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With the recent growth of sensors, cloud computing handles the data processing of many applications. Processing some of this data on the cloud raises, however, many concerns regarding, e.g., privacy, latency, or single points of failure. Alternatively, thanks to the development of embedded systems, smart wireless devices can share their computation capacity, creating a local wireless cloud for in-network processing. In this context, the processing of an application is divided into smaller jobs so that a device can run one or more jobs.
The contribution of this thesis to this scenario is divided into three parts. In part one, I focus on wireless aspects, such as power control and interference management, for deciding which jobs to run on which node and how to route data between nodes. Hence, I formulate optimization problems and develop heuristic and meta-heuristic algorithms to allocate wireless and computation resources. Additionally, to deal with multiple applications competing for these resources, I develop a reinforcement learning (RL) admission controller to decide which application should be admitted. Next, I look into acoustic applications to improve wireless throughput by using microphone clock synchronization to synchronize wireless transmissions.
In the second part, I jointly work with colleagues from the acoustic processing field to optimize both network and application (i.e., acoustic) qualities. My contribution focuses on the network part, where I study the relation between acoustic and network qualities when selecting a subset of microphones for collecting audio data or selecting a subset of optional jobs for processing these data; too many microphones or too many jobs can lessen quality by unnecessary delays. Hence, I develop RL solutions to select the subset of microphones under network constraints when the speaker is moving while still providing good acoustic quality. Furthermore, I show that autonomous vehicles carrying microphones improve the acoustic qualities of different applications. Accordingly, I develop RL solutions (single and multi-agent ones) for controlling these vehicles.
In the third part, I close the gap between theory and practice. I describe the features of my open-source framework used as a proof of concept for wireless in-network processing. Next, I demonstrate how to run some algorithms developed by colleagues from acoustic processing using my framework. I also use the framework for studying in-network delays (wireless and processing) using different distributions of jobs and network topologies.
Sich mit dem eigenen Denken und Handeln reflexiv auseinanderzusetzen ist ein zentrales Element im Selbstreifungsprozess von Lehrkräften im Studium, über die Ausbildung bis hin zur Lehrer:innentätigkeit im Alltag. Der Workshop ermöglichte Zugänge zur selbstreflexiven Auseinandersetzung mit den eigenen Handlungsmustern mithilfe vom Autor (weiter)entwickelter Coachingtools, die im Rahmen der seminaristischen Lehrer:innenbildung in der zweiten Phase konzipiert und erprobt wurden.
This thesis explores the variation in coreference patterns across language modes (i.e., spoken and written) and text genres. The significance of research on variation in language use has been emphasized in a number of linguistic studies. For instance, Biber and Conrad [2009] state that “register/genre variation is a fundamental aspect of human language” and “Given the ubiquity of register/genre variation, an understanding of how linguistic features are used in patterned ways across text varieties is of central importance for both the description of particular languages and the development of cross-linguistic theories of language use.”[p.23]
We examine the variation across genres with the primary goal of contributing to the body of knowledge on the description of language use in English. On the computational side, we believe that incorporating linguistic knowledge into learning-based systems can boost the performance of automatic natural language processing systems, particularly for non-standard texts. Therefore, in addition to their descriptive value, the linguistic findings we provide in this study may prove to be helpful for improving the performance of automatic coreference resolution, which is essential for a good text understanding and beneficial for several downstream NLP applications, including machine translation and text summarization.
In particular, we study a genre of texts that is formed of conversational interactions on the well-known social media platform Twitter. Two factors motivate us: First, Twitter conversations are realized in written form but resemble spoken communication [Scheffler, 2017], and therefore they form an atypical genre for the written mode. Second, while Twitter texts are a complicated genre for automatic coreference resolution, due to their widespread use in the digital sphere, at the same time they are highly relevant for applications that seek to extract information or sentiments from users’ messages. Thus, we are interested in discovering more about the linguistic and computational aspects of coreference in Twitter conversations. We first created a corpus of such conversations for this purpose and annotated it for coreference. We are interested in not only the coreference patterns but the overall discourse behavior of Twitter conversations. To address this, in addition to the coreference relations, we also annotated the coherence relations on the corpus we compiled. The corpus is available online in a newly developed form that allows for separating the tweets from their annotations.
This study consists of three empirical analyses where we independently apply corpus-based, psycholinguistic and computational approaches for the investigation of variation in coreference patterns in a complementary manner. (1) We first make a descriptive analysis of variation across genres through a corpus-based study. We investigate the linguistic aspects of nominal coreference in Twitter conversations and we determine how this genre relates to other text genres in spoken and written modes. In addition to the variation across genres, studying the differences in spoken-written modes is also in focus of linguistic research since from Woolbert [1922]. (2) In order to investigate whether the language mode alone has any effect on coreference patterns, we carry out a crowdsourced experiment and analyze the patterns in the same genre for both spoken and written modes. (3) Finally, we explore the potentials of domain adaptation of automatic coreference resolution (ACR) for the conversational Twitter data. In order to answer the question of how the genre of Twitter conversations relates to other genres in spoken and written modes with respect to coreference patterns, we employ a state-of-the-art neural ACR model [Lee et al., 2018] to examine whether ACR on Twitter conversations will benefit from mode-based separation in out-of-domain training data.
Innovat MOOC
(2023)
The COVID-19 pandemic has revealed the importance for university teachers to have adequate pedagogical and technological competences to cope with the various possible educational scenarios (face-to-face, online, hybrid, etc.), making use of appropriate active learning methodologies and supporting technologies to foster a more effective learning environment. In this context, the InnovaT project has been an important initiative to support the development of pedagogical and technological competences of university teachers in Latin America through several trainings aiming to promote teacher innovation. These trainings combined synchronous online training through webinars and workshops with asynchronous online training through the MOOC “Innovative Teaching in Higher Education.” This MOOC was released twice. The first run took place right during the lockdown of 2020, when Latin American teachers needed urgent training to move to emergency remote teaching overnight. The second run took place in 2022 with the return to face-to-face teaching and the implementation of hybrid educational models. This article shares the results of the design of the MOOC considering the constraints derived from the lockdowns applied in each country, the lessons learned from the delivery of such a MOOC to Latin American university teachers, and the results of the two runs of the MOOC.
Insight by de—sign
(2023)
The calculus of design is a diagrammatic approach towards the relationship between design and insight. The thesis I am evolving is that insights are not discovered, gained, explored, revealed, or mined, but are operatively de—signed. The de in design neglects the contingency of the space towards the sign. The — is the drawing of a distinction within the operation. Space collapses through the negativity of the sign; the command draws a distinction that neglects the space for the form's sake. The operation to de—sign is counterintuitively not the creation of signs, but their removal, the exclusion of possible sign propositions of space. De—sign is thus an act of exclusion; the possibilities of space are crossed into form.
Planets outside our solar system, so-called "exoplanets", can be detected with different methods, and currently more than 5000 exoplanets have been confirmed, according to NASA Exoplanet Archive. One major highlight of the studies on exoplanets in the past twenty years is the characterization of their atmospheres usingtransmission spectroscopy as the exoplanet transits. However, this characterization is a challenging process and sometimes there are reported discrepancies in the literature regarding the atmosphere of the same exoplanet. One potential reason for the observed atmospheric inconsistencies is called impact parameter degeneracy, and it is highly driven by the limb darkening effect of the host star. A brief introductionto those topics in presented in chapter 1, while the motivation and objectives of thiswork are described in chapter 2.The first goal is to clarify the origin of the transmission spectrum, which is anindicator of an exoplanet’s atmosphere; whether it is real or influenced by the impactparameter degeneracy. A second goal is to determine whether photometry from space using the Transiting Exoplanet Survey Satellite (TESS), could improve on the major parameters, which are responsible for the aforementioned degeneracy, of known exoplanetary systems. Three individual projects were conducted in order toaddress those goals. The three manuscripts are presented, in short, in the manuscriptoverview in chapter 3.More specifically, in chapter 4, the first manuscript is presented, which is an ex-tended investigation on the impact parameter degeneracy and its application onsynthetic transmission spectra. Evidently, the limb darkening of the host star isan important driver for this effect. It keeps the degeneracy persisting through different groups of exoplanets, based on the uncertainty of their impact parameter and on the type of their host star. The second goal, was addressed in the second and third manuscripts (chapter 5 and chapter 6 respectively). Using observationsfrom the TESS mission, two samples of exoplanets were studied; 10 transiting inflated hot-Jupiters and 43 transiting grazing systems. Potentially, the refinement or confirmation of their major system parameters’ measurements can assist in solving current or future discrepancies regarding their atmospheric characterization.In chapter 7 the conclusions of this work are discussed, while in chapter 8 itis proposed how TESS’s measurements can be able to discern between erroneousinterpretations of transmission spectra, especially on systems where the impact parameter degeneracy is likely not applicable.
High growth firms (HGFs) are important for job creation and considered to be precursors of economic growth. We investigate how formal institutions, like product- and labor-market regulations, as well as the quality of regional governments that implement these regulations, affect HGF development across European regions. Using data from Eurostat, OECD, WEF, and Gothenburg University, we show that both regulatory stringency and the quality of the regional government influence the regional shares of HGFs. More importantly, we find that the effect of labor- and product-market regulations ultimately depends on the quality of regional governments: in regions with high quality of government, the share of HGFs is neither affected by the level of product market regulation, nor by more or less flexibility in hiring and firing practices. Our findings contribute to the debate on the effects of regulations by showing that regulations are not, per se, “good, bad, and ugly”, rather their impact depends on the efficiency of regional governments. Our paper offers important building blocks to develop tailored policy measures that may influence the development of HGFs in a region.
The electrical resistivity tomography (ERT) method is widely used to investigate geological, geotechnical, and hydrogeological problems in inland and aquatic environments (i.e., lakes, rivers, and seas). The objective of the ERT method is to obtain reliable resistivity models of the subsurface that can be interpreted in terms of the subsurface structure and petrophysical properties. The reliability of the resulting resistivity models depends not only on the quality of the acquired data, but also on the employed inversion strategy. Inversion of ERT data results in multiple solutions that explain the measured data equally well. Typical inversion approaches rely on different deterministic (local) strategies that consider different smoothing and damping strategies to stabilize the inversion. However, such strategies suffer from the trade-off of smearing possible sharp subsurface interfaces separating layers with resistivity contrasts of up to several orders of magnitude. When prior information (e.g., from outcrops, boreholes, or other geophysical surveys) suggests sharp resistivity variations, it might be advantageous to adapt the parameterization and inversion strategies to obtain more stable and geologically reliable model solutions. Adaptations to traditional local inversions, for example, by using different structural and/or geostatistical constraints, may help to retrieve sharper model solutions. In addition, layer-based model parameterization in combination with local or global inversion approaches can be used to obtain models with sharp boundaries.
In this thesis, I study three typical layered near-surface environments in which prior information is used to adapt 2D inversion strategies to favor layered model solutions. In cooperation with the coauthors of Chapters 2-4, I consider two general strategies. Our first approach uses a layer-based model parameterization and a well-established global inversion strategy to generate ensembles of model solutions and assess uncertainties related to the non-uniqueness of the inverse problem. We apply this method to invert ERT data sets collected in an inland coastal area of northern France (Chapter~2) and offshore of two Arctic regions (Chapter~3). Our second approach consists of using geostatistical regularizations with different correlation lengths. We apply this strategy to a more complex subsurface scenario on a local intermountain alluvial fan in southwestern Germany (Chapter~4). Overall, our inversion approaches allow us to obtain resistivity models that agree with the general geological understanding of the studied field sites. These strategies are rather general and can be applied to various geological environments where a layered subsurface structure is expected. The flexibility of our strategies allows adaptations to invert other kinds of geophysical data sets such as seismic refraction or electromagnetic induction methods, and could be considered for joint inversion approaches.