In this tutorial, we will present the web-based video analysis platform TIB AV-Analytics (https://service.tib.eu/tibava).It provides users with state-of-the-art methods from artificial intelligence (AI) including generative AI as well as visualizations for AI-assisted video analysis.Besides a short introduction and basics on the underlying deep learning technologies, the tutorial provides a hands-on experience for the participants including practical tips to analyze their own videos.

Recommendation services for journals help scientists choose appropriate publication venues for their research results. They often use a semantic matching process to compare e.g. an abstract against already published articles. As these services can guide a researcher’s decision, their fairness and neutrality are critical qualities. However, the impact of journal characteristics (such as the abstract length) on recommendations is understudied. In this paper, we investigate whether editorial journal characteristics can lead to biased rankings from recommendation services, i.e. if editorial choices can systematically lead to a better ranking of one’s own journal. The performed experiments show that longer abstracts or a higher number of articles per journal can boost the rank of a journal in the recommendations. We apply these insights to an active, open-source journal recommendation system. The adaptation of the algorithm leads to an increased accuracy for smaller journals.

Abstract Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project.