Abstract
Smart governance is one of the characteristics of smart cities, having its roots in e-government, in the principles of good governance, and in the assumptions of citizens' participation and involvement in public decision-making. This chapter aims to answer the question: “What smart governance practices are being implemented in smart cities” through an extensive literature review in the areas of e-government, good governance, smart cities and smart governance, and content analysis of the websites of seven smart cities: Amsterdam, Barcelona, Copenhagen, Lisbon, Manchester, Singapore, and Stockholm. The objective was to identify the presence of factors related with e-participation; e-services; and public administration functioning on the cities' websites. The chapter ends with directions for future research and the conclusion that all the smart cities analyzed presented some factors related with smart governance, but with different levels of development and application.

Abstract
In a collaborative conceptualisation process, the existence of several solutions for a given domain is a very common problem. Given this, specialists must reach a consensus on the concepts that will encompass the final solution. Therefore, this work aims to provide a tool for the integration of conceptual models in order to help specialists during the negotiation phase of developing the final shared model. This approach analyses the concepts of two models and shows the similar concepts to the specialists. The semantic similarity is obtained after three stages, namely: normalization, syntax analysis and semantic analysis. To evaluate the proposed approach, the values of precision and recall measures were calculated in two practical application scenarios. The obtained results proved to be better when compared to the existing tools when applied to semi-formal models (conceptual maps), and very close to the best tools focused on formal models (ontologies) integration.

Abstract
In this paper we study the combined use of four different NLP toolkits-Stanford CoreNLP, GATE, OpenNLP and Twitter NLP tools-in the context of social media posts. Previous studies have shown performance comparisons between these tools, both on news and social media corporas. In this paper, we go further by trying to understand how differently these toolkits predict Named Entities, in terms of their precision and recall for three different entity types, and how they can complement each other in this task in order to achieve a combined performance superior to each individual one. Experiments on two publicly available datasets from the workshops WNUT-2015 and #MSM2013 show that using an ensemble of toolkits can improve the recognition of specific entity types - up to 10.62% for the entity type Person, 1.97% for the type Location and 1.31% for the type Organization, depending on the dataset and the criteria used for the voting. Our results also showed improvements of 3.76% and 1.69%, in each dataset respectively, on the average performance of the three entity types.

Abstract
Innovation is seen as a key element of an organization’s competitiveness. Along with the current imperative for innovation comes the need to adequately measure it. The purpose of this paper is to perform a literature review in the field of innovation performance models and metrics. The performed work aims to make an important contribution by facilitating the identification and categorization of innovation performance models and metrics formulated until the present time. A survey methodology was adopted to identify the most predominant contributions in the field. For that, the top three most popular bibliometric indexes (Web of Science, Scopus, and Google Scholar) were used. Then, the top ten most relevant studies on innovation performance models and metrics were compared to determine similarities and differences between each generation of models and metrics. Finally, the main innovation performance models and metrics were identified, classified and compared.

Abstract
Research data management is rapidly becoming a regular concern for researchers, and institutions need to provide them with platforms to support data organization and preparation for publication. Some institutions have adopted institutional repositories as the basis for data deposit, whereas others are experimenting with richer environments for data description, in spite of the diversity of existing workflows. This paper is a synthetic overview of current platforms that can be used for data management purposes. Adopting a pragmatic view on data management, the paper focuses on solutions that can be adopted in the long tail of science, where investments in tools and manpower are modest. First, a broad set of data management platforms is presented-some designed for institutional repositories and digital libraries-to select a short list of the more promising ones for data management. These platforms are compared considering their architecture, support for metadata, existing programming interfaces, as well as their search mechanisms and community acceptance. In this process, the stakeholders' requirements are also taken into account. The results show that there is still plenty of room for improvement, mainly regarding the specificity of data description in different domains, as well as the potential for integration of the data management platforms with existing research management tools. Nevertheless, depending on the context, some platforms can meet all or part of the stakeholders' requirements.

Abstract