Projects

Language varieties are a primary means of expressing a person's social affiliation and identity. Hence, computer systems that can adapt to the user by displaying a familiar socio-cultural identity are expected to raise the acceptance within certain contexts and target groups dramatically. However, current systems are far from achieving the fidelity required for realization of these benefits. For example, promising early results have been obtained in the context of speech synthesis through localized pronunciation, but it is clear that language variety is a multi-faceted concept that involves deviations from standard language on various linguistic levels. Our goal is to develop algorithmic methods that are capable of capturing and reproducing all major idiosyncrasies displayed by a language variety, be they syntactic, lexical or phonetic in nature. Conceptually, part of this task can be understood as a machine translation problem, which is, however, characterized by unique properties.

Quiew (quick review) ist ein Forschungsprojekt zur Entwicklung eines innovativen Online-Bewertungs- und Feedbacksystems für Produkte, Dienstleistungen und andere Objekte. Basierend auf der aus der Markt- und Konsumentenpsychologie stammenden Methode des Assoziationsgeflechts nach Kirchler und De Rosa (1996) wird ein computationelles System entwickelt zur automatischen Analyse und Gruppierung von sprachlichen Äußerungen (assoziativen Feedbacks), die zur Bewertung von Produkten, Diesntleistungen etc. abgegeben wurden.

In this project we develop core technology for a new kind of semantic search system that is able to automatically identify experts for clearly defined project tasks making use of textual similarities in expert and task profiles, and of models of explicit expert knowledge.

An important open research question in Music Information Retrieval (MIR) is the extraction of sound objects (e.g. a guitar chord, the beat of a bass drum, the bark of a dog) from polyphonic audio. Recent theoretical advances in mathematical signal processing indicate the possibility of a decisive improvement in identifying and extracting sound objects directly from the time-frequency plane. These mined sound objects can then automatically be organized into perceptually meaningful and easy to navigate sound libraries using the latest innovations in MIR based music similarity. Together this will form the core of a powerful new toolkit for audio manipulation with widespread applicability in fields like Sound Design, Computational Auditory Scene Analysis, Artefact Reduction or Audio Database Organisation.

IRIS (Integrating Research in Interactive Storytelling) is a European NoE (Network of Excellence) which aims at creating a virtual centre of excellence that will be able to achieve breakthroughs in the understanding of Interactive Storytelling and the development of corresponding technologies. Interactive Storytelling is a major endeavour to develop new media which could offer a radically new user experience, with a potential to revolutionise digital entertainment. The IRIS consortium involves ten partners in seven different countries in Europe.

In this project we aim at developing and implementing ontology-driven methods for domain-specific information extraction and retrieval.

In this project, we intend to go one step further and try to develop user-awareness and self-awareness through Theory of Mind (ToM) and Empathy in intelligent software agents or robots that have to take on complex interactive tasks. While context-awareness is based on "perception" of the environment, self-awareness is based in particular on registration of important "inner" states that can influence decisions. Time components, Theory of Mind and Empathy are aspects of self-awareness and user-awareness that have to be taken into account in this project.

In this project, we dealt with this topic in a theoretical and an applicable way with the aim of implementing aspects of context awareness in a software agent or a robot. Philosophical, psychological, and neuroscientific aspects of consciousness as well as their modeling are a central topic of interest in many disciplines of cognitive science. The approach of this issue has the aim of assessing human and animal consciousness, better understanding its underlying mechanisms and to develop and implement computational models.

L4S - Learning for Security is a European STReP (Small or medium-scale focused research project) that aims to develop simulation based learning experiences and to provide guidelines and tools for the development of soft skills necessary in effective crisis management.

Little is known in hard facts about why readers of online newspapers prefer some articles over others. Current news filtering systems assume that the topic of an article is the only factor that determines user satisfaction. But content accounts only for about 40% of a story's satisfaction rating. Factors that determine the remaining 60% can be as diverse as readability concerns, writing style, the type of a story, visual complexity, proper use of photographs, or, even less concretely, the appeal of a story. Contextual information, like previously read articles or the overall popularity and recentness of articles, needs to be considered as well. The goal of MAGNIFICENT is to gain deep insight into both the relevant parameters of stories and the adaptive training of user profiles along these parameters.