Abstract
We propose an approach to summarize large semantics graphs using namespaces. Semantic graphs based on the Resource Description Framework (RDF) use namespaces on their serializations. Although these namespaces are not part of RDF semantics, they have intrinsic meaning. Based on this insight, we use namespaces to create summary graphs of reduced size, more amenable to be visualized. In the summarization, object literals are also reduced to their data type and the blank nodes to a group of their own. The visualization created for the summary graph aims to give insight of the original large graph. This paper describes the proposed approach and reports on the results obtained with representative large semantic graphs.

Abstract
Currently, there is a vast amount of archival finding aids in Portuguese archives, however, these documents lack structure (are not annotated) making them hard to process and work with. In this way, we intend to extract and classify entities of interest, like geographical locations, people's names, dates, etc. For this, we will use an architecture that has been revolutionizing several NLP tasks, Transformers, presenting several models in order to achieve high results. It is also intended to understand what will be the degree of improvement that this new mechanism will present in comparison with previous architectures. Can Transformer-based models replace the LSTMs in NER? We intend to answer this question along this paper.

Abstract
Fintech has been one of the biggest agents of change in the financial sector worldwide, deserving an in-depth analysis as the aim of this study (including factors leading to its adoption, consequences, etc.). During the COVID-19 pandemic, the financial area and Fintech services allied to technology has increased efficiency, convenience, and security. To better understand this type of service, the research follows a quantitative methodology. The quantitative method included a questionnaire survey of companies that are Fintech customers, totaling 49 valid responses from firms (collected over a three-month period and which involved sending over a thousand emails to numerous companies). The response rate was low due to both the pandemic and the conjuncture with major war, which are generating uncertainty in business. The analysis was based on descriptive statistics, an assessment of the metric qualities of the scales, reliability and an Exploratory Factor Analysis, Pearson correlations and Hypothesis testing. The positive and significant effect of the technological context (perceived convenience, usefulness and effectiveness and perceived safety and trust) and the organizational context (ecological footprint reduction and internal cost reduction) on Fintech service adoption intention was confirmed. Hypothesis Three was partially confirmed since only consumer trends and reputation perception have a positive and significant effect on the intention to adopt Fintech by SMEs. The moderating effect of the environmental context in the relationship between the technological context and the intention to adopt Fintech by SMEs was partially proven, but the same was not verified in the relationship between the organizational context and the intention to adopt Fintech by SMEs. Portugal seems to be on the same adoption path as the rest of the western world, and Fintech services will undoubtedly increase, in a kind of revolution in which the strongest and those able to adapt to the markets and their needs will survive.

Abstract

Abstract
Strategic term rewriting and attribute grammars are two powerful programming techniques widely used in language engineering. The former relies on strategies (recursion schemes) to apply term rewrite rules in defining transformations, while the latter is suitable for expressing context-dependent language processing algorithms. Each of these techniques, however, is usually implemented by its own powerful and large processor system. As a result, it makes such systems harder to extend and to combine. We present the embedding of both strategic tree rewriting and attribute grammars in a zipper-based, purely functional setting. The embedding of the two techniques in the same setting has several advantages: First, we easily combine/zip attribute grammars and strategies, thus providing language engineers the best of the two worlds. Second, the combined embedding is easier to maintain and extend since it is written in a concise and uniform setting. We show the expressive power of our library in optimizing Haskell let expressions, expressing several Haskell refactorings and solving several language processing tasks for an Oberon-0 compiler.

Abstract
Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents 10% of total energy consumption. Major components are lighting (40% of consumption), electrical equipment (35%), and heating and central cooling systems (25\%). Occupants’ behaviours impact these energy consumption components, with solid evidence on the role of individual behaviours. In this work, we propose a methodology that uses virtual choreographies to identify and prioritize behaviour-change interventions towards office users based on the potential impact on energy consumption. The data shows that some behaviours with significant consumption have little potential for behavioural change impact, while other behaviours hold substantial potential for lowering energy consumption via behavioural change.