Contributions in Merlin

Governments across the globe are facing pressure to increase the speed of their digital transformation to meet the needs of the digital society while fulfilling their primary task of delivering public value. While researchers agree on the importance of the public sector for public value creation, recently, more and more studies have recognized the criticality of collaboration between the public and private sectors for successful public value creation. In our research, we conduct a qualitative survey. We examine the idea of collaboration between the public and the private sectors in more detail and add the citizens' perspective. We highlight the need for joint forces for optimal public value creation, identify ways to achieve this, and determine what digital technologies can support this process.

The Semantic Web is distributed yet interoperable: Distributed since resources are created and published by a variety of producers, tailored to their specific needs and knowledge; Interoperable as entities are linked across resources, allowing to use resources from different providers in concord. Complementary to the explicit usage of Semantic Web resources, embedding methods made them applicable to machine learning tasks. Subsequently, embedding models for numerous tasks and structures have been developed, and embedding spaces for various resources have been published. The ecosystem of embedding spaces is distributed but not interoperable: Entity embeddings are not readily comparable across different spaces. To parallel the Web of Data with a Web of Embeddings, we must thus integrate available embedding spaces into a uniform space. Current integration approaches are limited to two spaces and presume that both of them were embedded with the same method — both assumptions are unlikely to hold in the context of a Web of Embeddings. In this paper, we present FedCoder— an approach that integrates multiple embedding spaces via a latent space. We assert that linked entities have a similar representation in the latent space so that entities become comparable across embedding spaces. FedCoder employs an autoencoder to learn this latent space from linked as well as non-linked entities. Our experiments show that FedCoder substantially outperforms state-of-the-art approaches when faced with different embedding models, that it scales better than previous methods in the number of embedding spaces, and that it improves with more graphs being integrated whilst performing comparably with current approaches that assumed joint learning of the embeddings and were, usually, limited to two sources. Our results demonstrate that FedCoder is well adapted to integrate the distributed, diverse, and large ecosystem of embeddings spaces into an interoperable Web of Embeddings.

Corporate–startup collaboration (CSC) allows the co-development of innovations for pressing societal needs. Paradoxically, CSC is both fueled and challenged by diverging interests and approaches of the unequal actors. We apply a paradox lens to better understand the complex collaborative demands of CSC from the perspective of the corporate actors involved. Over the course of three years, we conducted 52 contextualized semi-structured interviews in a corporate-sponsored accelerator pursuing sustainability improvements. We identify five CSC paradoxes, which we translate into guiding principles for managing such paradoxes with a both/and mindset. Further, we show how these guiding principles help to address interdependencies between the CSC paradoxes. By disentangling the inherently paradoxical nature of the collaborative demands, we contribute to a fuller theoretical understanding of how organizational actors can manage these demands. We encourage companies engaging in CSC to use the guiding principles for empowering organizational actors’ understanding and approaches to CSC paradoxes.

We study the role of occupational tasks as drivers of West German wage inequality. We match administrative wage data with longitudinal task data, which allow us to account for within-occupation changes in task content over time. We run RIF regression-based decompositions to quantify the contribution of changes in the returns to tasks to overall changes in the wage distribution from 1978 to 2006. We find that changes in the returns to tasks explain up to half of the increase in wage inequality since the 1990s, both at the top and the bottom of the wage distribution. Specifically, abstract tasks drive the upper wage gap, while interactive and routine tasks drive the lower wage gap. Importantly, we find low-wage occupations to have the highest routine task intensity. The association between occupational tasks and West German wage inequality is thus both stronger and different than prior research has found.

Search engines serve as information gatekeepers on a multitude of topics that are prone to gender, ethnicity, and race misrepresentations. In this paper, we specifically look at the image search representation of migrant population groups that are often subjected to discrimination and biased representation in mainstream media, increasingly so with the rise of right-wing populist actors in the Western countries. Using multiple (n = 200) virtual agents to simulate human browsing behavior in a controlled environment, we collect image search results related to various terms referring to migrants (e.g., expats, immigrants, and refugees, seven queries in English and German used in total) from the six most popular search engines. Then, with the aid of manual coding, we investigate which features are used to represent these groups and whether the representations are subjected to bias. Our findings indicate that search engines reproduce ethnic and gender biases common for mainstream media representations of different subgroups of migrant population. For instance, migrant representations tend to be highly racialized, and female migrants as well as migrants at work tend to be underrepresented in the results. Our findings highlight the need for further algorithmic impact auditing studies in the context of representation of potentially vulnerable groups in web search results.

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This paper investigates responses of household debt to COVID-19 related data like confirmed cases and confirmed deaths within a panel VAR framework for OECD countries. We also employ a plethora of non-pharmaceutical and pharmaceutical interventions as shocks. In terms of methodology, we opt for a global panel VAR (GVAR) methodology that nests underlying country VARs. In addition, as linear factor models may be unable to capture the variability in the data, we use an artificial neural network (ANN) method. The number of factors, as well as the number of intermediate layers, are determined using the marginal likelihood criterion and we estimate the GVAR with MCMC techniques. Results reveal that household debt positively responds to COVID-19 infections and mortality as well as lockdowns, though this response is valid in the short term. However, vaccinations and testing appear to negatively affect household debt. Lockdown measures such as stay-at-home advice, and closing schools, all have a positive impact on household debt in GVAR, though of transitory nature.

Code review consists of manual inspection, discussion, and judgment of source code by developers other than the code's author. Due to discussions around competing ideas and group decision-making processes, interpersonal conflicts during code reviews are expected. This study systematically investigates how developers perceive code review conflicts and addresses interpersonal conflicts during code reviews as a theoretical construct. Through the thematic analysis of interviews conducted with 22 developers, we confirm that conflicts during code reviews are commonplace, anticipated and seen as normal by developers. Even though conflicts do happen and carry a negative impact for the review, conflicts-if resolved constructively-can also create value and bring improvement. Moreover, the analysis provided insights on how strongly conflicts during code review and its context (i.e., code, developer, team, organization) are intertwined. Finally, there are aspects specific to code review conflicts that call for the research and application of customized conflict resolution and management techniques, some of which are discussed in this paper. Data and material: https://doi.org/10.5281/zenodo.5848794

The automatic annotation of higher-level semantic information in long-form video content is still a challenging task. The Deep Video Understanding (DVU) Challenge aims at catalyzing progress in this area by offering common data and tasks. In this paper, we present our contribution to the 3rd DVU challenge. Our approach consists of multiple information streams extracted from both the visual and the audio modality. The streams can build on information generated by previous streams to increase their semantic descriptiveness. Finally, the output of all streams can be aggregated in order to produce a graph representation of the input movie to represent the semantic relationships between the relevant characters.

The retrieval of multimedia content remains a difficult problem where a high accuracy or specificity can often only be achieved interactively, with a user working closely and iteratively with a retrieval system. While there exist several venues for the exchange of insights in the area of information retrieval in general and multimedia retrieval specifically, there is little discussion on such interactive retrieval approaches. The Workshop on Interactive Multimedia Retrieval offers such a venue. Held for the 2nd time in 2022, it attracted a diverse set of contributions, six of which were accepted for presentation. The following provides a brief overview of the workshop itself as well as the contributions of 2022.