Valentine Friis (bananapoint0)

Biometric technologies have become the main focus in the design of state-of-the-art border security solutions. While respective research in the field of multimedia vision has been centred around improving quality and accuracy of identity recognition, the impact of such technologies upon society and legal regulations still remains a topic unaddressed, specifically within the engineering community. Research in technology can and in some respect must include collaboration with social sciences and social practice. Building on participation in the EU funded research project PERSONA [18] (Privacy, Ethical, Regulatory and SOcial No-gate crossing point solutions Acceptance), authors of this paper look at the challenges associated with biometrics-based solutions in no-gate border crossing point scenarios. This included the procedures needed for the assessment of their social, ethical, privacy and regulatory acceptance, particularly in view of the impact on both, the passengers and border control authorities as well as the potential pitfalls of biometric technology due to fraudulent activities. In consultation with the collaborating border control authorities, the paper reports on the formal assessment of biometric technologies for real-world acceptance to cope with the increasing demand of global travellers crossing state borders.The diffusion of COVID-19 represents a real threat for the health and economic system of a country. Therefore the governments have to adopt fast containment measures in order to stop its spread and to prevent the related devastating consequences. In this paper, a technique is proposed to optimally design the lock-down and reopening policies so as to minimize an aggregate cost function accounting for the number of individuals that decease due to the spread of COVID-19. A constraint on the maximal number of concomitant infected patients is also taken into account in order to prevent the collapse of the health system. The optimal procedure is built on the basis of a simple SIR model that describes the outbreak of a generic disease, without attempting to accurately reproduce all the COVID-19 epidemic features. This modeling choice is motivated by the fact that the containing measurements are actuated during the very first period of the outbreak, when the characteristics of the new emergent disease are not known but timely containment actions are required. In fact, as a consequence of dealing with poor preliminary data, the simplest modeling choice is able to reduce unidentifiability problems. Further, the relative simplicity of this model allows to compute explicitly its solutions and to derive closed-form expressions for the maximum number of infected and for the steady-state value of deceased individuals. These expressions can be then used to design static optimization problems so to determine the (open-loop) optimal lock-down and reopening policies for early-stage epidemics accounting for both the health and economic costs.The availability of innovative technologies (e.g., the Internet of Things, big data analytics, blockchain, the cloud, and applications) has led to a shift in the provision of home health-care (HHC) services from traditional institutions to service-sharing platforms. In the HHC context, one main challenge faced by service-sharing platforms is the matching of demand with supply, while considering the heterogeneity of care requests and service providers. From a centralized perspective of service-sharing platforms regarding three stakeholders (i.e., platform, caregiver, and customer), different matching strategies are used, including the "self-interested", "customer-first", "hard-work-happy-life", and "social-welfare" strategies. selleckchem When addressing the matching problem at an operational level, the platforms must comply with various requirements and rules, including break requirements, temporal dependencies, and flexible service durations. In this study, mixed-integer linear programmi