Current Projects
HEREDITARY
The HEREDITARY project addresses the critical challenge of leveraging multimodal health data to improve disease prevention, diagnosis, and treatment. Despite advancements in digitalization and machine learning, utilizing medical data remains complex due to its diverse nature—ranging from genomic data and bio-images to bio-signals and medical texts. Integrating this data from multiple sources is further complicated by technical and legal barriers, data heterogeneity, and privacy concerns. To address these challenges, HEREDITARY targets three key objectives overcoming the state-of-the-art in multimodal health data integration, access and re-use.
Roles: Gianmaria Silvello (Coordinator), Nicola Ferro (Task leader)
Members: 18 participants from 11 countries.
Type: Horizon Europe research and innovation programme (Contract n 101137074)
ENEOLI
Neology is the study of lexical innovation in natural languages, in multiple contexts and over time. Lexical innovation is a massive, permanent and universal phenomenon. From a strictly linguistic point of view, the study of neology “contributes to a better understanding of the lexical system of a given language and its evolution” (Sablayrolles 2019: 7), while from an extralinguistic point of view, “the inventory of neologisms also gives much information about language communities in their material lives and social representations” (ibid.). The key challenges addressed by the network may be summarised thus: 1) Define the core terminology of neology conceived as a discipline through the creation of a born-digital specialised multilingual glossary (none exist currently) in order to facilitate research on an international scale; 2) Adapt digital methodologies and tools to identify and account for lexical innovation; thanks to the involvement of institutions, experts and the general public (crowdsourcing), increase the awareness of lexical creations and their societal implications, foster creativity in mother tongues, clarity in institutional communication and in science; 3) Carry out comparative studies on lexical innovation in European languages, with a particular focus on borrowings and their equivalents; 4) Provide specific training in neology for translators, editors, journalists, technical writers and teachers through a specific protocol that could be replicated for any European language. Conferences, training schools and short-term scientific missions are also planned in order to achieve the aforementioned goals.
Roles: Federica Vezzani (Working Group Leader)
Type: European Cooperation in Science and Technology (Contract n 101137074)
BRAINTEASER
BRAINTEASER aims to integrate societal, environmental and health data to develop patient stratification and disease progression models for Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS). ALS and MS are two very complex degenerative neurological diseases, but with very different clinical picture, evolution, prognosis and therapies. Common features are that both these chronic diseases affect the nervous system and progressively modify the quality of life of the patients and their families in a significant way. BRAINTEASER will integrate large clinical datasets with novel patient-generated and environmental data collected using low-cost sensors and apps. The collected data will allow the development of Artificial Intelligence (AI) tools able to address the current needs of precision medicine, enabling early risk prediction of disease fast progression and adverse events. Technical solutions developed within the project will follow agile and user-centered approaches, accounting for the technical, medical, psychological and societal needs of the specific users.
Roles: Gianmaria Silvello (), Nicola Ferro (WP Leader)
Members: 11 participants from 6 countries.
Type: Horizon Europe research and innovation programme (Contract n 101017598)
CAMEO
Conversational agents have overwhelmingly gained importance in the last years, being widely used in chatbots, smartphones, and smart home devices, e.g., Google Home, Amazon Alexa, Apple Siri. However, they still lack the capability to understand and support real-time conversations between humans and digital counterparts in a natural, diverse and engaging way. CAMEO aims at enhancing conversational agents through an innovative use of user contextual information. In CAMEO, the user context becomes the internal knowledge of the system, including a representation of relevant information collected during past interactions of the user with the system. Such contextual information, enriched with external knowledge from the application domain considered, will be exploited to extract actionable insights enabling a rich and satisfying user interaction through advanced dialogue management, query answering, personalized search and recommendation. Moreover, CAMEO will, for the first time, jointly leverage search and recommendation approaches to improve conversational agents, also developing a principled unifying theoretical framework. Finally, CAMEO will provide researchers with better evaluation methodologies and a visual analytics environment for exploration and explanation of conversational agents behaviour in order to better support them in the design, implementation, and optimization of such systems. CAMEO will be driven and validated by a real-world use case. The Industry 4.0 use case concerns remote support to field workers in maintenance operations on industrial machines.
Roles: Nicola Ferro (Coordinator)
Members: 4 participants.
Type: PRIN: Progetti di ricerca di Rilevante Interesse Nazionale (Contract n 2022ZLL7MW)
Past Projects
EXAMODE
Exascale volumes of diverse data from distributed sources are continuously produced. Healthcare data stand out in the size produced (production is expected to be over 2000 exabytes in 2020), heterogeneity (many media, acquisition methods), included knowledge (e.g. diagnosis) and commercial value. The supervised nature of deep learning models requires large labeled, annotated data, which precludes models to extract knowledge and value. Examode solves this by allowing easy & fast, weakly supervised knowledge discovery of exascale heterogeneous data, limiting human interaction.
Roles: Gianmaria Silvello (WP and team unit leader)
Members: 30 participants from 14 countries.
Type: FP7 eContentPlus (Contract n 825292)
PREFORMA
It aims at addressing the challenge of implementing good quality standardised file formats for preserving data content in the long term. The main objective is to give memory institutions full control of the process of the conformity tests of files to be ingested into archives and to develop modular and flexible software tools to this end.
Roles: Nicola Ferro (WP Leader)
Members: 14 participants from 10 countries.
Type: FP7 Pre-commercial Procurement (Contract n 619568)
PROMISE
It aims at providing a virtual laboratory for conducting participative research and experimentation to carry out, advance and bring automation into the evaluation and benchmarking of complex multilingual and multimedia information systems, by facilitating management and offering access, curation, preservation, re-use, analysis, visualization, and mining of the collected experimental data.
Roles: Nicola Ferro (Coordinator)
Members: 13 participants from 10 countries.
Type: FP7 Network of Excellence (Contract n 258191)
EuropeanaConnect
It develops services and components for the Europeana digital library with a special focus on multilinguality and interoperability
Roles: Nicola Ferro (Task leader)
Members: 30 participants from 14 countries.
Type: FP7 eContentPlus (Contract n ECP-2008-DILI-528001)
TrebleCLEF
It fosters and promotes the experimental evaluation of multilingual information access systems
Roles: Nicola Ferro (WP leader)
Members: 7 participants from 5 countries.
Type: FP7 Coordination Action (Contract n 215231)