of multimodal fake news detection systems. In sub-task 2, we sought to gain insights into the interplay between text and images, specifically how they mutually influence the interpretation of content in the context of distinguishing between fake and real news. Both perspectives were framed as classification problems. The paper presents an overview of the task. In particular, we detail the key aspects of the task, including the creation of a new dataset for fake news detection in Italian, the evaluation methodology and criteria, the participant systems, and their results. In light of the obtained results, we argue that the problem is still open and propose some future directions.
Dell’Oglio, Pietro; Bondielli, Alessandro; Marcelloni, Francesco
A System to Support Readers in Automatically Acquiring Complete Summarized Information on an Event from Different Sources Journal Article
In: Algorithms, vol. 16, no. 11, 2023, ISSN: 1999-4893.
@article{a16110513,
title = {A System to Support Readers in Automatically Acquiring Complete Summarized Information on an Event from Different Sources},
author = {Pietro Dell’Oglio and Alessandro Bondielli and Francesco Marcelloni},
url = {https://www.mdpi.com/1999-4893/16/11/513},
doi = {10.3390/a16110513},
issn = {1999-4893},
year = {2023},
date = {2023-01-01},
journal = {Algorithms},
volume = {16},
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abstract = {Today, most newspapers utilize social media to disseminate news. On the one hand, this results in an overload of related articles for social media users. On the other hand, since social media tends to form echo chambers around their users, different opinions and information may be hidden. Enabling users to access different information (possibly outside of their echo chambers, without the burden of reading entire articles, often containing redundant information) may be a step forward in allowing them to form their own opinions. To address this challenge, we propose a system that integrates Transformer neural models and text summarization models along with decision rules. Given a reference article already read by the user, our system first collects articles related to the same topic from a configurable number of different sources. Then, it identifies and summarizes the information that differs from the reference article and outputs the summary to the user. The core of the system is the sentence classification algorithm, which classifies sentences in the collected articles into three classes based on similarity with the reference article: sentences classified as dissimilar are summarized by using a pre-trained abstractive summarization model. We evaluated the proposed system in two steps. First, we assessed its effectiveness in identifying content differences between the reference article and the related articles by using human judgments obtained through crowdsourcing as ground truth. We obtained an average F1 score of 0.772 against average F1 scores of 0.797 and 0.676 achieved by two state-of-the-art approaches based, respectively, on model tuning and prompt tuning, which require an appropriate tuning phase and, therefore, greater computational effort. Second, we asked a sample of people to evaluate how well the summary generated by the system represents the information that is not present in the article read by the user. The results are extremely encouraging. Finally, we present a use case.},
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Today, most newspapers utilize social media to disseminate news. On the one hand, this results in an overload of related articles for social media users. On the other hand, since social media tends to form echo chambers around their users, different opinions and information may be hidden. Enabling users to access different information (possibly outside of their echo chambers, without the burden of reading entire articles, often containing redundant information) may be a step forward in allowing them to form their own opinions. To address this challenge, we propose a system that integrates Transformer neural models and text summarization models along with decision rules. Given a reference article already read by the user, our system first collects articles related to the same topic from a configurable number of different sources. Then, it identifies and summarizes the information that differs from the reference article and outputs the summary to the user. The core of the system is the sentence classification algorithm, which classifies sentences in the collected articles into three classes based on similarity with the reference article: sentences classified as dissimilar are summarized by using a pre-trained abstractive summarization model. We evaluated the proposed system in two steps. First, we assessed its effectiveness in identifying content differences between the reference article and the related articles by using human judgments obtained through crowdsourcing as ground truth. We obtained an average F1 score of 0.772 against average F1 scores of 0.797 and 0.676 achieved by two state-of-the-art approaches based, respectively, on model tuning and prompt tuning, which require an appropriate tuning phase and, therefore, greater computational effort. Second, we asked a sample of people to evaluate how well the summary generated by the system represents the information that is not present in the article read by the user. The results are extremely encouraging. Finally, we present a use case.
Bárcena, José Luis Corcuera; Ducange, Pietro; Marcelloni, Francesco; Renda, Alessandro; Ruffini, Fabrizio; Schiavo, Alessio
Federated TSK Models for Predicting Quality of Experience in B5G/6G Networks Proceedings Article
In: 2023 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1-8, 2023.
@inproceedings{10309758,
title = {Federated TSK Models for Predicting Quality of Experience in B5G/6G Networks},
author = {José Luis Corcuera Bárcena and Pietro Ducange and Francesco Marcelloni and Alessandro Renda and Fabrizio Ruffini and Alessio Schiavo},
doi = {10.1109/FUZZ52849.2023.10309758},
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Bechini, Alessio; Daole, Mattia; Ducange, Pietro; Marcelloni, Francesco; Renda, Alessandro
An Application for Federated Learning of XAI Models in Edge Computing Environments Proceedings Article
In: 2023 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1-7, 2023.
@inproceedings{10309783,
title = {An Application for Federated Learning of XAI Models in Edge Computing Environments},
author = {Alessio Bechini and Mattia Daole and Pietro Ducange and Francesco Marcelloni and Alessandro Renda},
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Bárcena, José Luis Corcuera; Ducange, Pietro; Marcelloni, Francesco; Renda, Alessandro; Ruffini, Fabrizio
Federated Learning of Explainable Artificial Intelligence Models for Predicting Parkinson's Disease Progression Proceedings Article
In: Longo, Luca (Ed.): Explainable Artificial Intelligence, pp. 630–648, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-44064-9.
@inproceedings{10.1007/978-3-031-44064-9_34,
title = {Federated Learning of Explainable Artificial Intelligence Models for Predicting Parkinson's Disease Progression},
author = {José Luis Corcuera Bárcena and Pietro Ducange and Francesco Marcelloni and Alessandro Renda and Fabrizio Ruffini},
editor = {Luca Longo},
isbn = {978-3-031-44064-9},
year = {2023},
date = {2023-01-01},
booktitle = {Explainable Artificial Intelligence},
pages = {630–648},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Services based on Artificial Intelligence (AI) are becoming increasingly pervasive in our society. At the same time, however, we are also witnessing a growing awareness towards the ethical aspects and the trustworthiness of AI tools, especially in high stakes domains, such as the healthcare one. In this paper, we propose the adoption of AI techniques for predicting Parkinson's Disease progression with the overarching aim of accommodating the urgent need for trustworthiness. We address two key requirements towards trustworthy AI, namely privacy preservation in learning AI models and their explainability. As for the former aspect, we consider the (rather common) case of medical data coming from different health institutions, assuming that they cannot be shared due to privacy concerns. To address this shortcoming, we leverage federated learning (FL) as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data. As for the latter aspect, we focus on highly interpretable models, i.e., those for which humans are able to understand how decisions have been taken. An extensive experimental analysis carried out on a well-known Parkinson Telemonitoring dataset highlights how the proposed approach based on FL of fuzzy rule-based systems allows achieving, simultaneously, data privacy and interpretability. Results are reported for different data partitioning scenarios, also comparing the interpretable-by-design model with an opaque neural network model.},
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Services based on Artificial Intelligence (AI) are becoming increasingly pervasive in our society. At the same time, however, we are also witnessing a growing awareness towards the ethical aspects and the trustworthiness of AI tools, especially in high stakes domains, such as the healthcare one. In this paper, we propose the adoption of AI techniques for predicting Parkinson's Disease progression with the overarching aim of accommodating the urgent need for trustworthiness. We address two key requirements towards trustworthy AI, namely privacy preservation in learning AI models and their explainability. As for the former aspect, we consider the (rather common) case of medical data coming from different health institutions, assuming that they cannot be shared due to privacy concerns. To address this shortcoming, we leverage federated learning (FL) as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data. As for the latter aspect, we focus on highly interpretable models, i.e., those for which humans are able to understand how decisions have been taken. An extensive experimental analysis carried out on a well-known Parkinson Telemonitoring dataset highlights how the proposed approach based on FL of fuzzy rule-based systems allows achieving, simultaneously, data privacy and interpretability. Results are reported for different data partitioning scenarios, also comparing the interpretable-by-design model with an opaque neural network model.
2022
Bechini, Alessio; Bondielli, Alessandro; Bárcena, José Luis Corcuera; Ducange, Pietro; Marcelloni, Francesco; Renda, Alessandro
A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting Journal Article
In: ACM Trans. Knowl. Discov. Data, vol. 16, no. 6, 2022, ISSN: 1556-4681.
@article{10.1145/3532186,
title = {A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting},
author = {Alessio Bechini and Alessandro Bondielli and José Luis Corcuera Bárcena and Pietro Ducange and Francesco Marcelloni and Alessandro Renda},
url = {https://doi.org/10.1145/3532186},
doi = {10.1145/3532186},
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year = {2022},
date = {2022-07-01},
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journal = {ACM Trans. Knowl. Discov. Data},
volume = {16},
number = {6},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {In the last years, there has been an ever-increasing interest in profiling various aspects of city life, especially in the context of smart cities. This interest has become even more relevant recently when we have realized how dramatic events, such as the Covid-19 pandemic, can deeply affect the city life, producing drastic changes. Identifying and analyzing such changes, both at the city level and within single neighborhoods, may be a fundamental tool to better manage the current situation and provide sound strategies for future planning. Furthermore, such fine-grained and up-to-date characterization can represent a valuable asset for other tools and services, e.g., web mapping applications or real estate agency platforms. In this article, we propose a framework featuring a novel methodology to model and track changes in areas of the city by extracting information from online newspaper articles. The problem of uncovering clusters of news at specific times is tackled by means of the joint use of state-of-the-art language models to represent the articles, and of a density-based streaming clustering algorithm, properly shaped to deal with high-dimensional text embeddings. Furthermore, we propose a method to automatically label the obtained clusters in a semantically meaningful way, and we introduce a set of metrics aimed at tracking the temporal evolution of clusters. A case study focusing on the city of Rome during the Covid-19 pandemic is illustrated and discussed to evaluate the effectiveness of the proposed approach.},
keywords = {},
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In the last years, there has been an ever-increasing interest in profiling various aspects of city life, especially in the context of smart cities. This interest has become even more relevant recently when we have realized how dramatic events, such as the Covid-19 pandemic, can deeply affect the city life, producing drastic changes. Identifying and analyzing such changes, both at the city level and within single neighborhoods, may be a fundamental tool to better manage the current situation and provide sound strategies for future planning. Furthermore, such fine-grained and up-to-date characterization can represent a valuable asset for other tools and services, e.g., web mapping applications or real estate agency platforms. In this article, we propose a framework featuring a novel methodology to model and track changes in areas of the city by extracting information from online newspaper articles. The problem of uncovering clusters of news at specific times is tackled by means of the joint use of state-of-the-art language models to represent the articles, and of a density-based streaming clustering algorithm, properly shaped to deal with high-dimensional text embeddings. Furthermore, we propose a method to automatically label the obtained clusters in a semantically meaningful way, and we introduce a set of metrics aimed at tracking the temporal evolution of clusters. A case study focusing on the city of Rome during the Covid-19 pandemic is illustrated and discussed to evaluate the effectiveness of the proposed approach.
Renda, Alessandro; Ducange, Pietro; Marcelloni, Francesco; Sabella, Dario; Filippou, Miltiadis C.; Nardini, Giovanni; Stea, Giovanni; Virdis, Antonio; Micheli, Davide; Rapone, Damiano; Baltar, Leonardo Gomes
Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking Journal Article
In: Information, vol. 13, no. 8, 2022, ISSN: 2078-2489.
@article{info13080395,
title = {Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking},
author = {Alessandro Renda and Pietro Ducange and Francesco Marcelloni and Dario Sabella and Miltiadis C. Filippou and Giovanni Nardini and Giovanni Stea and Antonio Virdis and Davide Micheli and Damiano Rapone and Leonardo Gomes Baltar},
url = {https://www.mdpi.com/2078-2489/13/8/395},
doi = {10.3390/info13080395},
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year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Information},
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abstract = {This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications.},
keywords = {},
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This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications.
Fiaschi, Lorenzo; Cococcioni, Marco
A Non-Archimedean Interior Point Method and Its Application to the Lexicographic Multi-Objective Quadratic Programming Journal Article
In: Mathematics, vol. 10, no. 23, 2022, ISSN: 2227-7390.
@article{math10234536,
title = {A Non-Archimedean Interior Point Method and Its Application to the Lexicographic Multi-Objective Quadratic Programming},
author = {Lorenzo Fiaschi and Marco Cococcioni},
url = {https://www.mdpi.com/2227-7390/10/23/4536},
doi = {10.3390/math10234536},
issn = {2227-7390},
year = {2022},
date = {2022-01-01},
journal = {Mathematics},
volume = {10},
number = {23},
abstract = {This work presents a generalized implementation of the infeasible primal-dual interior point method (IPM) achieved by the use of non-Archimedean values, i.e., infinite and infinitesimal numbers. The extended version, called here the non-Archimedean IPM (NA-IPM), is proved to converge in polynomial time to a global optimum and to be able to manage infeasibility and unboundedness transparently, i.e., without considering them as corner cases: by means of a mild embedding (addition of two variables and one constraint), the NA-IPM implicitly and transparently manages their possible presence. Moreover, the new algorithm is able to solve a wider variety of linear and quadratic optimization problems than its standard counterpart. Among them, the lexicographic multi-objective one deserves particular attention, since the NA-IPM overcomes the issues that standard techniques (such as scalarization or preemptive approach) have. To support the theoretical properties of the NA-IPM, the manuscript also shows four linear and quadratic non-Archimedean programming test cases where the effectiveness of the algorithm is verified. This also stresses that the NA-IPM is not just a mere symbolic or theoretical algorithm but actually a concrete numerical tool, paving the way for its use in real-world problems in the near future.},
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This work presents a generalized implementation of the infeasible primal-dual interior point method (IPM) achieved by the use of non-Archimedean values, i.e., infinite and infinitesimal numbers. The extended version, called here the non-Archimedean IPM (NA-IPM), is proved to converge in polynomial time to a global optimum and to be able to manage infeasibility and unboundedness transparently, i.e., without considering them as corner cases: by means of a mild embedding (addition of two variables and one constraint), the NA-IPM implicitly and transparently manages their possible presence. Moreover, the new algorithm is able to solve a wider variety of linear and quadratic optimization problems than its standard counterpart. Among them, the lexicographic multi-objective one deserves particular attention, since the NA-IPM overcomes the issues that standard techniques (such as scalarization or preemptive approach) have. To support the theoretical properties of the NA-IPM, the manuscript also shows four linear and quadratic non-Archimedean programming test cases where the effectiveness of the algorithm is verified. This also stresses that the NA-IPM is not just a mere symbolic or theoretical algorithm but actually a concrete numerical tool, paving the way for its use in real-world problems in the near future.
Emilio, Paolini; Marinis, Lorenzo De; Cococcioni, Marco; Valcarenghi, Luca; Maggiani, Luca; Andriolli, Nicola; others,
Photonic-Aware Neural Networks Journal Article
In: NEURAL COMPUTING & APPLICATIONS, 2022.
@article{emilio2022photonic,
title = {Photonic-Aware Neural Networks},
author = {Paolini Emilio and Lorenzo De Marinis and Marco Cococcioni and Luca Valcarenghi and Luca Maggiani and Nicola Andriolli and others},
doi = {https://doi.org/10.1007/s00521-022-07243-z},
year = {2022},
date = {2022-01-01},
journal = {NEURAL COMPUTING & APPLICATIONS},
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Passaro, Lucia C.; Bondielli, Alessandro; Dell’Oglio, Pietro; Lenci, Alessandro; Marcelloni, Francesco
In-context annotation of topic-oriented datasets of fake news: A case study on the notre-dame fire event Journal Article
In: Information Sciences, vol. 615, pp. 657-677, 2022, ISSN: 0020-0255.
@article{PASSARO2022657,
title = {In-context annotation of topic-oriented datasets of fake news: A case study on the notre-dame fire event},
author = {Lucia C. Passaro and Alessandro Bondielli and Pietro Dell’Oglio and Alessandro Lenci and Francesco Marcelloni},
url = {https://www.sciencedirect.com/science/article/pii/S0020025522008167},
doi = {https://doi.org/10.1016/j.ins.2022.07.128},
issn = {0020-0255},
year = {2022},
date = {2022-01-01},
journal = {Information Sciences},
volume = {615},
pages = {657-677},
abstract = {The problem of fake news detection is becoming increasingly interesting for several research fields. Different approaches have been proposed, based on either the content of the news itself or the context and properties of its spread over time, specifically on social media. In the literature, it does not exist a widely accepted general-purpose dataset for fake news detection, due to the complexity of the task and the increasing ability to produce fake news appearing credible in particular moments. In this paper, we propose a methodology to collect and label news pertinent to specific topics and subjects. Our methodology focuses on collecting data from social media about real-world events which are known to trigger fake news. We propose a labelling method based on crowdsourcing that is fast, reliable, and able to approximate expert human annotation. The proposed method exploits both the content of the data (i.e., the texts) and contextual information about fake news for a particular real-world event. The methodology is applied to collect and annotate the Notre-Dame Fire Dataset and to annotate part of the PHEME dataset. Evaluation is performed with fake news classifiers based on Transformers and fine-tuning. Results show that context-based annotation outperforms traditional crowdsourcing out-of-context annotation.},
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The problem of fake news detection is becoming increasingly interesting for several research fields. Different approaches have been proposed, based on either the content of the news itself or the context and properties of its spread over time, specifically on social media. In the literature, it does not exist a widely accepted general-purpose dataset for fake news detection, due to the complexity of the task and the increasing ability to produce fake news appearing credible in particular moments. In this paper, we propose a methodology to collect and label news pertinent to specific topics and subjects. Our methodology focuses on collecting data from social media about real-world events which are known to trigger fake news. We propose a labelling method based on crowdsourcing that is fast, reliable, and able to approximate expert human annotation. The proposed method exploits both the content of the data (i.e., the texts) and contextual information about fake news for a particular real-world event. The methodology is applied to collect and annotate the Notre-Dame Fire Dataset and to annotate part of the PHEME dataset. Evaluation is performed with fake news classifiers based on Transformers and fine-tuning. Results show that context-based annotation outperforms traditional crowdsourcing out-of-context annotation.
Gallo, Gionatan; Rienzo, Francesco Di; Garzelli, Federico; Ducange, Pietro; Vallati, Carlo
A Smart System for Personal Protective Equipment Detection in Industrial Environments Based on Deep Learning at the Edge Journal Article
In: IEEE Access, vol. 10, pp. 110862-110878, 2022.
@article{9925236,
title = {A Smart System for Personal Protective Equipment Detection in Industrial Environments Based on Deep Learning at the Edge},
author = {Gionatan Gallo and Francesco Di Rienzo and Federico Garzelli and Pietro Ducange and Carlo Vallati},
doi = {10.1109/ACCESS.2022.3215148},
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Velez-Estevez, A.; Ducange, P.; Perez, I. J.; Cobo, M. J.
Conceptual structure of federated learning research field Journal Article
In: Procedia Computer Science, vol. 214, pp. 1374-1381, 2022, ISSN: 1877-0509, (9th International Conference on Information Technology and Quantitative Management).
@article{VELEZESTEVEZ20221374,
title = {Conceptual structure of federated learning research field},
author = {A. Velez-Estevez and P. Ducange and I. J. Perez and M. J. Cobo},
url = {https://www.sciencedirect.com/science/article/pii/S1877050922020312},
doi = {https://doi.org/10.1016/j.procs.2022.11.319},
issn = {1877-0509},
year = {2022},
date = {2022-01-01},
journal = {Procedia Computer Science},
volume = {214},
pages = {1374-1381},
abstract = {Nowadays there are a great amount of data that can be used to train artificial intelligent systems for classification, or prediction purposes. Although there are tons of publicly available data, there are also very valuable data that is private, and therefore, it can not be shared without breaking the data protections laws. For example, hospital data has great value, but it involves persons, so we must try to preserve their privacy rights. Furthermore, although it could be interesting to train a model with the data of only one entity (i.e. a hospital), it could have more value to train the model with the data of several entities. But, since the data of each entity might not be shared, it is not possible to train a global model. In that sense, Federated Learning has emerged as a research field that deals with the training of complex models, without the necessity to share data, and therefore, keeping the data private. In this contribution, we present a global conceptual analysis based on co-words networks of the Federated Learning research field. To do that, the field was delimited using an advance query in Web of Science. The corpus contain a total of 2444 documents. As the main result, it should be highlighted that the Federated Learning research field is focused on six main global areas: telecommunications, privacy and security, computer architecture and data modeling, machine learning, and applications.},
note = {9th International Conference on Information Technology and Quantitative Management},
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Nowadays there are a great amount of data that can be used to train artificial intelligent systems for classification, or prediction purposes. Although there are tons of publicly available data, there are also very valuable data that is private, and therefore, it can not be shared without breaking the data protections laws. For example, hospital data has great value, but it involves persons, so we must try to preserve their privacy rights. Furthermore, although it could be interesting to train a model with the data of only one entity (i.e. a hospital), it could have more value to train the model with the data of several entities. But, since the data of each entity might not be shared, it is not possible to train a global model. In that sense, Federated Learning has emerged as a research field that deals with the training of complex models, without the necessity to share data, and therefore, keeping the data private. In this contribution, we present a global conceptual analysis based on co-words networks of the Federated Learning research field. To do that, the field was delimited using an advance query in Web of Science. The corpus contain a total of 2444 documents. As the main result, it should be highlighted that the Federated Learning research field is focused on six main global areas: telecommunications, privacy and security, computer architecture and data modeling, machine learning, and applications.
Lai, Leonardo; Fiaschi, Lorenzo; Cococcioni, Marco; Deb, Kalyanmoy
On the Use of Grossone Methodology for Handling Priorities in Multi-objective Evolutionary Optimization Book Chapter
In: Sergeyev, Yaroslav D.; Leone, Renato De (Ed.): Numerical Infinities and Infinitesimals in Optimization, pp. 183–218, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93642-6.
@inbook{Lai2022,
title = {On the Use of Grossone Methodology for Handling Priorities in Multi-objective Evolutionary Optimization},
author = {Leonardo Lai and Lorenzo Fiaschi and Marco Cococcioni and Kalyanmoy Deb},
editor = {Yaroslav D. Sergeyev and Renato De Leone},
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abstract = {This chapter introduces a new class of optimization problems, called Mixed Pareto-Lexicographic Multi-objective Optimization Problems (MPL-MOPs), to provide a suitable model for scenarios where some objectives have priority over some others. Specifically, this work focuses on two relevant subclasses of MPL-MOPs, namely optimization problems having the objective functions organized as priority chains or priority levels. A priority chain (PC) is a sequence of objectives ordered lexicographically by importance; conversely, a priority level (PL) is a group of objectives having the same importance in terms of optimization, but a lexicographic ordering exists between the PLs. After describing these problems and discussing why the standard algorithms are inadequate, an innovative approach to deal with them is introduced: it leverages the Grossone Methodology, a recent theory that allows handling priorities by means of infinite and infinitesimal numbers. Most interestingly, this technique can be easily embedded in most of the existing evolutionary algorithms, without altering their core logic. Three algorithms for MPL-MOPs are shown: the first two, called PC-NSGA-II and PC-MOEA/D, are the generalization of NSGA-II and MOEA/D, respectively, in the presence of PCs; the third, named PL-NSGA-II, generalizes instead NSGA-II when PLs are present. Several benchmark problems, including some from the real world, are used to evaluate the effectiveness of the proposed approach. The generalized algorithms are compared to other famous evolutionary ones, either priority-based or not, through a statistical analysis of their performances. The experiments show that the generalized algorithms are consistently able to produce more solutions and of higher quality.},
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This chapter introduces a new class of optimization problems, called Mixed Pareto-Lexicographic Multi-objective Optimization Problems (MPL-MOPs), to provide a suitable model for scenarios where some objectives have priority over some others. Specifically, this work focuses on two relevant subclasses of MPL-MOPs, namely optimization problems having the objective functions organized as priority chains or priority levels. A priority chain (PC) is a sequence of objectives ordered lexicographically by importance; conversely, a priority level (PL) is a group of objectives having the same importance in terms of optimization, but a lexicographic ordering exists between the PLs. After describing these problems and discussing why the standard algorithms are inadequate, an innovative approach to deal with them is introduced: it leverages the Grossone Methodology, a recent theory that allows handling priorities by means of infinite and infinitesimal numbers. Most interestingly, this technique can be easily embedded in most of the existing evolutionary algorithms, without altering their core logic. Three algorithms for MPL-MOPs are shown: the first two, called PC-NSGA-II and PC-MOEA/D, are the generalization of NSGA-II and MOEA/D, respectively, in the presence of PCs; the third, named PL-NSGA-II, generalizes instead NSGA-II when PLs are present. Several benchmark problems, including some from the real world, are used to evaluate the effectiveness of the proposed approach. The generalized algorithms are compared to other famous evolutionary ones, either priority-based or not, through a statistical analysis of their performances. The experiments show that the generalized algorithms are consistently able to produce more solutions and of higher quality.
Cococcioni, Marco; Fiaschi, Lorenzo; Lambertini, Luca
Computing Optimal Decision Strategies Using the Infinity Computer: The Case of Non-Archimedean Zero-Sum Games Book Chapter
In: Sergeyev, Yaroslav D.; Leone, Renato De (Ed.): Numerical Infinities and Infinitesimals in Optimization, pp. 271–295, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93642-6.
@inbook{Cococcioni2022,
title = {Computing Optimal Decision Strategies Using the Infinity Computer: The Case of Non-Archimedean Zero-Sum Games},
author = {Marco Cococcioni and Lorenzo Fiaschi and Luca Lambertini},
editor = {Yaroslav D. Sergeyev and Renato De Leone},
url = {https://doi.org/10.1007/978-3-030-93642-6_11},
doi = {10.1007/978-3-030-93642-6_11},
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abstract = {As is well known, zero-sum games are appropriate instruments for the analysis of several issues across areas including economics, international relations and engineering, among others. In particular, the Nash equilibria of any two-player finite zero-sum game in mixed-strategies can be found solving a proper linear programming problem. This chapter investigates and solves non-Archimedean zero-sum games, i.e., games satisfying the zero-sum property allowing the payoffs to be infinite, finite and infinitesimal. Since any zero-sum game is coupled with a linear programming problem, the search for Nash equilibria of non-Archimedean games requires the optimization of a non-Archimedean linear programming problem whose peculiarity is to have the constraints matrix populated by both infinite and infinitesimal numbers. This fact leads to the implementation of a novel non-Archimedean version of the Simplex algorithm called Gross-Matrix-Simplex. Four numerical experiments served as test cases to verify the effectiveness and correctness of the new algorithm. Moreover, these studies helped in stressing the difference between numerical and symbolic calculations: indeed, the solution output by the Gross-Matrix Simplex is just an approximation of the true Nash equilibrium, but it still satisfies some properties which resemble the idea of a non-Archimedean $$backslashvarepsilon $$ε-Nash equilibrium. On the contrary, symbolic tools seem to be able to compute the ``exact'' solution, a fact which happens only on very simple benchmarks and at the price of its intelligibility. In the general case, nevertheless, they stuck as soon as the problem becomes a little more challenging, ending up to be of little help in practice, such as in real time computations. Some possible applications related to such non-Archimedean zero-sum games are also discussed.},
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As is well known, zero-sum games are appropriate instruments for the analysis of several issues across areas including economics, international relations and engineering, among others. In particular, the Nash equilibria of any two-player finite zero-sum game in mixed-strategies can be found solving a proper linear programming problem. This chapter investigates and solves non-Archimedean zero-sum games, i.e., games satisfying the zero-sum property allowing the payoffs to be infinite, finite and infinitesimal. Since any zero-sum game is coupled with a linear programming problem, the search for Nash equilibria of non-Archimedean games requires the optimization of a non-Archimedean linear programming problem whose peculiarity is to have the constraints matrix populated by both infinite and infinitesimal numbers. This fact leads to the implementation of a novel non-Archimedean version of the Simplex algorithm called Gross-Matrix-Simplex. Four numerical experiments served as test cases to verify the effectiveness and correctness of the new algorithm. Moreover, these studies helped in stressing the difference between numerical and symbolic calculations: indeed, the solution output by the Gross-Matrix Simplex is just an approximation of the true Nash equilibrium, but it still satisfies some properties which resemble the idea of a non-Archimedean $$backslashvarepsilon $$ε-Nash equilibrium. On the contrary, symbolic tools seem to be able to compute the ``exact'' solution, a fact which happens only on very simple benchmarks and at the price of its intelligibility. In the general case, nevertheless, they stuck as soon as the problem becomes a little more challenging, ending up to be of little help in practice, such as in real time computations. Some possible applications related to such non-Archimedean zero-sum games are also discussed.
Cococcioni, Marco; Cudazzo, Alessandro; Pappalardo, Massimo; Sergeyev, Yaroslav D.
Multi-objective Lexicographic Mixed-Integer Linear Programming: An Infinity Computer Approach Book Chapter
In: Sergeyev, Yaroslav D.; Leone, Renato De (Ed.): Numerical Infinities and Infinitesimals in Optimization, pp. 119–149, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-93642-6.
@inbook{Cococcioni2022b,
title = {Multi-objective Lexicographic Mixed-Integer Linear Programming: An Infinity Computer Approach},
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editor = {Yaroslav D. Sergeyev and Renato De Leone},
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abstract = {In this chapter we show how a lexicographic multi-objective linear programming problem (LMOLP) can be transformed into an equivalent, single-objective one, by using the Grossone Methodology. Then we provide a simplex-like algorithm, called GrossSimplex, able to solve the original LMOLP problem using a single run of the algorithm (its theoretical correctness is also provided). In the second part, we tackle a Mixed-Integer Lexicographic Multi-Objective Linear Programming problem (LMOMILP) and we solve it in an exact way, by using a Grossone-version of the Branch-and-Bound scheme (called GrossBB). After proving the theoretical correctness of the associated pruning rules and terminating conditions, we show a few experimental results, run on an Infinity Computer simulator.},
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In this chapter we show how a lexicographic multi-objective linear programming problem (LMOLP) can be transformed into an equivalent, single-objective one, by using the Grossone Methodology. Then we provide a simplex-like algorithm, called GrossSimplex, able to solve the original LMOLP problem using a single run of the algorithm (its theoretical correctness is also provided). In the second part, we tackle a Mixed-Integer Lexicographic Multi-Objective Linear Programming problem (LMOMILP) and we solve it in an exact way, by using a Grossone-version of the Branch-and-Bound scheme (called GrossBB). After proving the theoretical correctness of the associated pruning rules and terminating conditions, we show a few experimental results, run on an Infinity Computer simulator.
Bárcena, José Luis Corcuera; Ducange, Pietro; Ercolani, Alessio; Marcelloni, Francesco; Renda, Alessandro
An Approach to Federated Learning of Explainable Fuzzy Regression Models Proceedings Article
In: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8, 2022.
@inproceedings{9882881,
title = {An Approach to Federated Learning of Explainable Fuzzy Regression Models},
author = {José Luis Corcuera Bárcena and Pietro Ducange and Alessio Ercolani and Francesco Marcelloni and Alessandro Renda},
doi = {10.1109/FUZZ-IEEE55066.2022.9882881},
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Bechini, Alessio; Bárcena, José Luis Corcuera; Ducange, Pietro; Marcelloni, Francesco; Renda, Alessandro
Increasing Accuracy and Explainability in Fuzzy Regression Trees: An Experimental Analysis Proceedings Article
In: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8, 2022.
@inproceedings{9882604,
title = {Increasing Accuracy and Explainability in Fuzzy Regression Trees: An Experimental Analysis},
author = {Alessio Bechini and José Luis Corcuera Bárcena and Pietro Ducange and Francesco Marcelloni and Alessandro Renda},
doi = {10.1109/FUZZ-IEEE55066.2022.9882604},
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Lai, Leonardo; Fiaschi, Lorenzo; Cococcioni, Marco; Deb, Kalyanmoy
Pure and Mixed Lexicographic-Paretian Many-Objective Optimization: State of the Art Journal Article
In: Natural Computing, vol. 22, 2022.
@article{article,
title = {Pure and Mixed Lexicographic-Paretian Many-Objective Optimization: State of the Art},
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Paolini, E.; Marinis, L. De; Cococcioni, M.; Valcarenghi, L.; Maggiani, L.; Andriolli, N.
Photonic-Aware Neural Network: a fixed-point emulation of photonic hardware Proceedings Article
In: 2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC), pp. 01-03, 2022.
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title = {Photonic-Aware Neural Network: a fixed-point emulation of photonic hardware},
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Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Ducange, Pietro; Fazzolari, Michela; Pecori, Riccardo
An Explainable and Evolving Car Driver Identification System based on Decision Trees Proceedings Article
In: 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1-8, 2022.
@inproceedings{9787517,
title = {An Explainable and Evolving Car Driver Identification System based on Decision Trees},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Pietro Ducange and Michela Fazzolari and Riccardo Pecori},
doi = {10.1109/EAIS51927.2022.9787517},
year = {2022},
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Bondielli, Alessandro; Tortora, Giuseppe Cancello; Ducange, Pietro; Macri, Armando; Marcelloni, Francesco; Renda, Alessandro
Online Monitoring of Stance from Tweets: The case of Green Pass in Italy Proceedings Article
In: 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1-8, 2022.
@inproceedings{9787753,
title = {Online Monitoring of Stance from Tweets: The case of Green Pass in Italy},
author = {Alessandro Bondielli and Giuseppe Cancello Tortora and Pietro Ducange and Armando Macri and Francesco Marcelloni and Alessandro Renda},
doi = {10.1109/EAIS51927.2022.9787753},
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Bárcena, José Luis Corcuera; Ducange, Pietro; Marcelloni, Francesco; Renda, Alessandro; Ruffini, Fabrizio
Hoeffding Regression Trees for Forecasting Quality of Experience in B5G/6G Networks Conference
vol. 3380, 2022, (Cited by: 1).
@conference{Bárcena2022,
title = {Hoeffding Regression Trees for Forecasting Quality of Experience in B5G/6G Networks},
author = {José Luis Corcuera Bárcena and Pietro Ducange and Francesco Marcelloni and Alessandro Renda and Fabrizio Ruffini},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159341686&partnerID=40&md5=df0e50398592deb3771b3306a9e59ad6},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {CEUR Workshop Proceedings},
volume = {3380},
abstract = {Online data stream analysis is becoming more and more relevant, as the focus of daily life analyses shifts from offline processing to real-time acquisition and modeling of massive data from remote devices. In this paper, we focus our attention on the domain of telecommunications, in particular the video streaming services for moving devices (e.g., a passenger enjoying a movie during a car trip). Since the streaming service must provide a satisfactory level of quality of experience to the user, it is important to predict incoming problems on video quality. We used the well-known Hoeffding Decision Tree (HDT) for streaming data, tailored to regression problems, and we compared its performance with standard Regression Trees (RTs) to evaluate the potentiality of HDTs to forecast the quality of experience in terms of accuracy, time for learning, and memory used. Results show that, during the online learning process, the standard RT outperforms HDT in terms of accuracy, but is prone to under-performance in terms of timings and memory when applied to potentially massive data streaming scenarios. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).},
note = {Cited by: 1},
keywords = {},
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Online data stream analysis is becoming more and more relevant, as the focus of daily life analyses shifts from offline processing to real-time acquisition and modeling of massive data from remote devices. In this paper, we focus our attention on the domain of telecommunications, in particular the video streaming services for moving devices (e.g., a passenger enjoying a movie during a car trip). Since the streaming service must provide a satisfactory level of quality of experience to the user, it is important to predict incoming problems on video quality. We used the well-known Hoeffding Decision Tree (HDT) for streaming data, tailored to regression problems, and we compared its performance with standard Regression Trees (RTs) to evaluate the potentiality of HDTs to forecast the quality of experience in terms of accuracy, time for learning, and memory used. Results show that, during the online learning process, the standard RT outperforms HDT in terms of accuracy, but is prone to under-performance in terms of timings and memory when applied to potentially massive data streaming scenarios. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Bárcena, José Luis Corcuera; Daole, Mattia; Ducange, Pietro; Marcelloni, Francesco; Renda, Alessandro; Ruffini, Fabrizio; Schiavo, Alessio
Fed-XAI: Federated Learning of Explainable Artificial Intelligence Models Conference
vol. 3277, 2022, (Cited by: 0).
@conference{Bárcena2022104,
title = {Fed-XAI: Federated Learning of Explainable Artificial Intelligence Models},
author = {José Luis Corcuera Bárcena and Mattia Daole and Pietro Ducange and Francesco Marcelloni and Alessandro Renda and Fabrizio Ruffini and Alessio Schiavo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142821914&partnerID=40&md5=16d6a85b76ace1c888a7dbcbd8c9d504},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {CEUR Workshop Proceedings},
volume = {3277},
pages = {104 – 117},
abstract = {The current era is characterized by an increasing pervasiveness of applications and services based on data processing and often built on Artificial Intelligence (AI) and, in particular, Machine Learning (ML) algorithms. In fact, extracting insights from data is so common in daily life of individuals, companies, and public entities and so relevant for the market players, to become an important matter of interest for institutional organizations. The theme is so relevant that ad hoc regulations have been proposed. One important aspect is given by the capability of the applications to tackle the data privacy issue. Additionally, depending on the specific application field, paramount importance is given to the possibility for the humans to understand why a certain AI/ML-based application is providing that specific output. In this paper, we discuss the concept of Federated Learning of eXplainable AI (XAI) models, in short FED-XAI, purposely designed to address these two requirements simultaneously. AI/ML models are trained with the simultaneous goals of preserving the data privacy (Federated Learning (FL) side) and ensuring a certain level of explainability of the system (XAI side). We first introduce the motivations at the foundation of FL and XAI, along with their basic concepts; then, we discuss the current status of this field of study, providing a brief survey regarding approaches, models, and results. Finally, we highlight the main future challenges. © 2022 Copyright for this paper by its authors.},
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The current era is characterized by an increasing pervasiveness of applications and services based on data processing and often built on Artificial Intelligence (AI) and, in particular, Machine Learning (ML) algorithms. In fact, extracting insights from data is so common in daily life of individuals, companies, and public entities and so relevant for the market players, to become an important matter of interest for institutional organizations. The theme is so relevant that ad hoc regulations have been proposed. One important aspect is given by the capability of the applications to tackle the data privacy issue. Additionally, depending on the specific application field, paramount importance is given to the possibility for the humans to understand why a certain AI/ML-based application is providing that specific output. In this paper, we discuss the concept of Federated Learning of eXplainable AI (XAI) models, in short FED-XAI, purposely designed to address these two requirements simultaneously. AI/ML models are trained with the simultaneous goals of preserving the data privacy (Federated Learning (FL) side) and ensuring a certain level of explainability of the system (XAI side). We first introduce the motivations at the foundation of FL and XAI, along with their basic concepts; then, we discuss the current status of this field of study, providing a brief survey regarding approaches, models, and results. Finally, we highlight the main future challenges. © 2022 Copyright for this paper by its authors.
Bárcena, José Luis Corcuera; Ducange, Pietro; Marcelloni, Francesco; Nardini, Giovanni; Noferi, Alessandro; Renda, Alessandro; Stea, Giovanni; Virdis, Antonio
Towards Trustworthy AI for QoE prediction in B5G/6G Networks Conference
vol. 3189, 2022, (Cited by: 0).
@conference{CorcueraBárcena2022,
title = {Towards Trustworthy AI for QoE prediction in B5G/6G Networks},
author = {José Luis Corcuera Bárcena and Pietro Ducange and Francesco Marcelloni and Giovanni Nardini and Alessandro Noferi and Alessandro Renda and Giovanni Stea and Antonio Virdis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137816951&partnerID=40&md5=5de5c1a4829723066e922ea13a8bf106},
year = {2022},
date = {2022-01-01},
journal = {CEUR Workshop Proceedings},
volume = {3189},
abstract = {The ability to forecast Quality of Experience (QoE) metrics will be crucial in several applications and services offered by the future B5G/6G networks. However, QoE timeseries forecasting has not been adequately investigated so far, mainly due to the lack of available realistic datasets. In this paper, we first present a novel QoE forecasting dataset obtained from realistic 5G network simulations and characterized by Quality of Service (QoS) and QoE metrics for a video-streaming application; then, we embrace the topical challenge of trustworthiness in the adoption of AI systems for tackling the QoE prediction task. We show how an eXplainable Artificial Intelligence (XAI) model, namely Decision Tree, can be effectively leveraged for addressing the forecasting problem. Finally, we identify federated learning as a suitable paradigm for privacy-preserving collaborative model training and outline the related challenges from both an algorithmic and 6G network support perspective. © 2022 Copyright for this paper by its authors.},
note = {Cited by: 0},
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The ability to forecast Quality of Experience (QoE) metrics will be crucial in several applications and services offered by the future B5G/6G networks. However, QoE timeseries forecasting has not been adequately investigated so far, mainly due to the lack of available realistic datasets. In this paper, we first present a novel QoE forecasting dataset obtained from realistic 5G network simulations and characterized by Quality of Service (QoS) and QoE metrics for a video-streaming application; then, we embrace the topical challenge of trustworthiness in the adoption of AI systems for tackling the QoE prediction task. We show how an eXplainable Artificial Intelligence (XAI) model, namely Decision Tree, can be effectively leveraged for addressing the forecasting problem. Finally, we identify federated learning as a suitable paradigm for privacy-preserving collaborative model training and outline the related challenges from both an algorithmic and 6G network support perspective. © 2022 Copyright for this paper by its authors.
Filippou, Miltiadis C.; Lamprousi, Vasiliki; Mohammadi, Jafar; Merluzzi, Mattia; Soykan, Elif Ustundag; Borsos, Tamas; Rajatheva, Nandana; Rajapaksha, Nuwanthika; Magoarou, Luc Le; Piscione, Pietro; Benczur, Andras; Lampin, Quentin; Larue, Guillaume; Korpi, Dani; Ducange, Pietro; Renda, Alessandro; Farhadi, Hamed; Haraldson, Johan; Strinati, Leonardo Gomes Balta Emilio Calvanese; Demestichas, Panagiotis; Tomur, Emrah
Pervasive Artificial Intelligence in Next Generation Wireless: The Hexa-X Project Perspective Conference
vol. 3189, 2022, (Cited by: 0).
@conference{Filippou2022,
title = {Pervasive Artificial Intelligence in Next Generation Wireless: The Hexa-X Project Perspective},
author = {Miltiadis C. Filippou and Vasiliki Lamprousi and Jafar Mohammadi and Mattia Merluzzi and Elif Ustundag Soykan and Tamas Borsos and Nandana Rajatheva and Nuwanthika Rajapaksha and Luc Le Magoarou and Pietro Piscione and Andras Benczur and Quentin Lampin and Guillaume Larue and Dani Korpi and Pietro Ducange and Alessandro Renda and Hamed Farhadi and Johan Haraldson and Leonardo Gomes Balta Emilio Calvanese Strinati and Panagiotis Demestichas and Emrah Tomur},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137836688&partnerID=40&md5=dae417e9f6928bab4d2ce7e9fc686644},
year = {2022},
date = {2022-01-01},
journal = {CEUR Workshop Proceedings},
volume = {3189},
abstract = {The European 6G flagship project Hexa-X has the objective to conduct exploratory research on the next generation of mobile networks with the intention to connect human, physical and digital worlds with a fabric of technology enablers. Within this scope, one of the main research challenges is the ambition for beyond 5G (B5G)/6G systems to support, enhance and enable real-time trustworthy control by transforming Artificial Intelligence (AI) / Machine Learning (ML) technologies into a vital and trusted tool for large-scale deployment of interconnected intelligence available to the wider society. Hence, the study and development of concepts and solutions enabling AI-driven communication and computation co-design for a B5G /6G communication system is required. This paper focuses on describing the possibilities that emerge with the application of AI & ML mechanisms (with emphasis on ML) to 6G networks, identifying the resulting challenges and proposing some potential solution approaches. © 2022 Copyright for this paper by its authors.},
note = {Cited by: 0},
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The European 6G flagship project Hexa-X has the objective to conduct exploratory research on the next generation of mobile networks with the intention to connect human, physical and digital worlds with a fabric of technology enablers. Within this scope, one of the main research challenges is the ambition for beyond 5G (B5G)/6G systems to support, enhance and enable real-time trustworthy control by transforming Artificial Intelligence (AI) / Machine Learning (ML) technologies into a vital and trusted tool for large-scale deployment of interconnected intelligence available to the wider society. Hence, the study and development of concepts and solutions enabling AI-driven communication and computation co-design for a B5G /6G communication system is required. This paper focuses on describing the possibilities that emerge with the application of AI & ML mechanisms (with emphasis on ML) to 6G networks, identifying the resulting challenges and proposing some potential solution approaches. © 2022 Copyright for this paper by its authors.
Cococcioni, Marco; Grazzi, Marco; Li, Le; Ponchio, Federico
A toolbox for measuring heterogeneity and efficiency using zonotopes Journal Article
In: The Stata Journal, vol. 22, no. 1, pp. 25-59, 2022.
@article{doi:10.1177/1536867X221083854,
title = {A toolbox for measuring heterogeneity and efficiency using zonotopes},
author = {Marco Cococcioni and Marco Grazzi and Le Li and Federico Ponchio},
url = {https://doi.org/10.1177/1536867X221083854},
doi = {10.1177/1536867X221083854},
year = {2022},
date = {2022-01-01},
journal = {The Stata Journal},
volume = {22},
number = {1},
pages = {25-59},
abstract = {In this work, we describe the new command zonotope, which, by resorting to a geometry-based approach, provides a measure of productivity that fully accounts for the existing heterogeneity across firms within the same industry. The method we propose also enables assessment of the extent of multidimensional heterogeneity with applications to fields beyond that of production analysis. Finally, we detail the functioning of the software to perform the related empirical analysis, and we discuss the main computational issues encountered in its development.},
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In this work, we describe the new command zonotope, which, by resorting to a geometry-based approach, provides a measure of productivity that fully accounts for the existing heterogeneity across firms within the same industry. The method we propose also enables assessment of the extent of multidimensional heterogeneity with applications to fields beyond that of production analysis. Finally, we detail the functioning of the software to perform the related empirical analysis, and we discuss the main computational issues encountered in its development.
Benci, Vieri; Cococcioni, Marco; Fiaschi, Lorenzo
Non–Standard Analysis Revisited: An Easy Axiomatic Presentation Oriented Towards Numerical Applications Journal Article
In: International Journal of Applied Mathematics and Computer Science, vol. 32, no. 1, pp. 65–80, 2022.
@article{BenciCococcioniFiaschi+2022+65+80,
title = {Non–Standard Analysis Revisited: An Easy Axiomatic Presentation Oriented Towards Numerical Applications},
author = {Vieri Benci and Marco Cococcioni and Lorenzo Fiaschi},
url = {https://doi.org/10.34768/amcs-2022-0006},
doi = {doi:10.34768/amcs-2022-0006},
year = {2022},
date = {2022-01-01},
journal = {International Journal of Applied Mathematics and Computer Science},
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number = {1},
pages = {65–80},
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Biancani, Arianna; Bruti, Silvia; Cappellini, Paola; Marcelloni, Francesco; Marzano, Arturo; Polini, Marco; Roda, Chiara; Terranova, Adio
Managing Students from 23 Different Countries in Distance Learning: The Foundation Course Experience of the University of Pisa Proceedings Article
In: Casalino, Gabriella; Cimitile, Marta; Ducange, Pietro; Zea, Natalia Padilla; Pecori, Riccardo; Picerno, Pietro; Raviolo, Paolo (Ed.): Higher Education Learning Methodologies and Technologies Online, pp. 129–140, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-96060-5.
@inproceedings{10.1007/978-3-030-96060-5_10,
title = {Managing Students from 23 Different Countries in Distance Learning: The Foundation Course Experience of the University of Pisa},
author = {Arianna Biancani and Silvia Bruti and Paola Cappellini and Francesco Marcelloni and Arturo Marzano and Marco Polini and Chiara Roda and Adio Terranova},
editor = {Gabriella Casalino and Marta Cimitile and Pietro Ducange and Natalia Padilla Zea and Riccardo Pecori and Pietro Picerno and Paolo Raviolo},
isbn = {978-3-030-96060-5},
year = {2022},
date = {2022-01-01},
booktitle = {Higher Education Learning Methodologies and Technologies Online},
pages = {129–140},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The Foundation Course (FC) is a pre-university program primarily aimed at prospective students coming from countries where the national schooling system does not meet the minimum requirements requested for higher education access in Italy andbackslashor with a not appropriate pre-academic education for the enrollment in an Italian or European University Degree Program. The FC of the University of Pisa, in its fifth edition, up to March 2020 had been always delivered in presence, but in the last year due the COVID-19 emergency it has been moved to online learning mode.},
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The Foundation Course (FC) is a pre-university program primarily aimed at prospective students coming from countries where the national schooling system does not meet the minimum requirements requested for higher education access in Italy andbackslashor with a not appropriate pre-academic education for the enrollment in an Italian or European University Degree Program. The FC of the University of Pisa, in its fifth edition, up to March 2020 had been always delivered in presence, but in the last year due the COVID-19 emergency it has been moved to online learning mode.
Cococcioni, Marco; Rossi, Federico; Ruffaldi, Emanuele; Saponara, Sergio
Small Reals Representations for Deep Learning at the Edge: A Comparison Proceedings Article
In: Gustafson, John; Dimitrov, Vassil (Ed.): Next Generation Arithmetic, pp. 117–133, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-09779-9.
@inproceedings{10.1007/978-3-031-09779-9_8,
title = {Small Reals Representations for Deep Learning at the Edge: A Comparison},
author = {Marco Cococcioni and Federico Rossi and Emanuele Ruffaldi and Sergio Saponara},
editor = {John Gustafson and Vassil Dimitrov},
isbn = {978-3-031-09779-9},
year = {2022},
date = {2022-01-01},
booktitle = {Next Generation Arithmetic},
pages = {117–133},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The pervasiveness of deep neural networks (DNNs) in edge devices enforces new requirements on information representation. Low precision formats from 16 bits down to 1 or 2 bits have been proposed in the last years. In this paper we aim to illustrate a general view of the possible approaches of optimizing neural networks for DNNs at the edge. In particular we focused on these key points: i) limited non-volatile storage ii) limited volatile memory iii) limited computational power. Furthermore we explored the state-of-the-art of alternative representations for real numbers comparing their performance in recognition and detection tasks, in terms of accuracy and inference time. Finally we present our results using posits in several neural networks and datasets, showing the small accuracy degradation between 32-bit floats and 16-bit (or even 8-bit) posits, comparing the results also against the bfloat family.},
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The pervasiveness of deep neural networks (DNNs) in edge devices enforces new requirements on information representation. Low precision formats from 16 bits down to 1 or 2 bits have been proposed in the last years. In this paper we aim to illustrate a general view of the possible approaches of optimizing neural networks for DNNs at the edge. In particular we focused on these key points: i) limited non-volatile storage ii) limited volatile memory iii) limited computational power. Furthermore we explored the state-of-the-art of alternative representations for real numbers comparing their performance in recognition and detection tasks, in terms of accuracy and inference time. Finally we present our results using posits in several neural networks and datasets, showing the small accuracy degradation between 32-bit floats and 16-bit (or even 8-bit) posits, comparing the results also against the bfloat family.
Olivelli, Martina; Donati, Massimiliano; Alessio, Bechini; Fanucci, Luca
Enabling the E@syCare Telemedicine Platform with Push Notification with End-to-end Acknowledgment Proceedings Article
In: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), pp. 763-768, 2022.
@inproceedings{9767507,
title = {Enabling the E@syCare Telemedicine Platform with Push Notification with End-to-end Acknowledgment},
author = {Martina Olivelli and Massimiliano Donati and Bechini Alessio and Luca Fanucci},
doi = {10.1109/PerComWorkshops53856.2022.9767507},
year = {2022},
date = {2022-01-01},
booktitle = {2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},
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Cococcioni, Marco; Rossi, Federico; Ruffaldi, Emanuele; Saponara, Sergio
A Lightweight Posit Processing Unit for RISC-V Processors in Deep Neural Network Applications Journal Article
In: IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 4, pp. 1898-1908, 2022.
@article{9583876,
title = {A Lightweight Posit Processing Unit for RISC-V Processors in Deep Neural Network Applications},
author = {Marco Cococcioni and Federico Rossi and Emanuele Ruffaldi and Sergio Saponara},
doi = {10.1109/TETC.2021.3120538},
year = {2022},
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Cococcioni, Marco; Rossi, Federico; Ruffaldi, Emanuele; Saponara, Sergio
Experimental Results of Vectorized Posit-Based DNNs on a Real ARM SVE High Performance Computing Machine Proceedings Article
In: Saponara, Sergio; Gloria, Alessandro De (Ed.): Applications in Electronics Pervading Industry, Environment and Society, pp. 61–68, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-95498-7.
@inproceedings{10.1007/978-3-030-95498-7_9,
title = {Experimental Results of Vectorized Posit-Based DNNs on a Real ARM SVE High Performance Computing Machine},
author = {Marco Cococcioni and Federico Rossi and Emanuele Ruffaldi and Sergio Saponara},
editor = {Sergio Saponara and Alessandro De Gloria},
isbn = {978-3-030-95498-7},
year = {2022},
date = {2022-01-01},
booktitle = {Applications in Electronics Pervading Industry, Environment and Society},
pages = {61–68},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {With the pervasiveness of deep neural networks in scenarios that bring real-time requirements, there is the increasing need for optimized arithmetic on high performance architectures. In this paper we adopt two key visions: i) extensive use of vectorization to accelerate computation of deep neural network kernels; ii) adoption of the posit compressed arithmetic in order to reduce the memory transfers between the vector registers and the rest of the memory architecture. Finally, we present our first results on a real hardware implementation of the ARM Scalable Vector Extension.},
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With the pervasiveness of deep neural networks in scenarios that bring real-time requirements, there is the increasing need for optimized arithmetic on high performance architectures. In this paper we adopt two key visions: i) extensive use of vectorization to accelerate computation of deep neural network kernels; ii) adoption of the posit compressed arithmetic in order to reduce the memory transfers between the vector registers and the rest of the memory architecture. Finally, we present our first results on a real hardware implementation of the ARM Scalable Vector Extension.
Bechini, Alessio; Marcelloni, Francesco; Renda, Alessandro
TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams Journal Article
In: IEEE Transactions on Fuzzy Systems, vol. 30, no. 3, pp. 623-637, 2022.
@article{9281371,
title = {TSF-DBSCAN: A Novel Fuzzy Density-Based Approach for Clustering Unbounded Data Streams},
author = {Alessio Bechini and Francesco Marcelloni and Alessandro Renda},
doi = {10.1109/TFUZZ.2020.3042645},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Fuzzy Systems},
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