Abstract
Disability from musculoskeletal diseases and co-morbidities may lead to the worsening of social and economic well-being through a multitude of paths. Moreover since in European Union (EU) Member States it is projected that those aged 65 and over will become a much larger share (rising from 17% to 30% of the population), and those aged 80 and over (rising from 5% to 12%) will almost become as numerous as the young population in 2060, there is a great potential for Information and Communication Technologies (ICT) solutions for addressing the present and future living arrangements in older people. The UPPERCARE system is meant to affect positively both the intergenerational and partners care since it contributes to decrease usability barriers and promote collaborative environments for informal and self-care. UPPERCARE is a new approach for integrated care supported by ICT systems and services, focusing on post-operative rehabilitation of musculoskeletal pathologies, having as a case study the knee post-operative scenarios of prosthetic care. This paper presents the UPPERCARE system, that provides an integrated care solution, supported ICT, for empowering self-care and adherence to rehabilitation plans through natural interfaces, gamification and cross-modal paths for community care collaboration. The system addresses current barriers from technological, clinical, social and organisational perspectives in a multidisciplinary environment. Special attention is given to the patients' needs and behaviours entailing the participation of a wide care community, including clinical and non-clinical people, associations, institutions and authorities) through an user driven interaction within the system.

Abstract
The electricity markets restructuring process encouraged the use of computational tools in order to allow the study of different market mechanisms and the relationships between the participating entities. Automated negotiation plays a crucial role in the decision support for energy transactions due to the constant need for players to engage in bilateral negotiations. This paper proposes a methodology to estimate bilateral contract prices, which is essential to support market players in their decisions, enabling adequate risk management of the negotiation process. The proposed approach uses an adaptation of the Q-Learning reinforcement learning algorithm to choose the best from a set of possible contract prices forecasts that are determined using several methods, such as artificial neural networks (ANN), support vector machines (SVM), among others. The learning process assesses the probability of success of each forecasting method, by comparing the expected negotiation price with the historic data contracts of competitor players. The negotiation scenario identified as the most probable scenario that the player will face during the negotiation process is the one that presents the higher expected utility value. This approach allows the supported player to be prepared for the negotiation scenario that is the most likely to represent a reliable approximation of the actual negotiation environment. © 2017 IEEE.

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.