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Your search keyword '"Mazzeo, Pier Luigi"' showing total 190 results
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190 results on '"Mazzeo, Pier Luigi"'

1. Boosting House Price Estimations with Multi-Head Gated Attention

Author

Sellam, Zakaria Abdellah, Distante, Cosimo, Taleb-Ahmed, Abdelmalik, and Mazzeo, Pier Luigi

Subjects
Computer Science - Machine Learning
Abstract

Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to capture spatial dependencies and contextual information better. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention-based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.

Published
2024

2. Insights into Classifying and Mitigating LLMs' Hallucinations

Author

Bruno, Alessandro, Mazzeo, Pier Luigi, Chetouani, Aladine, Tliba, Marouane, and Kerkouri, Mohamed Amine

Subjects
Computer Science - Computation and Language
Abstract

The widespread adoption of large language models (LLMs) across diverse AI applications is proof of the outstanding achievements obtained in several tasks, such as text mining, text generation, and question answering. However, LLMs are not exempt from drawbacks. One of the most concerning aspects regards the emerging problematic phenomena known as "Hallucinations". They manifest in text generation systems, particularly in question-answering systems reliant on LLMs, potentially resulting in false or misleading information propagation. This paper delves into the underlying causes of AI hallucination and elucidates its significance in artificial intelligence. In particular, Hallucination classification is tackled over several tasks (Machine Translation, Question and Answer, Dialog Systems, Summarisation Systems, Knowledge Graph with LLMs, and Visual Question Answer). Additionally, we explore potential strategies to mitigate hallucinations, aiming to enhance the overall reliability of LLMs. Our research addresses this critical issue within the HeReFaNMi (Health-Related Fake News Mitigation) project, generously supported by NGI Search, dedicated to combating Health-Related Fake News dissemination on the Internet. This endeavour represents a concerted effort to safeguard the integrity of information dissemination in an age of evolving AI technologies., Comment: Accepted at AIxIA 2023

Published
2023

4. UAV Multi-object Tracking by Combining Two Deep Neural Architectures

Author

Mazzeo, Pier Luigi, Manica, Alessandro, Distante, Cosimo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Foresti, Gian Luca, editor, Fusiello, Andrea, editor, and Hancock, Edwin, editor

Published
2023
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6. Human Action Recognition with Transformers

Author

Mazzeo, Pier Luigi, Spagnolo, Paolo, Fasano, Matteo, Distante, Cosimo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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7. Comparison of Different Supervised and Self-supervised Learning Techniques in Skin Disease Classification

Author

Cino, Loris, Mazzeo, Pier Luigi, Distante, Cosimo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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8. Morphological Galaxies Classification According to Hubble-de Vaucouleurs Diagram Using CNNs

Author

Mazzeo, Pier Luigi, Rizzo, Antonio, Distante, Cosimo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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9. A Lightweight Model for Satellite Pose Estimation

Author

Carcagnì, Pierluigi, Leo, Marco, Spagnolo, Paolo, Mazzeo, Pier Luigi, Distante, Cosimo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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14. Classification of Skin Lesions by Combining Multilevel Learnings in a DenseNet Architecture

Author

Carcagnì, Pierluigi, Leo, Marco, Cuna, Andrea, Mazzeo, Pier Luigi, Spagnolo, Paolo, Celeste, Giuseppe, Distante, Cosimo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ricci, Elisa, editor, Rota Bulò, Samuel, editor, Snoek, Cees, editor, Lanz, Oswald, editor, Messelodi, Stefano, editor, and Sebe, Nicu, editor

Published
2019
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17. Multi-branch CNN for Multi-scale Age Estimation

Author

Del Coco, Marco, Carcagnì, Pierluigi, Leo, Marco, Spagnolo, Paolo, Mazzeo, Pier Luigi, Distante, Cosimo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Battiato, Sebastiano, editor, Gallo, Giovanni, editor, Schettini, Raimondo, editor, and Stanco, Filippo, editor

Published
2017
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21. A Technological Framework to Support Standardized Protocols for the Diagnosis and Assessment of ASD

Author

Leo, Marco, Del Coco, Marco, Carcagnì, Pierluigi, Mazzeo, Pier Luigi, Spagnolo, Paolo, Distante, Cosimo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hua, Gang, editor, and Jégou, Hervé, editor

Published
2016
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22. Intelligent Vision System for ASD Diagnosis and Assessment

Author

Leo, Marco, Del Coco, Marco, Carcagnì, Pierluigi, Mazzeo, Pier Luigi, Spagnolo, Paolo, Distante, Cosimo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Blanc-Talon, Jacques, editor, Distante, Cosimo, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2016
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23. Automatic Joint Attention Detection During Interaction with a Humanoid Robot

Author

Cazzato, Dario, Mazzeo, Pier Luigi, Spagnolo, Paolo, Distante, Cosimo, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Tapus, Adriana, editor, André, Elisabeth, editor, Martin, Jean-Claude, editor, Ferland, François, editor, and Ammi, Mehdi, editor

Published
2015
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24. BRISK Local Descriptors for Heavily Occluded Ball Recognition

Author

Mazzeo, Pier Luigi, Spagnolo, Paolo, Distante, Cosimo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Murino, Vittorio, editor, and Puppo, Enrico, editor

Published
2015
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29. Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study.

Author

Joshi, Kunj, Bhatt, Chintan, Shah, Kaushal, Parmar, Dwireph, Corchado, Juan M., Bruno, Alessandro, and Mazzeo, Pier Luigi

Subjects
PHISHING, MACHINE learning, BLOCKCHAINS, PHISHING prevention, CYBERTERRORISM, COMPARATIVE studies
Abstract

Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Soft Biometrics for a Socially Assistive Robotic Platform

Author

Carcagnì Pierluigi, Cazzato Dario, Del Coco Marco, Mazzeo Pier Luigi, Leo Marco, and Distante Cosimo

Subjects
human-robot interaction, biometrics, artificial intelligence, gender recognition, age group recognition, Technology
Abstract

In thiswork, a real-time system able to automatically recognize soft-biometric traits is introduced and used to improve the capability of a humanoid robot to interact with humans. In particular the proposed system is able to estimate gender and age of humans in images acquired from the embedded camera of the robot. This knowledge allows the robot to properly react with customized behaviors related to the gender/age of the interacting individuals. The system is able to handle multiple persons in the same acquired image, recognizing the age and gender of each person in the robot’s field of view. These features make the robot particularly suitable to be used in socially assistive applications.

Published
2015
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41. Computational assessment of facial expression production in ASD children

Author

Leo, Marco, Carcagnì, Pierluigi, Distante, Cosimo, Spagnolo, Paolo, Mazzeo, Pier Luigi, Rosato, Anna Chiara, Petrocchi, Serena, Pellegrino, Chiara, Levante, Annalisa, De Lumè, Filomena, Lecciso, Flavia, Leo, Marco, Carcagnì, Pierluigi, Distante, Cosimo, Spagnolo, Paolo, Mazzeo, Pier Luigi, Rosato, Anna Chiara, Petrocchi, Serena, Pellegrino, Chiara, Levante, Annalisa, De Lumè, Filomena, and Lecciso, Flavia

Abstract

In this paper, a computational approach is proposed and put into practice to assess the capability of children having had diagnosed Autism Spectrum Disorders (ASD) to produce facial expressions. The proposed approach is based on computer vision components working on sequence of images acquired by an off-the-shelf camera in unconstrained conditions. Action unit intensities are estimated by analyzing local appearance and then both temporal and geometrical relationships, learned by Convolutional Neural Networks, are exploited to regularize gathered estimates. To cope with stereotyped movements and to highlight even subtle voluntary movements of facial muscles, a personalized and contextual statistical modeling of non-emotional face is formulated and used as a reference. Experimental results demonstrate how the proposed pipeline can improve the analysis of facial expressions produced by ASD children. A comparison of system’s outputs with the evaluations performed by psychologists, on the same group of ASD children, makes evident how the performed quantitative analysis of children’s abilities helps to go beyond the traditional qualitative ASD assessment/diagnosis protocols, whose outcomes are affected by human limitations in observing and understanding multi-cues behaviors such as facial expressions.

Published
2020

42. Computational analysis of deep visual data for quantifying facial expression production

Author

Leo, Marco, Carcagnì, Pierluigi, Distante, Cosimo, Mazzeo, Pier Luigi, Spagnolo, Paolo, Levante, Annalisa, Petrocchi, Serena, Lecciso, Flavia, Leo, Marco, Carcagnì, Pierluigi, Distante, Cosimo, Mazzeo, Pier Luigi, Spagnolo, Paolo, Levante, Annalisa, Petrocchi, Serena, and Lecciso, Flavia

Abstract

The computational analysis of facial expressions is an emerging research topic that could overcome the limitations of human perception and get quick and objective outcomes in the assessment of neurodevelopmental disorders (e.g., Autism Spectrum Disorders, ASD). Unfortunately, there have been only a few attempts to quantify facial expression production and most of the scientific literature aims at the easier task of recognizing if either a facial expression is present or not. Some attempts to face this challenging task exist but they do not provide a comprehensive study based on the comparison between human and automatic outcomes in quantifying children’s ability to produce basic emotions. Furthermore, these works do not exploit the latest solutions in computer vision and machine learning. Finally, they generally focus only on a homogeneous (in terms of cognitive capabilities) group of individuals. To fill this gap, in this paper some advanced computer vision and machine learning strategies are integrated into a framework aimed to computationally analyze how both ASD and typically developing children produce facial expressions. The framework locates and tracks a number of landmarks (virtual electromyography sensors) with the aim of monitoring facial muscle movements involved in facial expression production. The output of these virtual sensors is then fused to model the individual ability to produce facial expressions. Gathered computational outcomes have been correlated with the evaluation provided by psychologists and evidence has been given that shows how the proposed framework could be effectively exploited to deeply analyze the emotional competence of ASD children to produce facial expressions.

Published
2020

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