Contributions in Merlin

Successful diffusion of emerging technologies requires coherent ideas for their use. However, such ideas can be difficult to negotiate when the involved discursive fields differ in their beliefs and discursive frames. To analyze how such diverse fields can nevertheless co-develop a shared linguistic repertoire and coherent ‘organizing vision’, we conduct an inductive, interpretive study on the use of blockchain in the financial services industry. Drawing on interviews with 46 experts, we unpack how three different discursive fields (non-custodians, custodians, regulators) participated in the development of a'decentralized finance'vision. We transfer these insights into a recursive process model for the guided negotiation and translation between discursive fields. Our study contributes a deeper understanding of the role of beliefs, discursive frames, and regulators for the emergence of a shared linguistic repertoire and coherent organizing vision.

This paper conducts a thorough examination of the 12th Video Browser Showdown (VBS) competition, a well-established international benchmarking campaign for interactive video search systems. The annual VBS competition has witnessed a steep rise in the popularity of multimodal embedding-based approaches in interactive video retrieval. Most of the thirteen systems participating in VBS 2023 utilized a CLIP-based cross-modal search model, allowing the specification of free-form text queries to search visual content. This shared emphasis on joint embedding models contributed to balanced performance across various teams. However, the distinguishing factors of the top-performing teams included the adept combination of multiple models and search modes, along with the capabilities of interactive interfaces to facilitate and refine the search process. Our work provides an overview of the state-of-the-art approaches employed by the participating systems and conducts a thorough analysis of their search logs, which record user interactions and results of their queries for each task. Our comprehensive examination of the VBS competition offers assessments of the effectiveness of the retrieval models, browsing efficiency, and user query patterns. Additionally, it provides valuable insights into the evolving landscape of interactive video retrieval and its future challenges.

The collection of lifelog data --- visual and multi-sensory data, including biometric and spatiotemporal metadata --- becomes easier and more supported by commercial products every year. Naturally, lifelog data is multi-modal, with arguably a major audio-visual component, such as captured videos, audio recordings and photos. For lifelog retrieval, the challenges of managing and accessing (visual) multimedia content are paired with the challenges of semi-structured and heterogeneous metadata. One approach to these challenges is the application of general-purpose, content-based multimedia retrieval in combination with traditional Boolean retrieval. In this paper, we present the latest iteration of vitrivr, a long-running participant in the Lifelog Search Challenge. After successfully replacing the retrieval engine Cineast with the vitrivr-engine for the structurally related Video Browser Showdown, we adjust the general purpose, content-based multimedia retrieval system to lifelog retrieval by extending the modular retrieval engine with Boolean retrieval and a model for metadata. In doing so, we continue to generalize the retrieval aspects also suitable for other applications and evaluate our system at the Lifelog Search Challenge 2024.

For the seventh time since 2018, the Lifelog Search Challenge (LSC) benchmarked interactive lifelog search systems in a live challenge. The LSC goal is to comparatively evaluate system capabilities to access large multimodal lifelogs comprising hundreds of thousands of records. LSC'24 attracted an unprecedented record number of twenty-one participating teams, where each team proposes innovative ideas implemented to new or already established interactive lifelog retrieval systems. The benchmark was organised in front of a live audience at the LSC workshop at ACM ICMR'24 in Phuket, Thailand. This short paper summarises the LSC workshop setting and presents the participating lifelog search systems.

To support the replication of "MMSF: A Multimodal Sentiment-Fused Method to Recognize Video Speaking Style", which was presented at ICMR'23, this companion paper provides the details of the artifacts. Speaking style recognition is aimed at recognizing the styles of conversations, which provides a fine-grained description about talking. In the original paper, we proposed a novel multimodal sentiment-fused method, MMSF, which extracts and integrates visual, audio and textual features of videos and introduced sentiment in MMSF with cross-attention mechanism to enhance the video feature to recognize speaking styles. In this paper, we explain the details of the implement code and the dataset used for experiments.

Implicit and explicit gender biases in media representations of individuals have long existed. Women are less likely to be represented in gender-neutral media content (representation bias), and their face-to-body ratio in images is often lower (face-ism bias). In this article, we look at representativeness and face-ism in search engine image results. We systematically queried four search engines (Google, Bing, Baidu, Yandex) from three locations, using two browsers and in two waves, with gender-neutral (person, intelligent person) and gendered (woman, intelligent woman, man, intelligent man) terminology, accessing the top 100 image results. We employed automatic identification for the individual’s gender expression (female/male) and the calculation of the face-to-body ratio of individuals depicted. We find that, as in other forms of media, search engine images perpetuate biases to the detriment of women, confirming the existence of the representation and face-ism biases. In-depth algorithmic debiasing with a specific focus on gender bias is overdue.

Return event studies generally involve several firms but there are also cases when only one firm is involved. This makes the relevant testing problems, abnormal return and cumulative abnormal return, more difficult since one cannot exploit the multitude of firms (by using a relevant central limit theorem, say) to design hypothesis tests. We propose a permutation test which is of nonparametric nature and more generally valid than the tests that have previously been proposed in the literature in this context. We address the question of the power of the test via a brief simulation study and also illustrate the method with two applications to real data.

This paper provides a novel theory of research joint ventures for financially constrained firms. When firms choose R&D portfolios, an RJV can help to coordinate research efforts, reducing investments in duplicate projects. This can free up resources, increase the variety of pursued projects and thereby increase the probability of discovering the innovation. RJVs improve innovation outcomes when market competition is weak or external financing conditions are bad. An RJV may increase the innovation probability and nevertheless lower total R&D costs. RJVs that increase innovation also increase consumer surplus and tend to be profitable, but innovation-reducing RJVs also exist. Finally, we compare RJVs to innovation-enhancing mergers.

In the era of digitalization, the availability of data is paramount for any scenario that requires informed decision-making. In the cybersecurity world, this is no different. This is especially the case for malware since, even though malware samples share common ancestors, implementations are commonly adapted into many strains, requiring frequent execution and analysis to implement appropriate detection and mitigation mechanisms based on malicious patterns.Sandboxes have emerged as an environment to execute and analyze malware dynamically. However, existing platforms lack real-time, interactive, reproducible malware analysis. Since they do not explore the applicability of container-based isolation, this paper proposes SecBox. To extract system calls, performance metrics, and network traffic, SecBox implements a real-time, visual, easy-to-use tool to execute malware from existing sample exchanges. The platform explores the suitability of lightweight, container-based sandboxing. Based on multiple experiments, SecBox achieves good results regarding the performance, isolation, reproducibility, and monitorability of malware.

This article examines the impact of non-pharmaceutical interventions on the initial exponential growth of the infected population and the final exponential decay of the infected population. We employ a Bayesian dynamic model to test whether there is learning, a random walk pattern, or another type of learning with evolving epidemiological data over time across 168 countries and 41,706 country-date observations. Although we show that Bayesian learning is not taking place, most policy measures appear to assert some effect. In particular, we show that economic policy variables are of importance for the main epidemiological parameters derived from the policy learning model. In an empirical second-stage application, we further investigate the underlying dynamics between the epidemiological parameters and household debt repayments, a key economic variable, in the UK. Results show no Bayesian learning, although a higher transmission rate would increase household debt repayments, while the recovery rate would have a negative impact. Therefore, suboptimal learning is taking place.