Abstract

Abstract
Millennials interact with technology like no other generation before them and this is affecting how they want to be taught in higher education and how they want to lead and expect to be led in organizations, after graduating. Though stating that they want to be enlightened in academia, some qualitative comments from millennials indicate the opposite, namely that they want to be prepared but also to be kept naive as to what business really entails. This is expected to help keep motivation levels high, as motivation is seen to be the key element to success in life. Millennials expect also to be led authentically and to be treated as valued human beings. This is in contrast to the current autocratic leadership profile found predominantly in Portuguese organizations, at the time of writing. This study had a sample of one hundred and eleven millennial students who answered a survey on attitudes towards leadership and their desired approach to higher education. Three interviews with seasoned executives were also performed, to establish a contrast and see other perspectives. With this research, we conclude that we may be in the presence of a hard working millennial generation, contrary to previous research findings which has indicated that they are lazy. Finally, information technology (IT) is a precious partner in class, in particular Padlet.com, Moodle, and online News Forums, as well as the challenge to create original videos about course content. Future research should focus on how technology has made society more transparent with employees wanting more democratic leaders in times when hierarchies are seen to hinder rather than aid productivity levels.

Abstract
This paper aims to identity the significant impact factors (IFs) of the residential electricity consumption level (RECL) and to better understand the influence mechanism of IFs on RECL. The analysis of influence mechanism is commonly through regression model where feature selection must first be performed to pick out non-redundant IFs that is highly correlated with RECL. In contrast to the existing studies, this study recognizes the problem that majority feature selection methods (e.g., step regression) are limited to the identification of linear relationships and proposes a novel wrapper feature selection (WFS) method to address this issue. The WFS is based on genetic algorithm (GA) and multinomial logistic regression (MLR). GA is a searching algorithm used to generate different feature subsets (FSs) that consist of several IFs. MLR is a modeling algorithm used to score these FSs. Further, maximal information coefficient (MIC) is utilized to verify the validity of WFS for selecting IFs. Finally, MLR based explanatory model is established to excavate the relationship between selected IFs and RECL. The results of Ireland dataset based case study show that WFS can identify the significant and non-redundant IFs that are linearly or nonlinearly related to RECL. The details about how selected IFs affect RECL are also provided via the explanatory model. Such research can provide useful guidance for a wide range of stakeholders including local governments, electric power companies, and individual households.

Abstract
Internationalization involves an active behavior to compete in international markets. Several theories, as well as several entry modes, have been developed to explain why and how firms compete internationally. Nevertheless, the internationalization process is difficult to implement as it involves not only historical reasons, as well as traditional strategies that sometimes are usually not questioned by the firm. This chapter aims to depict a case study in which a firm is trying to deploy an opportunity-driven internationalization, shifting its traditional modus operandi. The firm is analyzed based on several theories, namely the Uppsala model, the network-based theory, the born globals and the born again globals, and it is possible to conclude that despite its more than 80 years of existence, neither of the four theories can be properly used to explain the firm's international behavior. Moreover, to embrace international challenges, the firm needs to reposition its traditional business behavior.

Abstract
For the last decade, uebe.Q is being adopted by companies in different business areas and countries for managing compliance with solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive, solid and valuable business logic. When it is deployed for/on a company, it usually demands an extensive and specific adaptation (i.e. software refinement) and configuration process involving DigitalWind's ISO 9000 and ISO 1400 experts as well as software development and operation teams. However, a recent business model change imposed that the evolution and configuration of the software, shifts from DigitalWind (and especially from the development team) to external consultants and to other business partners, along with the fact that different third-party's systems and respective data/information need to be integrated with minimal intervention of the development team. This paper presents and overview of the re-engineering process taken to handle this business model change by adopting (i) ontologies for the specification of business concepts, (ii) closed-world assumption (CWA) rules for the specification of the dynamics of the system and (iii) Domain Specific Language (DSL) for the configuration of the system by domain/business experts. The DSL approach is further described in detail. © 2018 IEEE.

Abstract
The burst of social networks and the possibility of being continuously connected has provided a fast way for information diffusion. More specifically, real-time posting allowed news and events to be reported quicker through social networks than traditional news media. However, the massive data that is daily available makes newsworthy information a needle in a haystack. Therefore, our goal is to build models that can detect journalistic relevance automatically in social networks. In order to do it, it is essential to establish a ground truth with a large number of entries that can provide a suitable basis for the learning algorithms due to the difficulty inherent to the ambiguity and wide scope associated with the concept of relevance. In this paper, we propose and compare two different methodologies to annotate posts regarding their relevance: automatic and human annotation. Preliminary results show that supervised models trained with the automatic annotation methodology tend to perform better than using human annotation in a test dataset labeled by experts.