In this article, the author generalizes Dempster`s rule, Yager`s rule, and Dubois-Prade`s rule for belief functions combination in order to be applicable to hyper-power sets according to the Dezert-Smarandache (DSm) Theory. A comparison of the rules with the DSm rule of combination is further presented.

Continuous t-norm based fuzzy predicate logic is surveyed as a generalization of classical predicate logic; then a kind of fuzzy description logic based on our fuzzy predicate logic is briefly described as a powerful but still decidable formal system of description logic dealing with vague (imprecise) concepts.

This paper introduces a novel method for semantic annotation of web pages. Semantic annotation is performed with regard to unwritten and empirically proven agreement between users and web designers using Web patterns. This method is based on extraction of patterns which are characteristic for concrete domain. Patterns provide formalization of the agreement and allows assignment of semantics to parts of web pages. Experimental results verify the e®ectives of the proposed method.

In this paper we study learning with Regularization Networks (RN). RN are feedforward neural networks with one hidden layer. Since they have a very good theoretical background, we study their practical aspects and applicability. On experiments we demonstrate the role of the regularization parameter, compare RN with different kernels and parameter settings on benchmark data sets. Then we apply RN to a problem of a flow rate prediction, real data from Czech river Sázava are used. All experiments were made using the system Bang.

The problem of supervised learning is a subject of great interest at present. It covers wide range of tasks, such as various classification, prediction, or forecasting problems, i.e. problems that also often arise in semantic web applications. We study one approach to this problem - regularization networks. We introduce composite types of kernel functions, sum kernels and product kernels. On experiments we demonstrate the role of the regularization parameter and kernel function in the regularization network learning, and compare networks with different types of kernel functions.

It is shown that learning from data modelled as minimaization of error functionals can be reformulated in terms of inverse problems. This reformulation allows to characterize optimal input-output functions of networks with kernel units.

A crucial point in automated data processing is the way in which the data are expressed. One possibility is to employ existing features of the Semantic Web - ontologies. Ontologies play an important role in a knowledge representation. The aim of the research presented in this paper is to provide more automated VirGIS system. VirGIS is an integration system that works with GIS (Geographical Information Systems) data. As a first step of our research, we describe its data using common Semantic Web techniques and build a VirGIS ontology.

Integration has been an acknowledged problem for a long time. With the aim at combining data from different sources, data integration usually provides a unified global view over these data. A crucial part of the task is the establishment of the connection between the global view and the local sources. Two basic approaches have been proposed for this purpose: Global As View (GAV) and Local As View (LAV).With the Semantic Web and its data description means, there is also another possibility - to employ ontologies for the relationship description in an integration system.

Information Retrieval (IR) as a process of searching relevant information is a significant discipline of a data processing field. European Summer School in Information Retrieval ESSIR provides students, academic and industrial researchers and developers a grounding in the core objects of IR (models, architectures, algorithms), as well as covering some current topics, e.g. information retrieval from the Web. We have participated its 5th year that was held at Dublin City University in Dublin, Ireland.

Datová integrace je uznávaný problém v oblasti zpracování dat. Jejím cílem je obvykle poskytnout ucelený pohled na několik datových zdrojů. V případě nematerializovaného řešení je klíčové stanovení vazeb mezi poskytovaným virtuálním pohledem a daty uloženými ve zdrojích. článek se zabývá řešením stanovení těchto vazeb. Svůj přístup zakládá na ontologiích.

The aim of the paper is to present a novel, general approach to preference modelling in the framework of the relational data model. To allow nonmonotonic operations, the preferences are defined between sets of relational instances. The aim is the generalization of the relational algebra that is as minimal as possible, in the sense that the formal fundamentlas of the relational data model are preserved. At the same time, the extended model should be formal enough to provide a sound basis for the investigation of other new preference constructors and operations and for new possible applications.

The aim of the paper is to present a novel, general approach to preference modelling in the framework of the relational data model. The preferences are defined between sets of relational instances, which is a nontrivial generalization of the approach aiming at incorporating ordered attribute domains into the relational data model. The main goals are as follows: an effective representation of information representable by a partial order, an intuitive preference construction and its processing throughout the query execution plan, and a suitable data structure to support it all.

In this paper, a multi agent platform targeted toward the area of computational intelligence modeling is presented. We show the design of various computational agents creating multi agent systems, as well as the infrastructure capabilities. The focus of the system is the cooperation of agents, their interchangeability autonomous behavior, and emergence of new models. Two main areas of cooperation are presented: automated creation of a multi-agent system satisfying given constraints, and decision support for agent partner selection. It is demonstrated that such a system is able to assist in building hybrid artificial intelligence models based on data in a distributed environment.

In this paper we present an approach where a hybrid computational model is represented as a set of communicating agents composing a multi-agent system. A general concept of representation of connected groups of agents is introduced and utilized for automatic building of schemes to solve a given computational task. We propose a combination of an evolutionary algorithm and a formal logic resolution system which is able to generate and verify new schemes. Furthermore, the adaptive cooperation support of individual computational agents is described, which improves their efficiency in time. These features are implemented within a software system and demonstrated on several examples.

This paper describes ontological description of computational agents, their properties and abilities. The goal of the work is to allow for autonomous behavior and semi-automatic composition of agents within a multiagent system. The system has to be create foundation for the interchangeability of computational components, and emergence of new models. This paper focuses on ways of representing agents and systems in standard formalisms, such as description logics, OWL, and Jade.

We design a formal model of an amorphous computer suitable for theoretical investigation of its computational properties. The model consists of a finite set of nodes created by RAMs with restricted memory, which are dispersed uniformly in a given area. Within a limited radius the nodes can communicate with their neighbors via a single-channel radio. The assumptions on low-level communication abilities are among the weakest possible: the nodes work asynchronously, there is no broadcasting collision detection mechanism and no network addresses. For the underlying network we design a randomized communication protocol and analyze its efficiency. The subsequent experiments and combinatorial analysis of random networks show that the expectations under which our protocol was designed are met by the vast majority of the instances of our amorphous computer model.

The paper proposes a data structure modelling method, which aim is to estimate a structure model from a given input data set. The model can be seen as an estimate of data semantics ֠the obtained relations can be transformed into an RDF or OWL semantic web format documents to be included into the semantic web portfolio. The proposed method makes a connection between current web sources and the semantic web vision to be realized. Finally, the method usage and conversion rules are illustrated on an example.

Odhad struktury dat získaných například z webových zdrojů lze využít jednak pro uložení dat, tak pro netriviální dotazování nad těmito daty. Článek rozšiřuje metodu odhadu struktury dat získávající odpovídající schéma relačního modelu ze vstupních dat a popisuje metodu uložení dat pomocí jednoduchého asociativního úložiště dat právě na základě odhadnutého modelu. Článek diskutuje dvě možné implementace úložiště: první uchovávající data jako instance funkčních závislostí, druhou uchovávající pouze instance funkčních závislosti mezi jednoduchými atributy rozšířenou o podporu komplexních atributů pomocí metainformace.

Použití asociativního úložiště dat je jednou z možností, jak efektivním způsobem reprezentovat data. Článek se zabývá z převážné části metodou učení takového úložiště, přičemž využívá myšlenek vize sémantického webu. Dále ukazuje souvislosti této metody s teoriemi organizace paměti živých organismů včetně člověka a její učení bez snahy tyto procesy zpětně modelovat. Jelikož se nabízí možnost využít současných webových stránek jako vstupních dat, je učící algoritmus navržen inkrementálnì a výhody použití takového adaptivního přístupu jsou detailně popsány. Výsledkem algoritmu je asociativní úložiště navržené na základě všech dostupných (meta)informací, na které je možné pohlížet jako na extensionální úrovni odhadnutou sémantiku dat.

Mechanisms for accessing and training the data repository using a binary matrix formalism are presented. The repository is designed for a data storage through corresponding instances of simple attribute functional dependencies, which can be seen as similar to the binary predicate formalism being used by the RDF semantic web format. Two mechanisms for querying a repository, the generalisation and the specialisation, are given. Furthermore, the incremental repository training mechanism with no extra requirements on the input data form is described: The extensional functional dependency system is used as a generalised view on the stored data; the algorithm is inspired by the functional dependency discovery approach.

When dealing with image databases, we often need to solve the problem of how to retrieve a desired set of images effectively and efficiently. As a representation of images, there are commonly used some high-dimensional vectors of extracted features, since in such a way the content-based image retrieval is turned into a geometric-search problem. In this article we present a study of feature extraction from raw image data by means of the LSI method (singular-value decomposition, respectively). Simultaneously, we show how such a kind of feature extraction can be used for efficient and effective similarity retrieval using the M-tree index. Because of the application to image retrieval, we also show some interesting effects of LSI, which are not directly obvious in the area of text retrieval (where LSI came from).

In the paper, we present a quality of service and energy aware communication protocol, called RollingBall. We do believe that QoS and energy awareness are two of the most important parameters in wireless sensor networks. The protocol is completely distributed with no centralized control. The key idea is to introduce a resistance calculation for every connection in the network. The resistance reflects the distance to the sink together with energy capabilities of particular sensor. While the resistance is continually re-calculated, packets are sent to the sink via an appropriate path. Such a scheme allows to spend minimum messages on network management, whereby sensor network lifetime is extended and throughput remains high.

The paper presents a new approach for treating security issues in various environments with special emphasis on Mobile databases, Semantic web and Grids. A brief overview on possible security models and a discussion on their advantages and disadvantages is given. Our model based on virtual organization and is build up on mathematical background based on hypergraphs. We show that hypergraphs are the way how to reduce space complexity of the model. The complexity is important with respect to target environments where number of users might be huge. To verify our model an experimental implementation was programed and some graphical outputs are mentioned.

This paper gives a short overview on security issues widely found in the Semantic Web environment. It goes through each level of the proposed Semantic Web layers and discusses security, privacy and trust for each. Then, a list of possible solutions is given. In particular XML security, RDF security, secure information integration and trust on the Semantic Web are mentioned and short discussion is given. Finally, an approach for treating security and trust based on Virtual organization is described and its advantages are provided.

The paper describes a new approach for treatment security issues in reconfigurable groups of users (Virtual Organizations-VO). The proposed strategy combines a convenient mathematical model, efficient combinatorial algorithms which are robust with respect to changes in the VO structure, and an efficient implementation. The mathematical model uses properties of weighted hypergraphs. Model flexibility enables description of basic security relations between the nodes such that these relations are preserved under frequent changes in connections of the hypergraph nodes. The proposed implementation makes use of the techniques developed for time and space-critical applications in numerical linear algebra. The ideas can be generalized to other concepts describable by weighted hypergraphs. The consistency of the proposed ideas for security management in the changing VO was verified in a couple of tests with our pilot implementation SECGRID.

The technical report presents a brief overview on web search engines with deeper insight into their linear algebra background. The linear algebra plays very important role in modern web search algorithms (e.g. Google). The report presents two algorithms, particularly HITS and PageRank. The algorithms are discussed on their convergence problems and also some improvements to their personalization abilities. The computation complexity is also mentioned and briefly sketched.

In MANETs and similar link-free networks of communicating objects there is no central authority for naming and connection management. Autoconfiguration of network nodes is therefore desirable and, building on approaches in IPv6, a number of ‘zero configuration’ networking protocols have been proposed for this case. Typically these protocols do not easily scale and have difficulty with network partitioning and merging. In this paper we propose a number of novel, decentralized techniques for name resolution in zero configuration protocols that are more flexible and yet lead to name extensions of smallest possible length, assuming that objects sufficiently mix within their ranges. Finally, the techniques are converted into a fully decentralized, scalable autoconfiguration protocol for use in ad hoc networks with directional antennas. The protocol is ‘lazy’ in the sense that name resolution is postponed until the moment that conflicts prevent the correct functioning of the communication structure.

We present an overview of our results on model theoretic and fixpoint semantics for a relational algebra using a model of many valued Datalog with similarity. Using our previous results on equivalence of our model and certain variant of generalized annotated programs, we base our querying on fuzzy aggregation operators (also called annotation terms, combining functions, utility functions). Using of fuzzy aggregation operators (distinct from database aggregations) enables us to reduce tuning of various linguistic variables. In practice we can learn fuzzy aggregator operators by an ILP procedure for every user profile. Our approach enables also integration of data from different sources via aggregation and similarity. Extending domains we discuss difference between fuzzy elements and fuzzy subsets. We also discuss an alternative, when all extensional data are stored crisp and fuzziness is in rules interpreting data, context and in user query.

We present a design of cognitive system architecture with an internal world model. The internal world model is realized with the help of artificial mirror neurons. We consider generalized artificial mirror neurons acting both as a mechanism for assembling and learning multimodal sensorimotor information and as associative memory for invoking multimodal information given only some of its components. We show that within an artificial cognitive system a network of generalized mirror neurons can simultaneously serve as an internal world model recognized by the agent and as that of the agent`s position within this world. We also specify a self-organizing control mechanism, which is based on the basic operations over concepts that were essentially identified by the British 18th century philosopher David Hume. This control mechanism makes use of the internal world model constructed in agent`s interaction with real world and straightforwardly supports imitation learning. Building heavily on the properties of the generalized mirror net and on automatic abstract concept creation, we offer an algorithmic explanation of computational language acquisition, thinking and consciousness in our model. Rather than describing an implementation of the respective mechanisms, the aim of the paper is to establish a proof of the principle of algorithmic nature of higher cognitive functions.

Moderní teorie kognitivních systémů pohlíží na tyto systémy jako na autonomní vtělené výpočetní systémy, které se situují v okolí prostřednictvím svých senzomotorických jednotek. Přesto zejména v kruzích počítačových teoretikù je opakovaně slyšet názory, že na kognici lze pořád možné pohlížet i "klasicky", jako na problém specifického zpracování dat a že tudíž vtělení není nezbytné pro zachycení podstaty kognice. Ukážeme, že takto zjednodušený pohled opomíjí podstatnou vlastnost kognitivních systémů - a sice jejich aktivní vliv na výběr či dokonce vznik vstupních dat. Bez této zpětné vazby si systém nemůže vytvořit svůj vnitřní model světa poznaný prostřednictvím svých akcí. Pro vysvětlení povahy zmíněného problému použijeme výpočetní model kognitivních systémů zavedený autorem v předchozích pracích. Tento model umožní na principielní úrovni přemýšlet o fungování algoritmických mechanizmů imitace, komunikace, vzniku řeči, myšlení a vědomí a tím přispět i k jejich pochopení v živých systémech.

Moderní teorie kognitivních systémů pohlíží na tyto systémy jako na autonomní vtělené výpočetní systémy, které se situují v okolí prostřednictvím svých senzomotorických jednotek. Přesto zejména v kruzích počítačových teoretiků je opakovaně slyšet názory, že na kognici lze pořád možné pohlížet i „klasicky“, jako na problém specifického zpracování dat a že tudíž vtělení není nezbytné pro zachycení podstaty kognice. Ukážeme, že takto zjednodušený pohled opomíjí podstatnou vlastnost kognitivních systémů − a sice jejich aktivní vliv na výběr či dokonce vznik vstupních dat. Bez této zpětné vazby si systém nemůže vytvořit svůj vnitřní model světa poznaný prostřednictvím svých akcí. Pro vysvětlení povahy zmíněného problému použijeme výpočetní model kognitivních systémů zavedený autorem v předchozích pracích. Tento model umožní na principielní úrovni přemýšlet o fungování algoritmických mechanizmů imitace, komunikace, vzniku řeči, myšlení a vědomí a tím přispět i k jejich pochopení v živých systémech.

Existujú v súčasnej informatike výzvy zrovnateľné s veľkými problémami, s ktorými je konfrontované ľudstvo v oblasti fyziky, biológie, či iných vedných a inžinierskych oblastí? Má súčasná informatika výskumné zámery svojim významom zrovnateľné s odhalením podstaty gravitácie? S cestou na Mars? S rozlúštením genetického kódu? V prednáške sa zamyslíme, ktoré problémy v informatike majú podobný charakter, prečo je dobré sústrediť sa na ich riešenie a aké riešenia sa rysujú.