Abstract
The goal of the Semantic Web is to allow the software agents around us and AIs to extract information from the Internet as easily as humans do. This semantic web is a network of connected graphs, where relations between concepts and entities make up a layout that is very easy for machines to navigate. At the moment, there are only a few tools that enable humans to navigate this new layer of the Internet, and those that exist are for the most part very specialized tools that require from the user a lot of pre-existing knowledge about the technologies behind this structure. In this article we report on the development of DAOLOT, a search engine that allows users with no previous knowledge of the semantic web to take full advantage of its information network. This paper presents its design, the algorithm behind it and the results of the validation testing conducted with users. The results of our validation testing show that DAOLOT is useful and intuitive to users, even those without any previous knowledge of the field, and provides curated information from multiple sources instantly about any topic.

Abstract

Abstract
Several sophisticated machine learning tools (e.g., ensembles or deep networks) have shown outstanding performance in different regression forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Nevertheless, decision makers frequently require more than a black box model to be able to "trust" the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning research. This paper proposes a series of visualization tools that explain the relationship between the expected predictive performance of black box regression models and the values of the input variables of any given test case. This type of information thus allows end-users to correctly assess the risks associated with the use of a model, by showing how concrete values of the predictors may affect the performance of the model. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end-user. Furthermore, a thorough evaluation of the proposed tools is performed to showcase the reliability of this approach.

Abstract
Emotion is an essential part of what means to be human, but it is still disregarded by most technical fields as something not to be considered in scientific or engineering projects. However, the understanding of emotion as an aspect of decision-making processes and of modelling of human behavior is essential to create a better connection between humans and their tools and machines. With this work we focus on the measurement of emotion of users through the use of non-intrusive methods, like measuring inputs and reactions to stimuli, along with the creation of a tool that measures the emotional changes caused by visual output created by the tool itself. Usage of the tool in a test environment and the subsequent analysis of the data obtained will allow for conclusions about the effectiveness of the method, and if it is possible to apply it to future studies on human emotions by investigators in the fields of psychology and computation.

Abstract
Innovation matters. Business success increasingly depends upon sustainable innovation. Observing recent innovation best practices, the emergence of a new paradigm is traceable. Creating an innovative ecosystem has a multilayer effect: It contributes to regional digitalization, technological start-up emergence, open innovation promotion, and new policy enhancement retro-feeding the system. Public policy must create open innovation environments accordingly with the quintuple helix harmonizing the ecosystem to internalize emerging spillovers. The public sector should enhance the process, providing accurate legal framework, procurement of innovation, and shared risks in R&D. Opening the locks that confine the trunks of community, academic, industry, and government innovation will harness each dimension exploiting collective and collaborative potential of individuals towards a brighter sustainable future. In this sense, the aim of this study is to present how open innovation can enhance sustainable innovation ecosystems and boost the digital transition. For that, firstly, a diachronic perspective of the sustainable innovation ecosystem is traced, its connection to open innovation, and identification of the university linkages. Secondly, database exploration and econometric estimations are performed. Then, we will ascertain how far open innovation frameworks and in particular the knowledge flows unveiled by the university promote smart and responsible innovation cycles. Lastly, we will propose a policy package towards green governance, empowering the university in governance distributed ecosystem, embedded in the community, self-sustained with shared gains, and a meaningful sense of identity.

Abstract
Smart Transformers (STs) are being envisioned as a key element for the controllability of distribution networks in a future context of Renewable Energy Source (RES), Energy Storage System (ESS) and Electric Vehicle (EV) massification. Additionally, STs enable the deployment of hybrid AC/DC networks, which offer important advantages in this context. In addition to offering further degrees of controllability, hybrid AC/DC networks are more suited to integrate DC resources such as DC loads, PV generation, ESS and EV chargers. The purpose of the work developed in this paper is to address the feasibility of exploiting STs to actively coordinate a fleet of resources existing in a hybrid AC/DC network supplied by the ST aiming to provide active power-frequency regulation services to the upstream AC grid. The feasibility of the ST to coordinate the resources available in the hybrid distribution AC/DC network in order to provide active power-frequency regulation services is demonstrated in this paper through computational simulation. It is demonstrated that the aforementioned goal can be achieved using droop-based controllers that can modulate controlled variables in the ST.