GCS: A Quick and Dirty Guideline Compliance Scale

Expert-based usability evaluation methods offer valuable alternatives to traditional user testing in Human-Machine Interaction (HMI) development. While general measures of usability for user-based empirical studies are well-known throughout the community of researchers, expert-based approaches often lack such general measures of usability. This research introduces the Guideline Compliance Scale (GCS), a measure that can be applied during guideline reviews to assess the overall level of usability. Several guidelines relevant for the system being evaluated are rated by the evaluators according to their compliance. In the case study for our research, an automotive user interface was empirically evaluated in a user study as well as a guideline review with experts. The usability problem lists, which form part of the output, were made comparable by classification using the Usability Problem Classifier (UPC). An in-depth analysis revealed differences and similarities in the problem identification of both applied methods. Comparing the results of the GCS from the guideline review with the results of the System Usability Scale (SUS) from the user study, regarding the overall level of usability, showed similar results for both scales

Theory and implementation of inelastic Constitutive Artificial Neural Networks

Nature has always been our inspiration in the research, design and development of materials and has driven us to gain a deep understanding of the mechanisms that characterize anisotropy and inelastic behavior. All this knowledge has been accumulated in the principles of thermodynamics. Deduced from these principles, the multiplicative decomposition combined with pseudo potentials are powerful and universal concepts. Simultaneously, the tremendous increase in computational performance enabled us to investigate and rethink our history-dependent material models to make the most of our predictions. Today, we have reached a point where materials and their models are becoming increasingly sophisticated. This raises the question: How do we find the best model that includes all inelastic effects to explain our complex data? Constitutive Artificial Neural Networks (CANN) may answer this question. Here, we extend the CANNs to inelastic materials (iCANN). Rigorous considerations of objectivity, rigid motion of the reference configuration, multiplicative decomposition and its inherent non-uniqueness, restrictions of energy and pseudo potential, and consistent evolution guide us towards the architecture of the iCANN satisfying thermodynamics per design. We combine feed-forward networks of the free energy and pseudo potential with a recurrent neural network approach to take time dependencies into account. We demonstrate that the iCANN is capable of autonomously discovering models for artificially generated data, the response of polymers for cyclic loading and the relaxation behavior of muscle data. As the design of the network is not limited to visco-elasticity, our vision is that the iCANN will reveal to us new ways to find the various inelastic phenomena hidden in the data and to understand their interaction. Our source code, data, and examples are available at doi.org/10.5281/zenodo.10066805Comment: 54 pages, 14 figures, 14 table

Mechanical modeling of the maturation process for tissue-engineered implants: application to biohybrid heart valves

The development of tissue-engineered cardiovascular implants can improve the lives of large segments of our society who suffer from cardiovascular diseases. Regenerative tissues are fabricated using a process called tissue maturation. Furthermore, it is highly challenging to produce cardiovascular regenerative implants with sufficient mechanical strength to withstand the loading conditions within the human body. Therefore, biohybrid implants for which the regenerative tissue is reinforced by standard reinforcement material (e.g. textile or 3d printed scaffold) can be an interesting solution. In silico models can significantly contribute to characterizing, designing, and optimizing biohybrid implants. The first step towards this goal is to develop a computational model for the maturation process of tissue-engineered implants. This paper focuses on the mechanical modeling of textile-reinforced tissue-engineered cardiovascular implants. First, we propose an energy-based approach to compute the collagen evolution during the maturation process. Then, we apply the concept of structural tensors to model the anisotropic behavior of the extracellular matrix and the textile scaffold. Next, the newly developed material model is embedded into a special solid-shell finite element formulation with reduced integration. Finally, we use our framework to compute two structural problems: a pressurized shell construct and a tubular-shaped heart valve. The results show the ability of the model to predict collagen growth in response to the boundary conditions applied during the maturation process. Consequently, we can predict the implant's mechanical response, such as the deformation and stresses of the implant.Comment: Preprint submitted to Elsevie

MusicXML Analyzer. Ein Analysewerkzeug für die computergestützte Identifikation von Melodie-Patterns

Der Beitrag beschreibt Aktivitäten aus einem aktuellen Digital Humanities-Projekt, das im Schnittfeld von Informations-, Musik- und Kulturwissenschaft angesiedelt ist. Dabei soll eine Sammlung von ca. 50.000 handschriftlichen Liedblättern mit deutschsprachiger Volksmusik digitalisiert und maschinenlesbar in MusicXML kodiert werden, um schließlich über ein Informationssystem quantitative Analysen des Materials zu erlauben. Wir stellen einen ersten webbasierten Prototypen vor, der es erlaubt, Musikstücke im MusicXML-Format statistisch auszuwerten und nach konkreten Melodie-Patterns zu suchen. Das Tool ist zudem in der Lage, virtuelle Partituren und Audioausgaben auf Basis des MusicXML-Markups zu erstellen und direkt im Webbrowser verfügbar zu machen

The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective

We are interested in the role of field user interaction data in the development of IVIS, the potentials practitioners see in analyzing this data, the concerns they share, and how this compares to companies with digital products. We conducted interviews with 14 UX professionals, 8 from automotive and 6 from digital companies, and analyzed the results by emergent thematic coding. Our key findings indicate that implicit feedback through field user interaction data is currently not evident in the automotive UX development process. Most decisions regarding the design of IVIS are made based on personal preferences and the intuitions of stakeholders. However, the interviewees also indicated that user interaction data has the potential to lower the influence of guesswork and assumptions in the UX design process and can help to make the UX development lifecycle more evidence-based and user-centered

Evaluation of User Interaction Concepts for Driver Displays - Analysis of Expert-Based Approaches for Usability Evaluation During Development

This dissertation deals with the application of expert-based usability evaluation methods in vehicles. A deficit in this area of research could be shown by an exploratory literature review. In two comparative case studies the suitability of two concrete methods was systematically investigated. For the comparison, the methods cognitive walkthrough and guideline review were applied for the usability evaluation of different parts of an in-vehicle information system comparing the results with those of parallel usability tests. In order to satisfy the special context of use of an in-vehicle information system, established definitions of usability as well as their classification in the user-centered design process and the specific implications for the environment in the vehicle were considered. The two methods applied were then prepared specifically for the context of use in the vehicle. While no clear recommendation for the cognitive walkthrough in the context of use of a vehicle was found, the guideline review could achieve satisfactory results. Furthermore, the additional introduction of a new metric, based on the System Usability Scale (SUS), allows a comparison between individual versions of a system in the course of a project and thus closes a gap in the field of expert-based usability evaluation methods

When implicit prosociality trumps selfishness: the neural valuation system underpins more optimal choices when learning to avoid harm to others than to oneself

Humans learn quickly which actions cause them harm. As social beings, we also need to learn to avoid actions that hurt others. It is currently unknown if humans are as good at learning to avoid others' harm (prosocial learning) as they are at learning to avoid self-harm (self-relevant learning). Moreover, it remains unclear how the neural mechanisms of prosocial learning differ from those of self-relevant learning. In this fMRI study, 96 male human participants learned to avoid painful stimuli either for themselves or for another individual. We found that participants performed more optimally when learning for the other than for themselves. Computational modeling revealed that this could be explained by an increased sensitivity to subjective values of choice alternatives during prosocial learning. Increased value-sensitivity was further associated with empathic traits. On the neural level, higher value-sensitivity during prosocial learning was associated with stronger engagement of the ventromedial prefrontal cortex (VMPFC) during valuation. Moreover, the VMPFC exhibited higher connectivity with the right temporoparietal junction during prosocial, compared to self-relevant, choices. Our results suggest that humans are particularly adept at learning to protect others from harm. This ability appears implemented by neural mechanisms overlapping with those supporting self-relevant learning, but with the additional recruitment of structures associated to the social brain. Our findings contrasts with recent proposals that humans are egocentrically biased when learning to obtain monetary rewards for self or others. Prosocial tendencies may thus trump the egocentric bias in learning when another person's physical integrity is at stake

Vergleichende Evaluation zur Usability mobiler Web-Browser

Dieser Beitrag beschreibt eine vergleichende Usability-Evaluation der drei meistgenutzten Web-Browser für mobile Endgeräte. Dabei werden die zuvor identifizierten Hauptfunktionen der Browser durch entsprechend konstruierte Aufgaben mit sechs Studienteilnehmern ausführlich getestet. Die Ergebnisse werden in Form von think aloud-Protokollen sowie einer Abwandlung des User Experience Questionnaire (UEQ) dokumentiert und abschließend diskutiert