Picuslab-DIETI

Picuslab-DIETI is happy to share its last publication entitled "A community detection approach based on network representation learning for repository mining" - whose authors are Marco De Luca, Anna Rita Fasolino, Antonino Ferraro, Vincenzo Moscato, Giancarlo Sperlì, Porfirio Tramontana - has been accepted on Elsevier Expert Systems with Applications Journal.

In this paper, we propose a novel heterogeneous graph-based model for capturing and handling all the complex and strongly-correlated information of a software Developer Social Network (DSN) to support several analytic tasks. In particular, we challenge the problem of automatically discovering communities of software developers sharing interests for similar projects by relying on Social Network Analysis (SNA) findings. To overcome the huge graph-size issue, we leverage different graph embedding techniques.

https://lnkd.in/dq8V5igS

Picuslab-DIETI is pleased to announce that Professor Sansone has spoken at the entitled Seminar "False Truths" in the Forum PA 2023 event scheduled for May 16-18, 2023.

https://www.youtube.com/watch?v=z9Bw4AIaHlw

False verità 16 MAGGIO 2023 - FORUM PA

Picuslab-DIETI is happy to share its last publication entitled "Covid-19 sentiment analysis based on Tweets" - whose authors are Valerio La Gatta, Vincenzo Moscato, Marco Postiglione and Giancarlo Sperlì - has been accepted on IEEE Intelligent System Journal.

In this work, we investigate how individuals in Italy perceived the COVID-19 outbreak and its implications in real-life. Our analysis shows that while the overall sentiment is negative, Italians have shown upbeat responses to the pandemic, especially in regards to the vaccination campaign. The emotion analysis reveals that while fear progressively decreased after the first wave of the pandemic, the overall anger has remained constant but gradually turned into various narratives.

https://lnkd.in/dNUG4ghc

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Picuslab-DIETI is happy to share its last publication entitled "A deep attention based approach for predictive maintenance applications in IoT scenarios" - whose authors are Roberto De Luca, Antonino Ferraro, Antonio Galli, Mosè Gallo, Vincenzo Moscato, Giancarlo Sperlì - has been accepted on Journal of Manufacturing Technology Management.

In this paper, a deep learning-based approach has been designed for the predictive maintenance task. Its main novelty is to leverage a multi-head attention (MHA) mechanism for improving RUL estimation and reducing memory requirements.

https://lnkd.in/dwCErZvS

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A deep attention based approach for predictive maintenance applications in IoT scenarios | Emerald Insight A deep attention based approach for predictive maintenance applications in IoT scenarios - Author: Roberto De Luca, Antonino Ferraro, Antonio Galli, Mosè Gallo, Vincenzo Moscato, Giancarlo Sperlì