SMEthod is a research project aimed at delivering an improved methodology of identifying SME’s pathways to innovation and of segmenting innovating SMEs in order to determine optimal targeting and timing of support measures allocated by innovation agencies and other institutions. A complementary goal is to create a decision support tool (DST) ) which will be devolved in cooperation with the relevant stakeholders who implement innovation policies and by compiling the complex SME innovativeness determinants will offer them a flexible evaluation tool. Broad dissemination activities are also planned to reach innovation stakeholders, innovation and regional development agencies, ministries, etc. The SMEthod methodology will consider important factors that are likely to influence companies and the societal impact they create. In particular, enterprise lifecycle, industrial sectors, regional characteristics, and innovation cycles will be scrutinized. New segmenting criteria for innovation support policies will be prepared and specific variables will be determined and weighted in order to assure usability of the methodology. The holistic character of the project will be assured by proposing a more efficient allocation of appropriate pro-innovation measures. Most common and relevant methodologies of segmenting SMEs for the purpose of innovation support will be evaluated as well. Furthermore, efficiency of selected methods and techniques will be assessed. Conclusions from the research will be merged to deliver a final product of the project – a holistic methodology and the DST based on it. The project’s impact will be a better understanding of innovation dynamics and potentials in SMEs and of their efficient segmentation. New knowledge will be created on the effectiveness of innovation policies and most popular measures. Better targeting different instruments toward most promising SMEs will be possible thanks to evaluative analyses and new segmenting criteria.

Artificial intelligence is single-handedly changing decision-making at different levels and sectors in often unpredictable and uncontrolled ways. Due to their black-box nature, existing models are difficult to interpret, and hence trust. Explainable AI is an emergent field, but, to ensure no loss of predictive power, many of the proposed approaches just build local explanators on top of powerful black-box models. To change this paradigm and create an equally powerful, yet fully explainable model, we need to be able to learn its structure. However, searching for both structure and parameters is extremely challenging. Moreover, there is the risk that the necessary variables and operators are not provided to the algorithm, which leads to more complex and less general models. It is clear that state-of-the-art, yet practical, real-world solutions cannot come only from the computer science world. Our approach therefore consists in involving human intelligence in the discovery process, resulting in AI and humans working in concert to find better solutions (i.e. models that are effective, comprehensible and generalisable). This is made possible by employing ‘explainable-by-design’ symbolic models and learning algorithms, and by adopting a human-centric, ‘guided empirical’ learning process that integrates cognition, machine learning and human-machine interaction, ultimately resulting in a Transparent, Reliable and Unbiased Smart Tool. This proposal aims to design TRUST, ensure its adequacy to tackle predictive and prescriptive problems, and create an innovation ecosystem around it, whereby academia and companies can further exploit it, independently or in collaboration. The proposed ‘human-guided symbolic learning’ should be the next ‘go-to paradigm’ for a wide range of sectors, where human agency / accountability is essential. These include healthcare, retail, energy, banking, insurance and public administration (of which the first three are explored in this project).

Learning for Adult Social Care Practice Innovations and Skills Development ( LAPIS) aims to support adult social care in developing work-based learning (WBL) and learning for leadershi innovation. The reason we are focussing on this topic is because our previous project Helpcare (www.helpcare-project.org) found important gaps in provision which this project addresses. The care sector has been hard-hit by Covid 19, the LAPIS project will help develop learning for leadership, skills and innovation to build reslilence for the future. 1) Care workers taking part in HELPCARE identified over 400 individual unmet training needs, many could be delivered through WBL (Pavlidis et al, 2020)2) Care workers identified many opportunities for WBL which were not exploited by managers (Pavlidis et al, 2020)3) Care managers expressed lack of confidence at developing and delivering WBL programmes (Pavlidis et al, 2020)4) Care managers and commissioners did not have links with TVET or HE organisations to help them develop or gain recognition of WBL5) Care workers and care managers recognised there were many opportunities for innovation in social care but lacked knowledge of how to identify innovation, where to get support with developing innovation and how to implement innovation in care settings. 6) There is no effective method for sharing innovation across the extremely fragmented EU care sector The project team took the well-established principle that organisations where learning is embedded are better equipped to innovate (Landry, Becheikh, and Ouimet, 2008; Kerr and Lloyd, 2008; Holman, et al, 2012) and developed LAPIS in response to the problems outlined above. Our objectives and outputs centre around improving skills for leaders in the care sector in work-based and innovation learning, building networks to support work-based learning and innovation learning in social care and developing the LAPIS app.Thus, there are two policy-related outputs, IO1 covering barriers to innovation identification and development and implementation work-based learning in adult social care, and IO4 setting out strategies to overcome barriers to the recognition of work-based learning in adult social care. IO2, IO3, IO5, IO6 are a set of digital learning programmes based on information from IO1 and other project activities, which will provide a curriculum and guide for care managers, care company owners and care commissioners enabling them to develop and implement work-based learning, providing routes to partnerships with TVET and HEI to support the recognition of work-based learning, improve the capacity among care managers in particular to lead learning in the workplace and also enabling care managers and leaders to develop their skills in promoting, identifying and growing innovation in the sector. Our most innovative output is the LAPIS app, based on an innovation classification engine using machine learning to classify social care innovations and answer innovation-based queries from the adult social care sector. This unique output will allow crowd-sourcing of information about innovation and best practice, classified in line with the OSLO GUIDELINES FOR COLLECTING AND INTERPRETING TECHNOLOGICAL INNOVATION DATA, to support identification and sharing of innovations in social care. We will work with 500 direct participants over the life of the project. This includes care workers, care managers, care commissioners (social workers, doctors, nurses) policy makers (civil servants at regional and national level, responsible for health and education) national innovation agencies and HEI/TVET providers.Our underpinning methodology is participatory action research (PAR). This project approach is ideal for solving complex problems as it emphasizes participation and action, encouraging change through collaboration and reflection. PAR emphasizes collective inquiry and experimentation grounded in the lived experience of participants. We will use inclusive methods such as Ketso-based workshops (https://www.youtube.com/watch?v=y5cZmKmLI2M) that overcome power differentials and value all participants equally. The LAPIS project is innovative in proposing new tools to enable a fragmented sector to work more closely with each other on learning, and proposing new ways to build partnerships with TVET/HE providers so informal learning ( particularly important in the area of soft skills critical to social care) and formal work-based learning can be planned, implemented and recognised. The project is also innovative in recognising the barriers to innovation in the social care sector, and linking innovation to learning in the workplace. We envisage LAPIS facilitating more effective WBL, promoting innovation learning and practice in the sector and longer term evidence shows improving staff training improves retention and increases the status of care work in the general population, encouraging recruitment and supporting staff retention.

Measurement campaigns have shown major discrepancies in buildings energy performance between planned energy demand and real energy consumption, while nowadays most of the newly constructed offices buildings are equipped with BMS systems, integrating a more or less extended measurement layer providing large amounts of data. Their integration in the building management sector offers an improvement capability of 22 % as some studies demonstrate. The HIT2GAP project will develop a new generation of building monitoring and control tools based on advanced data treatment techniques allowing new approaches to assess building energy performance data, getting a better understanding of building’s behaviour and hence a better performance. From a strong research layer on data, HIT2GAP will build on existing measurement and control tools that will be embedded into a new software platform for performance optimization. The solution will be: - Fully modular: able to integrate several types and generations of data treatment modules (different algorithms) and data display solutions, following a plug and play approach - Integrating data mining for knowledge discovery (DMKD) as a core technique for buildings’ behaviour assessment and understanding The HIT2GAP solution will be applied as a novel intelligent layer offering new capability of the existing BMS systems and offering the management stakeholders opportunities for services with a novel added value. Applying the solutions to groups of buildings will also allow to test energy demand vs. local production management modules. This will be tested in various pilot sites across Europe. HIT2GAP work will be realized with a permanent concern about market exploitation of the solutions developed within the project, with specific partnerships about business integration of the tools in the activity of key energy services partners of the consortium.