Publications by authors named "Nicola Messina"

The automatic detection of violent actions in public places through video analysis is difficult because the employed Artificial Intelligence-based techniques often suffer from generalization problems. Indeed, these algorithms hinge on large quantities of annotated data and usually experience a drastic drop in performance when used in scenarios never seen during the supervised learning phase. In this paper, we introduce and publicly release the benchmark, the first large-scale collection of video clips for violence detection on public transport, where some actors simulated violent actions inside a moving bus in changing conditions, such as the background or light.

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Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues.

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Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there has been an increasing interest in convolutional neural network-based architectures to execute such a task. One of these supervised networks' critical goals is to generalize the knowledge learned during the training phase to new scenarios with different characteristics.

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Purpose of this work was to study the effect of UV irradiation on a microecosystem consisting of several interacting species. The system chosen was of a hypersaline type, where all the species present live at high salt concentration; it comprises different bacteria; a producer, the photosynthetic green alga Dunaliella salina; and a consumer, the ciliated protozoan Fabrea salina, which form a complete food chain. We were able to establish the initial conditions that give rise to a self-sustaining microecosystem, stable for at least 3 weeks.

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