Informatik und Kommunikation
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Abstract
Indigenous people across the globe have all too often been marginalised and not been considered in decisions that directly concern their life. We maintain the significance of incorporating indigenous perspectives in society to create a global dialogue through direct participation. In this light we propose a situated action in which our co-author from the Ju/’hoansi tribe, one of the San ethnicity in Southern Africa, digitally records semi-structured conversations with members of other indigenous communities at the conference and public spaces in Sibu. Then participants are engaged into a participatory exploration of processing the audio files into sound installations and soundscapes which “amplify indigenous voices“. We anticipate that the products can be reused for further initiatives in the different countries, raising awareness and calling for action on matters of concern for indigenous people.
Abstract
In this paper we present the co-design and implementation of an extended reality escape room with 26 primary school students. The aim of our study was to explore the co-design process with students and to co-create a playable escape room, providing an asymmetric immersive experience in which players collaborate. We realised the complexity of designing such an escape room with primary students. We share our experiences and learnings in regard to required capacities and skills of co-designers, and adjustment of complexity and timing to players. We also maintain that the integration of extended reality technologies into escape rooms requires further research to realise asymmetric co-located collaboration.
Journalism and Advertising: On the Separation of Editorial Content and Commercial Communication
(2024)
Abstract
The principle of separation between editorial content and commercial communication protects both the democratic and the commercial function of mass media. This article compiles all available statutory and professional regulations in Germany as an example of the various aspects of the principle of separation, such as the labeling obligation, the prohibition of paid content and tying transactions, as well as the handling of numerous forms of presentation of editorial advertising. Subsequently, the state of research is reported for the individual aspects of the principle of separation, in particular with regard to description and effect. Finally, proposed solutions for current application and desiderata are compiled.
Media Brand Management
(2024)
Abstract
The management of media brands faces challenges. In order to be able to point out possible solutions, this article first explains the concept and the nature of “media brands.” Subsequently, various theoretical approaches to the explanation of media brands and their management are presented. Regardless of theoretical preferences, it is important to keep in mind the brand-strategic complexity of media management that is subsequently described. Due to their specificity,
special attention is paid to the basic strategic positioning options and to the communication management of media brands. In this way, the special features of media brand management become clear in comparison with other products and services.
Focusing on the implementation of the Smart Specialisation Strategy (S3), the chapter examines the development of cluster policies in the Ruhr Metropolis as a post-industrial region. The chapter traces the historical development of the Ruhr Area from its industrial peak in the 20th century to its slow transformation into a post-industrial landscape characterised by high urban density, new knowledge-based clusters and a persistent structural lack of effective regional cooperation. The analysis shows the conceptual shift from traditional cluster policies to the S3 approach, introduced by the European Union in 2014. The Smart Specialisation Strategy calls for a focus on comparative regional strengths and the involvement of a wide range of stakeholders in the identification of clusters for sustainable economic growth. The chapter also discusses the challenges and milestones in developing a coherent and effective Smart Specialisation Strategy, emphasising the need for inter-municipal cooperation and a new multi-level approach to regional governance. Using the case of the Ruhr Metropolis, the chapter highlights the opportunities and constraints of S3 policies to revitalise post-industrial regions by promoting innovation and adapting to global economic trends in cluster development, thus showing a way forward for other regions with similar structural challenges.
Abstract
Earthquakes, fire, and floods often cause structural collapses of buildings. However, the inspection of such damaged buildings poses a high risk for emergency forces or is even impossible. We present three recently selected missions of the Robotics Task Force of the German Rescue Robotics Center (DRZ), where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. To make robots from research laboratories fit for real operations, realistic outdoor and indoor test environments were set up at the DRZ and used for tests in regular exercises by researchers and emergency forces. On the basis of this experience, the robots and their control software were significantly improved. Furthermore, expert teams of
researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.
Introduction: Drawing tasks are an elementary component of psychological assessment in the evaluation of mental health. With the rise of digitalization not only in psychology but healthcare in general, digital drawing tools (dDTs) have also been developed for this purpose. This scoping review aims at summarizing the state of the art of dDTs available to assess mental health conditions in people above preschool age. Methods: PubMed, PsycInfo, PsycArticles, CINAHL, and Psychology and Behavioral Sciences Collection were searched for dDTs from 2000 onwards. The focus was on dDTs, which not only evaluate the final drawing, but also process data. Results: After applying the search and selection strategy, a total of 37 articles, comprising unique dDTs, remained for data extraction. Around 75 % of these articles were published after 2014 and most of them target adults (86.5 %). In addition, dDTs were mainly used in two areas: tremor detection and assessment of cognitive states, utilizing, for example, the Spiral Drawing Test and the Clock Drawing Test. Conclusion: Early detection of mental diseases is an increasingly important field in healthcare. Through the integration of digital and art based solutions, this area could expand into an interdisciplinary science. This review shows that the first steps in this direction have already been taken and that the possibilities for further research, e.g., on the optimized application of dDTs, are still open.
Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data
(2024)
Early detection and diagnosis of dementia is a major challenge for medical research and practice. Hence, in the last decade, digital drawing tests became popular, showing sometimes even better performance than their paper-and-pencil versions. Combined with machine learning algorithms, these tests are used to differentiate between healthy people and people with mild cognitive impairment (MCI) or early Alzheimer’s disease (eAD), commonly using data from the Clock Drawing Test (CDT). In this investigation, a Random Forest Classification (RF) algorithm is trained on digital Tree Drawing Test (dTDT) data, containing socio-medical information and process data of 86 healthy people, 97 people with MCI, and 74 people with eAD. The results indicate that the binary classification works well for homogeneous groups, as demonstrated by a sensitivity of 0.85 and a specificity of 0.9 (AUC of 0.94). In contrast, the performance of both binary and multiclass classification degrades for groups with het erogeneous characteristics, which is reflected in a sensitivity of 0.91 and 0.29 and a specificity of 0.44 and 0.36 (AUC of 0.74 and 0.65), respectively. Nevertheless, as the early detection of cognitive impairment becomes increasingly important in healthcare, the results could be useful for models that aim for automatic identification
In the realm of digital situational awareness during disaster situations, accurate digital representations,
like 3D models, play an indispensable role. To ensure the
safety of rescue teams, robotic platforms are often deployed
to generate these models. In this paper, we introduce an
innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360° cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry—commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.
In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with utomatically labeled images. Finally, we evaluate the performance of different neural networks.
