Over the past decade, Russia’s ruling elites have massively stepped up their efforts to influ-ence media audiences abroad. Amongst others, Russia has been alleged to have sought to sway votes in Austria, France, Germany, Ukraine, and the US. This project’s overarching research ques-tion is: How, and with what consequences, have new Internet-based technologies contributed to the emergence of novel resources, techniques, and processes by which political elites in Moscow can influence media audiences abroad? In order to address this question, a theoretical work package (WP4) will undertake the first major systematic effort to interrogate how much, or how little, we can leverage extant in-depth knowledge of former-Soviet foreign propaganda, conducted in the broadcast era, in order to make sense of Russia’s recent digitally-enabled efforts. WP4 will be informed by three empirical WPs. They will scrutinize three heavily digitally-enabled elements of Russia’s recent efforts: • WP1 will conduct a comprehensive in-depth study of foreign active online audiences and other co-creators of Russia-related content. • WP2 will produce pioneering research about how social media platforms function as key transmission channels that connect Russia’s domestic media with Russian-speaking audiences abroad. • WP3 will be the first study to scrutinize the role of the Kremlin-controlled search engine Yan-dex as a resource of foreign influence. Methodologically, WP1-3 are highly innovative because they combine new computational methods (data mining, automated text analysis) with traditional methods (surveys, in-depth inter-views, grounded theory). In response to Russia’s recent efforts, countermeasures have been ushered in by a plurality of actors, including the EU, NATO, and NGOs. These actors will benefit immensely from the knowledge generated, which will enable them to enhance their initiatives to secure democratic elec-toral processes against undue informational interference.

Engaging children with ASC (Autism Spectrum Conditions) in communication centred activities during educational therapy is one of the cardinal challenges by ASC and contributes to its poor outcome. To this end, therapists recently started using humanoid robots (e.g., NAO) as assistive tools. However, this technology lacks the ability to autonomously engage with children, which is the key for improving the therapy and, thus, learning opportunities. Existing approaches typically use machine learning algorithms to estimate the engagement of children with ASC from their head-pose or eye-gaze inferred from face-videos. These approaches are rather limited for modeling atypical behavioral displays of engagement of children with ASC, which can vary considerably across the children. The first objective of EngageME is to bring novel machine learning models that can for the first time effectively leverage multi-modal behavioural cues, including facial expressions, head pose, vocal and physiological cues, to realize fully automated context-sensitive estimation of engagement levels of children with ASC. These models build upon dynamic graph models for multi-modal ordinal data, based on state-of-the-art machine learning approaches to sequence classification and domain adaptation, which can adapt to each child, while still being able to generalize across children and cultures. To realize this, the second objective of EngageME is to provide the candidate with the cutting-edge training aimed at expanding his current expertise in visual processing with expertise in wearable/physiological, and audio technologies, from leading experts in these fields. EngageME is expected to bring novel technology/models for endowing assistive robots with ability to accurately ‘sense’ engagement levels of children with ASC during robot-assisted therapy, while providing the candidate with a set of skills needed to become one of the frontiers in the emerging field of affect-sensitive assistive technology.