Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir’s Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.

The availability of open source assets for almost all imaginable domains has led the software industry toopportunistic design—an approach in which people develop new software systems in an ad hoc fashion by reusing and combining components that were not designed to be used together. In this paper we investigate this emerging approach. We demonstrate the approach with an industrial example in whichNode.jsmodules and various subsystems are used in an opportunistic way. Furthermore, to study opportunistic reuse as a phenomenon, we present the results of three contextual interviews and a survey with reuse practitioners to understand to what extent opportunistic reuse offers improvements over traditional systematic reuse approaches.

The exponentially growing amount of digital information and data analysis increase the ability to perceive the holistic situation of people. This article applies the digital twin paradigm to strengthen a person’s ability to utilize information about themselves by creating a digital representation of their situation to support their well-being. More specifically, we propose a blueprint to empower individuals by improving their self-determination regarding their personal data. The blueprint will help service and data providers, both public and private, to develop a common understanding of the role and possibilities of a citizen’s controlled personal digital twin of themselves—a citizen digital twin (CDT)—for creating people-centric solutions. The blueprint also provides a rational framework for service development based on CDTs and serves as a basis for strategic guidance of service development. We demonstrate this with a case study of confirmation class students.

Recent years have seen the rise of a new generation of UI frameworks for web application development. These frameworks differ from previous generations of JavaScript frameworks in that they define a declarative application development model, where transitions in the state of the UI are managed by the framework. This potentially greatly simplifies application development, but requires the framework to implement a rendering strategy which translates changes in application state into changes in the state of the UI. The performance characteristics of these rendering strategies have thus far been poorly studied. In this article, we describe the rendering strategies used in the frameworks Angular, React, Vue, Svelte and Blazor, which represent some of the most influential and widely used modern web frameworks. We find significant differences in the scaling of costs in their rendering strategies with potentially equally significant practical performance implications. To verify these differences, we implement a number of benchmarks that measure the scaling of rendering costs as an application grows in complexity. The results of our benchmarks confirm that under certain circumstances, performance differences between frameworks can range up to several orders of magnitude when performing the same tasks. Furthermore, we find that the relative performance of a rendering strategy can be effectively estimated based on factors affecting the input sizes of render loops. The best performing rendering strategies are found to be ones which minimize input sizes using techniques such as compile-time optimization and reactive programming models.