The current paradigm in service provisioning to future communication networks lacks thorough end-to-end interpretation from the quality viewpoint, while the end-users’/customers’ profiles and preferences are mostly not taken into account. The subjective perception of a provided service, known as Quality of Experience (QoE), is one of the most important factors for a user’s decision on retaining the service or giving it up, and the key parameter for enabling advanced customer experience management (CEM). The main objective of CASPER is to combine academic and industrial forces towards leveraging the expected benefits of QoE exploitation in future networks. In particular, CASPER will exploit the most recent approaches in communication networks, such as the Software Defined Networking (SDN) and the Network Functions Virtualisation (NFV), to design and implement a middleware architecture for QoE-driven service provisioning. The architecture will consist of three interlinked modules, one-to-one mapped to the three instrumental functionalities required for the beneficial exploitation of QoE: i) reliable, secure and passive QoE monitoring, ii) efficient, dynamic and objective QoE estimation, and iii) robust and real-time QoE-driven service management. The three modules will be optimised and released as an integrated solution, in order to accelerate the adoption of QoE-driven network service management. The cornerstone of this effort will be a carefully-planned inter-sectorial secondment programme for Experienced Researchers (ERs) and Early Stage Researchers (ESRs). Under this programme, CASPER is expected to foster the exchange of knowledge and strengthen the collaboration among academia and industry through a bidirectional knowledge-sharing approach, where the academic beneficiaries will contribute by conveying their knowledge in QoE analysis and modelling, while industrial beneficiaries will provide their expertise in service development and software implementation.

VOXReality is an ambitious project whose goal will be to facilitate and exploit the convergence of two important technologies, natural language processing (NLP) and computer vision (CV). Both technologies are experiencing a huge performance increase due to the emergence of data-driven methods, specifically machine learning (ML) and artificial intelligence (AI). On the one hand, CV/ML are driving the extended reality (XR) revolution beyond what was possible up to now, and, on the other, speech-based interfaces and text-based content understanding are revolutionising human-machine and human-human interaction. VOXReality will employ an economical approach to combine these two. VOXReality will pursue the integration of language- and vision-based AI models with either unidirectional or bidirectional exchanges between the two modalities. Vision systems drive both AR and VR, while language understanding adds a natural way for humans to interact with the back-ends of XR systems or create multimodal XR experiences combining vision and sound. The results of the project will be twofold: 1) a set of pretrained next-generation XR models combining, in various levels and ways, language and vision AI and enabling richer, more natural immersive experiences that are expected to boost XR adoption, and 2) a set of applications using these models to demonstrate innovations in various sectors. The above technologies will be validated through three use cases: 1) Personal Assistants that are an emerging type of digital technology that seeks to support humans in their daily tasks, with their core functionalities related to human-to-machine interaction; 2) Virtual Conferences that are completely hosted and run online, typically using a virtual conferencing platform that sets up a shared virtual environment, allowing their attendees to view or participate from anywhere in the world; 3) Theaters where VOXReality will combine language translation, audiovisual user associations and AR VFX triggered by predetermined speech.

The increasing amount and availability of collected data (cancer imaging) and the development of novel technological tools based on Artificial Intelligence (AI) and Machine Learning (ML), provide unprecedented opportunities for better cancer detection and classification, image optimization, radiation reduction, and clinical workflow enhancement. The INCISIVE project aims to address three major open challenges in order to explore the full potential of AI solutions in cancer imaging: (1) AI challenges unique to medical imaging, (2) Image labelling and annotation and (3) Data availability and sharing. In order to do that INCISIVE plans to develop and validate: (1) an AI-based toolbox that enhances the accuracy, specificity, sensitivity, interpretability and cost-effectiveness of existing cancer imaging methods, (2) an automated-ML based annotation mechanism to rapidly produce training data for machine learning research and (3) a pan-European repository federated repository of medical images, that will enable the secure donation and sharing of data in compliance with ethical, legal and privacy demands, increasing accessibility to datasets and enabling experimentation of AI-based solutions. The INCISIVE models and analytics will utilize various cancer imaging scans, biological data and EHRs, and will be trained with 1 PB of available data provided by 8 partners within the project. INCISIVE solution will be investigated in four validation studies for Breast, Prostate, Colorectal and Lung Cancer, taking place in 8 pilot sites, from 5 countries (Cyprus, Greece, Italy, Serbia and Spain), with participation of at least 2,600 patients and a total duration of 1.5 year. INCISIVE moves beyond the state of the art, by improving sensitivity and specificity of lower cost scanning methods, accurately predicting the tumor spread, evolution and relapse, enhancing interpretability of results and “democratizing” imaging data.