Krzysztof A. Cyran

Man-machine interactions

Keynote Talks.- Speech Man-Machine Communication.- Stochastic Effects in Signaling Pathways in Cells: Interaction between Visualization and Modeling.- Rough-Granular Computing in Human-Centric Information Processing.- Discovering Affinities between Perceptual Granules.- Human-Computer Interactions.- A Psycholinguistic Model of Man-Machine Interactions Based on Needs of Human Personality.- Adaptable Graphical User Interfaces for Player-Based Applications.- Case-Based Reasoning Model in Process of Emergency Management.- Enterprise Ontology According to Roman Ingarden Formal Ontology.- Hand Shape Recognition for Human-Computer Interaction.- System for Knowledge Mining in Data from Interactions between User and Application.- Computational Techniques in Biosciences.- Analyze of Maldi-TOF Proteomic Spectra with Usage of Mixture of Gaussian Distributions.- Energy Properties of Protein Structures in the Analysis of the Human RAB5A Cellular Activity.- Fuzzy Weighted Averaging of Biomedical Signal Using Bayesian Inference.- Fuzzy Clustering and Gene Ontology Based Decision Rules for Identification and Description of Gene Groups.- Estimation of the Number of Primordial Genes in a Compartment Model of RNA World.- Quasi Dominance Rough Set Approach in Testing for Traces of Natural Selection at Molecular Level.- Decision Support, Rule Inferrence and Representation.- The Way of Rules Representation in Composited Knowledge Bases.- Clustering of Partial Decision Rules.- Decision Trees Constructing over Multiple Data Streams.- Decision Tree Induction Methods for Distributed Environment.- Extensions of Multistage Decision Transition Systems: The Rough Set Perspective.- Emotion Recognition Based on Dynamic Ensemble Feature Selection.- Rough Fuzzy Investigations.- On Construction of Partial Association Rules with Weights.- Fuzzy Rough Entropy Clustering Algorithm Parametrization.- Data Grouping Process in Extended SQL Language Containing Fuzzy Elements.- Rough Sets in Flux: Crispings and Change.- Simplification of Neuro-Fuzzy Models.- Fuzzy Weighted Averaging Using Criterion Function Minimization.- Approximate String Matching by Fuzzy Automata.- Remark on Membership Functions in Neuro-Fuzzy Systems.- Capacity-Based Definite Rough Integral and Its Application.- Advances in Classification Methods.- Classifier Models in Intelligent CAPP Systems.- Classification Algorithms Based on Templates Decision Rules.- Fast Orthogonal Neural Network for Adaptive Fourier Amplitude Spectrum Computation in Classification Problems.- Relative Reduct-Based Selection of Features for ANN Classifier.- Enhanced Ontology Based Profile Comparison Mechanism for Better Recommendation.- Privacy Preserving Classification for Ordered Attributes.- Incorporating Detractors into SVM Classification.- Bayes Multistage Classifier and Boosted C4.5 Algorithm in Acute Abdominal Pain Diagnosis.- Pattern Recognition and Signal Processing.- Skrybot - A System for Automatic Speech Recognition of Polish Language.- Speaker Verification Based on Fuzzy Classifier.- Support Vector Classifier with Linguistic Interpretation of the Kernel Matrix in Speaker Verification.- Application of Discriminant Analysis to Distinction of Musical Instruments on the Basis of Selected Sound Parameters.- Computer Vision, Image Analysis and Virtual Reality.- Spatial Color Distribution Based Indexing and Retrieval Scheme.- Synthesis of Static Medical Images with an Active Shape Model.- New Method for Personalization of Avatar Animation.- Multidimensional Labyrinth - Multidimensional Virtual Reality.- Shape Recognition Using Partitioned Iterated Function Systems.- Computer Vision Support for the Orthodontic Diagnosis.- From Museum Exhibits to 3D Models.- Advances in Algorithmics.- A Method for Automatic Standardization of Text Attributes without Reference Data Sets.- Internal Conflict-Free Projection Sets.- The Comparison of an Adapted Evolutionary Algorithm with the Invasive Weed Optimization Algorithm Based on the Problem of Predetermining the Progress of Distributed Data Merging Process.- Cumulation of Pheromone Values in Web Searching Algorithm.- Mining for Unconnected Frequent Graphs with Direct Subgraph Isomorphism Tests.- Numerical Evaluation of the Random Walk Search Algorithm.- On Two Variants of the Longest Increasing Subsequence Problem.- Computing the Longest Common Transposition-Invariant Subsequence with GPU.- Databases and Data Warehousing.- Usage of the Universal Object Model in Database Schemas Comparison and Integration.- Computational Model for Efficient Processing of Geofield Queries.- Applying Advanced Methods of Query Selectivity Estimation in Oracle DBMS.- How to Efficiently Generate PNR Representation of a Qualitative Geofield.- RBTAT: Red-Black Table Aggregate Tree.- Performing Range Aggregate Queries in Stream Data Warehouse.- LVA-Index: An Efficient Way to Determine Nearest Neighbors.- Embedded Systems Applications.- Basic Component of Computational Intelligence for IRB-1400 Robots.- Factors Having Influence upon Efficiency of an Integrated Wired-Wireless Network.- FFT Based EMG Signals Analysis on FPGAs for Dexterous Hand Prosthesis Control.- The VHDL Implementation of Reconfigurable MIPS Processor.- Time Optimal Target Following by a Mobile Vehicle.- Improving Quality of Satellite Navigation Devices.

Rough sets in hybrid methods for pattern recognition

The article shows how rough sets can be applied to improve the classification ability of a hybrid pattern recognition system. The system presented here consists of a feature extractor based on a computer‐generated hologram (CGH) playing the role of a ring‐wedge detector. Features extracted by it are shift, rotation, and scale invariant. Although they can be optimized, no method has been proposed in the literature. This article presents an original method of optimizing the feature extraction abilities of a CGH. The method uses rough set theory (RST) to measure the amount of essential information contained in the feature vector. This measure is used to define an objective function in the optimization process. Since RST‐based factors are not differentiable, we use a nongradient approach for a search in the space of possible solutions. Finally, RST is used to determine decision rules for the classification of feature vectors. The alternative method of classification based on neural networks is also discussed. The whole method is illustrated by a system recognizing the class of speckle pattern images indicating the class of distortion of optical fibers. © 2001 John Wiley & Sons, Inc.

Fiberscope-based engine condition monitoring system

The article presents the engine condition monitoring system. The system uses dependence between amount of rub products gathered in lubricating oil and current wear condition of device. The measure method bases on ferrographic method. Images are taken from sample and magnified by fiberscope, and then converted by CCD camera into digital form. Then raster images are classified by dedicated computer system. This paper presents actual state of system research.

Indiscernibility Relation for Continuous Attributes: Application in Image Recognition

The paper presents an application of rough sets in a problem defined for the continuous feature space used by hybrid, high speed, pattern recognition system. The feature extraction part of this system is built as a holographic ring-wedge detector based on binary grating. Such feature extractor can be optimized and we apply for this purpose automatic knowledge acquisition and processing. Features from optimized extractor are then classified with the use of probabilistic neural network classifier. The methodology, proposed by one of the authors in earlier works, has been further enhanced here by application of modified indiscernibility relation. Modified version of this relation makes possible natural application of discrete type rough knowledge representation to problems defined in continuous space. We present an application of modified indiscernibility relation in the domain of image recognition.

Quasi Dominance Rough Set Approach in Testing for Traces of Natural Selection at Molecular Level

Testing for natural selection operating at molecular level has become one of the important issues in contemporary bioinformatics. In the paper the novel methodology called quasi dominance rough set approach (QDRSA) is proposed and applied for testing of balancing selection in four genes involved in human familial cancer. QDRSA can be considered as a hybrid of classical rough set approach (CRSA) and dominance rough set approach (DRSA). The advantages of QDRSA over CRSA and DRSA are illustrated for certain class of problems together with limitations of proposed methodology for other types of problems where CRSA or DRSA are better choice. The analysis of the reasons why QDRSA can produce decision algorithms yielding smaller error rates than DRSA is performed on the real world example, what shows that superiority of QDRSA in certain types of applications is of practical value.

Modified Indiscernibility Relation in the Theory of Rough Sets with Real-Valued Attributes: Application to Recognition of Fraunhofer Diffraction Patterns

The goal of the paper is to present the modification of classical indiscernibility relation, dedicated for rough set theory in a real-valued attributes space. Contrary to some other known generalizations, indiscernibility relation modified here, remains an equivalence relation and it is obtained by introducing a structure into collection of attributes. It defines real-valued subspaces, used in a multidimensional cluster analysis, partitioning the universe in a more natural way, as compared to one-dimensional discretization, iterated in classical model. Since the classical model is a special, extreme case of our modification, the modified version can be considered as more general. But more importantly, it allows for natural processing of real-valued attributes in a rough-set theory, broadening the scope of applications of classical, as well as variable precision rough set model, since the latter can utilize the proposed modification, equally well. In a case study, we show a real application of modified relation, a hybrid, opto-electronic recognizer of Fraunhofer diffraction patterns. Modified rough sets are used in an evolutionary optimization of the optical feature extractor implemented as a holographic ring-wedge detector. The classification is performed by a probabilistic neural network, whose error, assessed in an unbiased way is compared to earlier works.

Rough Sets in the Interpretation of Statistical Tests Outcomes for Genes Under Hypothetical Balancing Selection

Detection of natural selection at the molecular level is one of the crucial problems in contemporary population genetics. There exists a number of statistical tests designed for it, however, the interpretation of the outcomes is often obscure, because of the existence of factors like population growth, migration and recombination. In his earlier work, the author has proposed the multi-null methodology, and he applied it for four genes implicated in human familial cancer: ATM, RECQL, WRN and BLM. Because of high computational effort required for estimating critical values under nonclassical nulls, mentioned methodology is not appropriate for selection screening. In the current paper, the author presents novel, rough set based methodology, helpful in the interpretation of tests outcomes applied versus only classical nulls. This method does not require long-lasting simulations and, as it is shown in the paper, it gives reliable results.

Mathematical modelling of molecule evolution in protocells

In this article, we analyse the process of the emergence of RNA polynucleotides located in an enclosed environment, at an early stage of the RNA world. Therefore we prepared a mathematical model, composed of a set of differential equations, which simulates the behaviour of an early biological system bounded by a protocell membrane. There is evidence that enclosed environments were available on the primordial Earth. There are also experimental proofs that RNA strands can develop in these formations. The proposed model allows analysis of the influence of membrane permeability on the composition of internal material. It takes into account phenomena that lead to the elongation of an RNA strand (ligation), fission of molecules (phosphodiester bond breakage) and replication of polynucleotides. Results obtained from the model point out that the existence of protocells might support concentration of material and creation of longer molecules.

Estimation of the Number of Primordial Genes in a Compartment Model of RNA World

The Origin of life has been studied by researchers for many years. During this time a lot of computer models of early life were created and have given scientists a better view of this immemorial time. One of the simple models of early life proclaims that primitive genes (molecules) were enclosed in compartments (packages) which were submerged in the primordial broth. U. Niesert, D. Harnasch, and C. Bresch in the article ‘Origins of Life Between Scylla and Charybdis’ explained the basics of the model and predicted that there can be only 3 unlinked types of genes in a package. One of the important factor in the compartment model is NORM (Number of replicated molecules between two packages fission). We wanted to check whether NORM variation caused by environment changes, that certainly took place at this time, has an influence on the maximum number of unlinked types of genes in a package. Results of our researches, based on computer simulations, indicate that NORM variation has such an influence.

Classical and dominance-based rough sets in the search for genes under balancing selection

Since the time of Kimura’s theory of neutral evolution at molecular level the search for genes under natural selection is one of the crucial problems in population genetics. There exists quite a number of statistical tests designed for it, however, the interpretation of the results is often hard due to the existence of extra-selective factors, such as population growth, migration and recombination. The author, in his earlier work, has proposed the idea of multi-null hypotheses methodology applied for testing the selection in ATM, RECQL, WRN and BLM genes - the foursome implicated in human familial cancer. However, because of high computational effort required for estimating the critical values under nonclassical null hypotheses, mentioned strategy is not an appropriate tool for selection screening. The current article presents novel, rough set based methodology, helpful in the interpretation of the tests outcomes applied only versus classical nulls. The author considers for this purpose both classical and dominance based rough set frameworks. None of rough set based methods requires long-lasting simulations and, as it is shown in a paper, both give reliable results. The advantage of dominance based approach over classical one is more natural treatment of statistical test outcomes, resulting in better generalization without necessity of manual incorporating the domain-dependent reasoning to the process of knowledge processing. However, in testing this gain in generalization proved to be at the price of a slight loss of accuracy.