Catálogo de publicaciones - libros

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 paper we present some semantic web technologies of the EU integrated project X-Media that build bridges between the various information sources, the different media, the stations of knowledge management and the different communities. Core to this endeavour is the combination of information extraction with formal ontologies as well as with semantically lightweight folksonomies.

In this paper, some recent ideas will be presented about making machine learning (ML) more effective through mechanisms of argumentation. In this sense, (ABML) is defined as a refinement of the usual definition of ML. In ABML, some learning examples are accompanied by arguments, that are expert’s reasons for believing why these examples are as they are. Thus ABML provides a natural way of introducing domain-specific prior knowledge in a way that is different from the traditional, general background knowledge. The task of ABML is to find a theory that explains the “argumented” examples by making reference to the given reasons. 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 the expert. Ways of realising ABML by extending some existing ML techniques are discussed, and the aforementioned advantages of ABML are demonstrated experimentally.

It has been long established that when humans perform music, the result is never a literal mechanical rendering of the score. That is, humans deviate from the score. As far as these performance deviations are intentional, they are commonly thought of as conveying musical expressivity which is a fundamental aspect of music. Two main functions of musical expressivity are generally recognized. Firstly, expressivity is used to clarify the musical structure of the piece (metrical structure, phrasing, harmonic structure). secondly, expressivity is also used as a way of communicating, or accentuating, affective content.

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 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.

This paper proposes decision fusion method of shape and motion information based on Bayesian framework for object classification in image sequences. This method is designed for intelligent information and surveillance guard robots to detect and track a suspicious person and vehicle within a security region. For reliable and stable classification of targets, multiple invariant feature vectors to more certainly discriminate between targets are required. To do this, shape and motion information are extracted using Fourier descriptor, gradients, and motion feature variation on spatial and temporal images, and then local decisions are performed respectively. Finally, global decision is done using decision fusion method based on Bayesian framework. The experimental results on the different test sequences showed that the proposed method obtained good classification result than any other ones using neural net and other fusion methods.

In order to provide communication between the deaf-dumb people and the hearing people, a two-stage system translating Turkish Sign Language into Turkish is developed by using vision based approach. Hidden Markov models are utilized to determine the global feature group in the dynamic gesture recognition stage, and k nearest neighbor algorithm is used to compare the local features in the static gesture recognition stage. The system can perform person dependent recognition of 172 isolated signs.

Human social communication depends largely on exchanges of non-verbal signals, including non-lexical expression of emotions in speech. In this work, we propose a biologically plausible methodology for the problem of emotion recognition, based on the extraction of vowel information from an input speech signal and on the classification of extracted information by a spiking neural network. Initially, a speech signal is segmented into vowel parts which are represented with a set of salient features, related to the Mel-frequency cesptrum. Different emotion classes are then recognized by a spiking neural network and classified into five different emotion classes.

Information visualization is gaining importance in data mining and transactional data has long been an important target for data miners. We propose a novel approach for visualizing transactional data using multiple clustering results for knowledge discovery. This scheme necessitates us to relate different clustering results in a comprehensive manner. Thus we have invented a method for attributing colors to clusters of different clustering results based on minimal transversals. The effectiveness of our method has been confirmed with experiments using artificial and real-world data sets.

Visual cryptography schemes encrypt a secret image into shares so that any qualified set of shares enables one to visually decrypt the hidden secret; whereas any forbidden set of shares cannot leak out any secret information. In the study of visual cryptography, pixel expansion and contrast are two important issues. Since pixel-expansion based methods encode a pixel to many pixels on each share, the size of the share is larger than that of the secret image. Therefore, they result in distortion of shares and consume more storage space. In this paper, we propose a method to reach better contrast without pixel expansion. The concept of probability is used to construct an optimization model for general access structures, and the solution space is searched by genetic algorithms. Experimental result shows that the proposed method can reach better contrast and blackness of black pixels in comparison with Ateniese et al.’s.

Texture synthesis is a very active research area in computer vision and graphics, and temporal texture synthesis is one subset of it. We present a new temporal texture synthesis algorithm, in which a segment genetic algorithm is introduced into the processes of synthesizing videos. In the algorithm, by analyzing and processing a finite source video clip, Infinite video sequences that are played smoothly in vision can be obtained. Comparing with many temporal texture synthesis algorithms nowadays, this algorithm can get high-quality video results without complicated pre-processing of source video while it improves the efficiency of synthesis. It is analyzed in this paper that how the population size and the Max number of generations influence the speed and quality of synthesis.

In the previous paper, we proposed Quantum-behaved Particle Swarm Optimization (QPSO) that outperforms traditional standard Particle Swarm Optimization (SPSO) in search ability as well as less parameter to control. However, although QPSO is a global convergent search method, the intelligence of simulating the ability of human beings is deficient. In this paper, the immune operator based on the vector distance to calculate the density of antibody is introduced into Quantum-behaved Particle Swarm Optimization. The proposed algorithm incorporates the immune mechanism in life sciences and global search method QPSO to improve the intelligence and performance of the algorithm and restrain the degeneration in the process of optimization effectively. The results of typical optimization functions showed that QPSO with immune operator performs better than SPSO and QPSO without immune operator.