PREPARE aims at advancing rehabilitation care for patients with chronic non-communicable diseases. As rehabilitation is a complex, multifaceted, and highly personal process, we currently lack reliable patient stratification and outcome prediction tools. While big data approaches provide a path forward, existing data sets pose numerous challenges. These challenges can be overcome by combining advances in clinical research, socio-behavioral and public health research, data science, and advanced statistical and AI learning methods.

The aim of the project is to upgrade the open access semantic resources for the Slovenian language. The upgrade includes (1) the creation of a new, manually reviewed version of the Slovenian WordNet semantic lexicon; (2) (machine) creation of a database with semantic links between the new version of WordNet and the central open access Digital Dictionary Database (DSB) and other open sources with semantic data for the Slovenian language, and (3) upgrading the API (for DSB) to provide online access to selected linked data. Modern technological solutions (deep learning, neural networks) will be used to prepare existing data for manual review and to connect semantic data.

The project mainly addresses two shortcomings of the current version of the Slovenian WordNet, which arise from the methodology of its production: (1) coverage of the vocabulary or concepts and (2) data reliability. Depending on the results of the feasibility analysis, the goal of the project is full coverage of concepts or synsets (synonym sets) and manual validation of all synonym sets contained in the English WordNet. By linking data between WordNet and the Digital Dictionary, the project also ensures the availability of semantic data about the Slovenian language (DSB) in extensive semantic collections that are used in language technology and artificial intelligence systems (e.g. BabelNet, Wikidata).

Our vision is built around ethics values and trust (Responsible AI). These are intimately interwoven with the impact of AI on society, including problems associated with complex dynamic interactions between networked AI systems, the environment, and humans.

The overall objective of the Curated Multilingual Language Resources for CEF AT Action is to compile curated datasets in seven languages targeted by the consortium (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) in domains of relevance to the European Digital Service Infrastructures (DSIs) with a view to enhance the Automated Translation.

The project strives to meet the needs for computational tools and services in the field of language technologies for Slovene, which will be used by research organizations, companies, and the general public.

The development of language technologies is crucial for language survival in the digital age; these tools are the only way to keep participating in the new ways of communication, work and leisure that will be available in the future.

The EU-funded INFINITECH project will undertake an innovative effort to boost regulatory compliance and enhance directed investments.

Leaders in finance and ICT will aim to provide novel Big Data/IoT/AI technologies for managing and querying all kinds of data along with data governance capabilities.

These disruptive tools will enhance innovations in business sectors and support nine novelty testbeds and sandboxes to offer open application programming interfaces (APIs).

EnviroLENS bridges the gap for the utilization of the European Satellite capacities provided by Copernicus for environmental law enforcement and related cross-cutting sectors.

The project is responding to the demands of the jurisdictional sector for ready-to-access evidence and scenario information on environmental situations.

The main aim of the project is to deliver Earth observation-based services providing evidence on environmental incidences and legal violations in order to support the evidence data gathering process and to foster data-driven decision-making.

We are leading a breakthrough EU H2020 project, creating a solution that will help users/students find what they need not just in OER repositories, but across all open educational resources on the web.

This solution will adapt to the user’s needs and learn how to make ongoing customized recommendations and suggestions through a truly interactive and impactful learning experience.

This new AI-driven platform will deliver OER content from everywhere, for the students’ needs at the right time and place.

The European Data Science Academy (EDSA) designs curricula for data science training and data science education across the European Union (EU).

EDSA establishes a virtuous learning production cycle whereby we:

a) analyse the required sector-specific skillsets for data scientists across the main industrial sectors in Europe;

b) develop modular and adaptable data science curricula to meet industry expectations; and

c) deliver data science training supported by multi-platform and multilingual learning resources.

The curricula and learning resources are continuously evaluated by pedagogical and data science experts during both development and deployment.

QMiner is a data analytics platform for processing of large-scale real-time streams containing structured and unstructured data.

The platform provides support for preprocessing, feature engineering, data mining and machine learning.