Progetti di ricerca

With a vision “to drive responsible transformation for regions and communities, serve them with creative solutions by joint outcomes from incorporating research into education and turn contemporary challenges of our era into opportunities”, seven leading universities unite in their pledge to establish… Leggi tutto the INVEST - INnoVations of REgional Sustainability European university Alliance. We are committed into a deeper, sustainable and an effective transnational cooperation that promotes European Identity and European values leading to sustainable, short- and long-term impacts. With this premise, our aim is to advance knowledge, increase the quality and relevance of higher education and research, strengthen links between education, research, and innovation, improve employability and skills, make more effective use of digital technologies and open science, towards imparting inclusive education and training at all levels. We shall achieve this based on three strategic pillars: (a) Regional Transformation, Ecosystems & Entrepreneurship; (b) Flexible education & workbased pedagogy; (c) Developing a Centre of Excellence in RDI. Our main focus areas are Water, Energy, Food and Environment Nexus, Quality of Life and Entrepreneurship. We intensify the use of our Living Labs as innovative platforms for quadruple helix collaboration among research, education, innovation and service to society. The process is supported by innovative digital solutions such as the Virtual Campus and the EDUC8EU platforms. The INVEST Alliance endeavours to meet the needs and requirements of the new generation of Europeans who are able to cooperate and work within different European and global cultures, in different languages, across borders, sectors, academic disciplines, and respond to the current global challenges determined within the UN Sustainable Development Goals. INVEST will strive impact and create models for good practices for a more sustainable higher education and a more sustainable Europe.

Machine learning methods operate on formal representations of the data at hand and the models or patterns induced from the data. They also assume a suitable formalization of the learning task itself (e.g., as a classification problem), including a specification of… Leggi tutto the objective in terms of a suitable performance metric, and sometimes other criteria the induced model is supposed to meet. Different representations or problem formalizations may be more or less appropriate to address a particular task and to deal with the type of training information available. The goal of LEMUR is to create a novel branch of machine learning we call Learning with Multiple Representations. We aim to develop the theoretical foundations and a first set of algorithms for this new paradigma. Moreover, corresponding applications are to demonstrate the usefulness of the new family of approaches. We regard LEMUR as very timely, as LMR algorithms will allow to flexible representations (e.g., suitable for explainability, fairness) with diverse target functions (e.g., incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. We will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e., their data hunger, by means of weaker sources of labelling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competences relevant to successful careers in central AI areas.

DoSSIER (Domain Specific Systems for Information Extraction and Retrieval) will elucidate, model, and address the different information needs of professional users. It mobilizes an excellent and highly synergistic team of world-leading Information Retrieval (IR) experts from 5 EU States who,… Leggi tutto together with 3 academic partners (universities in US, Japan, and Australia), and 11 industrial partners (dynamic SMEs and large corporations) will produce fundamental insights into how users comprehend, formulate, and access information in professional environments. For this, DoSSIER takes a highly innovative intersectorial and multidisciplinary approach, addresses fundamental questions about the nature and representation of information needs, engages in novel qualitative and quantitative evaluation, and provides training towards a structured, rigorous, and practical approach to search systems. It connects premier universities and outstanding industrial partners to provide unique opportunities to young researchers. The research is structured in three areas: 1. fundamental models of users and domain specificity, 2. contextual and personalized search, and 3. workflow, task and the interface. Each area individually and in cross-field fertilisation, will produce breakthroughs in our understanding of computer-supported human information search workflows. The result will be a new generation of information access systems, which will accelerate innovation cycles in EU academia and industry, as well as in society as a whole. To be both concrete and generic, DoSSIER consists of 8 projects identifying a target domain and 7 projects acting horizontally across domains. Three vital domains are used: science & technology innovation, law, and healthcare. Questions currently unanswerable (e.g. What is the key innovation difference between these two patents?) will be answerable either directly by a system, or by the development of cognition-enhancing instruments for interacting with information.