Matti Pöllä

Adaptive filtering using multiplicative general parameters for zero-crossing detection

An adaptive, robust, and computationally efficient disturbance reduction method for line-frequency zero-crossing detectors is proposed. Our adaptive system consists of a fixed finite-impulse response filter block and two multiplicative general parameters. The predictive filter is able to handle wide variations in the line frequency without harmfully adapting to the harmonic components.

Concept Mining with Self-Organizing Maps for the Semantic Web

In this paper, we discuss problems related to the basic Semantic Web methodologies that are based on predicate logic and related formalisms. We discuss complementary and alternative approaches. In particular, we suggest how the Self-Organizing Map can be a basis for making the Semantic Web more semantic.

Retrieval of multimedia objects by combining semantic information from visual and textual descriptors

We propose a method of content-based multimedia retrieval of objects with visual, aural and textual properties. In our method, training examples of objects belonging to a specific semantic class are associated with their low-level visual descriptors (such as MPEG-7) and textual features such as frequencies of significant keywords. A fuzzy mapping of a semantic class in the training set to a class of similar objects in the test set is created by using Self-Organizing Maps (SOMs) trained from automatically extracted low-level descriptors. We have performed several experiments with different textual features to evaluate the potential of our approach in bridging the gap from visual features to semantic concepts by the use textual presentations. Our initial results show a promising increase in retrieval performance.

Inferring semantics from textual information in multimedia retrieval

We propose a method for inferring semantic information from textual data in content-based multimedia retrieval. Training examples of images and videos belonging to a specific semantic class are associated with their low-level visual and aural descriptors augmented with textual features such as frequencies of significant words. A fuzzy mapping of a semantic class in the training set to a class of similar objects in the test set is created by using Self-Organizing Maps (SOMs) trained from the low-level descriptors. Experiments with two databases and different textual features show promising results, indicating the usefulness of the approach in bridging the gap from low-level visual features to semantic concepts.

Content Collection for the Labelling of Health-Related Web Content

As the number of health-related web sites in various languages increases, so does the need for control mechanisms that give the users adequate guarantee on whether the web resources they are visiting meet a minimum level of quality standards. Based upon state-of-the-art technology in the areas of semantic web, content analysis and quality labelling, the MedIEQ project, integrates existing technologies and tests them in a novel application: the automation of the labelling process in health-related web content. MedIEQ provides tools that crawl the web to locate unlabelled health web resources, to label them according to pre-defined labelling criteria, as well as to monitor them. This paper focuses on content collection and discusses our experiments in the English language.

A generative model for self/non-self discrimination in strings

A statistical model is presented as an alternative to negative selection in anomaly detection of discrete data. We extend the use of probabilistic generative models from fixed-length binary strings into variable-length strings from a finite symbol alphabet using a mixture model of multinomial distributions for the frequency of adjacent symbols in a sliding window over a string. Robust and localized change analysis of text corpora is viewed as an application area.

Biologically Inspired Clustering: Comparing the Neural and Immune Paradigms

Biological systems have been an inspiration in the development of prototype-based clustering and vector quantization algorithms. The two dominant paradigms in biologically motivated clustering schemes are neural networks and, more recently, biological immune systems. These two biological paradigms are discussed regarding their benefits and shortcomings in the task of approximating multi-dimensional data sets. Further, simulation results are used to illustrate these properties. A class of novel hybrid models is outlined by combining the efficient use of a network topology of the neural models and the power of evolutionary computation of immune system models.

Probabilistic Text Change Detection Using an Immune Model

We present a probabilistic approach for detecting and analyzing changes in natural language motivated by biological immune systems. Contrary to traditional methods based on message-digest algorithms and line-by-line comparisons of two files, the proposed algorithm employs an implicit negative representation of text segments in the form of detector strings. A characteristic property of the presented change detection method is that it allows the analysis to be done without revealing the full contents of the original data to the authenticator. Implications of this property to security applications are outlined and an experiment is conducted to show how several incremental changes to a collaboratively maintained document can be analyzed.

Self‐Organizing Neural Network Models for State Anticipatory Systems

A vital mechanism of high‐level natural cognitive systems is the anticipatory capability of making decisions based on predicted events in the future. While in some cases the performance of computational cognitive systems can be improved by modeling anticipatory behavior, it has been shown that for many cognitive tasks anticipation is mandatory. In this paper, we review the use of self‐organizing artificial neural networks in constructing the state‐space model of an anticipatory system. The biologically inspired self‐organizing map (SOM) and its topologically dynamic variants such as the growing neural gas (GNG) are discussed using illustrative examples of their performance.