Method development for increasing the prediction quality of mechanical joining process simulations by friction modeling based on local joining process parameters - Rossel, Moritz Sebastian; Meschut, Gerson (Proceedings of the 6th Conference on Steels in Cars and Trucks)

Koopman analysis of quantum systems - Klus, Stefan; Nüske, Feliks; Peitz, Sebastian (Journal of Physics A: Mathematical and Theoretical)
Koopman operator theory has been successfully applied to problems from various research areas such as fluid dynamics, molecular dynamics, climate science, engineering, and biology. Applications include detecting metastable or coherent sets, coarse-graining, system identification, and control. There is an intricate connection between dynamical systems driven by stochastic differential equations and quantum mechanics. In this paper, we compare the ground-state transformation and Nelson's stochastic mechanics and demonstrate how data-driven methods developed for the approximation of the Koopman operator can be used to analyze quantum physics problems. Moreover, we exploit the relationship between Schrödinger operators and stochastic control problems to show that modern data-driven methods for stochastic control can be used to solve the stationary or imaginary-time Schrödinger equation. Our findings open up a new avenue towards solving Schrödinger's equation using recently developed tools from data science.

CamBench -- Cryptographic API Misuse Detection Tool Benchmark Suite - Schlichtig, Michael; Wickert, Anna-Katharina; Krüger, Stefan; Bodden, Eric; Mezini, Mira
Context: Cryptographic APIs are often misused in real-world applications. Therefore, many cryptographic API misuse detection tools have been introduced. However, there exists no established reference benchmark for a fair and comprehensive comparison and evaluation of these tools. While there are benchmarks, they often only address a subset of the domain or were only used to evaluate a subset of existing misuse detection tools. Objective: To fairly compare cryptographic API misuse detection tools and to drive future development in this domain, we will devise such a benchmark. Openness and transparency in the generation process are key factors to fairly generate and establish the needed benchmark. Method: We propose an approach where we derive the benchmark generation methodology from the literature which consists of general best practices in benchmarking and domain-specific benchmark generation. A part of this methodology is transparency and openness of the generation process, which is achieved by pre-registering this work. Based on our methodology we design CamBench, a fair "Cryptographic API Misuse Detection Tool Benchmark Suite". We will implement the first version of CamBench limiting the domain to Java, the JCA, and static analyses. Finally, we will use CamBench to compare current misuse detection tools and compare CamBench to related benchmarks of its domain.
Keywords: cryptography; benchmark; API misuse; static analysis

Chancen für einen rassismussensiblen Literaturunterricht - didaktische Perspektiven für das Lesen von Menschenzoogeschichten in der Grundschule am Beispiel von Rainer Maria Rilkes Gedicht "Die Aschanti. Jardin d´acclimatation" (1902) - Tönsing, Johanna (Rassismussensibler Literaturunterricht. Neue Perspektiven einer kulturwissenschaftlichen Literaturdidaktik)

Bringing Massive Parallelism and Hardware Acceleration to Linear Scaling Density Functional Theory Through Targeted Approximations - Lass, Michael

A Large-Scale Study of Usability Criteria Addressed by Static Analysis Tools - Nachtigall, Marcus; Schlichtig, Michael; Bodden, Eric (Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis)
Static analysis tools support developers in detecting potential coding issues, such as bugs or vulnerabilities. Research on static analysis emphasizes its technical challenges but also mentions severe usability shortcomings. These shortcomings hinder the adoption of static analysis tools, and in some cases, user dissatisfaction even leads to tool abandonment. To comprehensively assess the current state of the art, this paper presents the first systematic usability evaluation in a wide range of static analysis tools. We derived a set of 36 relevant criteria from the scientific literature and gathered a collection of 46 static analysis tools complying with our inclusion and exclusion criteria - a representative set of mainly non-proprietary tools. Then, we evaluated how well these tools fulfill the aforementioned criteria. The evaluation shows that more than half of the considered tools offer poor warning messages, while about three-quarters of the tools provide hardly any fix support. Furthermore, the integration of user knowledge is strongly neglected, which could be used for improved handling of false positives and tuning the results for the corresponding developer. Finally, issues regarding workflow integration and specialized user interfaces are proved further. These findings should prove useful in guiding and focusing further research and development in the area of user experience for static code analyses.
Keywords: Automated static analysis; Software usability

FUM - A Framework for API Usage constraint and Misuse Classification - Schlichtig, Michael; Sassalla, Steffen; Narasimhan, Krishna; Bodden, Eric (2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER))
Application Programming Interfaces (APIs) are the primary mechanism that developers use to obtain access to third-party algorithms and services. Unfortunately, APIs can be misused, which can have catastrophic consequences, especially if the APIs provide security-critical functionalities like cryptography. Understanding what API misuses are, and for what reasons they are caused, is important to prevent them, e.g., with API misuse detectors. However, definitions and nominations for API misuses and related terms in literature vary and are diverse. This paper addresses the problem of scattered knowledge and definitions of API misuses by presenting a systematic literature review on the subject and introducing FUM, a novel Framework for API Usage constraint and Misuse classification. The literature review revealed that API misuses are violations of API usage constraints. To capture this, we provide unified definitions and use them to derive FUM. To assess the extent to which FUM aids in determining and guiding the improvement of an API misuses detectors' capabilities, we performed a case study on CogniCrypt, a state-of-the-art misuse detector for cryptographic APIs. The study showed that FUM can be used to properly assess CogniCrypt's capabilities, identify weaknesses and assist in deriving mitigations and improvements. And it appears that also more generally FUM can aid the development and improvement of misuse detection tools.
Keywords: API misuses; API usage constraints; classification framework; API misuse detection; static analysis

Modelling and Proving Security for a Secure MPC Protocol for Stable Matching - Vahle, Ella

Visualisierung von Ultraschallfeldern mittels Schlierentechnik und fraktionaler Fouriertransformation - Hetkämper, Tim

Messverfahren für die akustische Absorption zur Bestimmung der Volumenviskosität - Claes, Leander

Heat regeneration model for external heat engines operating with dense working fluids - Mügge, Nils; Kronberg, Alexander; Glushenkov, Maxim; Kenig, Eugeny (Annual Meeting on Reaction Engineering and ProcessNet Subject Division Heat and Mass Transfer)

The k-Server with Preferences Problem - Castenow, Jannik; Feldkord, Björn; Knollmann, Till; Malatyali, Manuel; Meyer auf der Heide, Friedhelm (Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures)
The famous $k$-Server Problem covers plenty of resource allocation scenarios, and several variations have been studied extensively for decades. However, to the best of our knowledge, no research has considered the problem if the servers are not identical and requests can express which specific servers should serve them. Therefore, we present a new model generalizing the $k$-Server Problem by *preferences* of the requests and proceed to study it in a uniform metric space for deterministic online algorithms (the special case of paging). In our model, requests can either demand to be answered by any server (*general requests*) or by a specific one (*specific requests*). If only general requests appear, the instance is one of the original $k$-Server Problem, and a lower bound for the competitive ratio of $k$ applies. If only specific requests appear, a solution with a competitive ratio of $1$ becomes trivial since there is no freedom regarding the servers' movements. Perhaps counter-intuitively, we show that if both kinds of requests appear, the lower bound raises to $2k-1$. We study deterministic online algorithms in uniform metrics and present two algorithms. The first one has an adaptive competitive ratio dependent on the frequency of specific requests. It achieves a worst-case competitive ratio of $3k-2$ while it is optimal when only general or only specific requests appear (competitive ratio of $k$ and $1$, respectively). The second has a fixed close-to-optimal worst-case competitive ratio of $2k+14$. For the first algorithm, we show a lower bound of $3k-2$, while the second algorithm has a lower bound of $2k-1$ when only general requests appear. The two algorithms differ in only one behavioral rule for each server that significantly influences the competitive ratio. Each server acting according to the rule allows approaching the worst-case lower bound, while it implies an increased lower bound for $k$-Server instances. In other words, there is a trade-off between performing well against instances of the $k$-Server Problem and instances containing specific requests. We also show that no deterministic online algorithm can be optimal for both kinds of instances simultaneously.
Keywords: K-Server Problem; Heterogeneity; Online Caching

Künstliche Intelligenz fachfremd mittels Open Educational Resources unterrichten. Wie das Flipped-Classroom-Format bei der Einbettung in die Lehre der Anglistik hilft. - Mindt, Ilka (Künstliche Intelligenz mit offenen Lernangeboten an Hochschulen lehren. Erfahrungen und Erkenntnisse aus dem Fellowship-Programm des KI-Campus.)

Effect of Surface Hydrophobicity on the Adsorption of a Pilus-Derived Adhesin-like Peptide - Yang, Yu; Huang, Jingyuan; Dornbusch, Daniel; Grundmeier, Guido; Fahmy, Karim; Keller, Adrian; Cheung, David L. (Langmuir)
Keywords: Electrochemistry; Spectroscopy; Surfaces and Interfaces; Condensed Matter Physics; General Materials Science

Time-Dependent DNA Origami Denaturation by Guanidinium Chloride, Guanidinium Sulfate, and Guanidinium Thiocyanate - Hanke, Marcel; Hansen, Niklas; Tomm, Emilia; Grundmeier, Guido; Keller, Adrian (International Journal of Molecular Sciences)
Guanidinium (Gdm) undergoes interactions with both hydrophilic and hydrophobic groups and, thus, is a highly potent denaturant of biomolecular structure. However, our molecular understanding of the interaction of Gdm with proteins and DNA is still rather limited. Here, we investigated the denaturation of DNA origami nanostructures by three Gdm salts, i.e., guanidinium chloride (GdmCl), guanidinium sulfate (Gdm2SO4), and guanidinium thiocyanate (GdmSCN), at different temperatures and in dependence of incubation time. Using DNA origami nanostructures as sensors that translate small molecular transitions into nanostructural changes, the denaturing effects of the Gdm salts were directly visualized by atomic force microscopy. GdmSCN was the most potent DNA denaturant, which caused complete DNA origami denaturation at 50 °C already at a concentration of 2 M. Under such harsh conditions, denaturation occurred within the first 15 min of Gdm exposure, whereas much slower kinetics were observed for the more weakly denaturing salt Gdm2SO4 at 25 °C. Lastly, we observed a novel non-monotonous temperature dependence of DNA origami denaturation in Gdm2SO4 with the fraction of intact nanostructures having an intermediate minimum at about 40 °C. Our results, thus, provide further insights into the highly complex Gdm–DNA interaction and underscore the importance of the counteranion species.
Keywords: Inorganic Chemistry; Organic Chemistry; Physical and Theoretical Chemistry; Computer Science Applications; Spectroscopy; Molecular Biology; General Medicine; Catalysis

Return to play in elite rugby players after severe knee injuries - Robyn, Aneurin D.; Louw, Quinette A.; Baumeister, Jochen (South African Journal of Physiotherapy)
Background: Medical professionals working in an elite sport environment have the challenging task to balance the athlete’s readiness to return to the playing field after severe injury with other stakeholders’ (coaches, sponsors, teammates) opinions and objectives.Objectives: Our study aimed to evaluate differences in the physical profiles of elite rugby players at return to play (RTP) after a severe knee injury, compared with their pre-injury profiles and matched controls.Method: Before the injury, participants performed four performance tests during their preseason screening. These tests were repeated and compared to baseline once a player was declared fit to play.Results: Significant differences (p ≤ 0.05) were found in the injured players’ group who were slower over 10 m speed, in their decision-making time and the total time of the reactive agility tests at RTP, whilst controls were significantly faster over 10 m and 30 m speed tests. The countermovement jump outcomes showed significant improvement in the uninjured participants (p ≤ 0.05).Conclusion: Our study highlights that injured players’ running speeds and decision-making times are slower after injury. The uninjured players have a positive outcome to training and match stimulus by improving their running speed and lower body explosive power during the season.Clinical implications: Our study provides insight into the RTP profile of elite rugby players, and a novel finding was the decision-making time deficit. This highlights the importance of cognitive training during injury rehabilitation as athletes make numerous decisions in a pressured and uncontrolled environment during a match. Speed training development is recommended as the athletes were slower after severe knee injury.
Keywords: Physical Therapy; Sports Therapy and Rehabilitation

Anti-pattern Detection in Process-Driven Decision Support Systems - Kirchhoff, Jonas; Engels, Gregor (Software Business)
Decision makers increasingly rely on decision support systems for optimal decision making. Recently, special attention has been paid to process-driven decision support systems (PD-DSS) in which a process model prescribes the invocation sequence of software-based decision support services and the data exchange between them. Thus, it is possible to quickly combine available decision support services as needed for optimally supporting the decision making process of an individual decision maker. However, process modelers may accidentally create a process model which is technically well-formed and executable, but contains functional and behavioral flaws such as redundant or missing services. These flaws may result in inefficient computations or invalid decision recommendations when the corresponding PD-DSS is utilized by a decision maker. In this paper, we therefore propose an approach to validate functionality and behavior of a process model representing a PD-DSS. Our approach is based on expressing flaws as anti-patterns for which the process model can be automatically checked via graph matching. We prototypically implemented our approach and demonstrate its applicability in the context of decision making for energy network planning.

Psychological readiness of elite rugby players at return to play after severe knee injury - Robyn, A.D.; Louw, Q.A.; Baumeister, Jochen (African Journal for Physical Activity and Health Sciences (AJPHES))
Keywords: General Medicine

Innovative Leichtbau- und Kühlungskonzepte für elektrische Maschinen durch additive Fertigung (ILuKadd3D) - Haase, Michael; Tasche, Frederik; Bieber, Maximilian; Zibart, Alexander

Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning - Rieskamp, Jonas
Controversial topics like abortion or capital punishment inherently lack of correct answers or the right way to deal with. Thus, in order to find what is true, what is good, or what should be done, the involved parties need to debate. For the purpose of forming an opinion on a controversial topic someone needs to take in a lot of arguments on that topic to gather information which can be a time-consuming process. To increase efficiency, someone can use an argument search engine to quicken the retrieval of relevant arguments. Although the usage of such a service reduces the time to find arguments, there is still a lot of textual data that needs to be read. To this end, computational summarization approaches for arguments can limit the necessary time for information review by generating short snippets capturing the main gist of each argument. Yet, we suggest that approaches that consider one argument at a time show potential for further improvement in terms of efficiency during information review. In fact, arguments on the same topic, like those retrieved by a search engine for a certain query, partially cover the same content, e. g. arguments regarding the death penalty probably use deterrence as a point in favor of it. However, if the same aspect is central in multiple arguments, their snippets reflect this, which leads to redundancy among the snippets. Consequently, someone interested in gathering information on a controversial topic does not necessarily find new information in each snippet he or she reads. We introduce the task of Contrastive Argument Summarization (CAS) which addresses the aforementioned problem regarding existing argument summarization. An approach that addresses CAS aims to produce contrastive snippets for each argument in a set of topic-related arguments. A contrastive snippet should represent the main gist of its argument, it should account for the argumentative nature of the text, and it should be dissimilar to the other topic-related arguments in order to reduce redundancy among the snippets. We propose two approaches addressing CAS, namely an extended version of the LexRank derivation by Alshomary et al. (2020), and an advancement of the work by Bista et al. (2020). Additionally, we develop two automatic measures to assess to which extent the snippets of one set are opposed. For evaluation, we compile a corpus using the args.me search engine Wachsmuth et al. (2017b) to come close to the suggested area of application. Moreover, we conduct a manual annotation study to assess approaches’ effectiveness. We find that the graph-based approach is superior when it comes to contrastiveness (i. e. snippets being dissimilar to topic-related arguments), and that the second approach outperforms the previous one and the unmodified version of Alshomary et al. (2020) when it comes to representativeness (i. e. snippets capturing the main gist of an argument).