Universitätsbibliothek

This short letter proposes more consolidated explicit solutions for the forces and torques acting on typical rover wheels, that can be used as a method to determine their average mobility characteristics in planetary soils. The closed loop solutions stand in one of the verified methods, but at difference of the previous, observables are decoupled requiring a less amount of physical parameters to measure. As a result, we show that with knowledge of terrain properties, wheel driving performance rely in a single observable only. Because of their generality, the formulated equations established here can have further implications in autonomy and control of rovers or planetary soil characterization.

In recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays. However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own.

Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years, great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, character recognition, and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper, we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required ground truth for training stronger mixed models (for segmentation, as well as text recognition) is not available, yet, neither in the desired quantity nor quality. To deal with this issue in the short run, OCR4all offers a comfortable GUI that allows error corrections not only in the final output, but already in early stages to minimize error propagations. In the long run, this constant manual correction produces large quantities of valuable, high quality training material, which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. During experiments, the fully automated application on 19th Century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. Furthermore, on very complex early printed books, even users with minimal or no experience were able to capture the text with manageable effort and great quality, achieving excellent Character Error Rates (CERs) below 0.5%. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.

Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.

Virtual reality and related media and communication technologies have a growing impact on professional application fields and our daily life. Virtual environments have the potential to change the way we perceive ourselves and how we interact with others. In comparison to other technologies, virtual reality allows for the convincing display of a virtual self-representation, an avatar, to oneself and also to others. This is referred to as user embodiment. Avatars can be of varying realism and abstraction in their appearance and in the behaviors they convey. Such userembodying interfaces, in turn, can impact the perception of the self as well as the perception of interactions. For researchers, designers, and developers it is of particular interest to understand these perceptual impacts, to apply them to therapy, assistive applications, social platforms, or games, for example. The present thesis investigates and relates these impacts with regard to three areas: intrapersonal effects, interpersonal effects, and effects of social augmentations provided by the simulation. With regard to intrapersonal effects, we specifically explore which simulation properties impact the illusion of owning and controlling a virtual body, as well as a perceived change in body schema. Our studies lead to the construction of an instrument to measure these dimensions and our results indicate that these dimensions are especially affected by the level of immersion, the simulation latency, as well as the level of personalization of the avatar. With regard to interpersonal effects we compare physical and user-embodied social interactions, as well as different degrees of freedom in the replication of nonverbal behavior. Our results suggest that functional levels of interaction are maintained, whereas aspects of presence can be affected by avatar-mediated interactions, and collaborative motor coordination can be disturbed by immersive simulations. Social interaction is composed of many unknown symbols and harmonic patterns that define our understanding and interpersonal rapport. For successful virtual social interactions, a mere replication of physical world behaviors to virtual environments may seem feasible. However, the potential of mediated social interactions goes beyond this mere replication. In a third vein of research, we propose and evaluate alternative concepts on how computers can be used to actively engage in mediating social interactions, namely hybrid avatar-agent technologies. Specifically, we investigated the possibilities to augment social behaviors by modifying and transforming user input according to social phenomena and behavior, such as nonverbal mimicry, directed gaze, joint attention, and grouping. Based on our results we argue that such technologies could be beneficial for computer-mediated social interactions such as to compensate for lacking sensory input and disturbances in data transmission or to increase aspects of social presence by visual substitution or amplification of social behaviors. Based on related work and presented findings, the present thesis proposes the perspective of considering computers as social mediators. Concluding from prototypes and empirical studies, the potential of technology to be an active mediator of social perception with regard to the perception of the self, as well as the perception of social interactions may benefit our society by enabling further methods for diagnosis, treatment, and training, as well as the inclusion of individuals with social disorders. To this regard, we discuss implications for our society and ethical aspects. This thesis extends previous empirical work and further presents novel instruments, concepts, and implications to open up new perspectives for the development of virtual reality, mixed reality, and augmented reality applications.