Living in the Plastic Age: Perspectives from Humanities, Social Sciences and Environmental Sciences

Plastikmüll ist überall auf unserem Planeten zu finden. Er hinterlässt einen augenscheinlichen Fußabdruck des menschlichen Konsumverhaltens und der Massenproduktion. Unser ungebremster Plastikkonsum und dessen Auswirkungen prägen die gesellschaftlichen Naturverhältnisse in einer so tiefgreifenden Weise, dass wir vom Plastikzeitalter sprechen. Um Ansätze für einen Umgang mit diesem Problem zu entwickeln, müssen wir das Phänomen umfassend verstehen: Die Autor:innen beleuchten es aus interdisziplinärer Perspektive. Sie zeigen, welche Rolle Kunststoffe in unserer Gesellschaft spielen und welche Auswirkungen sie auf die Umwelt und die menschliche Gesundheit haben

A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

Facilitating low-carbon living? A comparison of intervention measures in different community-based initiatives

The challenge of facilitating a shift towards sustainable housing, food and mobility has been taken up by diverse community-based initiatives ranging from ‘top-down’ approaches in low-carbon municipalities to ‘bottom-up’ approaches in intentional communities. This paper compares intervention measures of these two types, focusing on their potential of re-configuring daily housing, food and mobility practices. Taking up critics on dominant intervention framings of diffusing low-carbon technical innovations and changing individual behaviour, we draw on social practice theory for the empirical analysis of four case studies. Framing interventions in relation to re-configuring daily practices, the paper reveals differences and weaknesses of current low-carbon measures of community-based initiatives in Germany and Austria. Low-carbon municipalities mainly focus on introducing technologies and offering additional infrastructure and information to promote low-carbon practices. They avoid interfering into residents’ daily lives and do not restrict carbon-intensive practices. In contrast, intentional communities base their interventions on the collective creation of shared visions, decisions and rules and thus provide social and material structures, which foster everyday low-carbon practices and discourage carbon-intensive ones. The paper discusses the relevance of organisational and governance structures for implementing different types of low-carbon measures and points to opportunities for broadening current policy strategies

Deep neural networks for identification of sentential relations

Natural language processing (NLP) is one of the most important technologies in the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate mostly in language: web search, advertisement, emails, customer service, language translation, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained exciting performance across a broad array of NLP tasks. These models can often be trained in an end-to-end paradigm without traditional, task-specific feature engineering. This dissertation focuses on a specific NLP task --- sentential relation identification. Successfully identifying the relations of two sentences can contribute greatly to some downstream NLP problems. For example, in open-domain question answering, if the system can recognize that a new question is a paraphrase of a previously observed question, the known answers can be returned directly, avoiding redundant reasoning. For another, it is also helpful to discover some latent knowledge, such as inferring ``the weather is good today'' from another description ``it is sunny today''. This dissertation presents some deep neural networks (DNNs) which are developed to handle this sentential relation identification problem. More specifically, this problem is addressed by this dissertation in the following three aspects. (i) Sentential relation representation is built on the matching between phrases of arbitrary lengths. Stacked Convolutional Neural Networks (CNNs) are employed to model the sentences, so that each filter can cover a local phrase, and filters in lower level span shorter phrases and filters in higher level span longer phrases. CNNs in stack enable to model sentence phrases in different granularity and different abstraction. (ii) Phrase matches contribute differently to the tasks. This motivates us to propose an attention mechanism in CNNs for these tasks, differing from the popular research of attention mechanisms in Recurrent Neural Networks (RNNs). Attention mechanisms are implemented in both convolution layer as well as pooling layer in deep CNNs, in order to figure out automatically which phrase of one sentence matches a specific phrase of the other sentence. These matches are supposed to be indicative to the final decision. Another contribution in terms of attention mechanism is inspired by the observation that some sentential relation identification task, like answer selection for multi-choice question answering, is mainly determined by phrase alignments of stronger degree; in contrast, some tasks such as textual entailment benefit more from the phrase alignments of weaker degree. This motivates us to propose a dynamic ``attentive pooling'' to select phrase alignments of different intensities for different task categories. (iii) In certain scenarios, sentential relation can only be successfully identified within specific background knowledge, such as the multi-choice question answering based on passage comprehension. In this case, the relation between two sentences (question and answer candidate) depends on not only the semantics in the two sentences, but also the information encoded in the given passage. Overall, the work in this dissertation models sentential relations in hierarchical DNNs, different attentions and different background knowledge. All systems got state-of-the-art performances in representative tasks.Die Verarbeitung natürlicher Sprachen (engl.: natural language processing - NLP) ist eine der wichtigsten Technologien des Informationszeitalters. Weiterhin ist das Verstehen komplexer sprachlicher Ausdrücke ein essentieller Teil künstlicher Intelligenz. Anwendungen von NLP sind überall zu finden, da Menschen haupt\-säch\-lich über Sprache kommunizieren: Internetsuchen, Werbung, E-Mails, Kundenservice, Übersetzungen, etc. Es gibt eine große Anzahl Tasks und Modelle des maschinellen Lernens für NLP-Anwendungen. In den letzten Jahren haben Deep-Learning-Ansätze vielversprechende Ergebnisse für eine große Anzahl verschiedener NLP-Tasks erzielt. Diese Modelle können oft end-to-end trainiert werden, kommen also ohne auf den Task zugeschnittene Feature aus. Diese Dissertation hat einen speziellen NLP-Task als Fokus: Sententielle Relationsidentifizierung. Die Beziehung zwischen zwei Sätzen erfolgreich zu erkennen, kann die Performanz für nachfolgende NLP-Probleme stark verbessern. Für open-domain question answering, zum Beispiel, kann ein System, das erkennt, dass eine neue Frage eine Paraphrase einer bereits gesehenen Frage ist, die be\-kann\-te Antwort direkt zurückgeben und damit mehrfaches Schlussfolgern vermeiden. Zudem ist es auch hilfreich, zu Grunde liegendes Wissen zu entdecken, so wie das Schließen der Tatsache "das Wetter ist gut" aus der Beschreibung "es ist heute sonnig". Diese Dissertation stellt einige tiefe neuronale Netze (eng.: deep neural networks - DNNs) vor, die speziell für das Problem der sententiellen Re\-la\-tions\-i\-den\-ti\-fi\-zie\-rung entwickelt wurden. Im Speziellen wird dieses Problem in dieser Dissertation unter den folgenden drei Aspekten behandelt: (i) Sententielle Relationsrepr\"{a}sentationen basieren auf einem Matching zwischen Phrasen beliebiger Länge. Tiefe convolutional neural networks (CNNs) werden verwendet, um diese Sätze zu modellieren, sodass jeder Filter eine lokale Phrase abdecken kann, wobei Filter in niedrigeren Schichten kürzere und Filter in höheren Schichten längere Phrasen umfassen. Tiefe CNNs machen es möglich, Sätze in unterschiedlichen Granularitäten und Abstraktionsleveln zu modellieren. (ii) Matches zwischen Phrasen tragen unterschiedlich zu unterschiedlichen Tasks bei. Das motiviert uns, einen Attention-Mechanismus für CNNs für diese Tasks einzuführen, der sich von dem bekannten Attention-Mechanismus für recurrent neural networks (RNNs) unterscheidet. Wir implementieren Attention-Mechanismen sowohl im convolution layer als auch im pooling layer tiefer CNNs, um herauszufinden, welche Phrasen eines Satzes bestimmten Phrasen eines anderen Satzes entsprechen. Wir erwarten, dass solche Matches die finale Entscheidung stark beeinflussen. Ein anderer Beitrag zu Attention-Mechanismen wurde von der Beobachtung inspiriert, dass einige sententielle Relationsidentifizierungstasks, zum Beispiel die Auswahl einer Antwort für multi-choice question answering hauptsächlich von Phrasen\-a\-lignie\-rungen stärkeren Grades bestimmt werden. Im Gegensatz dazu profitieren andere Tasks wie textuelles Schließen mehr von Phrasenalignierungen schwächeren Grades. Das motiviert uns, ein dynamisches "attentive pooling" zu entwickeln, um Phrasenalignierungen verschiedener Stärken für verschiedene Taskkategorien auszuwählen. (iii) In bestimmten Szenarien können sententielle Relationen nur mit entsprechendem Hintergrundwissen erfolgreich identifiziert werden, so wie multi-choice question answering auf der Grundlage des Verständnisses eines Absatzes. In diesem Fall hängt die Relation zwischen zwei Sätzen (der Frage und der möglichen Antwort) nicht nur von der Semantik der beiden Sätze, sondern auch von der in dem gegebenen Absatz enthaltenen Information ab. Insgesamt modellieren die in dieser Dissertation enthaltenen Arbeiten sententielle Relationen in hierarchischen DNNs, mit verschiedenen Attention-Me\-cha\-nis\-men und wenn unterschiedliches Hintergrundwissen zur Verf\ {u}gung steht. Alle Systeme erzielen state-of-the-art Ergebnisse für die entsprechenden Tasks

Overcoming digitalization-driven challenges in banks : An exploration of theory and practice towards improving Enterprise Architecture Management’s ability to support rapid change

Banks increasingly need the ability to implement rapid change to react to changes in technology, user demands, and regulations that are difficult to foresee. The complex information systems (IS) and process landscape of incumbent banks impede this ability. Enterprise architecture management (EAM), as a function that aims to oversee the coherent development of the IS and IT landscape in alignment with the business, is argued to have the capability to support this ability. However, the speed and uncertainty of changes, as well as a focus of banks to implement Agile project methodolo-gies and de-centralize decision-making, challenges EAM to effectively fulfill this role. A Theoretical Base model is constructed from the literature and promising approaches to increase the effectiveness are identified. An exploratory case study of three large banks that are affected by digitalization to different extents, is conducted on the basis of this model. The findings indicate non-technical issues to be the most challenging factors for EAM today, which need to be addressed to allow EAM to valuably support banks’ ability to accommodate rapid change by providing transparency, guidance for projects regarding processes and technology, as well as steering for the long-term evolution of the IT landscape. EAM can help banks most effectively by supporting cross-team communication and facilitating reduced complexity in the long-run

Connectionist Taxonomy Learning

The thesis presents an unsupervised connectionist network using spreading activation mechanism. By means of self-organization, the network is capable of creating a taxonomy of concepts which serves as a backbone for a respective ontology. The system is a biologically inspired constructivist hybrid between connectionist networks using distributed and localist data representation. Unlike most currently developed models it is capable to deal with analog signals and displays cognitive properties of categorization process. The thesis presents the general overview over the system’s architecture and method of network build-up and shows results of several experiments exploring the nature of categorization performed with the use of the described network

Success factors for implementation of novel decentralized diagnostics: How publicly funded multidisciplinary innovation networks can disrupt German Healthcare

Mobile diagnostics – or mobile health in general – is highly appealing, not only for clinicians, but also for patients. It implies empowerment, in particular of those who are really in need, such as inhabitants of less developed regions within the world who have limited access to healthcare. It also implies simplification: Easy data management – a continuous flow of information. Therefore, development of miniaturized and highly integrated diagnostic systems allowing near patient “instant” diagnostics gain a lot of momentum since more than a decade. However, system integration requires time and a significant amount of investment. In addition, there is strong competition on resources from other emergent technologies, such as next generation sequencing which made the collection of e.g. human genome data less expensive and much faster. A more severe challenge is that mobile diagnostics require a change in healthcare management, e.g. towards integrated practice units. This, in turn, requires implementation of adequate reimbursement, standards of interoperability, training of staff, quality control. In 2010, Germany’s Federal Ministry of Education and Research (BMBF) launched the grant initiative Mobile Diagnostic Systems (MD, 2011─2015) as part of its high-tech strategy. MD aimed at generating knowledge on how microsystem technologies fit into German healthcare environments. On the basis of interviews with multidisciplinary MD actors, this thesis evaluated retrospectively how the publicly funded innovation network managed to overcome pre-defined external barriers of diffusion, including technology, regulatory affairs and market access. Retrospectives reveal internal barriers involving knowledge and technology transfer, negatively influencing generation of innovation. In particular, financing still represents a high hurdle for biotech innovators in Germany: Larger firms look predominately for market-ready or in-market technologies rather than prototypes and venture capitalists are rare or extremely risk-averse. Another important finding was, that actors involved were highly focused on individual work packages. This risks of not seeing the whole environment embedding MD. Consequently, potential opportunities may be missed, e.g. synergies with relatively close (DIALOC) or more distant initiatives (Global Health Delivery Project-based discussion rounds). This could be partly due to the fact that publicly funded networking activities provide less freedom-to-operate because of pre-defined milestones. In addition, further development of actors with respect to role playing (e.g. boundary spanning or innovation selling) is often not included in such “innovation packages”, but can help to maneuver change. Internal barriers need to be addressed first before targeting the major remaining external hurdle: Reimbursement. Although the latter was covered within MD, standardization of technology evaluation is still an unmet need which strongly influences the willingness-to-implement novel mobile diagnostics. Thus, the value added is to be demonstrated to justify adequate reimbursement. Achieving this goal can be successful, when innovation networking finds its path towards a common vision, e.g. towards value-based integrated healthcare. Pathfinding and visioning can be facilitated by process promoter with excellent network management capabilities. In addition, such a promoter could help to further develop engagement, openness and commitment of collaborators. Therefore, transfer of MD activities to established “top” networks or clusters is recommended for securing valuable knowledge generated. In this environment, an important next step – globalization of MD for ensuring future return on investment – could be triggered as well. Since MD innovation was found to involve both product and service innovation, maneuvering change is particularly challenging for small and medium sized enterprises. These could benefit from engagement in innovation networking. Findings of this case study can help all direct and indirect actors in the field of MD innovation or in other high complex environments to reconsider pathfinding as well as role playing in networking

‘IMPLICIT CREATION’ – NON-PROGRAMMER CONCEPTUAL MODELS FOR AUTHORING IN INTERACTIVE DIGITAL STORYTELLING

Interactive Digital Storytelling (IDS) constitutes a research field that emerged from several areas of art, creation and computer science. It inquires technologies and possible artefacts that allow ‘highly-interactive’ experiences of digital worlds with compelling stories. However, the situation for story creators approaching ‘highly-interactive’ storytelling is complex. There is a gap between the available technology, which requires programming and prior knowledge in Artificial Intelligence, and established models of storytelling, which are too linear to have the potential to be highly interactive. This thesis reports on research that lays the ground for bridging this gap, leading to novel creation philosophies in future work. A design research process has been pursued, which centred on the suggestion of conceptual models, explaining a) process structures of interdisciplinary development, b) interactive story structures including the user of the interactive story system, and c) the positioning of human authors within semi-automated creative processes. By means of ‘implicit creation’, storytelling and modelling of simulated worlds are reconciled. The conceptual models are informed by exhaustive literature review in established neighbouring disciplines. These are a) creative principles in different storytelling domains, such as screenwriting, video game writing, role playing and improvisational theatre, b) narratological studies of story grammars and structures, and c) principles of designing interactive systems, in the areas of basic HCI design and models, discourse analysis in conversational systems, as well as game- and simulation design. In a case study of artefact building, the initial models have been put into practice, evaluated and extended. These artefacts are a) a conceived authoring tool (‘Scenejo’) for the creation of digital conversational stories, and b) the development of a serious game (‘The Killer Phrase Game’) as an application development. The study demonstrates how starting out from linear storytelling, iterative steps of ‘implicit creation’ can lead to more variability and interactivity in the designed interactive story. In the concrete case, the steps included abstraction of dialogues into conditional actions, and creating a dynamic world model of the conversation. This process and artefact can be used as a model illustrating non-programmer approaches to ‘implicit creation’ in a learning process. Research demonstrates that the field of Interactive Digital Storytelling still has to be further advanced until general creative principles can be fully established, which is a long-term endeavour, dependent upon environmental factors. It also requires further technological developments. The gap is not yet closed, but it can be better explained. The research results build groundwork for education of prospective authors. Concluding the thesis, IDS-specific creative principles have been proposed for evaluation in future work

Simplifying Authoring of Adaptive Hypermedia Structures in an eLearning Context

Full version unavailable due to 3rd party copyright restrictions.In an eLearning context, Adaptive Hypermedia Systems have been developed to improve learning success by increasing learner satisfaction, learning speed, and educational effectiveness. However, creating adaptive eLearning content and structures is still a time consuming and complicated task, in particular if individual lecturers are the intended authors. The way of thinking that is needed to create adaptive structures as well as the workflows is one that lecturers are unaccustomed to. The aim of this research project is to develop a concept that helps authors create adaptive eLearning content and structures, which focuses on its applicability for lecturers as intended authors. The research is targeted at the sequencing of content, which is one of the main aspects of adaptive eLearning. To achieve this aim the problem has been viewed from the author’s side. First, in terms of complexity of thoughts and threads, explanations about content structures have been found in storytelling theory. It also provides insights into how authors work, how story worlds are created, story lines intertwined, and how they are all merged together into one content. This helps us understand how non technical authors create content that is understandable and interesting for recipients. Second, the linear structure of learning content has been investigated to extract all the information that can be used for sequencing purposes. This investigation led to an approach that combines existing models to ease the authoring process for adaptive learning content by relating linear content from different authors and therefore defining interdependencies that delinearise the content structure. The technical feasibility of the authoring methods for adaptive learning content has been proven by the implementation of the essential parts in a research prototype and by authoring content from real life lectures with the prototype’s editor. The content and its adaptive structure obtained by using the concept of this research have been tested with the prototype’s player and monitor. Additionally, authoring aspects of the concept have been shown along with practical examples and workflows. Lastly, the interviewees who took part in expert interviews have agreed that the concept significantly reduces authoring complexity and potentially increases the amount of lecturers that are able to create adaptive content. The concept represents the common and traditional authoring process for linear content to a large extent. Compared to existing approaches the additional work needed is limited, and authors do not need to delve into adaptive structures or other authors’ content structures and didactic approaches