In scientific papers, citations refer to relevant previous work in order to underline the current line of argumentation, compare to other work and/or avoid repetition in writing. Self-citations, e.g. authors citing own previous work might have the same motivation but have also gained negative attention w.r.t. unjustified improvement of scientific performance indicators. Previous studies on self-citations do not provide a detailed analysis in the domain of computer science. In this work, we analyse the prevalence of self-citations in the DBLP, a digital library for computer science. We find, that approx. 10% of all citations are self-citations, while the rates vary with year after publication and the position of the author in the list as well as with the gender of the lead author. Further, we find that C-ranked venues have the highest incoming self-citation rate, while the outgoing rate is stable across all ranks.

<p>Foehn winds are accelerated, warm and dry winds that can have significant environmental impacts as they descend into the lee of a mountain range. For example, in the McMurdo Dry Valleys in Antarctica, foehn events can cause ice and glacial melt and destabilise ice shelves, which if lost, resulting in a rise in sea level. Consequently, there is a strong interest in a deeper understanding of foehn winds and their meteorological signatures. Most current automatic detection methods rely on rule-based methodologies that require static thresholds of meteorological parameters. However, the patterns of foehn winds are hard to define and differ between alpine valleys around the world. Consequently, data-driven solutions might help create more accurate detection and prediction methodologies. </p> <p>State-of-the-art machine learning approaches to this problem have shown promising results but follow a supervised learning paradigm. As such, these approaches require accurate labels, which for the most part, are being created by imprecise static rule-based algorithms. Consequently, the resulting machine-learning models are trained to recognise the same static definitions of the foehn wind signatures. </p> <p>In this paper, we introduce and compare the first unsupervised machine-learning approaches for detecting foehn wind events. We focus on data from the Mc Murdo Dry Valleys as an example, however, due to the unsupervised nature of these approaches, our solutions can recognise a more dynamic definition of foehn wind events and are therefore, independent of the location. The first approach is based on multivariate time-series clustering, while the second utilises a deep autoencoder-based anomaly detection method to identify foehn wind events. Our best model achieves an f1-score of 88%, matching or surpassing previous machine-learning methods while providing a more flexible and inclusive definition of foehn events. </p>

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Citations are a means to refer to previous, relevant scientific bodies of work. However, little is known about how citations behave with respect to venue reputation. Do A* papers get more often cited by C papers or vice versa? What is the source and sink of a citation in terms of venue reputation? In this work, we investigate this issue by analysing the DBLP database of computer science publications, utilizing rank information from the CORE database. Our analysis shows that authors tend to cite publications from the same or higher ranked venues more often than from lower tier venues. Self-citations, on the contrary, are especially focused on same-tier venues. The gender of the first author does not seem to have any impact on the citations from and to differently ranked mediums.