Towards a crowdsourced solution for the authoring bottleneck in interactive narratives

Interactive Storytelling research has produced a wealth of technologies that can be employed to create personalised narrative experiences, in which the audience takes a participating rather than observing role. But so far this technology has not led to the production of large scale playable interactive story experiences that realise the ambitions of the field. One main reason for this state of affairs is the difficulty of authoring interactive stories, a task that requires describing a huge amount of story building blocks in a machine friendly fashion. This is not only technically and conceptually more challenging than traditional narrative authoring but also a scalability problem. This thesis examines the authoring bottleneck through a case study and a literature survey and advocates a solution based on crowdsourcing. Prior work has already shown that combining a large number of example stories collected from crowd workers with a system that merges these contributions into a single interactive story can be an effective way to reduce the authorial burden. As a refinement of such an approach, this thesis introduces the novel concept of Crowd Task Adaptation. It argues that in order to maximise the usefulness of the collected stories, a system should dynamically and intelligently analyse the corpus of collected stories and based on this analysis modify the tasks handed out to crowd workers. Two authoring systems, ENIGMA and CROSCAT, which show two radically different approaches of using the Crowd Task Adaptation paradigm have been implemented and are described in this thesis. While ENIGMA adapts tasks through a realtime dialog between crowd workers and the system that is based on what has been learned from previously collected stories, CROSCAT modifies the backstory given to crowd workers in order to optimise the distribution of branching points in the tree structure that combines all collected stories. Two experimental studies of crowdsourced authoring are also presented. They lead to guidelines on how to employ crowdsourced authoring effectively, but more importantly the results of one of the studies demonstrate the effectiveness of the Crowd Task Adaptation approach

Entwicklung neuer Studiengänge - Curricula kooperativ und kompetenzorientiert gestalten

Mit einer "kooperativen Curriculumsentwicklung" soll ein Beitrag zur Öffnung von Hochschulen geleistet werden: Neue Studiengänge werden nicht mehr nur von der Hochschule, sondern gemeinsam mit Unternehmen und Einrichtungen der beruflichen Bildung entwickelt. Die Fachhochschule der Diakonie in Bielefeld hat dazu ein Modell entwickelt und setzt dieses in einem Forschungsprojekt im Rahmen der BMBF-Initiative "Aufstieg durch Bildung – Offene Hochschulen" um. Der Werkstattbericht gibt einen Einblick in die konkreten Entwicklungsschritte und diskutiert Chancen und Hürden des Modells. 21.03.2014 | Miriam Schäfer, Michael Kriegel & Tim Hagemann (Bielefeld

St. Patrick’s Day 2015 geomagnetic storm analysis based on Real Time Ionosphere Monitoring

A detailed analysis is presented for the days in March, 2015 surrounding St. Patrick’s Day 2015 geomagnetic storm, based on the existing real-time and near real-time ionospheric models (global or regional) within the group, which are mainly based on Global Navigation Satellite Systems (GNSS) and ionosonde data. For this purpose, a variety of ionospheric parameters is considered, including Total Electron Content (TEC), F2 layer critical frequency (foF2), F2 layer peak (hmF2), bottomside halfthickness (B0) and ionospheric disturbance W-index. Also, ionospheric high-frequency perturbations such as Travelling Ionospheric Disturbances (TIDs), scintillations and the impact of solar flares facing the Earth will be presented to derive a clear picture of the ionospheric dynamicsPostprint (published version

System-wide transcriptome damage and tissue identity loss in COVID-19 patients

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections., • Across all organs, fibroblast, and immune cell populations increase in COVID-19 patients • Organ-specific cell types and functional markers are lost in all COVID-19 tissue types • Lung compartment identity loss correlates with SARS-CoV-2 viral loads • COVID-19 uniquely disrupts co-occurrence cell type clusters (different from IAV/ARDS) , Park et al. report system-wide transcriptome damage and tissue identity loss wrought by SARS-CoV-2, influenza, and bacterial infection across multiple organs (heart, liver, lung, kidney, and lymph nodes) and provide a spatiotemporal landscape of COVID-19 in the lung

Fusing Color and Geometry Information for Understanding Cluttered Scenes

In this paper, we introduce a new image processing pipeline for scene recognition and pose estimation in robotic applications. Unknown objects are autonomously modeled resulting in geometric 3D models and color images. Theses models are then used for object recognition in cluttered scenes by merging color and geometry information. Our recognition approach generates new suitable feature vectors and uses RANSAC to obtain promising hypotheses of recognized object poses for the scene. RANSAC is widely used for scene understanding. For making RANSAC applicable, it is very important to implement this algorithm efficiently and to reject hypotheses as early as possible in the scene understanding pipeline. By using color information many hypotheses can be rejected early in the recognition pipeline. With our approach we provide an efficient implementation of a scene analyzing pipeline while fusing color and geometric information. Moreover, we are able to learn new objects by a fast autonomous scanning process and no further runs through time consuming learning algorithms are necessary. The complete pipeline from scanning to scene understanding is described. The evaluated scenes consist of several household objects. Some of them vary only in texture and not in shape