Informatica

A computational model of the spatial‐frequency filtering processes at the level of 4Cβ layer of the visual striate cortex is being proposed. The model does not interfere with the filtering performed by the cortical receptive fields itself, and the focus of attention is restricted to the cortical input. The model is based on the literature data concerning the conformal mapping of the visual field representation at the primary visual cortex and uniformity of short‐range horizontal connections of cortical neurons. To test the model, the illusory figures were used as input stimuli, responses to which were computed and the output patterns constructed. The psychophysical experiments employing the same illusory figures were performed. A rather good correspondence between the model predictions and the experimental measurements of perceived size distortions was observed. In practice, the neurophysiological model provides a simplified and relatively fast algorithm of evaluation of distortions caused by filtering.

A proxy signature allows a designated person, called a proxy signer, to sign the message on behalf of the original signer. Proxy signatures are very useful tools when one needs to delegate his/her signing capability to other party. A number of proxy signature schemes have been proposed and succeeded for proxy delegations, but the schemes are in defective in proxy revocations. In this paper, we propose two proxy signature schemes based on RSA cryptosystems. The proposed first scheme does not consider proxy revocation mechanism; however, it will help us to compare our protocol with the existing RSA‐based schemes. The proposed second scheme provides an effective proxy revocation mechanism. The proposed schemes do not require any secure channel to proxy key delivery and support the necessary security requirements of proxy signature.

The development of Lithuanian HMM/ANN speech recognition system, which combines artificial neural networks (ANNs) and hidden Markov models (HMMs), is described in this paper. A hybrid HMM/ANN architecture was applied in the system. In this architecture, a fully connected three‐layer neural network (a multi‐layer perceptron) is trained by conventional stochastic back‐propagation algorithm to estimate the probability of 115 context‐independent phonetic categories and during recognition it is used as a state output probability estimator. The hybrid HMM/ANN speech recognition system based on Mel Frequency Cepstral Coefficients (MFCC) was developed using CSLU Toolkit. The system was tested on the VDU isolated‐word Lithuanian speech corpus and evaluated on a speaker‐independent ∼750 distinct isolated‐word recognition task. The word recognition accuracy obtained was about 86.7%.

In this paper, the main measure, an amount of information, of the information theory is analyzed and corrected. The three conceptions of the theory on the microstate, dissipation pathways, and self‐organization levels with a tight connection to the statistical physics are discussed. The concepts of restricted information were introduced as well as the proof of uniqueness of the entropy function, when the probabilities are rational numbers, is presented.

The paper considers moving locally predefined (MLP) finite element remeshing technique for deep penetration of the rigid cone into homogeneous and porous medium. Remeshing presents a computational tool implemented in the form of postprocessor type software compatible with standard FEM codes. It involves a transfer operation combining both the moving least square method based on stress patch recovery and the interpolation method for transfer of state variables. The developed MLP remeshing is able to overcome numerical difficulties occurring due to large distortions of the Lagrangian mesh and contact sliding and capture steady‐state behavior. It shows good performance in modeling of cone penetration into elasto‐plastic homogeneous and porous media reaching several diameters of the cone.

Tree is one of the most studied and practically useful classes of graphs and is the attention of a great number of studies. There is absence of generalized results for tree as a class and even for one kind of labeling as whole. Only specialized results exist limited to specific types of trees only. A number of conjectures stand being unsolved. Graham and Sloane (1980) conjectured trees to be Harmonious and Ringel‐Kotzig conjectured trees to be Graceful about three decades ago. Kotzig and Rosa (1970) ask the question whether all trees are Magic or not. No generalized result for Antimagic labeling is given for trees so far. This paper presents the methodologies to obtain the major labeling schemes for trees viz., Harmonious, Sequential, Felicitous, Graceful, Antimagic and found the trees to be not Magic except T(2,1), thus solving the said conjectures. These findings could also be useful for those working in fields where graphs serve as models.

Walras theory is well known and widely used in models of market economy. Various iterative methods are developed to search for the equilibrium conditions.

Text categorization – the assignment of natural language documents to one or more predefined categories based on their semantic content – is an important component in many information organization and management tasks. Performance of neural networks learning is known to be sensitive to the initial weights and architecture. This paper discusses the use multilayer neural network initialization with decision tree classifier for improving text categorization accuracy. Decision tree from root node until a final leave is used for initialization of each single unit. Growing decision trees with increasingly larger amounts of training data will result in larger decision tree sizes. As a result, the neural networks constructed from these decision trees are often larger and more complex than necessary. Appropriate choice of certainty factor is able to produce trees that are essentially constant in size in the face of increasingly larger training sets. Experimental results support the conclusion that error based pruning can be used to produce appropriately sized trees, which are directly mapped to optimal neural network architecture with good accuracy. The experimental evaluation demonstrates this approach provides better classification accuracy with Reuters‐21578 corpus, one of the standard benchmarks for text categorization tasks. We present results comparing the accuracy of this approach with multilayer neural network initialized with traditional random method and decision tree classifiers.

This paper describes our research on statistical language modeling of Lithuanian. The idea of improving sparse n‐gram models of highly inflected Lithuanian language by interpolating them with complex n‐gram models based on word clustering and morphological word decomposition was investigated. Words, word base forms and part‐of‐speech tags were clustered into 50 to 5000 automatically generated classes. Multiple 3‐gram and 4‐gram class‐based language models were built and evaluated on Lithuanian text corpus, which contained 85 million words. Class‐based models linearly interpolated with the 3‐gram model led up to a 13% reduction in the perplexity compared with the baseline 3‐gram model. Morphological models decreased out‐of‐vocabulary word rate from 1.5% to 1.02%.