Research of the Influence of Methods of Compression on the Co-efficient of Information Security of Objects Under Attacks

In this paper a possibility for quantitative measuring of information security of objects, exposed to information attacks and processed with methods of compression, is represented. A co-efficient of information security, which reflects the influence of the level of compression obtained after applying methods of compression to objects and the time, required by the attack to get access to the corresponding object, is proposed. Methods’ groups with the highest and respectively the lowest values of the co-efficient of information security for all methods’ groups in relation to all attacks’ groups are determined. Assessments and conclusions for future investigations are proposed.

Application of Methods of Multi-criteria Evaluation in Choosing a Method of Compression as a Means of Heightening the Information Security of System and Applied Objects

In this paper is proposed a model for researching the capability to influence, by selected methods’ groups of compression, to the co-efficient of information security of selected objects’ groups, exposed to selected attacks’ groups. With the help of methods for multi-criteria evaluation are chosen the methods’ groups with the lowest risk with respect to the information security. Recommendations for future investigations are proposed.

Analysis of Information Security of Objects under Attacks and Processed by Methods of Compression

In this paper a methodology for evaluation of information security of objects under attacks, processed by methods of compression, is represented. Two basic parameters for evaluation of information security of objects – TIME and SIZE – are chosen and the characteristics, which reflect on their evaluation, are analyzed and estimated. A co-efficient of information security of object is proposed as a mean of the coefficients of the parameter TIME and SIZE. From the simulation experiments which were carried out methods with the highest co-efficient of information security had been determined. Assessments and conclusions for future investigations are proposed.

Forming of Learning Set for Neural Networks in Problems of Losless Data Compression

questions of forming of learning sets for artificial neural networks in problems of lossless data compression are considered. Methods of construction and use of learning sets are studied. The way of forming of learning set during training an artificial neural network on the data stream is offered.

Applying A Normalized Compression Metric To The Measurement Of Dialect Distance

The paper discusses the application of a similarity metric based on compression to the measurement of the distance among Bulgarian dia- lects. The similarity metric is de ned on the basis of the notion of Kolmo- gorov complexity of a le (or binary string). The application of Kolmogorov complexity in practice is not possible because its calculation over a le is an undecidable problem. Thus, the actual similarity metric is based on a real life compressor which only approximates the Kolmogorov complexity. To use the metric for distance measurement of Bulgarian dialects we rst represent the dialectological data in such a way that the metric is applicable. We propose two such representations which are compared to a baseline distance between dialects. Then we conclude the paper with an outline of our future work.

Study of the Information Security of File Objects under Information Attacks with a Record of Effect of the Methods of Compression

This report examines important issues pertaining to the different ways of affecting the information security of file objects under information attacks through methods of compression. Accordingly, the report analyzes the three-way relationships which may exist among a selected set of attacks, methods and objects. Thus, a methodology is proposed for evaluation of information security, and a coefficient of information security is created. With respects to this coefficient, using different criteria and methods for evaluation and selection of alternatives, the lowest-risk methods of compression are selected.

Conceptual Information Compression and Efficient Pattern Search

This paper introduces an encoding of knowledge representation statements as regular languages and proposes a two-phase approach to processing of explicitly declared conceptual information. The idea is presented for the simple conceptual graphs where conceptual pattern search is implemented by the so called projection operation. Projection calculations are organised into off-line preprocessing and run-time computations. This enables fast run-time treatment of NP-complete problems, given that the intermediate results of the off-line phase are kept in suitable data structures. The experiments with randomly-generated, middle-size knowledge bases support the claim that the suggested approach radically improves the run-time conceptual pattern search.

Compression Technologies of Graphic Information

The classification of types of information redundancy in symbolic and graphical forms representation of information is done. The general classification of compression technologies for graphical information is presented as well. The principles of design, tasks and variants for realizations of semantic compression technology of graphical information are suggested.

Local Goals Driven Hierarchical Reinforcement Learning

* This research was partially supported by the Latvian Science Foundation under grant No.02-86d.

Multiplierless DCT Algorithm for Image Compression Applications

This paper presents a novel error-free (infinite-precision) architecture for the fast implementation of 8x8 2-D Discrete Cosine Transform. The architecture uses a new algebraic integer encoding of a 1-D radix-8 DCT that allows the separable computation of a 2-D 8x8 DCT without any intermediate number representation conversions. This is a considerable improvement on previously introduced algebraic integer encoding techniques to compute both DCT and IDCT which eliminates the requirements to approximate the transformation matrix ele- ments by obtaining their exact representations and hence mapping the transcendental functions without any errors. Apart from the multiplication-free nature, this new mapping scheme fits to this algorithm, eliminating any computational or quantization errors and resulting short-word-length and high-speed-design.