Multimedia Annotation Interoperability Framework

Multimedia systems typically contain digital documents of mixed media types, which are indexed on the basis of strongly divergent metadata standards. This severely hamplers the inter-operation of such systems. Therefore, machine understanding of metadata comming from different applications is a basic requirement for the inter-operation of distributed Multimedia systems. In this document, we present how interoperability among metadata, vocabularies/ontologies and services is enhanced using Semantic Web technologies. In addition, it provides guidelines for semantic interoperability, illustrated by use cases. Finally, it presents an overview of the most commonly used metadata standards and tools, and provides the general research direction for semantic interoperability using Semantic Web technologies

Scientific Knowledge fit for society - Scoring scientific accuracy in climate change related news articles

The quantity of information is increasing exponentially, and there is a vast amount of content viewed on the internet that lacks an indicator as to whether it is scientifically accurate and correct or scientifically inaccurate and incorrect. This thesis proposes the development of an indicator of scientific accuracy in online media. This should help in public debates and help in the detection of misinformation. The thesis presents a baseline score and clear interfaces for further improvement. The necessity for such a score has been validated by a user survey, and the employed methodologies were evaluated and updated through interviews with experts from the ORKG team. Furthermore, an overview of the knowledge required to conduct research in this field and a discussion for future work is provided.Die Menge an Informationen nimmt exponentiell zu, und es gibt eine riesige Menge an Inhalten im Internet, für die es keinen Indikator dafür gibt, ob sie wissenschaftlich genau und korrekt oder wissenschaftlich ungenau und falsch sind. In dieser Arbeit wird die Entwicklung eines Indikators für wissenschaftliche Genauigkeit in Online- Medien vorgeschlagen. Dies sollte in öffentlichen Debatten und bei der Aufdeckung von Fehlinformationen helfen. Die Arbeit legt einen Grundstein und schafft klare Schnittstellen für weitere Verbesserungen. Die Notwendigkeit eines solchen Scores wurde durch eine Nutzerbefragung validiert, und die verwendeten Methoden wurden durch Interviews mit Experten aus dem ORKG-Team evaluiert und aktualisiert. Darüber hinaus wird ein Überblick über das für die Forschung in diesem Bereich erforderliche Wissen gegeben und eine Diskussion über künftige Arbeiten geführt

Digital Subjectivation and Financial Markets: Criticizing Social Studies of Finance with Lazzarato

The recently rising field of Critical Data Studies is still facing fundamental questions. Among these is the enigma of digital subjectivation. Who are the subjects of Big Data? A field where this question is particularly pressing is finance. Since the 1990s traders have been steadily integrated into computerized data assemblages, which calls for an ontology that eliminates the distinction between human sovereign subjects and non-human instrumental objects. The latter subjectivize traders in pre-conscious ways, because human consciousness runs too slow to follow the volatility of the market. In response to this conundrum Social Studies of Finance has drawn on Actor-Network Theory to interpret financial markets as technically constructed networks of human and non-human actors. I argue that in order to develop an explicitly critical data study it might be advantageous to refer to Maurizio Lazzarato’s theory of machinic subjugation instead. Although both accounts describe financial digital subjectivation similarly, Lazzarato has the advantage of coupling his description to a clear critique of and resistance to finance

Semantic Knowledge Graphs for the News: A Review

ICT platforms for news production, distribution, and consumption must exploit the ever-growing availability of digital data. These data originate from different sources and in different formats; they arrive at different velocities and in different volumes. Semantic knowledge graphs (KGs) is an established technique for integrating such heterogeneous information. It is therefore well-aligned with the needs of news producers and distributors, and it is likely to become increasingly important for the news industry. This article reviews the research on using semantic knowledge graphs for production, distribution, and consumption of news. The purpose is to present an overview of the field; to investigate what it means; and to suggest opportunities and needs for further research and development.publishedVersio

Dreaming of drams: Authenticity in Scottish whisky tourism as an expression of unresolved Habermasian rationalities

In this paper, the production of whisky tourism at both independently owned and corporately owned distilleries in Scotland is explored by focusing on four examples (Arran, Glengoyne, Glenturret and Bruichladdich). In particular, claims of authenticity and Scottishness of Scottish whiskies through commercial materials, case studies, website-forum discussions and 'independent' writing about such whisky are analysed. It is argued that the globalisation and commodification of whisky and whisky tourism, and the communicative backlash to these trends typified by the search for authenticity, is representative of a Habermasian struggle between two irreconcilable rationalities. This paper will demonstrate that the meaning and purpose of leisure can be understood through such explorations of the tension between the instrumentality of commodification and the freedom of individuals to locate their own leisure lives in the lifeworld that remains. © 2011 Taylor & Francis

Bringing the Market to Life: Screen Aesthetics and the Epistemic Consumption Object

This article argues that the new ‘visuality’ (Schroeder, 2002) of the Internet transforms the stock market into an epistemic consumption object. The aesthetics of the screen turn the market into an interactive and response-present surface representation. On the computer screen, the market becomes an object of constant movement and variation, changing direction and altering appearance at any time. Following Knorr Cetina (1997, 2002b) we argue that the visual logic of the screen ‘opens up’ the market ontologically. The ontological liquidity of the market-on-screen simulates the indefiniteness of other life forms. We suggest that the continuing fascination with online investing is a function of the reflexive looping of the investor, who aspires to discern what the market is lacking, through the market-on-screen that continuously signals to the investor what it still lacks. Implications for existing theories on relationships and involvement are discussed

Ursula von der Leyen: The study of her leadership from the media perspective

The dissertation concerns assessing Ursula von der Leyen´s political leadership in the European press from December 2019 to September 2021. Considering that Ursula von der Leyen is the first woman president of the European Commission, this dissertation focuses on how the media frames her leadership through an interdisciplinary approach. More specifically, this investigation seeks to characterize Ursula von der Leyen´s political leadership through agenda-setting and the tone coverage in media discourses; and to identify whether there is a symbolic empowerment presence in media outlets of Ursula Von der Leyen. To achieve the established objectives, a thematic analysis is carried out with headlines in the original language collected from four European newspapers (Guardian, Público, Le Monde, El País). A qualitative approach is employed, allowing us to interpret findings by applying both a constructivist lens to characterize Ursula von der Leyen´s political leadership as well as a gender lens. The predominant political event of thematic analysis is the coronavirus pandemic, which reflected both higher visibility of the European values pillar in Ursula von der Leyen´s political agenda, as well as in the reduction of stereotyped coverage in the media discourses. The way in which the European press frames Ursula von der Leyen´s political leadership is inseparable from the context of the coronavirus pandemic in which this leadership takes place, contributing to revealing the impact of exceptional contexts on international political leadership.A dissertação trata de avaliar a liderança política de Ursula von der Leyen na imprensa europeia de dezembro de 2019 a setembro de 2021. Ao considerar que Ursula von der Leyen é a primeira mulher presidente da Comissão Europeia, esta dissertação foca como a mídia enquadra sua liderança através de uma abordagem interdisciplinar. Mais especificamente, esta investigação busca caracterizar a liderança política de Ursula von der Leyen através do agendamento e a cobertura de tom nos discursos midiáticos; como também, identificar se há uma presença simbólica de empoderamento de Ursula von der Leyen nos veículos de comunicação. Para alcançar os objetivos estabelecidos, é realizada a análise temática de manchetes com língua original coletadas de quatro jornais europeus (The Guardian, Público, Le Monde, El País). É empregada uma abordagem qualitativa, permitindo interpretar os resultados através tanto de uma lente construtivista para caracterizar liderança política de Ursula von der Leyen, como também, de uma lente de gênero. O evento político predominante da análise temática é a pandemia do coronavírus, no qual refletiu tanto numa maior visibilidade no pilar Valores europeus da agenda política de Ursula von der Leyen, como também, na redução de uma cobertura estereoptizada nos discursos midiáticos. O modo como a imprensa europeia enquadra a liderança política de Ursula von der Leyen é indissociável do contexto de pandemia de coronavírus em que essa liderança tem lugar, contribuindo para revelar o impacto de contextos excepcionais na liderança política internacional

Two-Level Text Classification Using Hybrid Machine Learning Techniques

Nowadays, documents are increasingly being associated with multi-level category hierarchies rather than a flat category scheme. To access these documents in real time, we need fast automatic methods to navigate these hierarchies. Today’s vast data repositories such as the web also contain many broad domains of data which are quite distinct from each other e.g. medicine, education, sports and politics. Each domain constitutes a subspace of the data within which the documents are similar to each other but quite distinct from the documents in another subspace. The data within these domains is frequently further divided into many subcategories. Subspace Learning is a technique popular with non-text domains such as image recognition to increase speed and accuracy. Subspace analysis lends itself naturally to the idea of hybrid classifiers. Each subspace can be processed by a classifier best suited to the characteristics of that particular subspace. Instead of using the complete set of full space feature dimensions, classifier performances can be boosted by using only a subset of the dimensions. This thesis presents a novel hybrid parallel architecture using separate classifiers trained on separate subspaces to improve two-level text classification. The classifier to be used on a particular input and the relevant feature subset to be extracted is determined dynamically by using a novel method based on the maximum significance value. A novel vector representation which enhances the distinction between classes within the subspace is also developed. This novel system, the Hybrid Parallel Classifier, was compared against the baselines of several single classifiers such as the Multilayer Perceptron and was found to be faster and have higher two-level classification accuracies. The improvement in performance achieved was even higher when dealing with more complex category hierarchies