M
Marco Furini
Researcher at University of Modena and Reggio Emilia
Publications - 107
Citations - 3756
Marco Furini is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: Computer science & The Internet. The author has an hindex of 20, co-authored 100 publications receiving 3493 citations. Previous affiliations of Marco Furini include University of Eastern Piedmont & University of Bologna.
Papers
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Proceedings ArticleDOI
On Ameliorating the Perceived Playout Quality in Chunk-Driven P2P Media Streaming Systems
TL;DR: A novel approach that uses both low-level audio and video information to divide the video in stand-alone segments is proposed that shows that if the underlying P2P network causes inter-segments playout delay, the perceived playout quality is less affected if the approach is employed.
Book ChapterDOI
Real-Time Traffic Transmission over the Internet
Marco Furini,Don Towsley +1 more
TL;DR: This paper introduces a BAM that can increase bandwidth utilization and decrease the allocated bandwidth without affecting the QoS of the delivered real-time stream and without introducing any modification in the Premium Service.
Proceedings ArticleDOI
TV commercials: Improving viewers engagement through gamification and second screen
Roberta De Michele,Marco Furini +1 more
TL;DR: This paper proposes to use the gamification approach to increase the engagement between viewers and commercials by modifying the visual layout of commercial breaks by introducing game elements (i.e., a quiz with simple questions related to some selected commercials).
Proceedings ArticleDOI
Beyond Passive Audiobook: How Digital Audiobooks Get Interactive
TL;DR: This paper proposes an architecture that produces interactive audiobooks in a transparent and secure way and introduces interactivity into digital audiobook, so that a user can interact with the storyline and the story develops according to user choices.
Journal ArticleDOI
A Predictive Method to Improve the Effectiveness of Twitter Communication in a Cultural Heritage Scenario
TL;DR: A novel method is proposed that exploits interpretable machine learning techniques to predict whether a tweet will likely be appreciated by Twitter users or not and present simple suggestions that will help to enhance the message and increase the probability of its success.