Foreign birth and marriage documents : the voice of Belgian and Dutch public servants

In 2019, the Institute of Private International Law at Ghent University (Belgium) launched a bilingual (Dutch/French) online survey in Belgium and the Netherlands. The objective of the survey was to examine how Belgian and Dutch public servants deal with foreign documents that record the personal status of people. Thanks to the cooperation of the Belgian and Dutch associations of registrars (Vlavabbs, Gapec and NVVB), the Belgian Immigration Office (DVZ/OE), the Dutch Immigration and Naturalisation Service (IND), the Belgian Federal Public Service Foreign Affairs and the Dutch Ministry of Foreign Affairs, it was possible to use and analyse the data of 219 respondents. This article elaborates on the Belgian and Dutch rules on the recognition of foreign marriage and birth certificates, and explores how those rules are (not) applied in practice by examining the results of the survey

Isometric Representations in Neural Networks Improve Robustness

Artificial and biological agents cannon learn given completely random and unstructured data. The structure of data is encoded in the metric relationships between data points. In the context of neural networks, neuronal activity within a layer forms a representation reflecting the transformation that the layer implements on its inputs. In order to utilize the structure in the data in a truthful manner, such representations should reflect the input distances and thus be continuous and isometric. Supporting this statement, recent findings in neuroscience propose that generalization and robustness are tied to neural representations being continuously differentiable. In machine learning, most algorithms lack robustness and are generally thought to rely on aspects of the data that differ from those that humans use, as is commonly seen in adversarial attacks. During cross-entropy classification, the metric and structural properties of network representations are usually broken both between and within classes. This side effect from training can lead to instabilities under perturbations near locations where such structure is not preserved. One of the standard solutions to obtain robustness is to add ad hoc regularization terms, but to our knowledge, forcing representations to preserve the metric structure of the input data as a stabilising mechanism has not yet been studied. In this work, we train neural networks to perform classification while simultaneously maintaining within-class metric structure, leading to isometric within-class representations. Such network representations turn out to be beneficial for accurate and robust inference. By stacking layers with this property we create a network architecture that facilitates hierarchical manipulation of internal neural representations. Finally, we verify that isometric regularization improves the robustness to adversarial attacks on MNIST.Comment: 14 pages, 4 figure

Consumer responses to brands placed in YouTube movies: the effect of prominence and endorser expertise

Despite the vast growth of web 2.0., academic research has not kept pace with the development of advertising techniques for user-generated content. The present study is, to the best of our knowledge, the first to investigate the effects of brand placement techniques in user-generated content. Using a 2x2 full-factorial between-subjects design with self-produced videos posted on a major social media platform (YouTube), we investigate the effects of prominence (how conspicuously the brand is used or mentioned), celebrity endorser expertise (celebrity expert versus amateur) and their interaction on brand recognition and purchase intention of brands that appear in the video. While the prominence of one brand was manipulated, we also tested the effects on both the manipulated brand and the other brands that subtly appeared in the video. We further study the moderating role of video liking on these relationships using associative network theory and the Persuasion Knowledge Model. The results indicate a strong positive effect of brand placement prominence on brand recognition of both the manipulated brand and a subtly placed complementary brand (a brand that is explicitly used together with the manipulated brand). A prominent endorsement by a celebrity expert enhances the purchase intention of the focal brand compared to a subtle endorsement. This effect is stronger for viewers who strongly liked the video than for viewers who liked the video less. Although our study is limited to only one platform and content type, our results are of importance to practitioners who are interested in integrating their brands in online content. The study aims to advance both the theoretical and practical knowledge of brand placement effects by studying the effects of different placement characteristics and brands in a user-generated content setting