Efficient Algorithms for Coded Multicasting in Heterogeneous Caching Networks

Autor: Giuseppe Vettigli, Mingyue Ji, Karthikeyan Shanmugam, Jaime Llorca, Antonia M. Tulino, Giuseppe Caire

Publikováno v: Entropy, Vol 21, Iss 3, p 324 (2019)

Coded multicasting has been shown to be a promising approach to significantly improve the performance of content delivery networks with multiple caches downstream of a common multicast link. However, the schemes that have been shown to achieve order-

Externí odkaz: https://doaj.org/article/527c3c1055214149b8d3c5d6acb810e1

Python Data Visualization Cookbook (Second Edition) : Visualize Data Using Python's Most Popular Libraries

Autor: Igor Milovanovic, Dimitry Foures, Giuseppe Vettigli

Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization.Key Features[•] Learn how to set up an optimal Python environment for data visualization[•] Understand how to import, clean and

CompNA at SemEval-2021 Task 1: Prediction of lexical complexity analyzing heterogeneous features

Autor: Antonio Sorgente, Giuseppe Vettigli

Publikováno v: SemEval@ACL/IJCNLP

This paper describes the CompNa model that has been submitted to the Lexical Complexity Prediction (LCP) shared task hosted at SemEval 2021 (Task 1). The solution is based on combining features of different nature through an ensambling method based o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::da33887c704140678cdf239b79bad92a
https://doi.org/10.18653/v1/2021.semeval-1.69

Audio content-based framework for emotional music recognition

Autor: Angelo Ciaramella, Giuseppe Vettigli, Davide Nardone, Antonino Staiano

Publikováno v: Advances in Data Science: Methodologies and Applications ISBN: 9783030518691

Music is a language of emotions and music emotional recognition has been addressed by different disciplines (e.g., psychology, cognitive science and musicology). Nowadays, the music fruition mechanism is evolving, focusing on the music content. In th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13ce5dbb5521d7e1e8ca298547c316ef
http://hdl.handle.net/11367/87246

A hybrid approach for the automatic extraction of causal relations from text

Autor: Giuseppe Vettigli, Francesco Mele, Antonio Sorgente

Publikováno v: pp. 15–29, 2018
info:cnr-pdr/source/autori:Sorgente, Antonio; Vettigli, Giuseppe; Mele, Francesco/titolo:A hybrid approach for the automatic extraction of causal relations from text/titolo_volume:/curatori_volume:/editore:/anno:2018
Emerging Ideas on Information Filtering and Retrieval ISBN: 9783319683904

This chapter presents an approach for the discovery of causal relations from open domain text in English. The approach is hybrid, indeed it joins rules based and machine learning methodologies in order to combine the advantages of both. The approach

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71259a477cb5fb543a4fd0d420e77e39
http://www.scopus.com/record/display.url?eid=2-s2.0-85033449502&origin=inward