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50 results on '"Meo, P."'

5. Platelet gel in cutaneous radiation dermatitis

Author

Iervolino, Vincenzo, Di Costanzo, Gaetano, Azzaro, Rosa, Diodato, Anna Maria, Di Macchia, Catia Addolorata, Di Meo, Tommaso, Petruzziello, Arnolfo, Loquercio, Giovanna, Muto, Paolo, Apice, Gaetano, and Cacciapuoti, Carmela

Published
2013
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21. Rural Surgery in Southern Sudan

Author

Meo, Giuseppe, Andreone, Dario, De Bonis, Umberto, Cometto, Giorgio, Enrico, Stefano, Giustetto, Guido, Kiss, Alberto, Landra, Marino, Palmas, Maria, Sacchi, Laura, Taliente, Peter, and Vergnano, Guido

Published
2006
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34. Similarity and Trust to Form Groups in Online Social Networks

Author

Panetto H.,Debruyne C.,Dillon T.,An Y.,Ardagna C.A.,Weichhart G.,Meersman R., DE MEO, P, Messina, F, Pappalardo, G, Rosaci, D, Sarne', G, DE MEO P, MESSINA F, PAPPALARDO G, ROSACI D, SARNE' G, Panetto H.,Debruyne C.,Dillon T.,An Y.,Ardagna C.A.,Weichhart G.,Meersman R., DE MEO, P, Messina, F, Pappalardo, G, Rosaci, D, Sarne', G, DE MEO P, MESSINA F, PAPPALARDO G, ROSACI D, and SARNE' G

Abstract

Social Sciences identify similarity and mutual trust as main criteria to consider in group formation processes. On this basis, we present a group formation technique which exploits measures of both similarity and trust, in order to improve the compactness of groups in Online Social Networks. Similarity and trust have been jointly exploited to design two algorithms designed to match groups and users, in order to capture the gain of a user who desires to join with a group and the benefit of the group itself. Experimental results show that trust is more valuable than similarity in forming groups and that the two proposed algorithms are capable to deal with large networks.

Published
2015

50. Forming classes in an e-Learning social network scenario

Author

Pasquale De Meo, Giuseppe M. L. Sarné, Lidia Fotia, Fabrizio Messina, Domenico Rosaci, BADICA C EL FALLAH S SEGHROUCHNI A BEYNIER A CAMACHO D HERPSON C HINDRIKS K NOVAIS P, De Meo, P, Fotia, L, Messina, F, Rosaci, D, and Sarne', G

Subjects
Class (computer programming), MULTI-AGENT SYSTEMS, Knowledge management, Social network, Exploit, business.industry, Computer science, E-learning (theory), 05 social sciences, E-LEARNING, 050301 education, 050801 communication & media studies, Artificial Intelligence, Online Social Networks, Data science, Trust relationship, 0508 media and communications, ONLINE SOCIAL NETWORK, Distributed algorithm, Software agent, Convergence (relationship), business, 0503 education
Abstract

The use of network technology to provide online courses is the latest trend in the training and development industry and has been defined as the “e-Learning revolution”. On the other hand, Online Social Networks (OSNs) represent today an effective possibility to have common and easy-to-use platforms for supporting e-Learning activities. However, as underlined by previous studies, many of the proposed e-Learning systems can result in confusion and decrease the learner’s interest. In this paper, we introduce the possibility to form e-Learning classes in the context of OSNs. At the best of our knowledge, any of the approaches proposed in the past considers the evolution of on-line classes as a problem of matching the individual users’ profiles with the profiles of the classes. In this paper, we propose an algorithm that exploits a multi-agent system to suitably distribute such a matching computation on all the user devices. The good effectiveness and the promising efficiency of our approach is shown by the experimental results obtained on simulated On-line Social Networks data.

Published
2017

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