Access control models describe frameworks that dictate how subjects (e.g. users) access resources. In the Role-Based Access Control (RBAC) model access to resources is based on the role the user holds within the organization. RBAC is a rigid model where access control decisions have only two output options: Grant or Deny. Break The Glass (BTG) policies on the other hand are flexible and allow users to break or override the access controls in a controlled and justifiable manner. The main objective of this paper is to integrate BTG within the NIST/ANSI RBAC model in a transparent and secure way so that it can be adopted generically in any domain where unanticipated or emergency situations may occur. The new proposed model, called BTG-RBAC, provides a third decision option BTG, which grants authorized users permission to break the glass rather than be denied access. This can easily be implemented in any application without major changes to either the application code or the RBAC authorization infrastructure, apart from the decision engine. Finally, in order to validate the model, we discuss how the BTG-RBAC model is being introduced within a Portuguese healthcare institution where the legislation requires that genetic information must be accessed by a restricted group of healthcare professionals. These professionals, advised by the ethical committee, have required and asked for the implementation of the BTG concept in order to comply with the said legislation.

A detailed error analysis is a fundamental step in every natural language processing task, as to be able to diagnose what went wrong will provide cues to decide which research directions are to be followed. In this paper we focus on error analysis in Machine Translation (MT). We significantly extend previous error taxonomies so that translation errors associated with Romance language specificities can be accommodated. Furthermore, based on the proposed taxonomy, we carry out an extensive analysis of the errors generated by four different systems: two mainstream online translation systems Google Translate (Statistical) and Systran (Hybrid Machine Translation), and two in-house MT systems, in three scenarios representing different challenges in the translation from English to European Portuguese. Additionally, we comment on how distinct error types differently impact translation quality.

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