News

In recent years, the Metaverse has sparked an increasing interest across the globe and is projected to reach a market size of more than $1000B by 2030. This is due to its many potential applications in highly heterogeneous fields, such as entertainment and multimedia consumption, training, and industry. This new technology raises many research challenges since, as opposed to the more traditional scene understanding, metaverse scenarios contain additional multimedia content, such as movies in virtual cinemas and operas in digital theaters, which greatly influence the relevance of the metaverse to a user query. For instance, if a user is looking for Impressionist exhibitions in a virtual museum, only the museums that showcase exhibitions featuring various Impressionist painters should be considered relevant. We introduce the novel problem of text-to-metaverse retrieval, to support the users in finding the most suitable metaverse according to a given textual query. It is a challenging task, since the multimedia content present in the metaverse greatly influences […]

AILAB-Udine is proud to be one of the organizers of the 2nd European Summer School on Quantum AI, to be held in Udine on May 29 – June 01, 2023. Find the latest updated information in the official website:http://eqai.eu The summer school will take place in Udine (Italy) on May 29 – June 1, 2023, but it can also be followed remotely. The main topic of this edition is “Quantum Machine and Deep Learning”. Deadlines for application are: ▶ on-site: April 29, 2023, ▶ remote: May 15, 2023. All the speakers will be there in person to make the experience more immersive and interactive. The program will include lectures, tutorials, and dissemination opportunities. The participants may also present their own research work during a dedicated poster session, having the opportunity to interact and discuss with their peers. More information can be found on the dedicated website: http://eqai.eu/ Feel free to share this invitation with anyone interested in Quantum Computing, Quantum […]

This research project focuses on the application of machine and deep learning methods for forestry applications. In particular, the main focus is forest growing stock prediction in the Friuli Venezia Giulia region (Italy), but the developed methods can be applied to produce estimations of biophysical forest attributes on any large territory. This study will take into account different sources of data such as Forest inventory data from Nationla surveys, multispectral satellite images, climatic data and various environmental features collected through different services. Several methods will be applied to produce a forest-growing stock volume map, which will be useful to create management plans for forestry areas in the region. Traditionally, the growing stock is considered an important indicator of forest health and productivity. The growing stock is estimated through forest inventory under which both qualitative and quantitative parameters are recorded to know the overall health of growing forests. So, we will produce the results that can be considered as a basis […]

Inscriptions are a testimony to the past but their poor condition, caused by the deterioration of the material on which they are engraved upon, often makes them partially or completely illegible. The process of restoring these inscriptions is time-consuming and requires the involvement of an expert epigraphist. It is possible to speed-up this process by adopting a semi-automatic assisting tool based on deep neural networks. This project aims to develop a complete methodology, from the acquisition of the inscriptions, to the description of four possible approaches to predict the missing text in a Latin inscription, that our research team plans to implement in the near future as part of a interdisciplinary research project. Related publications: Alessandro Locaputo, Beatrice Portelli, Emanuela Colombi, Giuseppe Serra: “Filling the Lacunae in ancient Latin inscriptions”, 19th The Conference on Information and Research science Connecting to Digital and Library science (IRCDL), 2023. Andrea Brunello, Emanuela Colombi, Alessandro Locaputo, Stefano Magnani, Nicola Saccomanno, Giuseppe Serra: “Usage of […]