Informatica

In this paper, the opening work on the development of a Lithuanian HMM speech recognition system is described. The triphone single‐Gaussian HMM speech recognition system based on Mel Frequency Cepstral Coefficients (MFCC) was developed using HTK toolkit. Hidden Markov model's parameters were estimated from phone‐level hand‐annotated Lithuanian speech corpus. The system was evaluated on a speaker‐independent ∼750 distinct isolated‐word recognition task. Though the speaker adaptation and language modeling techniques were not used, the system was performing at 20% word error rate.

The paper presents a technique that defines creation of ESTELLE/Ag specifications using knowledge bases (KB). Application KB is created using the knowledge acquisition technique joined with a piece‐linear aggregate model. The production rules of the application KB are transformed to decision tables, and the static properties of the KB are checked in PROLOGA system. Further, the application KB is combined with the defined KB of validated properties and validation method, and application KB dynamic properties are checked in the expert system in CLIPS. A validated application KB is used defining a framework of ESTELLE/Ag specification using PRAXIS editor and supplementing PRAXIS generated framework with the application functional description. The technique is illustrated with an example of a single channel queuing system.

The Markowitz model for single period portfolio optimization quantifies the problem by means of only two criteria: the mean, representing the expected outcome, and the risk, a scalar measure of the variability of outcomes. The classical Markowitz model uses the variance as the risk measure, thus resulting in a quadratic optimization problem. Following Sharpe's work on linear approximation to the mean‐variance model, many attempts have been made to linearize the portfolio optimization problem. There were introduced several alternative risk measures which are computationally attractive as (for discrete random variables) they result in solving Linear Programming (LP) problems. The LP solvability is very important for applications to real‐life financial decisions where the constructed portfolios have to meet numerous side constraints and take into account transaction costs. This paper provides a systematic overview of the LP solvable models with a wide discussion of their properties.

There are a growing number of applications, which extensively use the visual media. A key requirement in those applications is efficient access to the stored visual information for the purposes of indexing, fast retrieval, and scene analysis. To support this vision, many scene change detection algorithms have been developed for both sudden and gradual scene change in uncompressed domain. This paper presents a comparison of shot boundary detection and classification techniques and their variations including histogram, pixel‐differences, likelihood ratio, and motion vector. In addition, the pros and cons of each approach with many clarifying comments are presented.

We discuss the application of heterogeneous metaprogramming (MPG) for the interface‐based design, which deals with the synthesis of the communication interfaces between Intellectual Property components (IPs). Heterogeneous MPG is based on the simultaneous usage of a domain language for describing domain functionality and a metalanguage for developing generic components and program generators. We present a design framework based on the MPG techniques. The novelty of our approach is that we apply the concept of heterogeneous MPG for the automatic generation of IP wrappers for communication between the third‐party soft IPs systematically. We present a case study for the synthesis of the communication interfaces using a FIFO protocol.

The goal of the paper is to create a model for investigating the character of relationships between the freedom and restrictions in the terrorism context, in order to find out how sensitive is the probability of the population survival to small changes in these two parameters. A model for simulating relationships between access to resources and possibility of termination of the population is presented. The model comprises an agent world, its properties, interactions, and a world life cycle algorithm. As the “right” properties and interactions of the model are a question of experimentation, the model is introduced in two steps: a general model and a specific sub-model. Preliminary analysis of results received on a sub-model implementation demonstrate that in certain cases the relationship between the level of access to information and the overall aggressiveness value implying the end of population may has a stepwise character.

This paper presents model-based forecasting of the Lithuanian education system in the period of 2001–2010. In order to obtain satisfactory forecasting results, development of models used for these aims should be grounded on some interactive data mining. The process of the development is usually accompanied by the formulation of some assumptions to background methods or models. The accessibility and reliability of data sources should be verified. Special data mining of data sources may verify the assumptions. Interactive data mining of the data, stored in the system of the Lithuanian teachers' database, and that of other sources representing the state of the education system and demographic changes in Lithuania was used. The models cover the estimation of data quality in the databases, analysis of the flow of teachers and pupils, clustering of schools, the model of dynamics of the pedagogical staff and pupils, and the quality analysis of teachers. The main results of forecasting and integrated analysis of the Lithuanian teachers' database with other data reflecting the state of the education system and demographic changes in Lithuania are presented.

The presented article is about a research using artificial neural network (ANN) methods for compound (technical and fundamental) analysis and prognosis of Lithuania's National Stock Exchange (LNSE) indices LITIN, LITIN-A and LITIN-VVP. We employed initial pre-processing (analysis for entropy and correlation) for filtering out model input variables (LNSE indices, macroeconomic indicators, Stock Exchange indices of other countries such as the USA – Dow Jones and S&P, EU – Eurex, Russia – RTS). Investigations for the best approximation and forecasting capabilities were performed using different backpropagation ANN learning algorithms, configurations, iteration numbers, data form-factors, etc. A wide spectrum of different results has shown a high sensitivity to ANN parameters. ANN autoregressive, autoregressive causative and causative trend model performances were compared in the approximation and forecasting by a linear discriminant analysis.

Application of knowledge discovery in databases (data mining) for medical decision support is discussed in this work. The aim of the study was to use decision support algorithm for the differential diagnosis of intraocular tumors using parameters from eye images obtained by the ultrasound examination. Application of predictive modeling algorithm for decision tree formation using See5.0/C5.0 data mining system is presented. The decision tree was build using tumor geometry and microstructure parameters. The use of decision tree allows to differentiate tumors from other tumor-like formations. Low percentage of diagnostic errors shows that decision tree is reliable enough to offer it as “second opinion” for physician's decision support.

The amount of products and services available over the Internet increases significantly and it soon becomes beyond users ability to analyze and compare them. At the same time the number of potential customers available via the Internet also increases dramatically and starts to be beyond the service providers ability to perform efficient targeted marketing. A possible way for relaxing the above-mentioned limitations could be in usage of electronic assistants, both for customers and providers. Such assistants may serve as mediators for commercial Internet-based activity. Software agents could play role of such mediators representing customers and providers in the network. In this paper we present our experience and a solution to using agent technology in customer services management for mobile users. The solution is intended to increase granularity and personalization in targeted advertising while ensuring customer privacy. The proposed solution has been implemented in a prototype system for providing services for users of mobile devices.