Milos Kudelka

Social Network Reduction Based on Stability

The analysis of social networks is concentrated especially on uncovering hidden relations and properties of network members (vertices). Most of the current approaches are focused mainly on different network types and different network coefficients. On one hand, the analysis can be relatively simple, on the other hand some complex approaches to network dynamics can be used. This paper introduces a novel aspect of network analysis based on the so-called Forgetting Curve. For network vertices and edges, we define two coefficients, which describe their role in the network depending on their long-term behavior. Using one of these parameters we reduce the network to smaller components. We provide some experimental results using DBLP dataset. Our research illustrates the usefulness of the proposed approach.

Semantic Analysis of Web Pages Using Web Patterns

This paper introduces a novel method for semantic analysis of Web pages. Analysis 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 characteristics for concrete domain. Patterns provide formalization of the agreement and allow assignment of semantics to parts of Web pages. Experimental results verify the effectives of the proposed method

Semantic Annotation of Web Pages Using Web Patterns

This paper introduces a novel method for semantic annotation of web pages. We perform semantic annotation 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 a particular domain. A pattern provides formalization of the agreement and allows assigning semantics to parts of web pages. We will introduce experiments with this method and show its benefits for querying the web.

Forcoa.NET: An interactive tool for exploring the significance of authorship networks in DBLP data

This paper presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science. The developed tool is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. The visualization of co-authorship networks allows to describe the author and his/her current surroundings while still incorporating the historical aspect. The analysis is based on using the forgetting function to hold the information relevant to the selected date. After this analysis, we are capable of computing several measures, which can describe different aspects of user behaviour from the point of view of scientific social network.

Web Page Analysis: Experiments Based on Discussion and Purchase Web Patterns

In this paper we introduce experiments with our method used for analysis and evaluation of Web pages. This method is based on Web patterns. The Web patterns which are used by the Web designers in their Web page implementations. Using our method we can find out whether the pattern is presented on the page with high level of relevance. In this paper we explain the essentials of our method and the experiments with two patterns.

Correlation Network Analysis Reveals Relationships between MicroRNAs, Transcription Factor T-bet, and Deregulated Cytokine/Chemokine-Receptor Network in Pulmonary Sarcoidosis

Sarcoidosis is an inflammatory granulomatous disease with unknown etiology driven by cytokines and chemokines. There is limited information regarding the regulation of cytokine/chemokine-receptor network in bronchoalveolar lavage (BAL) cells in pulmonary sarcoidosis, suggesting contribution of miRNAs and transcription factors. We therefore investigated gene expression of 25 inflammation-related miRNAs, 27 cytokines/chemokines/receptors, and a Th1-transcription factor T-bet in unseparated BAL cells obtained from 48 sarcoidosis patients and 14 control subjects using quantitative RT-PCR. We then examined both miRNA-mRNA expressions to enrich relevant relationships. This first study on miRNAs in sarcoid BAL cells detected deregulation of miR-146a, miR-150, miR-202, miR-204, and miR-222 expression comparing to controls. Subanalysis revealed higher number of miR-155, let-7c transcripts in progressing (n = 20) comparing to regressing (n = 28) disease as assessed by 2-year follow-up. Correlation network analysis revealed relationships between microRNAs, transcription factor T-bet, and deregulated cytokine/chemokine-receptor network in sarcoid BAL cells. Furthermore, T-bet showed more pronounced regulatory capability to sarcoidosis-associated cytokines/chemokines/receptors than miRNAs, which may function rather as “fine-tuners” of cytokine/chemokine expression. Our correlation network study implies contribution of both microRNAs and Th1-transcription factor T-bet to the regulation of cytokine/chemokine-receptor network in BAL cells in sarcoidosis. Functional studies are needed to confirm biological relevance of the obtained relationships.

Web page classification based on Schema.org collection

The internet is a library of a huge amount of information and there is a need for categorize its content based on web page classification. Classification of web page content can improve the quality of web search and its accuracy. Unfortunately the high dimensionality of the web pages dataset has made the process of classification difficult. The use of an automatic method for web page classification can simplify the whole process and assist the search engine in getting more relevant results. Nowadays information on the web is generally structured and formatted in a not formal way. This absence of semantics leads to create formal methods to provide more semantics information into web page. Search engines including Bing, Google, Yahoo! and Yandex formed collection of schemas Schema.org to support web page semantics and improve their search results. This paper explores the use of formal source code structure for classifying a large collection of the web content. Is focused on use of schemas collection Schema.org to classify web pages and categorize them unambiguously.

Weighted Co-authorship Network Based on Forgetting

In this paper we focus on the analysis of weighted networks and their properties. We describe a new way to weigh network vertices and edges based on the Forgetting curve. We denote the weight as a stability changing gradually over time. Based on the stability, we propose new measures. For our experiments we have selected the DBLP database, therefore we can evaluate our approach on a real network with more than 830,000 vertices.

FCA as a Tool for Inaccuracy Detection in Content-Based Image Analysis

In this paper we focus on the detection of inaccuracies in the results of content-based image analysis. During the analysis process we detect a set of features, which are later used in Image Retrieval. This detection is based on multiple algorithms specific to particular features. These algorithms use parameters, which have been obtained by the analysis of our test collection. However it seems that in the real application deployment produces some inaccuracies in the results. Our goal is to support the process of feature analysis by detecting these inaccuracies, or at least showing the most probable sources of them. This support can be helpful in tuning these algorithms on less known input data. In the article we describe both the image features detection algorithms as well as usage of Formal Concept Analysis (FCA) as a tool for detection of inaccuracies.

Social Aspects of Web Page Contents

In this paper we try to consider a Web page as information with social aspects. Each Web page is the result of invisible social interaction. This interaction between different groups of people translates into a certain unification of Web page creation. External signs of this unification are the features of the Web page, that meets the user’s expectations. Through analysis of the features, we can obtain information that can simply describe the Web page. This simple description contains strong information about the social group the page is intended for. If the user uses this information to refine the search, then he identifies himself as a member of a certain social group. For the description of the social aspects of Web pages we use the term MicroGenre. This paper describes the fundamental concepts of MicroGenre and also illustrate experiments for the detection and usage of MicroGenres.