An approach to semantic text similarity matching is concept-based characterization of entities and themes that can be automatically extracted from content. This is useful to build an effective recommender system on top of similarity measures and its usage for document retrieval and ranking. In this work, our research goal is to create an expert system for education recommendation, based on skills, capabilities, areas of expertise present in someone's curriculum vitae and personal preferences. This form of semantic text matching challenge needs to take into account all the personal educational experiences (formal, informal, and on-the-job), but also work-related know-how, to create a concept based profile of the person. This will allow a reasoned matching process from CVs and career vision to descriptions of education programs. Taking inspiration from the explicit semantic analysis (ESA), we developed a domain-specific approach to semantically characterize short texts and to compare their content for semantic similarity. Thanks to an enriching and a filtering process, we transform the general purpose German Wikipedia into a domain specific model for our task. The domain is defined also through a German knowledge base or vocabulary of description for educational experiences and for job offers. Initial testing with a small set of documents demonstrated that our approach

The rapid and pervasive development of methods from Artificial Intelligence (AI) affects our everyday life. Its application improves the users' experience of many daily tasks. Despite the enhancements provided, such approaches have a substantial limitation in the shortfall of people's trust connected with their lack of explainability. In natural language understanding (NLU) and processing (NLP), a fundamental objective is to support human interactions using sense-making of the language for communication. Such methods try to comprehend and reproduce the self-evident processes of human communication. This applies either in receiving speech signals or in extracting relevant information from a text. Furthermore, the pervasiveness of AI methods in the workplace and on the free time demands a sustainable and verified support of users' trust, as a natural condition for their acceptance. The objective of this work is to introduce a framework for the calculation and selection of understandable text features. Such features can increase the confidence placed into adopted NLP solutions. The following work outlines the Text Feature Framework and its text features, based on statistical information coming from a general text corpus. The showcase experiment uses those features to verify them on the concept recognition task. The results shows their capability to explain a model and its predictions. The resulting concept recognition models are competitive with other methods existing in the literature. It has the definitive advantage of being able to externalize the supporting evidence for a choice of concept identification.

This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers. Starting from the existing tension between the intrinsically hierarchical current structure of the electricity distribution network and the substantially distributed and self-organising nature of the self-generation, we explore the limitations imposed by the current conditions. Initially, we introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid. Secondly, we analyse the consequent changes for the different users' roles, also in connection with some incentive models connected with the decentralisation of the power production. To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability. Thirdly, we present an example of a typical P2P settlement, showcasing the role of all the previously analysed aspects. To conclude, we performed a review of relevant activities in this domain, to showcase where existing projects are going and what are the most important themes covered. Being this a work in progress, many open questions are still on the table and will be addressed in the next stages of the research. Eventually, by providing a reference model as base for further discussions and improvements, we would like to engage ourselves in a dialog with the different users and the broad community, oriented towards a more fair and ecological-friendly solution for the electricity market of the future.

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