Pedro Campos

Abstract
The increasing use of medical software as an interface between patients and medical staff has raised alarming questions on the safety of data privacy and assurance of patients' rights. This issue has reached a new level with the emergent use of medical social networks in Health Information Systems. Medical networks, which work as an interface between the patient medical data and geographical and/or social connections, as well as between the patient individual needs and the attending medical doctor, can allow feasible and fast visualization/information systems. As new models for medical social networks and health data visualization and information systems are planned and presented, the need for protocols regarding data privacy in this context is becoming a subject of analysis and discussion. This chapter reviews the evolution and status quo of prospective medical social networks within data privacy and patients' rights, and discusses the ideal model and its future venues and interaction with ethics in the areas of Law, Health Policies, and Human Rights.

Abstract
After 12 years of EMOS experience it is time to open the discussion on the future of EMOS. This papers briefly describes the experience from the perspective of the Universities, trying also to describe the needs and role of the NSIs, Banks and other possible actors to join the network, and unlock the future. EMOS should reload (or evolute) to stay current and attractive. Statistical 'thinking' evolved and a major change and challenge for EMOS is to pick up this trend in its cooperation with the universities.

Abstract
As the ongoing evolution in the higher education sector changes the roles of universities, entrepreneurial practices become more prominent in their agendas. The literature on academic entrepreneurship focuses predominantly on the commercialization of research and less on other intrapreneurial activities—namely those performed by non-academic employees. To fill this gap, this study aims to provide a comprehensive understanding of the factors that influence universities’ faculty members and non-academic staff to engage in intrapreneurial activities. The article analyzes Latvian university employees’ perceptions of 13 organizational, individual, and environmental factors and how they influence intrapreneurial behavior. Regarding the organizational factors, the results show that higher trust in managers, more available resources for innovative ideas, less formal rules and procedures, and greater freedom in decision-making can lead to higher levels of intrapreneurial behavior. With regard to individual factors, intrapreneurial behavior is associated with an employee’s initiative, but is not correlated with risk-taking and personal initiative. As to external factors, while environmental munificence is positively correlated with innovativeness, dynamism and unfavorable change influence employees’ engagement in intrapreneurial activities.

Abstract

Abstract
Community detection techniques use only the information about the network topology to find communities in networks Similarly, classic clustering techniques for vector data consider only the information about the values of the attributes describing the objects to find clusters. In real-world networks, however, in addition to the information about the network topology, usually there is information about the attributes describing the vertices that can also be used to find communities. Using both the information about the network topology and about the attributes describing the vertices can improve the algorithms' results. Therefore, authors started investigating methods for community detection in attributed networks. In the past years, several methods were proposed to uncover this task, partitioning a graph into sub-graphs of vertices that are densely connected and similar in terms of their descriptions. This article focuses on the analysis and comparison of some of the proposed methods for community detection in attributed networks. For that purpose, several applications to both synthetic and real networks are conducted. Experiments are performed on both weighted and unweighted graphs. The objective is to establish which methods perform generally better according to the validation measures and to investigate their sensitivity to changes in the networks' structure and homogeneity.