Programme <<<

Presentation of Papers

Each presentation will be allotted the following amount of time, including questions:
  • Regular papers: 30 mins
  • Short papers: 15 mins.
(figures may be changed during the final scheduling)
Conference Schedule
Download it as a PDF file.

Co-located Workshop
Methods and Experiences of Ambient Intelligence

Invited Talks

Steffen StaabSteffen Staab

Institute for Computer Science • Computer Science Faculty of the University of Koblenz-Landau • Koblenz, Germany


Abstract Knowledge and information spanning multiple information sources, multiple media, multiple versions and multiple communities challenge the capabilities of existing knowledge and information management infrastructures by far --- primarily in terms of intellectually exploiting the stored knowledge and information. In this talk we present some semantic web technologies of the EU integrated project X-media that build bridges the various information sources, the different media, the stations of knowledge management and the different communities. Core to this endeavour is the combination of informtaion extraction with formal ontologies as well as with semantically lightweight folksonomies.

Ivan BratkoIvan Bratko

Lubiana University and Department of Intelligent Systems • Jožef Stefan Institute • Lubiana, Slovenia


Abstract In this talk, some recent ideas will be presented about making machine learning (ML) more effective through mechanisms from the field of argumentation. In this sense, argument-based machine learning (ABML) is defined as a refinement of the usual definition of ML. In ABML, some learning examples are accompanied by arguments, that is expert's reasons for believing why these examples are as they are. For instance, why the class of the example is as given. The task of ABML is to find a theory that explains the "argumented" examples in terms of the given arguments. ABML, so defined, is motivated by the following advantages in comparison with standard learning from examples: (1) arguments impose constraints over the space of possible hypotheses, thus reducing search complexity, and (2) induced theories should make more sense to an expert. Ways of realising ABML by extending some existing ML techniques will be discussed, and the aforementioned advantages of ABML will be demonstrated experimentally.

Ramon López de MántarasRamon López de Mántaras

Artificial Intelligence Research Institute (IIIA) • Spanish Council for Scientific Research (CSIC) • Barcelona, Spain


Abstract An important issue when performing music is the effect of tempo on expressivity. It has been argued that temporal aspects of performance scale uniformly when tempo changes. That is, the durations of all performed notes maintain their relative proportions. This hypothesis is called relational invariance (of timing under tempo changes). However, counter-evidence for this hypothesis has been provided, and a recent study shows that listeners are able to determine above chance-level whether audio recordings of jazz and classical performances are uniformly time stretched or original recordings, based solely on expressive aspects of the performances. In my talk I will address this issue by focusing on our research on tempo transformations of audio recordings of saxophone jazz performances. More concretely, we have investigated the problem of how a performance played at a particular tempo can be automatically rendered at another tempo while preserving its expressivity. To do so we have developed a case-based reasoning system called TempoExpress. Our approach also experimentally refutes the relational invariance hypothesis by comparing the automatic transformations generated by TempoExpress against uniform time stretching.