Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices - Arias-Cabarcos, Patricia; Fallahi, Matin; Habrich, Thilo; Schulze, Karen; Becker, Christian; Strufe, Thorsten (ACM Transactions on Privacy and Security)
Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.
Keywords: Safety; Risk; Reliability and Quality; General Computer Science

'Surprised, Shocked, Worried'}: User Reactions to Facebook Data Collection from Third Parties - Arias-Cabarcos, Patricia; Khalili, Saina; Strufe, Thorsten (Proceedings on Privacy Enhancing Technologies)
Data collection and aggregation by online services happens to an extent that is often beyond awareness and comprehension of its users. Transparency tools become crucial to inform people, though it is unclear how well they work. To investigate this matter, we conducted a user study focusing on Facebook, which has recently released the 'Off-Facebook Activity' transparency dashboard that informs about personal data collection from third parties. We exposed a group of n = 100 participants to the dashboard and surveyed their level of awareness and reactions to understand how transparency impacts users' privacy attitudes and intended behavior. Our participants were surprised about the massive amount of collected data, became significantly less comfortable with data collection, and more likely to take protective measures. Collaterally, we observed that current consent schemes are inadequate. Based on the survey findings, we make recommendations for more usable transparency and highlight the need to raise awareness about transparency tools and to provide easily actionable privacy controls.
Keywords: General Medicine

Pseudo-Labeling Machine Learning Algorithm for Predictive Maintenance of Relays - Winkel, Fabian; Wallscheid, Oliver; Scholz, Peter; Böcker, Joachim (IEEE Open Journal of the Industrial Electronics Society)
Keywords: Electrical and Electronic Engineering; Industrial and Manufacturing Engineering; Control and Systems Engineering

Run-to-Failure Relay Dataset for Predictive Maintenance Research With Machine Learning - Winkel, Fabian; Deuse-Kleinsteuber, Johannes; Böcker, Joachim (IEEE Transactions on Reliability)
Keywords: Electrical and Electronic Engineering; Safety; Risk; Reliability and Quality

BrainNet: Improving Brainwave-based Biometric Recognition with Siamese Networks - Fallahi, Matin; Strufe, Thorsten; Arias-Cabarcos, Patricia (2023 IEEE International Conference on Pervasive Computing and Communications (PerCom))

Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features - Prager, Raphael Patrick; Dietrich, Konstantin; Schneider, Lennart; Schäpermeier, Lennart; Bischl, Bernd; Kerschke, Pascal; Trautmann, Heike; Mersmann, Olaf (Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms)
Artificial benchmark functions are commonly used in optimization research because of their ability to rapidly evaluate potential solutions, making them a preferred substitute for real-world problems. However, these benchmark functions have faced criticism for their limited resemblance to real-world problems. In response, recent research has focused on automatically generating new benchmark functions for areas where established test suites are inadequate. These approaches have limitations, such as the difficulty of generating new benchmark functions that exhibit exploratory landscape analysis (ELA) features beyond those of existing benchmarks.The objective of this work is to develop a method for generating benchmark functions for single-objective continuous optimization with user-specified structural properties. Specifically, we aim to demonstrate a proof of concept for a method that uses an ELA feature vector to specify these properties in advance. To achieve this, we begin by generating a random sample of decision space variables and objective values. We then adjust the objective values using CMA-ES until the corresponding features of our new problem match the predefined ELA features within a specified threshold. By iteratively transforming the landscape in this way, we ensure that the resulting function exhibits the desired properties. To create the final function, we use the resulting point cloud as training data for a simple neural network that produces a function exhibiting the target ELA features. We demonstrate the effectiveness of this approach by replicating the existing functions of the well-known BBOB suite and creating new functions with ELA feature values that are not present in BBOB.
Keywords: Benchmarking; Instance Generator; Black-Box Continuous Optimization; Exploratory Landscape Analysis; Neural Networks

Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features - Prager, Raphael Patrick; Trautmann, Heike (Applications of Evolutionary Computation)
Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a comprehensive and large set of numerical features characterizing problem instances. Those foster problem understanding and serve as basis for constructing automated algorithm selection models choosing the best suited algorithm for a problem at hand based on the aforementioned features computed prior to optimization. This work specifically points to the sensitivity of a substantial proportion of these features to absolute objective values, i.e., we observe a lack of shift and scale invariance. We show that this unfortunately induces bias within automated algorithm selection models, an overfitting to specific benchmark problem sets used for training and thereby hinders generalization capabilities to unseen problems. We tackle these issues by presenting an appropriate objective normalization to be used prior to ELA feature computation and empirically illustrate the respective effectiveness focusing on the BBOB benchmark set.

Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets - Schäpermeier, Lennart; Kerschke, Pascal; Grimme, Christian; Trautmann, Heike (Evolutionary Multi-Criterion Optimization)
The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. However, they lack diversity in their landscape properties and do not provide a mechanism for creating multiple distinct problem instances. Other suites, like bi-objective BBOB, possess diverse and challenging landscape properties, but their optima are not well understood and can only be approximated empirically without any guarantees.

Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python - Prager, Raphael Patrick; Trautmann, Heike (Evolutionary Computation)
The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization.

K-12 Data Science Education: Outcomes of a National Workshop; International Perspectives; and Next Steps for the Learning Sciences - Wilkerson, Michelle; Ben-Zvi, Dani; Dvir, Michal; Matuk, Camilla; Podworny, Susanne; Stephens, Amy; Zapata-Cardona, Lucia (General Proceedings of the ISLS Annual Meeting: Building Knowledge and Sustaining our Community)

Statistics and Probability Education in Germany - Podworny, Susanne (Advances in Mathematics Education)

Tax Morale and the Church: How Catholic Clergies Adapted Norms of Paying Taxes to Secular Institutions (1940s–1950s) - Schönhärl, Korinna (Reassessing the Moral Economy Religion and Economic Ethics from Ancient Greece to the 20th Century)
Paying taxes is a field of economic activity that has always been highly morally charged: the question of who pays how much or can avoid or evade the prescribed payments is always closely related to debate about a fair societal distribution of burdens. In the process of moralisation, therefore, faith communities such as the Catholic Church also repeatedly seized the floor to propagate certain norms. The article examines the contributions of theologians from Spain, the USA and West Germany in the 1940s and 1950s. It concludes that the norms of taxation they propagated differed greatly depending on the institutional and economic frameworks within which they operated. The analysis proves taxation to be a field of economic action and societal dispute where economics and morality are indissolubly interconnected.
Keywords: Tax history; religious history: financial history; catholic church; history of economic thought

Bildungsforschung – Erziehungswissenschaftliche Perspektiven

Correlation between SLS-Powder processability and particle properties - Rüther, Moritz Johannes; Klippstein, Sven Helge; Schmid, Hans-Joachim (PARTEC International Congress on Particle Technology - Book of Abstracts)

Vergleichskonstruktionen im Mittelniederdeutschen - Wallmeier, Nadine; Wich-Reif, Claudia (Historische (Morpho-)Syntax des Deutschen)

Sarah Ihnen (2000): Relativsätze im Mittelniederdeutschen. Korpuslinguistische Untersuchungen zu Struktur und Gebrauch. - Wallmeier, Nadine (Linguistische Berichte)

Hybrid renewable energy utility systems for industrial sites: A review - Walmsley, Timothy Gordon; Philipp, Matthias; Picón-Núñez, Martín; Meschede, Henning; Taylor, Matthew Thomas; Schlosser, Florian; Atkins, Martin John (Renewable and Sustainable Energy Reviews)
Keywords: Renewable Energy; Sustainability and the Environment

Regierende Herrscherwitwen und die Angst vor Fremdherrschaft. Zum Verhältnis von Dynastie und Geschlecht - Foerster, Anne (Regentinnen und andere Stellvertreterfiguren Vom 10. bis zum 15. Jahrhundert)

Die Stimmen der Eroberten bei Ermoldus Nigellus. Eine Mahnung an Ludwig den Frommen und Pippin von Aquitanien - Foerster, Anne (Eroberte im Mittelalter: Umbruchssituationen erleben, bewältigen, gestalten)

To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?