Contributions in Merlin

In 5G and beyond, the figure of cross-operator/domain connections and relationships grows exponentially among stakeholders, resources, and services, being reputation-based trust models one of the capital technologies leveraged for trustworthy decision-making. This work studies novel 5G assets on which trust can be used to overcome unsuitable decision-making and address current requirements. First, it introduces a background and general architecture of reputation-based trust models. Afterward, it analyzes pivotal 5G assets on which trust can enhance their performance. Besides, this article performs a comprehensive review of the current reputation models applied to 5G assets and compares their properties, features, techniques, and results. Finally, it provides current trends and future challenges to conducting forthcoming research in the area.

We examine how education expansions affect the job opportunities for workers with and without the new education. To identify causal effects, we exploit a quasi-random establishment of Universities of Applied Sciences (UASs), bachelor-granting three-year colleges that teach and conduct applied research. By applying machine-learning methods to job advertisement data, we analyze job content before and after the education expansion. We find that, in regions with the newly established UASs, not only job descriptions of the new UAS graduates but also job descriptions of workers without this degree (i.e., middle-skilled workers with vocational training) contain more high-skill job content. This upskilling in job content is driven by an increase in high-skill R&D-related tasks and linked to employment and wage gains. The task spillovers likely occur because UAS graduates with applied research skills build a bridge between middle-skilled workers and traditional university graduates, facilitating the integration of the former into R&D-related tasks.

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Objective We examined changes in value interrelations during middle childhood. In line with the Personal Values Theory, we expected a value system, with individuals similarly valuing related motivations, and setting priorities between conflicting motivations. We hypothesized this system to develop dynamically during middle childhood as children deepen their understanding of their own values. Method Using unfolding analysis, we estimated intraindividual value structure coherence, that is, the extent to which the interrelations among a child's values are similar to the hypothesized interrelations. Cross-Cultural Study 1 (N = 4615, 6–12-year-old children) included children from 12 countries. Cross-Sequential Study 2 (N = 629, 6–10-year-old children at Time 1) included three annual measurements. Results In Study 1, we found a curvilinear association between age and intraindividual value structure coherence: Children's values were more coherent at ages 9–10 than before or after. Study 2 confirmed this pattern of within-individual development. Conclusions We propose that development in coherence with the theoretical value structure offers insight into children's understanding of values as well as changes in value priorities.

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Scientific datasets often have hierarchical structure: for example, in surveys, individual participants (samples) might be grouped at a higher level (units) such as their geographical region. In these settings, the interest is often in exploring the structure on the unit level rather than on the sample level. Units can be compared based on the distance between their means, however this ignores the within-unit distribution of samples. Here we develop an approach for exploratory analysis of hierarchical datasets using the Wasserstein distance metric that takes into account the shapes of within-unit distributions. We use t-SNE to construct 2D embeddings of the units, based on the matrix of pairwise Wasserstein distances between them. The distance matrix can be efficiently computed by approximating each unit with a Gaussian distribution, but we also provide a scalable method to compute exact Wasserstein distances. We use synthetic data to demonstrate the effectiveness of our Wasserstein t-SNE, and apply it to data from the 2017 German parliamentary election, considering polling stations as samples and voting districts as units. The resulting embedding uncovers meaningful structure in the data.

Länder mit einer starken Betonung der beruflichen Bildung in ihren Bildungs- und Innovationssystemen, beispielsweise Deutschland und die Schweiz, zählen seit Jahren zu den innovativsten der Welt. Gleichzeitig konstatiert internationale Innovationsforschung, dass ein hoher Akademisierungsgrad (und damit gerade nicht die berufliche Bildung) für starke Innovationsleistungen unabdinglich ist. Wie lässt sich dieser Widerspruch erklären? In diesem Überblicksartikel zeigen Uschi Backes-Gellner und Patrick Lehnert anhand einer Aufarbeitung neuer Forschungserkenntnisse, warum die deutschsprachigen Länder nicht trotz, sondern gerade auch wegen ihres Berufsbildungssystems hochinnovativ sind. Zu den erforderlichen institutionellen Rahmenbedingungen für diesen Effekt gehören dabei die zukunftsorientierte Gestaltung und Aktualisierung von Berufsausbildungscurricula, die Vermittlung von breiten beruflichen (statt engen betriebsspezifischen) Kompetenzen in dualen Berufsausbildungsprogrammen, ein durchlässiges Bildungssystem mit attraktiven Karrierepfaden sowie eine Verknüpfung von tertiärer beruflicher Bildung und angewandter Forschung.

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