Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications.

Today's IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners and third party developers. Overall, these require scalable, decentralized and intelligent IoT security mechanisms and services which are addressed by the SecureIoT project. This paper presents the definition, implementation and validation of a SecureIoT-enabled socially assisted robots (SAR) usage scenario. The aim of the SAR scenario is to integrate and validate the SecureIoT services in the scope of personalized healthcare and ambient assistive living (AAL) scenarios, involving the integration of two AAL platforms, namely QTrobot (QT) and CloudCare2U (CC2U). This includes risk assessment of communications security, predictive analysis of security risks, implementing access control policies to enhance the security of solution, and auditing of the solution against security, safety and privacy guidelines and regulations. Future perspectives include the extension of this security paradigm by securing the integration of healthcare platforms with IoT solutions, such as Healthentia with QTRobot, by means of a system product assurance process for cyber-security in healthcare applications, through the PANACEA toolkit.

Staring us in the face? An embodied theory of innate face preference.

Human expertise in face perception grows over development, but even within minutes of birth, infants exhibit an extraordinary sensitivity to face-like stimuli. The dominant theory accounts for innate face detection by proposing that the neonate brain contains an innate face detection device, dubbed 'Conspec'. Newborn face preference has been promoted as some of the strongest evidence for innate knowledge, and forms a canonical stage for the modern form of the nature-nurture debate in psychology. Interpretation of newborn face preference results has concentrated on monocular stimulus properties, with little mention or focused investigation of potential binocular involvement. However, the question of whether and how newborns integrate the binocular visual streams bears directly on the generation of observable visual preferences. In this theoretical paper, we employ a synthetic approach utilizing robotic and computational models to draw together the threads of binocular integration and face preference in newborns, and demonstrate cases where the former may explain the latter. We suggest that a system-level view considering the binocular embodiment of newborn vision may offer a mutually satisfying resolution to some long-running arguments in the polarizing debate surrounding the existence and causal structure of newborns' 'innate knowledge' of faces.