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606 results on '"Francesco Palmieri"'

151. Effective classification of android malware families through dynamic features and neural networks

Author

Antonio Robustelli, Gianni D'Angelo, Francesco Palmieri, and Arcangelo Castiglione

Subjects
Malware analysis, malware dataset, Computer science, convolutional neural network, 02 engineering and technology, Machine learning, computer.software_genre, Convolutional neural network, Artificial Intelligence, Android malware, 0202 electrical engineering, electronic engineering, information engineering, Android (operating system), machine learning, recurrent neural network, Artificial neural network, business.industry, 020207 software engineering, Popularity, Human-Computer Interaction, Recurrent neural network, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Software
Abstract

Due to their open nature and popularity, Android-based devices have attracted several end-users around the World and are one of the main targets for attackers. Because of the reasons given above, i...

Published
2021

159. A Fraud-Resilient Blockchain-Based Solution for Invoice Financing

Author

Luca Verderame, Alessio Merlo, Meriem Guerar, Francesco Palmieri, and Mauro Migliardi

Subjects
blockchain, Blockchain, Invoice, Smart contract, Transparency (market), invoice factoring, Strategy and Management, IPFS, Cryptography, Auction, ethereum, reputation system, smart contract, Contracts, Insurance, Insurance policy, 0502 economics and business, Confidentiality, Electrical and Electronic Engineering, Finance, business.industry, 05 social sciences, Companies, Waste materials, Reputation system, business, 050203 business & management
Abstract

Invoice financing has been a steadily growing component of the financing market as a whole for the last few years, and, in 2016, it became the third largest financing market. Nonetheless, the risk of frauds is still very high, and most solutions proposed so far are based on private, proprietary platforms that cannot match the global nature of such a market. Even the most recent proposals based on blockchain are mainly adopting a private, permissioned blockchain due to the lack of confidentiality in public blockchain. In this article, we propose an Invoice financing platform based on a public blockchain supporting both fully open and group-restricted auctioning of invoices. We addressed the confidentiality issue by storing the confidential data encrypted in IPFS and the corresponding hash in the smart contract hosted on Ethereum blockchain. Our blockchain-based solution ensures data confidentiality and benefits from the main properties of the public blockchain required in Invoice financing systems, such as transparency, immutability, trustworthiness, and security. Furthermore, our platform introduces a reputation system based on the past behavior of entities, computed using the blockchain global ledger. Such a reputation system allows insurance companies to modulate the cost of the insurance contracts they offer. This combination guarantees the complete transparency and tamperproofness of a public blockchain, while it allows reducing insurance costs and fraud possibilities.

Published
2020

160. Discovering genomic patterns in SARS‐CoV‐2 variants

Author

Francesco Palmieri and Gianni D'Angelo

Subjects
0209 industrial biotechnology, Mutation rate, sequence analysis, Sequence analysis, viruses, coronavirus, 02 engineering and technology, Disease, Computational biology, Biology, spike protein, medicine.disease_cause, Genome, Virus, Theoretical Computer Science, 020901 industrial engineering & automation, Artificial Intelligence, Pandemic, 0202 electrical engineering, electronic engineering, information engineering, medicine, genomic patterns, Coronavirus, dynamic programming, SARS-CoV-2, common subsequence, pattern recognition, COVID-19, medicine.disease, Human-Computer Interaction, genomic variants, Middle East respiratory syndrome, 020201 artificial intelligence & image processing, Software
Abstract

SARS-CoV-2 is a novel severe acute respiratory syndrome-like coronavirus (SARS-CoV), which is responsible of the ongoing world pandemic of COVID-19 disease. Although many approaches are being investigated to address this issue, nowaday there are no vaccines available and there is little evidence supporting the efficiency of potential therapeutic agents. Moreover, the high mutation rate of this virus heavily affects the understanding of its evolution and diffusion mechanisms, and, in turn, the development of effective solutions. In this study, two novel algorithms are provided for finding out recurrent patterns of nucleotide subsequences of different SARS-CoV-2 genomes as a unique signature capable of identifying the most peculiar features of the pathogen. In particular, we provide several subsequence patterns related to the Spike glycoprotein, which is believed to be the main target for developing effective drugs and vaccines against the COVID-19 disease because of its role in the entrance of coronaviruses into host cells. The experimental results, obtained by analyzing 5000 genomes of SARS-CoV-2, have shown that the extracted patterns are able to recognize the Spyke protein in the 99.35% of the considered genomes. In addition, such patterns have proven to be highly discriminating with respect to other pathogenic genomes, such as SARS, Middle East respiratory syndrome, Nipah, and the streptococcus bacteria. We hope that the findings presented in this study can help specialists in speeding up the design of more accurate drugs or vaccines against SARS-CoV-2.

Published
2020

161. Malware detection in mobile environments based on Autoencoders and API-images

Author

Francesco Palmieri, Massimo Ficco, Gianni D'Angelo, D'Angelo, G., Ficco, M., and Palmieri, F.

Subjects
Computer Networks and Communications, Computer science, Autoencoders, 02 engineering and technology, computer.software_genre, Machine learning, Dynamic analysi, Malware, Theoretical Computer Science, Android, Artificial Intelligence, Dynamic analysis, 0202 electrical engineering, electronic engineering, information engineering, Android (operating system), Artificial neural network, business.industry, Deep learning, 020206 networking & telecommunications, Autoencoder, Hardware and Architecture, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Classifier (UML), Software
Abstract

Due to their open nature and popularity, Android-based devices represent one of the main targets for malware attacks that may adversely affect the privacy of their users. Considering the huge Android market share, it is necessary to build effective tools able to reliably detect zero-day malware on these platforms. Therefore, several static and dynamic analysis methods based on Neural Networks and Deep Learning have been proposed in the literature. Despite machine learning can be considered the most promising approach for classifying applications into malware or legitimate ones, its success strongly depends on the choice of the right features used for building the detection model. This is definitely not an easy task that requires a systematic solution. Accordingly, this work represents the sequences of API calls invoked by apps during their execution as sparse matrices looking like images (API-images), which can be used as fingerprints of the apps’ behavior over time. We also used autoencoders to autonomously extract the most representative and discriminating features from these matrices, that, once provided to an artificial neural network-based classifier have shown to be effective in detecting malware, also when the network is trained on a reduced number of samples. Experimental results show that the resulting framework is able to outperform more complex and sophisticated machine learning approaches in malware classification.

Published
2020

162. Securing visual search queries in ubiquitous scenarios empowered by smart personal devices

Author

Bruno Carpentieri, Xiaofei Xing, Arcangelo Castiglione, Alfredo De Santis, Francesco Palmieri, and Raffaele Pizzolante

Subjects
Information Systems and Management, Spoofing attack, Ubiquitous computing, Computer science, Access control, 02 engineering and technology, Watermarking, Theoretical Computer Science, Data acquisition, Artificial Intelligence, Robustness (computer science), Human–computer interaction, MPEG-CDVS Standard, Secure data streaming, Security, Visual search techniques, Health care, 0202 electrical engineering, electronic engineering, information engineering, Digital watermarking, Server-side, Visual search, Authentication, business.industry, 05 social sciences, 050301 education, Watermark, Computer Science Applications, Control and Systems Engineering, 020201 artificial intelligence & image processing, Internet of Things, business, 0503 education, Software
Abstract

Smart personal devices are assuming a fundamental role in the ubiquitous communication and computing arena. They provide new sophisticated cameras and new visual search interfaces and facilities that can drastically improve their presence and role in complex IoT-based critical infrastructures, such as healthcare monitoring and emergency systems, or remote access control facilities and smart authentication services. This new scenario calls for strong secure and resilient visual query mechanisms for these devices. In this work we propose an innovative secure visual search system, which is well-suited for ubiquitous computing scenarios empowered by modern smart personal devices. More precisely, we show how to insert, at the visual data acquisition time, a watermark inside the already compressed descriptor characterizing an MPEG-CDVS data stream used in visual queries, to make it possible to decode the watermark on the server side in order to improve the robustness against image-based identity spoofing. Such a security enforcement solution may be practical in several real-life applications involving visual queries performed from personal trusted devices, and it is particularly suitable in all those application domains that require performing visual queries with a high degree of security. It has been extensively tested and achieved satisfactory results: the presence of such a watermark does not affect the image matching performance and functionality.

Published
2020

163. Knowledge elicitation based on genetic programming for non destructive testing of critical aerospace systems

Author

Francesco Palmieri and Gianni D'Angelo

Subjects
Artificial intelligence, Symbolic regression (SR), Computer Networks and Communications, Computer science, Process (engineering), media_common.quotation_subject, Evolutionary algorithm, Genetic programming (GP), Genetic programming, 02 engineering and technology, Field (computer science), Non-destructive testing (NDT), Nondestructive testing, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), Aerospace, Reliability (statistics), Eddy-current testing (ECT), media_common, business.industry, Genetic algorithms (GA), Carbon-fiber reinforced aluminum (FRA), Carbon-fiber reinforced plastic (CFRP), Composite materials, 020206 networking & telecommunications, Reliability engineering, Hardware and Architecture, 020201 artificial intelligence & image processing, business, Software
Abstract

In non-destructive testing of aerospace structures’ defects, the tests reliability is a crucial issue for guaranteeing security of both aircrafts and passengers. Most of the widely recognized approaches rely on precision and reliability of testing equipment, but also the methods and techniques used for processing measurement results, in order to detect defects, may heavily influence the overall quality of the testing process. The effectiveness of such methods strongly depends on specific field knowledge that is definitely not easy to be formalized and codified within the results processing practices. Although many studies have been conducted in this direction, such issue is yet an open-problem. This work describes the use of Genetic Programming for the diagnosis and modeling of aerospace structural defects. The resulting approach aims at extracting such knowledge by providing a mathematical model of the considered defects, which can be used for recognizing other similar ones. Eddy-Current Testing has been selected as a case study in order to assess both the performance and functionality of the whole framework, and a publicly available dataset of specific measures for aircraft structures has been considered. The experimental results put into evidence the effectiveness of the proposed approach in building reliable models of the aforementioned defects, so that it can be considered a successful option for building the knowledge needed by tools for controlling the quality of critical aerospace systems.

Published
2020

165. DNS tunnels detection via DNS-images

Author

Gianni D’Angelo, Arcangelo Castiglione, and Francesco Palmieri

Subjects
DNS security, Data exfiltration, DNS tunneling, Anomaly detection, Classification, Convolutional neural network, Media Technology, Library and Information Sciences, Management Science and Operations Research, Computer Science Applications, Information Systems
Published
2022

166. coMpliAnce with evideNce-based cliniCal guidelines in the managemenT of acute biliaRy pancreAtitis): The MANCTRA-1 international audit

Author

Mauro Podda, Daniela Pacella, Gianluca Pellino, Federico Coccolini, Alessio Giordano, Salomone Di Saverio, Francesco Pata, Benedetto Ielpo, Francesco Virdis, Dimitrios Damaskos, Belinda De Simone, Ferdinando Agresta, Massimo Sartelli, Ari Leppaniemi, Cristiana Riboni, Vanni Agnoletti, Damian Mole, Yoram Kluger, Fausto Catena, Adolfo Pisanu, Chiara Gerardi, Salomone di Saverio, Dimitris Damaskos, Stavros Gourgiotis, Gaetano Poillucci, Kumar Jayant, Luca Ferrario, Mikel Prieto Calvo, Michael Wilson, Fiammetta Soggiu, Alaa Hamdan, Carlos Augusto Gomes, Gustavo Fraga, Argyrios Ioannidis, Zaza Demetrashvili, Saaz Sahani, Lovenish Bains, Almu'atasim Khamees, Hazim Ababneh, Osama Aljaiuossi, Samuel Pimentel, Ikhwan Sani Mohamad, Ahmad Ramzi Yusoff, Narcis Octavian Zarnescu, Valentin Calu, Andrey Litvin, Dusan Lesko, Ahmed Elmehrath, Mohamedraed Elshami, Martin de Santibañes, Justin Gundara, Kamel Alawadhi, Rashid Lui, Alexander Julianov, Sergio Ralon, Ibrahim-Umar Garzali, Gustavo M. Machain, Darwin Artidoro Quispe-Cruz, Abigail Cheska C. Orantia, Maciej Walędziak, Tiago Correia de Sá, Syed Muhammad Ali, Bojan Kovacevic, Colin Noel, Haidar M. Abdalah, Ali Kchaou, Arda Isik, Luca Ansaloni, Walter Biffl, Mario Guerrieri, Alberto Sartori, Manuel Abradelo, Giuseppe Nigri, Nicola Di Lorenzo, Andrea Mingoli, Massimo Chiarugi, Juliana Di Menno Stavron, Oscar Mazza, José Ignacio Valenzuela, Diana Alejandra Pantoja Pachajoa, Fernando Andrés Alvarez, Julian Ezequiel Liaño, Joan Tefay, Abdulrahman Alshaikh, Layla Hasan, Felipe Couto Gomes, Gustavo P. Fraga, Thiago R.A. Calderan, Elcio S. Hirano, Dragomir Dardanov, Azize Saroglu, Boyko Atanasov, Nikolay Belev, Nikola Kovachev, Shannon Melissa Chan, Hon-Ting Lok, Diego Salcedo, Diana Robayo, María Alejandra Triviño, Jan Manak, Jorann de Araujo, Ananya Sethi, Ahmed Awad, Merihan Elbadawy, Ahmed Farid, Asmaa Hanafy, Ahmed Nafea, null Sherief-Ghozy, Alzhraa Salah – Abbas, Wafaa Abdelsalam, Sameh Emile, Ahmed Elfallal, Hossam Elfeki, Hosam Elghadban, Ashraf Shoma, Mohamed Shetiwy, Mohamed Elbahnasawy, Salem- Mohamed, Emad Fawzi Hamed, Usama Ahmed Khalil, Elie Chouillard, Andrew Gumbs, Andréa Police, Andrea Mabilia, Kakhi Khutsishvili, Anano Tvaladze, Orestis Ioannidis, Elissavet Anestiadou, Lydia Loutzidou, Konstantinis Konstantinidis, Sofia Konstantinidou, Dimitrios Manatakis, Vasileios Acheimastos, Nikolaos Tasis, Nikolaos Michalopoulos, Panagiotis Kokoropoulos, Maria Papadoliopoulou, Maria Sotiropoulou, Stylianos Kapiris, Panagiotis Metaxas, Ioannis Tsouknidas, Despoina Kefili, George Petrakis, Konstantinos Dakis, Eirini Alexandridou, Eirini Synekidou, Kostas Dakis, Aristeidis Papadopoulos, Christos Chouliaras, Odysseas Mouzakis, Francesk Mulita, Ioannis Maroulis, Michail Vailas, Tania Triantafyllou, Dimitrios Theodorou, Eftychios Lostoridis, Eleni-Aikaterini Nagorni, Paraskevi Tourountzi, Efstratia Baili, Alexandros Charalabopoulos, Theodore Liakakos, Dimitrios Schizas, Alexandros Kozadinos, Athanasios Syllaios, Nikolaos Machairas, Stylianos Kykalos, Paraskevas Stamopoulos, Spiros Delis, Christos Farazi-Chongouki, Evangelos Kalaitzakis, Miltiadis Giannarakis, Konstantinos Lasithiotakis, Giorgia Petra, Amit Gupta, Noushif Medappil, Vijayanand Muthukrishnan, Jubin Kamar, Pawan Lal, Rajendra Agarwal, Matteo Magnoli, Paolo Aonzo, Alberto Serventi, Antonio Giuliani, Pierpaolo Di Lascio, Margherita Pinto, Carlo Bergamini, Andrea Bottari, Laura Fortuna, Jacopo Martellucci, Atea Cicako, Claudio Miglietta, Mario Morino, Daniele Delogu, Andrea Picchetto, Marco Assenza, Giancarlo D'Ambrosio, Giulio Argenio, Mariano Fortunato Armellino, Giovanna Ioia, Savino Occhionorelli, Dario Andreotti, Lacavalla Domenico, Davide Luppi, Massimiliano Casadei, Luca Di Donato, Farshad Manoochehri, Tiziana Rita Lucia Marchese, William Sergi, Roberto Manca, Raimondo Murgia, Enrico Piras, Lorenzo Conti, Simone Gianazza, Andrea Rizzi, Edoardo Segalini, Marco Monti, Elena Iiritano, Nicolò Maria Mariani, Enrico De Nicola, Giovanna Scifo, Giusto Pignata, Jacopo Andreuccetti, Francesco Fleres, Guglielmo Clarizia, Alessandro Spolini, Alan Biloslavo, Paola Germani, Manuela Mastronardi, Selene Bogoni, Silvia Palmisano, Nicolo’ De Manzini, Marco Vito Marino, Gennaro Martines, Giuseppe Trigiante, Elpiniki Lagouvardou, Gabriele Anania, Cristina Bombardini, Dario Oppici, Tiziana Pilia, Valentina Murzi, Emanuela Gessa, Umberto Bracale, Maria Michela Di Nuzzo, Roberto Peltrini, Francesco Salvetti, Jacopo Viganò, Gabriele Sganga, Valentina Bianchi, Pietro Fransvea, Tommaso Fontana, Giuliano Sarro, Vincenza Paola Dinuzzi, Luca Scaravilli, Mario Virgilio Papa, Elio Jovine, Giulia Ciabatti, Laura Mastrangelo, Matteo Rottoli, Claudio Ricci, Iris Shari Russo, Alberto Aiolfi, Davide Bona, Francesca Lombardo, Pasquale Cianci, Roberto Bini, Osvaldo Chiara, Stefano Cioffi, Stefano Cantafio, Guido Coretti, Edelweiss Licitra, Grazia Savino, Sergio Grimaldi, Raffaele Porfidia, Elisabetta Moggia, Mauro Garino, Chiara Marafante, Antonio Pesce, Nicolò Fabbri, Carlo Vittorio Feo, Ester Marra, Marina Troian, Davide Drigo, Carlo Nagliati, Muratore Andrea, Riccardo Danna, Alessandra Murgese, Michele Crespi, Claudio Guerci, Alice Frontali, Luca Ferrari, Francesco Favi, Erika Picariello, Alessia Rampini, Fabrizio D'Acapito, Giorgio Ercolani, Leonardo Solaini, Francesco Palmieri, Matteo Calì, Francesco Ferrara, Irnerio Angelo Muttillo, Edoardo Maria Muttillo, Biagio Picardi, Raffaele Galleano, Ali Badran, Omar Ghazouani, Maurizio Cervellera, Gaetano Campanella, Gennaro Papa, Annamaria Di Bella, Gennaro Perrone, Gabriele Luciano Petracca, Concetta Prioriello, Mario Giuffrida, Federico Cozzani, Matteo Rossini, Marco Inama, Giovanni Butturini, Gianluigi Moretto, Luca Morelli, Giulio Di Candio, Simone Guadagni, Enrico Cicuttin, Camilla Cremonini, Dario Tartaglia, Valerio Genovese, Nicola Cillara, Alessandro Cannavera, Antonello Deserra, Arcangelo Picciariello, Vincenzo Papagni, Leonardo Vincenti, Giulia Bagaglini, Giuseppe Sica, Pierfrancesco Lapolla, Gioia Brachini, Dario Bono, Antonella Nicotera, Marcello Zago, Fabrizio Sammartano, Laura Benuzzi, Marco Stella, Stefano Rossi, Alessandra Cerioli, Caterina Puccioni, Stefano Olmi, Carolina Rubicondo, Matteo Uccelli, Andrea Balla, Anna Guida, Pasquale Lepiane, Diego Sasia, Giorgio Giraudo, Sara Salomone, Elena Belloni, Alessandra Cossa, Francesco Lancellotti, Roberto Caronna, Piero Chirletti, Paolina Saullo, Raffaele Troiano, Felice Mucilli, Mirko Barone, Massimo Ippoliti, Michele Grande, Bruno Sensi, Leandro Siragusa, Monica Ortenzi, Andrea Santini, Isidoro Di Carlo, Massimiliano Veroux, Rossella Gioco, Gastone Veroux, Giuseppe Currò, Michele Ammendola, Iman Komaei, Giuseppe Navarra, Valeria Tonini, Lodovico Sartarelli, Samuele Vaccari, Marco Ceresoli, Stefano Perrone, Linda Roccamatisi, Paolo Millo, Riccardo Brachet Contul, Elisa Ponte, Matteo Zuin, Giuseppe Portale, Alice Sabrina Tonello, Geri Fratini, Matteo Bianchini, Bruno Perotti, Emanuele Doria, Elia Giuseppe Lunghi, Diego Visconti, Khayry Al-Shami, Sajeda Awadi, Mohammad Musallam Khalil Buwaitel, Mo'taz Fawzat Naief Naffa', Ahmad Samhouri, Hatem Sawalha, Mohd Firdaus Che Ani, Ida Nadiah Ahmed Fathil, Jih Huei, Andee Dzulkarnaen Zakaria, Mohammad Zawawi Ya'acob, Jose-Luis Beristain-Hernandez, Alejandro Garcia-Meza, Rafael Sepulveda-Rdriguez, Edgard Efren Lozada Hernández, Camilo Levi Acuña Pinzón, Jefferson Nieves Condoy, Francisco C. Becerra García, Mohammad Sadik, null Jalpa, Bushra kadir, Jalpa Devi, Nandlal Seerani, null Zainab, Mohammad Sohail- Asghar, Ameer Afzal, Ali Akbar, Helmut Segovia Lohse, Herald Segovia Lohse, Zamiara Solange Leon Cabrera, Gaby Susana Yamamoto Seto, José Ríos Chiuyari, Jorge Ordemar, Martha Rodríguez, Abigail Cheska C. Orantia-Carlos, Margie Antionette Quitoy, Andrzej Kwiatkowski, Maciej Mawlichanów, Mónica Rocha, Carlos Soares, Alexandru Rares Stoian, Andreea Diana Draghici, Valentin Titus Grigorean, Raluca Bievel Radulescu, Radu Virgil Costea, Eugenia Claudia Zarnescu, Mikhail Kurtenkov, George Gendrikson, Volovich Alla-Angelina, Tsurbanova Arina, Ayrat Kaldarov, Mahir Gachabayov, Abakar Abdullaev, Milica Milentijevic, Milovan Karamarkovic, Arpád Panyko, Jozef Radonak, Marek Soltes, Laura Álvarez Morán, Haydée Calvo García, Pilar Suárez Vega, Sergio Estevez, Fabio Ausania, Jordi Farguell, Carolina González-Abós, Santiago Sánchez-Cabús, Belén Martín, Víctor Molina, Luis Oms, Lucas Ilzarbe, Eva Pont Feijóo, Elena Sofia Perra, Noel Rojas-Bonet, Rafael Penalba-Palmí, Susana Pérez-Bru, Jaume Tur-Martínez, Andrea Álvarez-Torrado, Marta Domingo-Gonzalez, Javier Tejedor-Tejada, Marcello Di Martino, Yaiza García del Alamo, Fernando Mendoza-Moreno, Francisca García-Moreno-Nisa, Belén Matías-García, Manuel Durán, Rafael Calleja-Lozano, José Manuel Perez de Villar, Luis Sánchez-Guillén, Iban Caravaca, Daniel Triguero-Cánovas, Antonio Carlos Maya Aparicio, Blas Durán Meléndez, Andrea Masiá Palacios, Aitor Landaluce-olavarria, Mario De Francisco, Begoña Estraviz-Mateos, Felipe Alconchel, Tatiana Nicolás-López, Pablo Ramírez, Virginia Duran Muñoz-Cruzado, Felipe Pareja Ciuró, Eduardo Perea del Pozo, Sergio Olivares Pizarro, Vicente Herrera Cabrera, Jose Muros Bayo, Hytham K.S. Hamid, Raffaello Roesel, Alessandra Cristaudi, Kinan Abbas, Iyad Ali, Ahmed Tlili, Hüseyin Bayhan, Mehmet Akif Türkoğlu, Mustafa Yener Uzunoglu, Ibrahim Fethi Azamat, Nail Omarov, Derya Salim Uymaz, Fatih Altintoprak, Emrah Akin, Necattin First, Koray Das, Nazmi Ozer, Ahmet Seker, Yasin Kara, Mehmet Abdussamet Bozkurt, Ali Kocataş, Semra Demirli Atici, Murat Akalin, Bulent Calik, Elif Colak, Yuksel Altinel, Serhat Meric, Yunus Emre Aktimur, Victoria Hudson, Jean-Luc Duval, Mansoor Khan, Ahmed Saad, Mandeep Kaur, Alison Bradley, Katherine Fox, Ivan Tomasi, Daniel Beasley, Alekhya Kotta Prasanti, Pinky Kotecha, Husam Ebied, Michaela Paul, Hemant Sheth, Ioannis Gerogiannis, Mohannad Gaber, Zara Sheikh, Shatadru Seth, Maria Kunitsyna, Cosimo Alex Leo, Vittoria Bellato, Noman - Zafar, Amr Elserafy, Giles Bond-smith, Giovanni Tebala, Pawan Mathur, Izza Abid, Nnaemeka Chidumije, Pardip Sandhar, Syed Osama Zohaib Ullah, Tamara Lezama, Muhammad Hassan Anwaar, Conor Magee, Salma Ahmed, Brooke Davies, Jeyakumar Apollos, Kieran McCormack, Hasham Choudhary, Triantafyllos Doulias, Tamsin Morrison, Anna Palepa, Fernando Bonilla Cal, Lianet Sánchez, Fabiana Domínguez, Ibrahim Al-Raimi, Haneen Alshargabi, Abdullah Meead, Podda, Mauro, Pacella, Daniela, Pellino, Gianluca, Coccolini, Federico, Giordano, Alessio, Di Saverio, Salomone, Pata, Francesco, Ielpo, Benedetto, Virdis, Francesco, Damaskos, Dimitrio, De Simone, Belinda, Agresta, Ferdinando, Sartelli, Massimo, Leppaniemi, Ari, Riboni, Cristiana, Agnoletti, Vanni, Mole, Damian, Kluger, Yoram, Catena, Fausto, Pisanu, Adolfo, de Manzini, Nicolo', Palmisano, Silvia, Podda, M, Pacella, D, Pellino, G, Coccolini, F, Giordano, A, Di Saverio, S, Pata, F, Ielpo, B, Virdis, F, Damaskos, D, De Simone, B, Agresta, F, Sartelli, M, Leppaniemi, A, Riboni, C, Agnoletti, V, Mole, D, Kluger, Y, Catena, F, Pisanu, A, Gerardi, C, Gourgiotis, S, Poillucci, G, Jayant, K, Ferrario, L, Calvo, M, Wilson, M, Soggiu, F, Hamdan, A, Gomes, C, Fraga, G, Ioannidis, A, Demetrashvili, Z, Sahani, S, Bains, L, Khamees, A, Ababneh, H, Aljaiuossi, O, Pimentel, S, Mohamad, I, Yusoff, A, Zarnescu, N, Calu, V, Litvin, A, Lesko, D, Elmehrath, A, Elshami, M, de Santibanes, M, Gundara, J, Alawadhi, K, Lui, R, Julianov, A, Ralon, S, Garzali, I, Machain, G, Quispe-Cruz, D, Orantia, A, Waledziak, M, Correia de Sa, T, Ali, S, Kovacevic, B, Noel, C, Abdalah, H, Kchaou, A, Isik, A, Ansaloni, L, Biffl, W, Guerrieri, M, Sartori, A, Abradelo, M, Nigri, G, Di Lorenzo, N, Mingoli, A, Chiarugi, M, Di Menno Stavron, J, Mazza, O, Valenzuela, J, Pantoja Pachajoa, D, Alvarez, F, Liano, J, Tefay, J, Alshaikh, A, Hasan, L, Augusto Gomes, C, Gomes, F, Calderan, T, Hirano, E, Dardanov, D, Saroglu, A, Atanasov, B, Belev, N, Kovachev, N, Chan, S, Lok, H, Salcedo, D, Robayo, D, Trivino, M, Manak, J, de Araujo, J, Sethi, A, Awad, A, Elbadawy, M, Farid, A, Hanafy, A, Nafea, A, Sherief-Ghozy, Salah - Abbas, A, Abdelsalam, W, Emile, S, Elfallal, A, Elfeki, H, Elghadban, H, Shoma, A, Shetiwy, M, Elbahnasawy, M, Mohamed, S, Hamed, E, Khalil, U, Chouillard, E, Gumbs, A, Police, A, Mabilia, A, Khutsishvili, K, Tvaladze, A, Ioannidis, O, Anestiadou, E, Loutzidou, L, Konstantinidis, K, Konstantinidou, S, Manatakis, D, Acheimastos, V, Tasis, N, Michalopoulos, N, Kokoropoulos, P, Papadoliopoulou, M, Sotiropoulou, M, Kapiris, S, Metaxas, P, Tsouknidas, I, Kefili, D, Petrakis, G, Dakis, K, Alexandridou, E, Synekidou, E, Papadopoulos, A, Chouliaras, C, Mouzakis, O, Mulita, F, Maroulis, I, Vailas, M, Triantafyllou, T, Theodorou, D, Lostoridis, E, Nagorni, E, Tourountzi, P, Baili, E, Charalabopoulos, A, Liakakos, T, Schizas, D, Kozadinos, A, Syllaios, A, Machairas, N, Kykalos, S, Stamopoulos, P, Delis, S, Farazi-Chongouki, C, Kalaitzakis, E, Giannarakis, M, Lasithiotakis, K, Petra, G, Gupta, A, Medappil, N, Muthukrishnan, V, Kamar, J, Lal, P, Agarwal, R, Magnoli, M, Aonzo, P, Serventi, A, Giuliani, A, Di Lascio, P, Pinto, M, Bergamini, C, Bottari, A, Fortuna, L, Martellucci, J, Cicako, A, Miglietta, C, Morino, M, Delogu, D, Picchetto, A, Assenza, M, D'Ambrosio, G, Argenio, G, Armellino, M, Ioia, G, Occhionorelli, S, Andreotti, D, Domenico, L, Luppi, D, Casadei, M, Di Donato, L, Manoochehri, F, Lucia Marchese, T, Sergi, W, Manca, R, Murgia, R, Piras, E, Conti, L, Gianazza, S, Rizzi, A, Segalini, E, Monti, M, Iiritano, E, Mariani, N, De Nicola, E, Scifo, G, Pignata, G, Andreuccetti, J, Fleres, F, Clarizia, G, Spolini, A, Biloslavo, A, Germani, P, Mastronardi, M, Bogoni, S, Palmisano, S, De Manzini, N, Marino, M, Martines, G, Trigiante, G, Lagouvardou, E, Anania, G, Bombardini, C, Oppici, D, Pilia, T, Murzi, V, Gessa, E, Bracale, U, Di Nuzzo, M, Peltrini, R, Salvetti, F, Vigano, J, Sganga, G, Bianchi, V, Fransvea, P, Fontana, T, Sarro, G, Dinuzzi, V, Scaravilli, L, Papa, M, Jovine, E, Ciabatti, G, Mastrangelo, L, Rottoli, M, Ricci, C, Russo, I, Aiolfi, A, Bona, D, Lombardo, F, Cianci, P, Bini, R, Chiara, O, Cioffi, S, Cantafio, S, Coretti, G, Licitra, E, Savino, G, Grimaldi, S, Porfidia, R, Moggia, E, Garino, M, Marafante, C, Pesce, A, Fabbri, N, Feo, C, Marra, E, Troian, M, Drigo, D, Nagliati, C, Andrea, M, Danna, R, Murgese, A, Crespi, M, Guerci, C, Frontali, A, Ferrari, L, Favi, F, Picariello, E, Rampini, A, D'Acapito, F, Ercolani, G, Solaini, L, Palmieri, F, Cali, M, Ferrara, F, Muttillo, I, Muttillo, E, Picardi, B, Galleano, R, Badran, A, Ghazouani, O, Cervellera, M, Campanella, G, Papa, G, Di Bella, A, Perrone, G, Petracca, G, Prioriello, C, Giuffrida, M, Cozzani, F, Rossini, M, Inama, M, Butturini, G, Moretto, G, Morelli, L, Di Candio, G, Guadagni, S, Cicuttin, E, Cremonini, C, Tartaglia, D, Genovese, V, Cillara, N, Cannavera, A, Deserra, A, Picciariello, A, Papagni, V, Vincenti, L, Bagaglini, G, Sica, G, Lapolla, P, Brachini, G, Bono, D, Nicotera, A, Zago, M, Sammartano, F, Benuzzi, L, Stella, M, Rossi, S, Cerioli, A, Puccioni, C, Olmi, S, Rubicondo, C, Uccelli, M, Balla, A, Guida, A, Lepiane, P, Sasia, D, Giraudo, G, Salomone, S, Belloni, E, Cossa, A, Lancellotti, F, Caronna, R, Chirletti, P, Saullo, P, Troiano, R, Mucilli, F, Barone, M, Ippoliti, M, Grande, M, Sensi, B, Siragusa, L, Ortenzi, M, Santini, A, Di Carlo, I, Veroux, M, Gioco, R, Veroux, G, Curro, G, Ammendola, M, Komaei, I, Navarra, G, Tonini, V, Sartarelli, L, Vaccari, S, Ceresoli, M, Perrone, S, Roccamatisi, L, Millo, P, Contul, R, Ponte, E, Zuin, M, Portale, G, Tonello, A, Fratini, G, Bianchini, M, Perotti, B, Doria, E, Lunghi, E, Visconti, D, Al-Shami, K, Awadi, S, Khalil Buwaitel, M, Naief Naffa', M, Samhouri, A, Sawalha, H, Ramzi Yusoff, A, Che Ani, M, Ahmed Fathil, I, Huei, J, Zakaria, A, Ya'Acob, M, Beristain-Hernandez, J, Garcia-Meza, A, Sepulveda-Rdriguez, R, Lozada Hernandez, E, Acuna Pinzon, C, Condoy, J, Becerra Garcia, F, Sadik, M, Jalpa, Kadir, B, Devi, J, Seerani, N, Zainab, Asghar, M, Afzal, A, Akbar, A, Lohse, H, Artidoro Quispe-Cruz, D, Leon Cabrera, Z, Yamamoto Seto, G, Chiuyari, J, Ordemar, J, Rodriguez, M, Orantia-Carlos, A, Quitoy, M, Kwiatkowski, A, Mawlichanow, M, Rocha, M, Soares, C, Muhammad Ali, S, Stoian, A, Diana Draghici, A, Draghici, A, Grigorean, V, Radulescu, R, Costea, R, Zarnescu, E, Kurtenkov, M, Gendrikson, G, Alla-Angelina, V, Arina, T, Kaldarov, A, Gachabayov, M, Abdullaev, A, Milentijevic, M, Karamarkovic, M, Panyko, A, Radonak, J, Soltes, M, Alvarez Moran, L, Garcia, H, Vega, P, Estevez, S, Ausania, F, Farguell, J, Gonzalez-Abos, C, Sanchez-Cabus, S, Martin, B, Molina, V, Oms, L, Ilzarbe, L, Feijoo, E, Perra, E, Rojas-Bonet, N, Penalba-Palmi, R, Perez-Bru, S, Tur-Martinez, J, Alvarez-Torrado, A, Domingo-Gonzalez, M, Tejedor-Tejada, J, Di Martino, M, Garcia del Alamo, Y, Mendoza-Moreno, F, Garcia-Moreno-Nisa, F, Matias-Garcia, B, Duran, M, Calleja-Lozano, R, Perez de Villar, J, Sanchez-Guillen, L, Caravaca, I, Triguero-Canovas, D, Maya Aparicio, A, Melendez, B, Palacios, A, Landaluce-olavarria, A, De Francisco, M, Estraviz-Mateos, B, Alconchel, F, Nicolas-Lopez, T, Ramirez, P, Munoz-Cruzado, V, Ciuro, F, Perea del Pozo, E, Pizarro, S, Cabrera, V, Bayo, J, Hamid, H, Roesel, R, Cristaudi, A, Abbas, K, Ali, I, Tlili, A, Bayhan, H, Turkoglu, M, Uzunoglu, M, Azamat, I, Omarov, N, Uymaz, D, Altintoprak, F, Akin, E, First, N, Das, K, Ozer, N, Seker, A, Kara, Y, Bozkurt, M, Kocatas, A, Atici, S, Akalin, M, Calik, B, Colak, E, Altinel, Y, Meric, S, Aktimur, Y, Hudson, V, Duval, J, Khan, M, Saad, A, Kaur, M, Bradley, A, Fox, K, Tomasi, I, Beasley, D, Prasanti, A, Kotecha, P, Ebied, H, Paul, M, Sheth, H, Gerogiannis, I, Gaber, M, Sheikh, Z, Seth, S, Kunitsyna, M, Leo, C, Bellato, V, Zafar, N, Elserafy, A, Bond-smith, G, Tebala, G, Mathur, P, Abid, I, Chidumije, N, Sandhar, P, Zohaib Ullah, S, Lezama, T, Anwaar, M, Magee, C, Ahmed, S, Davies, B, Apollos, J, Mccormack, K, Choudhary, H, Doulias, T, Morrison, T, Palepa, A, Cal, F, Sanchez, L, Dominguez, F, Al-Raimi, I, Alshargabi, H, and Meead, A

Subjects
Acute pancreatitis, Biliary pancreatitis, Global surgery, Guidelines compliance, International audit, Hepatology, Endocrinology, Diabetes and Metabolism, Gastroenterology, Settore MED/18, Hospitalization, Enteral Nutrition, Pancreatitis, Acute Disease, Humans, Biliary pancreatiti, Cholecystectomy, Acute pancreatiti, Human
Abstract

Background/objectives: Reports about the implementation of recommendations from acute pancreatitis guidelines are scant. This study aimed to evaluate, on a patient-data basis, the contemporary practice patterns of management of biliary acute pancreatitis and to compare these practices with the recommendations by the most updated guidelines. Methods: All consecutive patients admitted to any of the 150 participating general surgery (GS), hepatopancreatobiliary surgery (HPB), internal medicine (IM) and gastroenterology (GA) departments with a diagnosis of biliary acute pancreatitis between 01/01/2019 and 31/12/2020 were included in the study. Categorical data were reported as percentages representing the proportion of all study patients or different and well-defined cohorts for each variable. Continuous data were expressed as mean and standard deviation. Differences between the compliance obtained in the four different subgroups were compared using the Mann-Whitney U, Student's t, ANOVA or Kruskal-Wallis tests for continuous data, and the Chi-square test or the Fisher's exact test for categorical data. Results: Complete data were available for 5275 patients. The most commonly discordant gaps between daily clinical practice and recommendations included the optimal timing for the index CT scan (6.1%, χ2 6.71, P = 0.081), use of prophylactic antibiotics (44.2%, χ2 221.05, P < 0.00001), early enteral feeding (33.2%, χ2 11.51, P = 0.009), and the implementation of early cholecystectomy strategies (29%, χ2 354.64, P < 0.00001), with wide variability based on the admitting speciality. Conclusions: The results of this study showed an overall poor compliance with evidence-based guidelines in the management of ABP, with wide variability based on the admitting speciality. Study protocol registered in ClinicalTrials.Gov (ID Number NCT04747990).

Published
2022

169. NOT ALL THAT GLITTERS IS TUMOUR: Radiological finding of a plugoma and review of the literature

Author

Francesco Palmieri, Davide Gobatti, Serena Marmaggi, Francesco Calabrese, and Roberto Sampietro

Subjects
General Economics, Econometrics and Finance
Abstract

Background. The imaging of “plugoma” (or meshoma) has not been largely described in the literature and could conduct to misdiagnosis. Therefore, there is a need for surgeons and radiologists to further their knowledge about postsurgical anatomic alterations after hernia repair. Case presentation In this paper, we describe our finding of a right inguinal mass at CT scan and 18F FDG PET/CT in differential diagnosis with metastasis, which ultimately resulted in a plugoma. Conclusion Our imagining effort together with our considerations may help other colleague to identify this alteration, thus avoiding unnecessary treatment for patients.

Published
2023

182. Distributed temporal link prediction algorithm based on label propagation

Author

Nan Hu, Aniello Castiglione, Georgios Kontonatsios, Tao Li, Xiaolong Xu, Marcello Trovati, and Francesco Palmieri

Subjects
Computer Networks and Communications, Computer science, Complex networks, Label propagation, Link prediction, Network dynamics, Software, Hardware and Architecture, Stability (learning theory), 020206 networking & telecommunications, 02 engineering and technology, Link (geometry), Complex network, Similarity (network science), Scalability, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Algorithm, Network analysis
Abstract

Link prediction has steadily become an important research topic in the area of complex networks. However, the current link prediction algorithms typi cally neglect the network evolution and tend to exhibit low accuracy and scal ability when applied to large-scale organisations. In this article, we propose a novel distributed temporal link prediction algorithm based on label propagation (DTLPLP), governed by the dynamical properties of the interactions between nodes. In particular, nodes are associated with labels, which include details of their sources and the corresponding similarity value. When such labels are propagated across neighbouring nodes, they are updated based on the weights of the incident links, and the values from same source nodes are aggregated to evaluate the scores of links in the predicted network. Furthermore, DTLPLP has been designed to be distributed and parallelised, and thus is suitable for large-scale network analysis. As part of the validation process, we have de signed a prototype system developed in Pregel, which is a distributed network analysis framework. Experiments are conducted on the Enron e-mail network and the General Relativity and Quantum Cosmology Scientific Collaboration network. The experimental results show that when compared to the most of link prediction algorithms, DTLPLP offers enhanced accuracy, stability and scalability.

Published
2019

183. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection

Author

Paolo Zanetti, Francesca Perla, Alfredo De Santis, Ugo Fiore, and Francesco Palmieri

Subjects
Generative adversarial networks, Information Systems and Management, Computer science, media_common.quotation_subject, 02 engineering and technology, Machine learning, computer.software_genre, Theoretical Computer Science, Deep learning, Fraud detection, Supervised classification, Software, Control and Systems Engineering, Computer Science Applications1707 Computer Vision and Pattern Recognition, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, media_common, Training set, business.industry, Credit card fraud, Fraud detection, Supervised classification, Deep learning, Generative adversarial networks, 020206 networking & telecommunications, Payment, Computer Science Applications, Credit card, Binary classification, 020201 artificial intelligence & image processing, Artificial intelligence, business, Classifier (UML), computer
Abstract

In the last years, the number of frauds in credit card-based online payments has grown dramatically, pushing banks and e-commerce organizations to implement automatic fraud detection systems, performing data mining on huge transaction logs. Machine learning seems to be one of the most promising solutions for spotting illicit transactions, by distinguishing fraudulent and non-fraudulent instances through the use of supervised binary classification systems properly trained from pre-screened sample datasets. However, in such a specific application domain, datasets available for training are strongly imbalanced, with the class of interest considerably less represented than the other. This significantly reduces the effectiveness of binary classifiers, undesirably biasing the results toward the prevailing class, while we are interested in the minority class. Oversampling the minority class has been adopted to alleviate this problem, but this method still has some drawbacks. Generative Adversarial Networks are general, flexible, and powerful generative deep learning models that have achieved success in producing convincingly real-looking images. We trained a GAN to output mimicked minority class examples, which were then merged with training data into an augmented training set so that the effectiveness of a classifier can be improved. Experiments show that a classifier trained on the augmented set outperforms the same classifier trained on the original data, especially as far the sensitivity is concerned, resulting in an effective fraud detection mechanism.

Published
2019

184. MIH-SPFP: MIH-based secure cross-layer handover protocol for Fast Proxy Mobile IPv6-IoT networks

Author

Jiyoon Kim, Francesco Palmieri, Ilsun You, Soonhyun Kwon, Vishal Sharma, Jianfeng Guan, and Mario Collotta

Subjects
Handover, IoT, Computer Networks and Communications, Computer science, SPFP, 02 engineering and technology, F-PMIPv6, Internet security, Media-independent handover, 5G networks, Default gateway, 0202 electrical engineering, electronic engineering, information engineering, Proxy Mobile IPv6, Access network, business.industry, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, MIH, Computer Science Applications1707 Computer Vision and Pattern Recognition, 020206 networking & telecommunications, Cryptographic protocol, Computer Science Applications, Hardware and Architecture, Security, Cellular network, 020201 artificial intelligence & image processing, Mobile telephony, business, Mobile device, Computer network
Abstract

With the proliferation of mobile devices characterizing modern cyber-physical systems, service switching and handoff over large coverage areas become key aspects of the Internet of Things (IoT), mainly when remotely controlling and interacting with mission-critical autonomous vehicles that potentially may cover quite large distances such as driverless cars and Unmanned Aerial Vehicles (UAVs). These requirements can now be fully satisfied by the widespread Fast handover for Proxy Mobile IPv6 (F-PMIPv6) technology, that can be yet considered as a cornerstone in emerging 5G communications, but, unfortunately, such an approach only supports homogeneous handover, that may result in a nontrivial problem due to the heterogeneity in mobile communications technologies characterizing the available cyber-physical solutions and IoT network access devices. Recently, many researchers developed efficient solutions for the integration of F-PMIPv6 and Media Independent Handover (MIH) to allow fast handover in a highly heterogeneous mobile network. However, these models lack the security features which are necessary to protect IoT devices during handoffs. In this paper, a new security protocol, MIH-based secure cross-layer handover protocol for Fast Proxy Mobile IPv6 networks (MIH-SPFP), is proposed, incorporating the features of Secure Protocol for Fast-PMIPv6 (SPFP) into F-PMIPv6-MIH and reducing the security risks during the handover. The proposed solution also provides low latency by reducing the re-authentication path during the inter-Mobile Access Gateway (MAG) handovers. The security of the proposed protocol has been analyzed by using Burrows–Abadi–Needham (BAN) logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and its performance has been evaluated through numerical simulation by selecting “Marathon Broadcasting” as a case study. Results show that the proposed protocol not only effectively secures the handover process but is also more efficient compared with the standard MIH handover solution.

Published
2019

185. On the undetectability of payloads generated through automatic tools: A human‐oriented approach

Author

Bruno Carpentieri, Arcangelo Castiglione, Raffaele Pizzolante, and Francesco Palmieri

Subjects
Computer Networks and Communications, Computer science, business.industry, malicious code, computer.file_format, code obfuscation technique, Computer Science Applications, Theoretical Computer Science, shellcode inspection, Computational Theory and Mathematics, antivirus evasion, emerging threats, executable file, Executable, Software engineering, business, computer, Software
Published
2021

186. Special Issue: Novel Algorithms and Protocols for Networks

Author

Miroslaw Klinkowski, Francesco Palmieri, and Davide Careglio

Subjects
Fluid Flow and Transfer Processes, Computer science, lcsh:T, Process Chemistry and Technology, General Engineering, ComputerApplications_COMPUTERSINOTHERSYSTEMS, lcsh:Technology, lcsh:QC1-999, Computer Science Applications, Computational science, lcsh:Chemistry, n/a, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, Core (graph theory), General Materials Science, Enhanced Data Rates for GSM Evolution, lcsh:Engineering (General). Civil engineering (General), Instrumentation, lcsh:QH301-705.5, lcsh:Physics
Abstract

Today, applications can be instantiated in a number of data centers located in different segments of the network, from the core to the edge [...]

Published
2021

187. Time-Delay Fractional Optimal Control Problems: A Survey Based on Methodology

Author

Leila Moradi, Eslam Farsimadan, Beatrice Paternoster, Dajana Conte, and Francesco Palmieri

Subjects
Time-delay fractional optimal control problems, Discrete Chebyshev polynomials, Chelyshkov wavelets, Discrete and continuous chebyshev polynomials, Optimal control, Chebyshev filter, Chelyshkov wavelets, ChebyshevWavelet, Discrete and continuous chebyshev polynomials, Fractional Calculus, Time-delay fractional optimal control problems, Fractional calculus, Wavelet, Applied mathematics, Fractional Calculus, ChebyshevWavelet, Fractional differential, Mathematics
Abstract

We survey some representative results on time-delay fractional differential optimal control problems. In this paper we provide a review of the techniques, developed in the last decade, for the numerical solution of time-delay fractional optimal control problems. In particular, Chebyshev and Chelyshkov wavelet methods, continuous and discrete Chebyshev polynomials methods are focused on this study.

Published
2021

188. PERMS: An efficient rescue route planning system in disasters

Author

Eleana Asimakopoulou, Lei Zhang, Marcello Trovati, Nikolaos Bessis, Francesco Palmieri, Olayinka Johnny, and Xiaolong Xu

Subjects
Computer science, Heuristic (computer science), Distributed computing, k-means clustering, Swarm behaviour, Glow-worm swarm optimisation, Emergency rescue planning, Travelling salesman problem, Mobile cloud computing, Chaotic search, K-means++ clustering algorithm, Task (computing), Management system, Cluster analysis, Software
Abstract

The occurrence of natural and man-made disasters usually leads to significant social and economic disruption, as well as high numbers of casualties. Such occurrences are difficult to predict due to the huge number of parameters with mutual interdependencies which need to be investigated to provide reliable predictive capabilities. In this work, we present high-Performance Emergent Rescue Management e-System (PERMS), an efficient rescue route planning scheme operating within a high-performance emergent rescue management system for vehicles based on the mobile cloud computing paradigm. More specifically, an emergency rescue planning problem (ERRP) is investigated as a multiple travelling salesman problem (MTSP), as well as a novel phased heuristic rescue route planning scheme. This consists of an obstacle constraints and task of equal division-based K-means++ clustering algorithm (OT-K-means++), which is more suitable for clustering victims in disaster environments, and a glow-worm swarm optimisation algorithm based on chaotic initialisation (GSOCI), which provides the appropriate rescue route for each vehicle. A prototype is developed to evaluate the performance of this proposed approach, which demonstrates a better performance compared to other well-known and widely used algorithms. As demonstrated by the validation process, our approach enhances the accuracy and convergence speed for solving the emergency rescue planning problem. Furthermore, it shortens the length of the rescue route, as well as rescue time, and it leads to reasonable and balanced allocation of emergency rescue tasks, whilst achieving an overall efficient rescue process. More specifically, by considering scenarios with 200 victims, compared with K-means and K-means++, OT-K-means++ reduces the time cost of clustering by 9.52% and 17.39% respectively, and reduces the number of iterations by 11.11% and 15.78% respectively. Compared with ACO or GA, GSOCI reduces the length of rescue route by 9.81% and 16.36% respectively, and reduces the time of rescue by 4.35% and 15.38% respectively.

Published
2021

189. A distributed flow correlation attack to anonymizing overlay networks based on wavelet multi-resolution analysis

Author

Francesco Palmieri

Subjects
Overlay networks, Computer science, business.industry, Distributed computing, Overlay network, Distributed traffic interception, Wavelets, Encryption, Networking hardware, Attack model, Lawful interception, Obfuscation, The Internet, Electrical and Electronic Engineering, business, Correlation attack, Anonymity, Multi-resolution analysis, Flow correlation
Abstract

Government agencies rely more and more heavily on the availability of flexible and intelligent solutions for the interception and analysis of Internet-based telecommunications. Unfortunately, the global lawful interception market has been recently put into a corner by the emerging sophisticated encryption, obfuscation and anonymization technologies provided by modern overlay communication infrastructures. To face this challenge, this work proposes a novel strategy for defeating the anonymity of traffic flows, collected within and at the exit of these anonymizing networks, relying on distributed flow-capture, characterization and correlation attacks driven by wavelet-based multi-resolution analysis. Such a strategy, starting from a properly formalized attack model, results in an effective and promising framework that can be easily deployed on real-life network equipment and can potentially scale by working according to different distribution/parallelization scenarios.

Published
2021

190. A Unified View of Algorithms for Path Planning Using Probabilistic Inference on Factor Graphs

Author

Francesco Palmieri, Krishna R. Pattipati, Giovanni Di Gennaro, Giovanni Fioretti, Francesco Verolla, Amedeo Buonanno, Palmieri, Francesco, Pattipati, Krishna R., DI GENNARO, Giovanni, Fioretti, Giovanni, Verolla, Francesco, and Buonanno, Amedeo

Subjects
FOS: Computer and information sciences, Computer Science - Learning, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)
Abstract

Even if path planning can be solved using standard techniques from dynamic programming and control, the problem can also be approached using probabilistic inference. The algorithms that emerge using the latter framework bear some appealing characteristics that qualify the probabilistic approach as a powerful alternative to the more traditional control formulations. The idea of using estimation on stochastic models to solve control problems is not new and the inference approach considered here falls under the rubric of Active Inference (AI) and Control as Inference (CAI). In this work, we look at the specific recursions that arise from various cost functions that, although they may appear similar in scope, bear noticeable differences, at least when applied to typical path planning problems. We start by posing the path planning problem on a probabilistic factor graph, and show how the various algorithms translate into specific message composition rules. We then show how this unified approach, presented both in probability space and in log space, provides a very general framework that includes the Sum-product, the Max-product, Dynamic programming and mixed Reward/Entropy criteria-based algorithms. The framework also expands algorithmic design options for smoother or sharper policy distributions, including generalized Sum/Max-product algorithm, a Smooth Dynamic programming algorithm and modified versions of the Reward/Entropy recursions. We provide a comprehensive table of recursions and a comparison through simulations, first on a synthetic small grid with a single goal with obstacles, and then on a grid extrapolated from a real-world scene with multiple goals and a semantic map.

Published
2021
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191. Optimized realization of Bayesian networks in reduced normal form using latent variable model

Author

Amedeo Buonanno, Giovanni Di Gennaro, Francesco Palmieri, Di Gennaro, G, Buonanno, A, and Palmieri, F

Subjects
FOS: Computer and information sciences, Optimization, Computer Science - Machine Learning, Theoretical computer science, Computer science, Inference, Bayesian network, Context (language use), Computational intelligence, Machine Learning (stat.ML), Theoretical Computer Science, Machine Learning (cs.LG), Cost reduction, Statistics - Machine Learning, Latent variable, Geometry and Topology, Belief propagation, Latent variable model, Factor graph, Software, Realization (probability)
Abstract

Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable, even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. An online version of the classic batch learning algorithm is also analyzed, showing very similar results (in an unsupervised context); which is essential even if multilevel structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood (ML) algorithms. The results are discussed with particular reference to a Latent Variable Model (LVM) structure., Comment: 20 pages, 8 figures

Published
2021

192. A stacked autoencoder-based convolutional and recurrent deep neural network for detecting cyberattacks in interconnected power control systems

Author

Gianni D'Angelo and Francesco Palmieri

Subjects
IoT, Computer science, remote control systems, Convolutional neural network, Theoretical Computer Science, Long short term memory, Artificial Intelligence, convolutional neural networks, anomaly detection, cyber-attacks, long short-term memory, power grids, recurrent neural networks, Artificial neural network, business.industry, Pattern recognition, Autoencoder, Human-Computer Interaction, Recurrent neural network, Anomaly detection, Artificial intelligence, business, Internet of Things, Software, Power control
Published
2021

193. Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images

Author

Francesco Palmieri and Gianni D'Angelo

Subjects
IoT, Computer science, 02 engineering and technology, Accelerometer, Machine learning, computer.software_genre, Convolutional neural network, Electromagnetic interference, Activity recognition, CNN, COVID-19, Health monitoring devices, Health monitoring system, Healthcare, Tracking app, e-Health, Artificial Intelligence, Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, Artificial neural network, business.industry, 020206 networking & telecommunications, Crowding, Special Issue on IoT-based Health Monitoring System, 020201 artificial intelligence & image processing, Artificial intelligence, business, Raw data, computer, Software
Abstract

With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

Published
2021

194. Vehicle-to-Everything (V2X) Communication Scenarios for Vehicular Ad-hoc Networking (VANET): An Overview

Author

Leila Moradi, Dajana Conte, Eslam Farsimadan, Beatrice Paternoster, and Francesco Palmieri

Subjects
Vehicle to everything, Vehicular ad hoc network, Traffic congestion, Wireless ad hoc network, Computer science, Network communication, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, Control (management), Computer security, computer.software_genre, computer, Intelligent transportation system, Field (computer science)
Abstract

Nowadays both sciences and technology, including Intelligent Transportation Systems, are involved in improving current approaches. Overview studies give you fast, comprehensive, and easy access to all of the existing approaches in the field. With this inspiration, and the effect of traffic congestion as a challenging issue that affects the regular daily lives of millions of people around the world, in this work, we concentrate on communications paradigms which can be used to address traffic congestion problems. Vehicular Ad-hoc Networking (VANET), a modern networking technology, provides innovative techniques for vehicular traffic control and management. Virtual traffic light (VTL) methods for VANET seek to address traffic issues through using vehicular network communication models. These communication paradigms can be classified into four scenarios: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) and Vehicle-to-Pedestrian (V2P). In general, these four scenarios are included in the category of vehicle-to-everything (V2X). Therefore, in this paper, we provide an overview of the most important scenarios of V2X communications based on their characteristics, methodologies, and assessments. We also investigate the applications and challenges of V2X.

Published
2021

195. Association rule-based malware classification using common subsequences of API calls

Author

Gianni D'Angelo, Francesco Palmieri, Massimo Ficco, D'Angelo, G., Ficco, M., and Palmieri, F.

Subjects
Malware dynamic analysi, 0209 industrial biotechnology, Exploit, Association rule learning, Computer science, Markov chain, Evasion (network security), 02 engineering and technology, API call sequence, Association rules, Machine learning, Malware dynamic analysis, Markov chains, Sequence alignment, computer.software_genre, 020901 industrial engineering & automation, Obfuscation, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), Obfuscation (software), Association rule, Malware, 020201 artificial intelligence & image processing, Data mining, computer, Software
Abstract

Emerging malware pose increasing challenges to detection systems as their variety and sophistication continue to increase. Malware developers use complex techniques to produce malware variants, by removing, replacing, and adding useless API calls to the code, which are specifically designed to evade detection mechanisms, as well as do not affect the original functionality of the malicious code involved. In this work, a new recurring subsequences alignment-based algorithm that exploits associative rules has been proposed to infer malware behaviors. The proposed approach exploits the probabilities of transitioning from two API invocations in the call sequence, as well as it also considers their timeline, by extracting subsequence of API calls not necessarily consecutive and representative of common malicious behaviors of specific subsets of malware. The resulting malware classification scheme, capable to operate within dynamic analysis scenarios in which API calls are traced at runtime, is inherently robust against evasion/obfuscation techniques based on the API call flow perturbation. It has been experimentally compared with two detectors based on Markov chain and API call sequence alignment algorithms, which are among the most widely adopted approaches for malware classification. In such experimental assessment the proposed approach showed an excellent classification performance by outperforming its competitors.

Published
2021

196. Global attitudes in the management of acute appendicitis during COVID-19 pandemic: ACIE Appy Study

Author

Benedetto, Ielpo, Mauro, Podda, Gianluca, Pellino, Pata, Francesco, Gianpiero, Gravante, Salomone Di Saverio, Gallo, Gaetano, Rashid, Lui, Adam, Orengia, Aditya, Chowdary, Aditya, Kulkarni, Adnan, Kuvvetli, Adolfo, Navarro, Adolfo, Pisanu, Adrian, Smith, Adriana Cavero Ibiricu, Aeris Jane, D Nacion, Ahmad, Alsaleh, Ahmad, Alhazmi, Ahmad, Elmabri, Ajaz, Wani, Ahmet, Rencuzogullari, Aingeru Sarriugarte Lasarte, Ainhoa Valle Rubio, Akshay, Bavikatte, Akshay, Kumar, Al-Radjid, Jamiri, Alain Michel Alvarado Padilla, Alban, Cacurri, Alberto de San Ildefonso, Alberto, Porcu, Alberto, Sartori, Aldo, Rocca, Alejandro Paz Yáñez, Alejandro, Becaria, Alejandro, Solís-Peña, Aleksandar, Sretenović, Alex, Urbistondo, Alfonso, Bandin, Alfonso, Najar, Alessandro De Luca, Alex, Boddy, Alexandros, Charalabopoulos, Alexios, Tzivanakis, Alfonso, Amendola, Alfredo Ramirez-Gutierrez de Velasco, Ali Cihat Yildirim, Alice, Frontali, Alpha Oumar Toure, Alvaro, García-Granero, Amaia Martínez Roldan, Amaia Sanz Larrainzar, Amila Sanjiva Ratnayake, Ana María Gonzalez-Ganso, Ana, M Minaya-Bravo, Andre, Das, Andrea, Bondurri, Andrea, Costanzi, Andrea, Lucchi, Andrea, Mazzari, Andrea, Musig, Andrea, Peloso, Andrea, Piano, Andrea, Police, Andrei, Mihailescu, Andrés, Pouy, Angela, Romano, Iossa, Angelo, Anna Carmen Leonetti, Anna, Guariniello, Anna, Isaac, Anna Pia Delli Bovi, Antonella, Chessa, Antonella, Tromba, Antonio Álvarez Martínez, Antonio, Brillantino, Antonio, Caira, Antonio, Castaldi, Antonio, Ferronetti, Antonio, Giuliani, Antonio, Prestera, Antonio Ramos-De la Medina, Antonio, Tarasconi, Antonino, Tornambè, Arcangelo, Picciariello, Argyrios, Ioannidis, Ari, Leppäniemi, Arshad, Khan, Arshad, Rashid, Arteaga Luis Eduardo Pérez-Sánchez, Ashok, Mittal, Ashrarur Rahman Mitul, Asif, Mehraj, Asim, Laharwal, Asnel, Dorismé, Athanasios, Marinis, Atif, Iqbal, Augusto, Moncada, Bartolomeo, Braccio, Basim, Alkhafaji, Beatriz de Andrés Asenjo, Beatriz, Martin-Perez, Belinda Sánchez Pérez, Ben, Creavin, Benedetto, Calì, Beniamino, Pascotto, Benjamin, Stubbs, Benjamin Zavala Retes, Branislav, Jovanovic, Brian Kp Goh, Bruno, Sensi, Carlo, Biddau, Carlo, Gazia, Carlo, Vallicelli, Carlos Alberto Fagundes, Carlos Cerdán Santacruz, Carlos, Chirico, Carlos Javier Gómez, Carlos, Petrola, Carlos Sánchez Rodriguez, Carlos Yánez Benítez, Carmelisa, Dammaro, Carmelo Lo Faro, Caroline, Reinke, Casandra Dominguez Paez, Catalina, Oliva, Charudutt, Paranjape, Charlotte, Thomas, Chi Fung Chia, Chi Kwan Kong, Chiara De Lucia, Christian Ovalle Chao, Claudio, Arcudi, Claudio, Guerci, Clement, Chia, Cristiano, Parise, Cristina, Folliero, Cristopher, Varela, Dalya, M Ferguson, Daniel, Camacho, Daniel, Popowich, Daniel Souza Lima, Daniela, Rega, Daniele, Delogu, Daniele, Zigiotto, Danilo, Vinci, Dario, D'Antonio, Dario, Parini, David Alessio Merlini, David DE Zimmerman, David, Moro-Valdezate, Davide, Pertile, Deborah Maria Giusti, Deborah, S Keller, Delko, Tarik, Denis, Kalivaçi, Dennis, Mazingi, Diana Gabriela Maldonado-Pintado, Diego, Sasia, Dimitrios, Linardoutsos, Dixon, Osilli, Domenico, Murrone, Domenico, Russello, Edgar, Rodas, Edisson Alberto Acuña Roa, Edoardo, Ricciardi, Edoardo, Rosso, Edoardo, Saladino, Eduardo, Flores-Villalba, Eduardo Ruiz Ajs, Eduardo, Smith-Singares, Efstratia, Baili, Efstratios, Kouroumpas, Eirini, Bourmpouteli, Eleftheria, Douka, Elena, Martin-Perez, Eleonora, Guaitoli, Elgun, Samadov, Elisa, Francone, Elisa, Vaterlini, Emilio, Morales, Emilio, Peña, Enhao, Zhao, Eneko Del Pozo Andres, Enrico, Benzoni, Enrico, Erdas, Enrico, Pinotti, Enrique, Colás-Ruiz, Erman, Aytac, Ernesto, Laterza, Ervis, Agastra, Esteban, Foianini, Esteban, Moscoso, Estefania, Laviano, Ester, Marra, Eugenia, Cardamone, Eugenio, Licardie, Eustratia, Mpaili, Eva, Pinna, Evaristo, Varo, Fabian Martín Navarro, Fabio, Marino, Fabio, Medas, Fabio, Romano, Fatlum, Maraska, Fatmir, Saliu, Fausto, Madrid, Fausto, Rosa, Federica, Mastella, Federico, Gheza, Federico, Luvisetto, Felipe, Alconchel, Felipe Monge Vieira, Felipe, Pareja, Ferdinando, Agresta, Fernanda, Luna, Fernando, Bonilla, Fernando, Cordera, Fernando, Burdió, Fernando, Mendoza-Moreno, Fernando Muñoz Flores, Fernando Pardo Aranda, Fiona, Taylor, Flavia, L Ramos, Flavio, Fernandes, Francesca Paola Tropeano, Francesco, Balestra, Francesco, Bianco, Francesco, Ceci, Francesco, Colombo, Francesco Di Marzo, Francesco, Ferrara, Francesco, Lancellotti, Francesco, Lazzarin, Francesco, Litta, Francesco, Martini, Francesco, Pizza, Francesco, Roscio, Francesco, Virdis, Francisco Blanco Antona, Francisco Cervantes Ramírez, Francisco Miguel Fernandez, Francisco Oliver Llinares, Francisco, Quezada, Francisco, Schlottmann, Gabriel, Herrera-Almario, Gabriel, Massaferro, Gabriele, Bislenghi, Gabrielle van Ramshorst, Gaetano, Gallo, Gaetano, Luglio, Georgios, Bointas, Georgios, Kampouroglou, Georgios, Papadopoulos, Gerardo Arredondo Manrique, Giacomo, Calini, Giacomo, Nastri, Giampaolo, Formisano, Giampaolo, Galiffa, Gian Marco Palini, Gianluca, Colucci, Gianluca, Pagano, Gianluca, Vanni, Gianmaria Casoni Pattacini, Gilda De Paola, Giorgio, Lisi, Giovanna, Partida, Giovanni, Bellanova, Giovanni De Nobili, Giovanni Sammy Necchi, Giovanni, Sinibaldi, Giovanni, Tebala, Giulia, Bagaglini, Giuliano, Izzo, Giulio, Argenio, Giuseppe, Brisinda, Giuseppe, Candilio, Giuseppe Di Grezia, Giuseppe, Esposito, Giuseppe, Faillace, Giuseppe, Frazzetta, Giuseppe La Gumina, Giuseppe, Nigri, Giuseppe, Romeo, Gloria Chocarro Amatriaín, Gloria, Ortega, Gonzalo, Martin-Martin, Gregor, A Stavrou, Gunadi, Gustavo Armand Ugon, Gustavo, Machain, Gustavo, Marcucci, Gustavo, Martínez-Mier, Gustavo Miguel Machain, Gustavo, Nari, Haydée, Calvo, Hamada, Fathy, Hamilto, Hazem, Ahmed, Hazem, Faraj, Hector, Nava, Hector Ordas Macias, Herald, Nikaj, Heriberto, Solano, Huma Ahmed Khan, Humberto Sánchez Alarcón, Husam, Ebied, Iacopo, Giani, Ibabe Villalabeitia Ateca, Ignacio, Neri, Igor Alberdi San Roman, Iliya, Fidoshev, Iñaki Martinez Rodriguez, Ionut, Negoi, Irene, Ortega, Irina, Bernescu, Iris Shari Russo, Irune Vincente Rodríguez, Irving, Palomares, Isaac, Baltazar, Isabel Jaén Torrejimeno, Isabel María Cornejo Jurado, Isabella, Reccia, Ishtiyaq, Hussain, Ismael Brito Toledo, Ismael, Mora-Guzmán, Iulia, Dogaru, Ivan, Romic, Izaskun, Balciscueta, J Cleo Kenington, Jackison, Sagolsem, Jae, Y Jang, James, Olivier, Jan, Lammel-Lindemann, Jana, Dziakova, Javier Ismael Roldán Villavicencio, Javier, Salinas, Jelena, Pejanovic, Jose Gustavo Parreira, Jovanovic, Jeny Rincón Pérez, Jeryl, Asreyes, Jesus Antonio Medina Luque, Joanna, Mak, Joanne Salas Rodriguez, Johnn Henry Herrera Kok, Jon, Krook, Jose Antonio Diaz-Elizondo, Jose, Castell, José Eduardo García-Flores, José María Jover Navalón, Jose Mauro Silva Rodrigues, José, Pereira, José Tomas Castell Gómez, Juan Bellido Luque, Juan Carlos Martín Del Olmo, Juan Carlos Salamea, Juan Francisco Coronel Olivier, Juan Luis Blas Laina, Juliana Maria Ordoñez, Julieta, Gutierrez, Julio, Abba, Junaid Ahmad Sofi, Kashaf, Sherafgan, Kapil, Sahnan, Katsuhiko, Yanaga, Kevin, Beatson, Laharwal, Asim, Laura, Alvarez, Leandro, Siragusa, Lee, Farber, Lester, Ong, Liarakos, Athanasios, Lorena, García-Bruña, Luca De Martino, Luca, Ferrario, Luca, Giordano, Luca, Gordini, Luca, Pio, Luca, Ponchietti, Lucia, Moletta, Luciano, Curella, Luciano, Poggi, Lucio, Taglietti, Luigi, Bonavina, Luigi, Conti, Luigi, Goffredi, Luis Angel Garcia Ruiz, Luis, Barrionuevo, Luis Enrique Fregoso, Luis, F Cabrera, Luis, G Rodriguez, Luis, Grande, Luis Gregorio Osoria, Luis Javier Kantun Gonzalez, Luis, Sánchez-Guillén, Luis, Tallon-Aguilar, Luis, Tresierra, Luisa, Giavarini, Mahmoud, Hasabelnabi, Maja, Odovic, Mamoru, Uemura, Mansoor, Khan, Manuel, Artiles-Armas, Mara, David, Marcello Di Martino, Marcello Giuseppe Spampinato, Marcelo A, F Ribeiro, Marcelo, Viola, Marco, Angrisani, Marco, Calussi, Marco, Cannistrà, Marco, Catarci, Marco, Cereda, Marco, Conte, Marco, Giordano, Marco, Pellicciaro, Marco Vito Marino, Maria, E Vaterlini, María, F Jiménez, María Giulia Lolli, Bellini, MARIA IRENE, Maria, Lemma, Maria Michela Chiarello, Maria, Nicola, Mario, Arrigo, Mario Caneda Mejia, Mario Montes Manrique, Mario, Rodriguez-Lopez, Mario, Serradilla-Martín, Mario Zambrano Lara, Marisa, Martínez, Mark, Bagnall, Mark, Peter, Marta Cañón Lara, Marta Jimenez Gomez, Marta, Paniagua-Garcia-Señorans, Marta Perez Gonzalez, Martin, Rutegård, Martin, Salö, Marzia, Franceschilli, Massimiliano, Silveri, Massimiliano, Veroux, Massimo, Pezzulo, Matteo, Nardi, Matteo, Rottoli, Matti, Tolonen, Mauricio Pedraza Ciro, Mauricio, Zuluagua, Maurizio, Cannavò, Maurizio, Cervellera, Maurizio, Iacobone, Mauro, Montuori, Melody García Domínguez, Meltem, Bingol-Kologlu, Mian, Tahir, Michael, Lim, Michael Sj Wilson, Michael, Wilson, Michela, Campanelli, Michele, Bisaccia, Michele De Rosa, Michele, Maruccia, Michele, Paterno, Michele, Pisano, Michele, Torre, Michele, Treviño, Michele, Zuolo, Miguel, A Hernandez Bartolome, Miguel, Farina, Miguel, Pera, Miguel Prieto Calvo, Milagros, Sotelo, Min Myat Thway, Mohamed, Hassan, Mohamed Salah Eldin Hassan, Mohammad, Azfar, Mohammad, Bouhuwaish, Mohammad, Taha, Mohammad, Zaieem, Mohammed, Korkoman, Montserrat, Guraieb, Mostafa, Shalaby, Muhammad Asif Raza, Muhammad Umar Younis, Muhammed, Elhadi, Mujahid Zulfiqar Ali, Nadeem, Quazi, Nagendra, N Dudi-Venkata, Nahar, Alselaim, Natasha, Loria, Nathalie Villan Ramírez, Nay Win Than, Neil, Smart, Nelson, Trelles, Nicanor, Pinto, Niccolò, Allievi, Niccolo, Petrucciani, Nicola, Antonacci, Nicola, Cillara, Nicolae, Gica, Nicolaescu Diana Cristiana, Nicolás, Nicolás, Nicolò, Falco, Nicolò, Pecorelli, Nicolò, Tamini, Nikolaos Andreas Dallas, Nikolaos, Machairas, Noelia, Brito, Nura Ahmed Fieturi, Nuria, Ortega, Octavio, Avilamercado, Oktay, Irkorucu, Omar, Alsherif, Orestes, Valles, Orestis, Ioannidis, Oscar Hernández Palmas, Oscar Isaac Hernandez Palmas, Oscar Sanz Guadarrama, Osman, Bozbiyik, Pablo, Omelanczuk, Pablo, Ottolino, Pablo, Rodrigues, Pablo, Ruiz, Paola, Campenni, Paola, Chiarade, Paola Prieto Olivares, Paolo, Baroffio, Pascal, Wintringer, Pasquale Di Fronzo, Pasquale, Talento, Pasqualino, Favoriti, Patricia, Sendino, Patrizia, Marsanic, Patricia, Mifsut, Paúl, Andrade, Pawel, Ajawin, Valentina, Ferri, Giuseppe Massimiliano de Luca, Sara, Ingallinella, Eva, Pueyo, Francesco, Palmieri, Jesus, Silva, Ken Min Chin, Nicholas, Syn, Brian K, P Goh, Ye Xin Koh, Valeria, Tonini, Ana, Gonzales-Ganso, Vicente, Simó, Maria Victoria Diago, Pedro, Abadía-Barnó, Pedro Alfonso Najar Castañeda, Pedro Omar Sillas Arevalos, Pedro Palazón Bellver, Peng Soon Koh, Petry, Souza, Piotr, Major, Rajandeep Singh Bali, Rakesh Mohan Khattar, Renato Bessa Melo, Reza, Ebrahiminia, Ricardo, Azar, Ricardo López Murga, Riccardo, Caruso, Riccardo, Pirolo, Richard, Brady, Richard Justin Davies, Rishi, Dholakia, Rishi, Rattan, Rishi, Singhal, Robert, Lim, Roberta, Angelico, Roberta Maria Isernia, Roberta, Tutino, Roberto, Faccincani, Roberto, Peltrini, Rocío, Carrera-Ceron, Rodrigo, Tejos, Rohit, Kashyap, Roosevelt, Fajardo, Rosa, Lozito, Royer Madariaga Pareja, Sabrina, Garbarino, Salvador, Morales-Conde, Sami, Benli, Sami, Mansour, Samir, Flores, Samuel Limon Suarez, Santiago Lopez Ben, Sara, Fuentes, Sara, Napetti, Sara Ortiz de Guzmán, Selmy, Awad, Sergio, A Weckmann Luján, Sergio, Gentilli, Sergio, Grimaldi, Sergio Olivares Pizarro, Serkan, Tayar, Shakeeb, Nabi, Shannon, M Chan, Sheikh, Junaid, Sidney, Rojas, Silvana, Monetti, Silvia, García, Silvia, Salvans, Silvia, Tenconi, Simon, Shaw, Simone, Santoni, Sofia Andrea Parra, Sofía, Cárdenas, Sonia, Pérez-Bertólez, Sonja, Chiappetta, Sophie, Dessureault, Spiros, Delis, Stefano Amore Bonapasta, Stefano, Rausei, Stefano, Scaringi, Sundeep, Keswani, Syed Muhammad Ali, Süleyman, Cetinkunar, Tak Lit Derek Fung, Tariq, Rawashdeh, Tatiana Nicolás López, Tercio De Campos, Teresa Calderon Duque, Teresa, Perra, Theodore, Liakakos, Theodoros, Daskalakis, Theodoros, Liakakos, Thomas, Barnes, Tijmen, Koëter, Tiku, Zalla, Tomás, E González, Tomás, Elosua, Tommaso, Campagnaro, Tommy, Brown, Topi, Luoto, Touré Alpha Oumar, Ugo, Giustizieri, Ugo, Grossi, Umberto, Bracale, Uriel, Rivas, Valentina, Sosa, Valentina, Testa, Valeria, Andriola, Valerio, Balassone, Valerio, Celentano, Valerio, Progno, Varun, Raju, Vanessa, Carroni, Venera, Cavallaro, Venkateswara Rao Katta, Veronica De Simone, Vicent Primo Romaguera, Victor Hugo García Orozco, Victor, Luraschi, Victor, Rachkov, Victor, Turrado-L, Victor, Visag-Castillo, Victoria, Dowling, Victoria, Graham, Vincenzo, Papagni, Vincenzo, Vigorita, Vinicius Cordeiro Fonseca, Virginia Jimenez Carneros, Vittoria, Bellato, Walyson, Gonçalves, William, F Powers, William, Grigg, Wolf, O Bechstein, Yu Bing Lim, Yuksel, Altinel, Zoran, Golubović, Zutoia, Balciscueta, Ielpo B., Podda M., Pellino G., Pata F., Caruso R., Gravante G., Di Saverio S., Gallo G., Lui R., Orengia A., Chowdary A., Kulkarni A., Kuvvetli A., Navarro A., Pisanu A., Smith A., Ibiricu A.C., Nacion A.J.D., Alsaleh A., Alhazmi A., Elmabri A., Wani A., Rencuzogullari A., Lasarte A.S., Rubio A.V., Bavikatte A., Kumar A., Jamiri A.-R., Padilla A.M.A., Cacurri A., de San Ildefonso A., Porcu A., Sartori A., Rocca A., Yanez A.P., Becaria A., Solis-Pena A., Sretenovic A., Urbistondo A., Bandin A., Najar A., De Luca A., Boddy A., Charalabopoulos A., Tzivanakis A., Amendola A., de Velasco A.R.-G., Yildirim A.C., Frontali A., Toure A.O., Garcia-Granero A., Roldan A.M., Larrainzar A.S., Ratnayake A.S., Gonzalez-Ganso A.M., Minaya-Bravo A.M., Das A., Bondurri A., Costanzi A., Lucchi A., Mazzari A., Musig A., Peloso A., Piano A., Police A., Mihailescu A., Pouy A., Romano A., Iossa A., Leonetti A.C., Guariniello A., Isaac A., Bovi A.P.D., Chessa A., Tromba A., Martinez A.A., Brillantino A., Caira A., Castaldi A., Ferronetti A., Giuliani A., Prestera A., la Medina A.R.-D., Tarasconi A., Tornambe A., Picciariello A., Ioannidis A., Leppaniemi A., Khan A., Rashid A., Perez-Sanchez A.L.E., Mittal A., Mitul A.R., Mehraj A., Laharwal A., Dorisme A., Marinis A., Iqbal A., Moncada A., Braccio B., Alkhafaji B., de Andres Asenjo B., Martin-Perez B., Perez B.S., Creavin B., Cali B., Pascotto B., Stubbs B., Retes B.Z., Jovanovic B., Goh B.K.P., Sensi B., Biddau C., Gazia C., Vallicelli C., Fagundes C.A., Santacruz C.C., Chirico C., Diaz C.J.G., Petrola C., Rodriguez C.S., Benitez C.Y., Dammaro C., Faro C.L., Reinke C., Paez C.D., Oliva C., Paranjape C., Thomas C., Chia C.F., Kong C.K., De Lucia C., Chao C.O., Arcudi C., Guerci C., Chia C., Parise C., Folliero C., Varela C., Ferguson D.M., Camacho D., Popowich D., Lima D.S., Rega D., Delogu D., Zigiotto D., Vinci D., D'Antonio D., Parini D., Merlini D.A., Zimmerman D.D.E., Moro-Valdezate D., Pertile D., Giusti D.M., Keller D.S., Tarik D., Kalivaci D., Mazingi D., Maldonado-Pintado D.G., Sasia D., Linardoutsos D., Osilli D., Murrone D., Russello D., Rodas E., Roa E.A.A., Ricciardi E., Rosso E., Saladino E., Flores-Villalba E., Ajs E.R., Smith-Singares E., Baili E., Kouroumpas E., Bourmpouteli E., Douka E., Martin-Perez E., Guaitoli E., Samadov E., Francone E., Vaterlini E., Morales E., Pena E., Zhao E., Andres E.D.P., Benzoni E., Erdas E., Pinotti E., Colas-Ruiz E., Aytac E., Laterza E., Agastra E., Foianini E., Moscoso E., Laviano E., Marra E., Cardamone E., Licardie E., Mpaili E., Pinna E., Varo E., Navarro F.M., Marino F., Medas F., Romano F., Maraska F., Saliu F., Madrid F., Rosa F., Mastella F., Gheza F., Luvisetto F., Alconchel F., Vieira F.M., Pareja F., Agresta F., Luna F., Bonilla F., Cordera F., Burdio F., Mendoza-Moreno F., Flores F.M., Aranda F.P., Taylor F., Ramos F.L., Fernandes F., Tropeano F.P., Balestra F., Bianco F., Ceci F., Colombo F., Di Marzo F., Ferrara F., Lancellotti F., Lazzarin F., Litta F., Martini F., Pizza F., Roscio F., Virdis F., Antona F.B., Ramirez F.C., Fernandez F.M., Llinares F.O., Quezada F., Schlottmann F., Herrera-Almario G., Massaferro G., Bislenghi G., van Ramshorst G., Luglio G., Bointas G., Kampouroglou G., Papadopoulos G., Manrique G.A., Calini G., Nastri G., Formisano G., Galiffa G., Palini G.M., Colucci G., Pagano G., Vanni G., Pattacini G.C., De Paola G., Lisi G., Partida G., Bellanova G., De Nobili G., Necchi G.S., Sinibaldi G., Tebala G., Bagaglini G., Izzo G., Argenio G., Brisinda G., Candilio G., Di Grezia G., Esposito G., Faillace G., Frazzetta G., La Gumina G., Nigri G., Romeo G., Amatriain G.C., Ortega G., Martin-Martin G., Stavrou G.A., Gunadi, Ugon G.A., Machain G., Marcucci G., Martinez-Mier G., Machain G.M., Nari G., Calvo H., Fathy H., Hamilto, Ahmed H., Faraj H., Nava H., Macias H.O., Nikaj H., Solano H., Khan H.A., Alarcon H.S., Ebied H., Giani I., Ateca I.V., Neri I., Roman I.A.S., Fidoshev I., Rodriguez I.M., Negoi I., Ortega I., Bernescu I., Russo I.S., Rodriguez I.V., Palomares I., Baltazar I., Torrejimeno I.J., Jurado I.M.C., Reccia I., Hussain I., Toledo I.B., Mora-Guzman I., Dogaru I., Romic I., Balciscueta I., Kenington J.C., Sagolsem J., Jang J.Y., Olivier J., Lammel-Lindemann J., Dziakova J., Villavicencio J.I.R., Salinas J., Parreira J.P.J.G., Jovanovic, Perez J.R., Reyes J.A.S., Luque J.A.M., Mak J., Rodriguez J.S., Kok J.H.H., Krook J., Diaz-Elizondo J.A., Castell J., Garcia-Flores J.E., Navalon J.M.J., Rodrigues J.M.S., Pereira J., Gomez J.T.C., Luque J.B., del Olmo J.C.M., Salamea J.C., Olivier J.F.C., Laina J.L.B., Ordonez J.M., Gutierrez J., Abba J., Sofi J.A., Sherafgan K., Sahnan K., Yanaga K., Beatson K., Asim L., Alvarez L., Siragusa L., Farber L., Ong L., Athanasios L., Garcia-Bruna L., De Martino L., Ferrario L., Giordano L., Gordini L., Pio L., Ponchietti L., Moletta L., Curella L., Poggi L., Taglietti L., Bonavina L., Conti L., Goffredi L., Ruiz L.A.G., Barrionuevo L., Fregoso L.E., Cabrera L.F., Rodriguez L.G., Grande L., Osoria L.G., Gonzalez L.J.K., Sanchez-Guillen L., Tallon-Aguilar L., Tresierra L., Giavarini L., Hasabelnabi M., Odovic M., Uemura M., Khan M., Artiles-Armas M., David M., Di Martino M., Spampinato M.G., Ribeiro M.A.F., Viola M., Angrisani M., Calussi M., Cannistra M., Catarci M., Cereda M., Conte M., Giordano M., Pellicciaro M., Marino M.V., Vaterlini M.E., Jimenez M.F., Lolli M.G., Bellini M.I., Lemma M., Chiarello M.M., Nicola M., Arrigo M., Mejia M.C., Manrique M.M., Rodriguez-Lopez M., Serradilla-Martin M., Lara M.Z., Martinez M., Bagnall M., Peter M., Lara M.C., Gomez M.J., Paniagua-Garcia-Senorans M., Gonzalez M.P., Rutegard M., Salo M., Franceschilli M., Silveri M., Veroux M., Pezzulo M., Nardi M., Rottoli M., Tolonen M., Ciro M.P., Zuluagua M., Cannavo M., Cervellera M., Iacobone M., Montuori M., Dominguez M.G., Bingol-Kologlu M., Tahir M., Lim M., Wilson M.S., Wilson M., Campanelli M., Bisaccia M., De Rosa M., Maruccia M., Paterno M., Pisano M., Torre M., Trevino M., Zuolo M., Hernandez Bartolome M.A., Farina M., Pera M., Calvo M.P., Sotelo M., Thway M.M., Hassan M., Hassan M.S.E., Azfar M., Bouhuwaish M., Taha M., Zaieem M., Korkoman M., Guraieb M., Shalaby M., Raza M.A., Younis M.U., Elhadi M., Ali M.Z., Quazi N., Dudi-Venkata N.N., Alselaim N., Loria N., Ramirez N.V., Than N.W., Smart N., Trelles N., Pinto N., Allievi N., Petrucciani N., Antonacci N., Cillara N., Gica N., Cristiana N.D., Krystek N., Falco N., Pecorelli N., Tamini N., Dallas N.A., Machairas N., Brito N., Fieturi N.A., Ortega N., Mercado O.A., Irkorucu O., Alsherif O., Valles O., Ioannidis O., Palmas O.H., Palmas O.I.H., Guadarrama O.S., Bozbiyik O., Omelanczuk P., Ottolino P., Rodrigues P., Ruiz P., Campenni P., Chiarade P., Olivares P.P., Baroffio P., Panaccio P., Wintringer P., Di Fronzo P., Talento P., Favoriti P., Sendino P., Marsanic P., Mifsut P., Andrade P., Ajawin P., Abadia-Barno P., Castaneda P.A.N., Arevalos P.O.S., Bellver P.P., Koh P.S., Souza P., Major P., Bali R.S., Khattar R.M., Melo R.B., Ebrahiminia R., Azar R., Murga R.L., Pirolo R., Brady R., Davies R.J., Dholakia R., Rattan R., Singhal R., Lim R., Angelico R., Isernia R.M., Tutino R., Faccincani R., Peltrini R., Carrera-Ceron R., Tejos R., Kashyap R., Fajardo R., Lozito R., Pareja R.M., Garbarino S., Morales-Conde S., Benli S., Mansour S., Flores S., Suarez S.L., Ben S.L., Fuentes S., Napetti S., de Guzman S.O., Awad S., Weckmann Lujan S.A., Gentilli S., Grimaldi S., Pizarro S.O., Tayar S., Nabi S., Chan S.M., Junaid S., Rojas S., Monetti S., Garcia S., Salvans S., Tenconi S., Shaw S., Santoni S., Parra S.A., Cardenas S., Perez-Bertolez S., Chiappetta S., Dessureault S., Delis S., Bonapasta S.A., Rausei S., Scaringi S., Keswani S., Ali S.M., Cetinkunar S., Fung T.L.D., Rawashdeh T., Lopez T.N., De Campos T., Duque T.C., Perra T., Liakakos T., Daskalakis T., Barnes T., Koeter T., Zalla T., Gonzalez T.E., Elosua T., Campagnaro T., Brown T., Luoto T., Oumar T.A., Giustizieri U., Grossi U., Bracale U., Rivas U., Sosa V., Testa V., Andriola V., Tonini V., Balassone V., Celentano V., Progno V., Raju V., Carroni V., Cavallaro V., Katta V.R., De Simone V., Romaguera V.P., Orozco V.H.G., Luraschi V., Rachkov V., Turrado-L V., Visag-Castillo V., Dowling V., Graham V., Papagni V., Vigorita V., Fonseca V.C., Carneros V.J., Bellato V., Goncalves W., Powers W.F., Grigg W., Bechstein W.O., Lim Y.B., Altinel Y., Golubovic Z., Balciscueta Z., Ielpo, B., Podda, M., Pellino, G., Pata, F., Caruso, R., Gravante, G., Di Saverio, S., Gallo, G., Lui, R., Orengia, A., Chowdary, A., Kulkarni, A., Kuvvetli, A., Navarro, A., Pisanu, A., Smith, A., Ibiricu, A. C., Nacion, A. J. D., Alsaleh, A., Alhazmi, A., Elmabri, A., Wani, A., Rencuzogullari, A., Lasarte, A. S., Rubio, A. V., Bavikatte, A., Kumar, A., Jamiri, A. -R., Padilla, A. M. A., Cacurri, A., de San Ildefonso, A., Porcu, A., Sartori, A., Rocca, A., Yanez, A. P., Becaria, A., Solis-Pena, A., Sretenovic, A., Urbistondo, A., Bandin, A., Najar, A., De Luca, A., Boddy, A., Charalabopoulos, A., Tzivanakis, A., Amendola, A., de Velasco, A. R. -G., Yildirim, A. C., Frontali, A., Toure, A. O., Garcia-Granero, A., Roldan, A. M., Larrainzar, A. S., Ratnayake, A. S., Gonzalez-Ganso, A. M., Minaya-Bravo, A. M., Das, A., Bondurri, A., Costanzi, A., Lucchi, A., Mazzari, A., Musig, A., Peloso, A., Piano, A., Police, A., Mihailescu, A., Pouy, A., Romano, A., Iossa, A., Leonetti, A. C., Guariniello, A., Isaac, A., Bovi, A. P. D., Chessa, A., Tromba, A., Martinez, A. A., Brillantino, A., Caira, A., Castaldi, A., Ferronetti, A., Giuliani, A., Prestera, A., la Medina, A. R. -D., Tarasconi, A., Tornambe, A., Picciariello, A., Ioannidis, A., Leppaniemi, A., Khan, A., Rashid, A., Perez-Sanchez, A. L. E., Mittal, A., Mitul, A. R., Mehraj, A., Laharwal, A., Dorisme, A., Marinis, A., Iqbal, A., Moncada, A., Braccio, B., Alkhafaji, B., de Andres Asenjo, B., Martin-Perez, B., Perez, B. S., Creavin, B., Cali, B., Pascotto, B., Stubbs, B., Retes, B. Z., Jovanovic, B., Goh, B. K. P., Sensi, B., Biddau, C., Gazia, C., Vallicelli, C., Fagundes, C. A., Santacruz, C. C., Chirico, C., Diaz, C. J. G., Petrola, C., Rodriguez, C. S., Benitez, C. Y., Dammaro, C., Faro, C. L., Reinke, C., Paez, C. D., Oliva, C., Paranjape, C., Thomas, C., Chia, C. F., Kong, C. K., De Lucia, C., Chao, C. O., Arcudi, C., Guerci, C., Chia, C., Parise, C., Folliero, C., Varela, C., Ferguson, D. M., Camacho, D., Popowich, D., Lima, D. S., Rega, D., Delogu, D., Zigiotto, D., Vinci, D., D'Antonio, D., Parini, D., Merlini, D. A., Zimmerman, D. D. E., Moro-Valdezate, D., Pertile, D., Giusti, D. M., Keller, D. S., Tarik, D., Kalivaci, D., Mazingi, D., Maldonado-Pintado, D. G., Sasia, D., Linardoutsos, D., Osilli, D., Murrone, D., Russello, D., Rodas, E., Roa, E. A. A., Ricciardi, E., Rosso, E., Saladino, E., Flores-Villalba, E., Ajs, E. R., Smith-Singares, E., Baili, E., Kouroumpas, E., Bourmpouteli, E., Douka, E., Martin-Perez, E., Guaitoli, E., Samadov, E., Francone, E., Vaterlini, E., Morales, E., Pena, E., Zhao, E., Andres, E. D. P., Benzoni, E., Erdas, E., Pinotti, E., Colas-Ruiz, E., Aytac, E., Laterza, E., Agastra, E., Foianini, E., Moscoso, E., Laviano, E., Marra, E., Cardamone, E., Licardie, E., Mpaili, E., Pinna, E., Varo, E., Navarro, F. M., Marino, F., Medas, F., Romano, F., Maraska, F., Saliu, F., Madrid, F., Rosa, F., Mastella, F., Gheza, F., Luvisetto, F., Alconchel, F., Vieira, F. M., Pareja, F., Agresta, F., Luna, F., Bonilla, F., Cordera, F., Burdio, F., Mendoza-Moreno, F., Flores, F. M., Aranda, F. P., Taylor, F., Ramos, F. L., Fernandes, F., Tropeano, F. P., Balestra, F., Bianco, F., Ceci, F., Colombo, F., Di Marzo, F., Ferrara, F., Lancellotti, F., Lazzarin, F., Litta, F., Martini, F., Pizza, F., Roscio, F., Virdis, F., Antona, F. B., Ramirez, F. C., Fernandez, F. M., Llinares, F. O., Quezada, F., Schlottmann, F., Herrera-Almario, G., Massaferro, G., Bislenghi, G., van Ramshorst, G., Luglio, G., Bointas, G., Kampouroglou, G., Papadopoulos, G., Manrique, G. A., Calini, G., Nastri, G., Formisano, G., Galiffa, G., Palini, G. M., Colucci, G., Pagano, G., Vanni, G., Pattacini, G. C., De Paola, G., Lisi, G., Partida, G., Bellanova, G., De Nobili, G., Necchi, G. S., Sinibaldi, G., Tebala, G., Bagaglini, G., Izzo, G., Argenio, G., Brisinda, G., Candilio, G., Di Grezia, G., Esposito, G., Faillace, G., Frazzetta, G., La Gumina, G., Nigri, G., Romeo, G., Amatriain, G. C., Ortega, G., Martin-Martin, G., Stavrou, G. A., Ugon, G. A., Machain, G., Marcucci, G., Martinez-Mier, G., Machain, G. M., Nari, G., Calvo, H., Fathy, H., Ahmed, H., Faraj, H., Nava, H., Macias, H. O., Nikaj, H., Solano, H., Khan, H. A., Alarcon, H. S., Ebied, H., Giani, I., Ateca, I. V., Neri, I., Roman, I. A. S., Fidoshev, I., Rodriguez, I. M., Negoi, I., Ortega, I., Bernescu, I., Russo, I. S., Rodriguez, I. V., Palomares, I., Baltazar, I., Torrejimeno, I. J., Jurado, I. M. C., Reccia, I., Hussain, I., Toledo, I. B., Mora-Guzman, I., Dogaru, I., Romic, I., Balciscueta, I., Kenington, J. C., Sagolsem, J., Jang, J. Y., Olivier, J., Lammel-Lindemann, J., Dziakova, J., Villavicencio, J. I. R., Salinas, J., Parreira, J. P. J. G., Perez, J. R., Reyes, J. A. S., Luque, J. A. M., Mak, J., Rodriguez, J. S., Kok, J. H. H., Krook, J., Diaz-Elizondo, J. A., Castell, J., Garcia-Flores, J. E., Navalon, J. M. J., Rodrigues, J. M. S., Pereira, J., Gomez, J. T. C., Luque, J. B., del Olmo, J. C. M., Salamea, J. C., Olivier, J. F. C., Laina, J. L. B., Ordonez, J. M., Gutierrez, J., Abba, J., Sofi, J. A., Sherafgan, K., Sahnan, K., Yanaga, K., Beatson, K., Asim, L., Alvarez, L., Siragusa, L., Farber, L., Ong, L., Athanasios, L., Garcia-Bruna, L., De Martino, L., Ferrario, L., Giordano, L., Gordini, L., Pio, L., Ponchietti, L., Moletta, L., Curella, L., Poggi, L., Taglietti, L., Bonavina, L., Conti, L., Goffredi, L., Ruiz, L. A. G., Barrionuevo, L., Fregoso, L. E., Cabrera, L. F., Rodriguez, L. G., Grande, L., Osoria, L. G., Gonzalez, L. J. K., Sanchez-Guillen, L., Tallon-Aguilar, L., Tresierra, L., Giavarini, L., Hasabelnabi, M., Odovic, M., Uemura, M., Khan, M., Artiles-Armas, M., David, M., Di Martino, M., Spampinato, M. G., Ribeiro, M. A. F., Viola, M., Angrisani, M., Calussi, M., Cannistra, M., Catarci, M., Cereda, M., Conte, M., Giordano, M., Pellicciaro, M., Marino, M. V., Vaterlini, M. E., Jimenez, M. F., Lolli, M. G., Bellini, M. I., Lemma, M., Chiarello, M. M., Nicola, M., Arrigo, M., Mejia, M. C., Manrique, M. M., Rodriguez-Lopez, M., Serradilla-Martin, M., Lara, M. Z., Martinez, M., Bagnall, M., Peter, M., Lara, M. C., Gomez, M. J., Paniagua-Garcia-Senorans, M., Gonzalez, M. P., Rutegard, M., Salo, M., Franceschilli, M., Silveri, M., Veroux, M., Pezzulo, M., Nardi, M., Rottoli, M., Tolonen, M., Ciro, M. P., Zuluagua, M., Cannavo, M., Cervellera, M., Iacobone, M., Montuori, M., Dominguez, M. G., Bingol-Kologlu, M., Tahir, M., Lim, M., Wilson, M. S., Wilson, M., Campanelli, M., Bisaccia, M., De Rosa, M., Maruccia, M., Paterno, M., Pisano, M., Torre, M., Trevino, M., Zuolo, M., Hernandez Bartolome, M. A., Farina, M., Pera, M., Calvo, M. P., Sotelo, M., Thway, M. M., Hassan, M., Hassan, M. S. E., Azfar, M., Bouhuwaish, M., Taha, M., Zaieem, M., Korkoman, M., Guraieb, M., Shalaby, M., Raza, M. A., Younis, M. U., Elhadi, M., Ali, M. Z., Quazi, N., Dudi-Venkata, N. N., Alselaim, N., Loria, N., Ramirez, N. V., Than, N. W., Smart, N., Trelles, N., Pinto, N., Allievi, N., Petrucciani, N., Antonacci, N., Cillara, N., Gica, N., Cristiana, N. D., Krystek, N., Falco, N., Pecorelli, N., Tamini, N., Dallas, N. A., Machairas, N., Brito, N., Fieturi, N. A., Ortega, N., Mercado, O. A., Irkorucu, O., Alsherif, O., Valles, O., Ioannidis, O., Palmas, O. H., Palmas, O. I. H., Guadarrama, O. S., Bozbiyik, O., Omelanczuk, P., Ottolino, P., Rodrigues, P., Ruiz, P., Campenni, P., Chiarade, P., Olivares, P. P., Baroffio, P., Panaccio, P., Wintringer, P., Di Fronzo, P., Talento, P., Favoriti, P., Sendino, P., Marsanic, P., Mifsut, P., Andrade, P., Ajawin, P., Abadia-Barno, P., Castaneda, P. A. N., Arevalos, P. O. S., Bellver, P. P., Koh, P. S., Souza, P., Major, P., Bali, R. S., Khattar, R. M., Melo, R. B., Ebrahiminia, R., Azar, R., Murga, R. L., Pirolo, R., Brady, R., Davies, R. J., Dholakia, R., Rattan, R., Singhal, R., Lim, R., Angelico, R., Isernia, R. M., Tutino, R., Faccincani, R., Peltrini, R., Carrera-Ceron, R., Tejos, R., Kashyap, R., Fajardo, R., Lozito, R., Pareja, R. M., Garbarino, S., Morales-Conde, S., Benli, S., Mansour, S., Flores, S., Suarez, S. L., Ben, S. L., Fuentes, S., Napetti, S., de Guzman, S. O., Awad, S., Weckmann Lujan, S. A., Gentilli, S., Grimaldi, S., Pizarro, S. O., Tayar, S., Nabi, S., Chan, S. M., Junaid, S., Rojas, S., Monetti, S., Garcia, S., Salvans, S., Tenconi, S., Shaw, S., Santoni, S., Parra, S. A., Cardenas, S., Perez-Bertolez, S., Chiappetta, S., Dessureault, S., Delis, S., Bonapasta, S. A., Rausei, S., Scaringi, S., Keswani, S., Ali, S. M., Cetinkunar, S., Fung, T. L. D., Rawashdeh, T., Lopez, T. N., De Campos, T., Duque, T. C., Perra, T., Liakakos, T., Daskalakis, T., Barnes, T., Koeter, T., Zalla, T., Gonzalez, T. E., Elosua, T., Campagnaro, T., Brown, T., Luoto, T., Oumar, T. A., Giustizieri, U., Grossi, U., Bracale, U., Rivas, U., Sosa, V., Testa, V., Andriola, V., Tonini, V., Balassone, V., Celentano, V., Progno, V., Raju, V., Carroni, V., Cavallaro, V., Katta, V. R., De Simone, V., Romaguera, V. P., Orozco, V. H. G., Luraschi, V., Rachkov, V., Turrado-L, V., Visag-Castillo, V., Dowling, V., Graham, V., Papagni, V., Vigorita, V., Fonseca, V. C., Carneros, V. J., Bellato, V., Goncalves, W., Powers, W. F., Grigg, W., Bechstein, W. O., Lim, Y. B., Altinel, Y., Golubovic, Z., Balciscueta, Z., Ielpo, B, Podda, M, Pellino, G, Pata, F, Caruso, R, Gravante, G, Di Saverio, S, and Luglio, G

Subjects
medicine.medical_specialty, Anti-Bacterial Agents, Appendectomy, Appendicitis, COVID-19 Testing, Hospital Administration, Humans, Pandemics, Personal Protective Equipment, Practice Patterns, Physicians', Surveys and Questionnaires, Attitude of Health Personnel, COVID-19, Surgeons, Coronavirus disease 2019 (COVID-19), Settore MED/18 - CHIRURGIA GENERALE, COVID-19 pandemic. Acute appendicitis, MEDLINE, Practice Patterns, 030230 surgery, Health administration, 03 medical and health sciences, 0302 clinical medicine, Anti-Bacterial Agent, Pandemic, medicine, Surveys and Questionnaire, Appendiciti, General, Laparoscopy, Personal protective equipment, Physicians', medicine.diagnostic_test, business.industry, General surgery, Original Articles, medicine.disease, Anti-bacterial agents, appendectomy, appendicitis, COVID-19 testing, hospital administration, humans, pandemics, personal protective equipment, practice patterns physicians', surveys and questionnaires, attitude of health personnel, surgeons, appendicitis - COVI-19 - ACIE study - management, Acute appendicitis, Original Article, Surgery, Covid-19, business, Human
Abstract

Background Surgical strategies are being adapted to face the COVID‐19 pandemic. Recommendations on the management of acute appendicitis have been based on expert opinion, but very little evidence is available. This study addressed that dearth with a snapshot of worldwide approaches to appendicitis. Methods The Association of Italian Surgeons in Europe designed an online survey to assess the current attitude of surgeons globally regarding the management of patients with acute appendicitis during the pandemic. Questions were divided into baseline information, hospital organization and screening, personal protective equipment, management and surgical approach, and patient presentation before versus during the pandemic. Results Of 744 answers, 709 (from 66 countries) were complete and were included in the analysis. Most hospitals were treating both patients with and those without COVID. There was variation in screening indications and modality used, with chest X‐ray plus molecular testing (PCR) being the commonest (19·8 per cent). Conservative management of complicated and uncomplicated appendicitis was used by 6·6 and 2·4 per cent respectively before, but 23·7 and 5·3 per cent, during the pandemic (both P, The COVID‐19 pandemic required reorganization of surgical services, affecting patients with common surgical diseases including acute appendicitis. No evidence is available on the topic. This study found global variation in screening policies, use of personal protective equipment and intraoperative directives. There has been increased adoption of non‐operative management and open appendicectomy. Hands off

Published
2021

197. Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form

Author

Giuseppe Cacace, Francesco Palmieri, Giovanni Di Gennaro, Giorgio Graditi, Maria Valenti, Amedeo Buonanno, Antonio Nogarotto, Buonanno, A, Nogarotto, A, Cacace, G, Di Gennaro, G, Palmieri, F., Valenti, M, Graditi, G, Buonanno, A., Nogarotto, A., Cacace, G., Gennaro, G. D., Palmieri, F. A. N., Valenti, M., and Graditi, G.

Subjects
Computer science, factor graph, 02 engineering and technology, Belief propagation, computer.software_genre, lcsh:Technology, 01 natural sciences, lcsh:Chemistry, 010309 optics, Robustness (computer science), bayesian networks, 0103 physical sciences, bayesian network, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, lcsh:QH301-705.5, Instrumentation, belief propagation, Fluid Flow and Transfer Processes, lcsh:T, Process Chemistry and Technology, Document classification, General Engineering, Probabilistic logic, Bayesian network, Sensor fusion, Missing data, lcsh:QC1-999, Data Fusion, Computer Science Applications, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, 020201 artificial intelligence & image processing, Data mining, lcsh:Engineering (General). Civil engineering (General), computer, lcsh:Physics, Factor graph
Abstract

In this work, we investigate an Information Fusion architecture based on a Factor Graph in Reduced Normal Form. This paradigm permits to describe the fusion in a completely probabilistic framework and the information related to the different features are represented as messages that flow in a probabilistic network. In this way we build a sort of context for observed features conferring to the solution a great flexibility for managing different type of features with wrong and missing values as required by many real applications. Moreover, modifying opportunely the messages that flow into the network, we obtain an effective way to condition the inference based on the different reliability of each information source or in presence of single unreliable signal. The proposed architecture has been used to fuse different detectors for an identity document classification task but its flexibility, extendibility and robustness make it suitable to many real scenarios where the signal can be wrongly received or completely missing.

Published
2021

198. Considerations about learning Word2Vec

Author

Amedeo Buonanno, Francesco Palmieri, Giovanni Di Gennaro, Di Gennaro, G., Buonanno, A., Palmieri, F. A. N., DI GENNARO, Giovanni, Buonanno, Amedeo, and Palmieri, Francesco

Subjects
Hyperparameter, Point (typography), Computer science, business.industry, Natural language processing, Matrix (music), Word embedding, 02 engineering and technology, Overfitting, Machine learning, computer.software_genre, Theoretical Computer Science, Hardware and Architecture, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Embedding, 020201 artificial intelligence & image processing, Word2vec, Artificial intelligence, business, computer, Software, Neural networks, Information Systems
Abstract

Despite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters. In this work, we try to shed some light on various issues focusing on a typical dataset. It is shown that the learning rate prevents the exact mapping of the co-occurrence matrix, that Word2Vec is unable to learn syntactic relationships, and that it does not suffer from the problem of overfitting. Furthermore, through the creation of an ad-hoc network, it is also shown how it is possible to improve Word2Vec directly on the analogies, obtaining very high accuracy without damaging the pre-existing embedding. This analogy-enhanced Word2Vec may be convenient in various NLP scenarios, but it is used here as an optimal starting point to evaluate the limits of Word2Vec.

Published
2021

199. Comparison Between Protein-Protein Interaction Networks CD4$$^+$$T and CD8$$^+$$T and a Numerical Approach for Fractional HIV Infection of CD4$$^{+}$$T Cells

Author

Beatrice Paternoster, Leila Moradi, Eslam Farsimadan, Dajana Conte, and Francesco Palmieri

Subjects
Maple, Source code, Discrete Chebyshev polynomials, media_common.quotation_subject, engineering.material, Quantitative Biology::Cell Behavior, Fractional calculus, Nonlinear system, Algebraic equation, Scheme (mathematics), Orthogonal polynomials, engineering, Applied mathematics, media_common, Mathematics
Abstract

This research examines and compares the construction of protein-protein interaction (PPI) networks of CD4\(^{+}\) and CD8\(^{+}\)T cells and investigates why studying these cells is critical after HIV infection. This study also examines a mathematical model of fractional HIV infection of CD4\(^{+}\)T cells and proposes a new numerical procedure for this model that focuses on a recent kind of orthogonal polynomials called discrete Chebyshev polynomials. The proposed scheme consists of reducing the problem by extending the approximated solutions and by using unknown coefficients to nonlinear algebraic equations. For calculating unknown coefficients, fractional operational matrices for orthogonal polynomials are obtained. Finally, there is an example to show the effectiveness of the recommended method. All calculations were performed using the Maple 17 computer code.

Published
2021

200. An empirical evaluation of prediction by partial matching in assembly assistance systems

Author

Ugo Fiore, Francesco Palmieri, Arpad Gellert, Bogdan-Constantin Pirvu, Stefan-Alexandru Precup, and Constantin-Bala Zamfirescu

Subjects
0209 industrial biotechnology, Industry 4.0, Computer science, 02 engineering and technology, Prediction by partial matching, Machine learning, computer.software_genre, lcsh:Technology, lcsh:Chemistry, 020901 industrial engineering & automation, Multidisciplinary approach, Component (UML), 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Instrumentation, lcsh:QH301-705.5, Fluid Flow and Transfer Processes, Markov chain, business.industry, lcsh:T, Process Chemistry and Technology, Smart factory, Assembly assistance system, General Engineering, Training station, lcsh:QC1-999, Computer Science Applications, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, Factory (object-oriented programming), 020201 artificial intelligence & image processing, Artificial intelligence, business, Internet of Things, lcsh:Engineering (General). Civil engineering (General), computer, lcsh:Physics
Abstract

Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors.

Published
2021

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