Reduction of systematic measurement deviation in acoustic absorption measurement systems - Claes, Leander; Baumhögger, Elmar; Rüther, Torben; Gierse, Jan; Tröster, Thomas; Henning, Bernd (Fortschritte der Akustik - DAGA 2020)

Capabilities for Ambidextrous Innovation of Digital Service - Wolf, Verena; Lüttenberg, Hedda (Proceedings of the 15th International Conference on Wirtschaftsinformatik)

Establishing Smart Service Systems is a Challenge: A Case Study on Pitfalls and Implications - Wolf, Verena; Franke, Alena; Bartelheimer, Christian; Beverungen, Daniel (Proceedings of the 15th International Conference on Wirtschaftsinformatik)

Interview with Utz-Uwe Haus on “High Performance Computing in Economic Environments: Opportunities and Challenges" - Schryen, Guido; Kliewer, Natalia; Fink, Andreas (Business & Information Systems Engineering)
This interview is part of the special issue (01/2020) on “High Performance Business Computing” to be published in the journal Business & Information Systems Engineering. The interviewee Utz-Uwe Haus is Senior Research Engineer @ CRAY European Research Lab (CERL)). A bio of him is included at the end of the interview.

Aspect Phrase Extraction in Sentiment Analysis with Deep Learning - Kersting, Joschka; Geierhos, Michaela (Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020))
This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.
Keywords: Deep Learning; Natural Language Processing; Aspect-based Sentiment Analysis

Detection of Privacy Disclosure in the Medical Domain: A Survey - Buff, Bianca; Kersting, Joschka; Geierhos, Michaela (Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020))
When it comes to increased digitization in the health care domain, privacy is a relevant topic nowadays. This relates to patient data, electronic health records or physician reviews published online, for instance. There exist different approaches to the protection of individuals’ privacy, which focus on the anonymization and masking of personal information subsequent to their mining. In the medical domain in particular, measures to protect the privacy of patients are of high importance due to the amount of sensitive data that is involved (e.g. age, gender, illnesses, medication). While privacy breaches in structured data can be detected more easily, disclosure in written texts is more difficult to find automatically due to the unstructured nature of natural language. Therefore, we take a detailed look at existing research on areas related to privacy protection. Likewise, we review approaches to the automatic detection of privacy disclosure in different types of medical data. We provide a survey of several studies concerned with privacy breaches in the medical domain with a focus on Physician Review Websites (PRWs). Finally, we briefly develop implications and directions for further research.
Keywords: Identity Disclosure; Privacy Protection; Physician Review Website; De-Anonymization; Medical Domain

Concept-based Co-Migration of Test Cases - Jovanovikj, Ivan; Yigitbas, Enes; Sauer, Stefan; Engels, Gregor (Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD)

Test Case Co-Migration Method Patterns - Jovanovikj, Ivan; Yigitbas, Enes; Sauer, Stefan; Engels, Gregor (Software Engineering 2020 Workshopband)

Structural Elements Regulating the Photochromicity in a Cyanobacteriochrome - Xu, Xiuling; Port, Astrid; Wiebeler, Christian; Zhao, Kai-Hong; Schapiro, Igor; Gärtner, Wolfgang (Proceedings of the National Academy of Sciences)
The three-dimensional (3D) crystal structures of the GAF3 domain of cyanobacteriochrome Slr1393 ( Synechocystis PCC6803) carrying a phycocyanobilin chromophore could be solved in both 15- Z dark-adapted state, Pr, λ max = 649 nm, and 15- E photoproduct, Pg, λ max = 536 nm (resolution, 1.6 and 1.86 Å, respectively). The structural data allowed identifying the large spectral shift of the Pr-to-Pg conversion as resulting from an out-of-plane rotation of the chromophore’s peripheral rings and an outward movement of a short helix formed from a formerly unstructured loop. In addition, a third structure (2.1-Å resolution) starting from the photoproduct crystals allowed identification of elements that regulate the absorption maxima. In this peculiar form, generated during X-ray exposition, protein and chromophore conformation still resemble the photoproduct state, except for the D-ring already in 15- Z configuration and tilted out of plane akin the dark state. Due to its formation from the photoproduct, it might be considered an early conformational change initiating the parental state-recovering photocycle. The high quality and the distinct features of the three forms allowed for applying quantum-chemical calculations in the framework of multiscale modeling to rationalize the absorption maxima changes. A systematic analysis of the PCB chromophore in the presence and absence of the protein environment showed that the direct electrostatic effect is negligible on the spectral tuning. However, the protein forces the outer pyrrole rings of the chromophore to deviate from coplanarity, which is identified as the dominating factor for the color regulation.

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification - Wever, Marcel Dominik; Tornede, Alexander; Mohr, Felix; Hüllermeier, Eyke
In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.