Kateřina Slaninová

Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities

Enhancing the the effectiveness of web-based eduction has become one of the most important concerns within both educational engineering and information system fields. The development of information technologies has contributed to the growth in elearning as an important education method. This learning environment enables learners to participate in ’any time, any place’ personalized training. It has been known that the application of data mining and computational intelligent approaches can provide better learning environments, and in their effort to participate in this field, the authors introduced this study which consists in its first part of a survey of the applications of data mining and computational intelligence in web based education during (2004-2009), and the second part is a case study that aims to analyze students’ activities performed in a Learning Management System.

The ANTAREX approach to autotuning and adaptivity for energy efficient HPC systems

The ANTAREX project aims at expressing the application self-adaptivity through a Domain Specific Language (DSL) and to runtime manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to Exascale. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. We show through a mini-app extracted from one of the project application use cases some initial exploration of application precision tuning by means enabled by the DSL.

Control Loop Model of Virtual Company in BPM Simulation

The motivation of the paper is to introduce agent-based technology in the business process simulation. As in other cases, such simulation needs sufficient input data. However, in the case of business systems, real business data are not always available. Therefore, multi-agent systems often operate with randomly (resp. pseudo randomly) generated parameters. This method can also represent unpredictable phenomena. The core of the paper is to introduce the control loop model methodology in JADE business process simulation implementation. At the end of this paper the analysis of agent-based simulation outputs through process mining methods and methods for analysis of agents’ behavior in order to verify the correctness of used methodology is presented. The business process simulation inputs are randomly generated using the normal distribution. The results obtained show that using random number generation function with normal distribution can lead to the correct output data and therefore can be used to simulate real business processes.

Spectral Clustering: Left-Right-Oscillate Algorithm for Detecting Communities

Detection of communities in the complex networks is an actual problem solved in research area. The paper describes a new algorithm for this purpose. Left-Right-Oscillate algorithm (LRO) is based on spectral ordering of graph vertices. This approach allows us to detect a desired community – either by the size of the smallest communities or by the level of modularity. Since the LRO algorithm detects efficiently communities in large network even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social, complex or coauthor networks. In this paper, proposed algorithm is used for finding communities in a large coauthor network - DBLP.

Analysis and Visualization of Relations in eLearning

The popularity of eLearning systems is growing rapidly; this growth is enabled by the consecutive development in Internet and multimedia technologies. Web-based education became wide spread in the past few years. Various types of learning management systems facilitate development of Web-based courses. Users of these courses form social networks through the different activities performed by them. This chapter focuses on searching the latent social networks in eLearning systems data. These data consist of students activity records wherein latent ties among actors are embedded. The social network studied in this chapter is represented by groups of students who have similar contacts and interact in similar social circles. Different methods of data clustering analysis can be applied to these groups, and the findings show the existence of latent ties among the group members. The second part of this chapter focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships as well as the amount of independent groups in a given network. When applied to the field of eLearning, data visualization simplifies the process of monitoring the study activities of individuals or groups, as well as the planning of educational curriculum, the evaluation of study processes, etc.

Author Cooperation Based on Terms of Article Titles from DBLP

Very interesting source of information about scientific publishing in computer science is database DBLP. This database allows bibliographic information about main publications from conferences, journals and books in this area. In the article we deal with strength extraction between authors based on their association. The research presented in this article is partly motivated by work of Mori et al. From this paper we have used the approach for extraction of initial metadata, and we have inspired how to take advantage from Jaccard coefficient principals for description of the strength of associations between authors. Method is usable for development of synthetic coauthors network, where as input is used the set of words, which will describe the network (the authors used these words in publication titles).

Local Community Detection and Visualization: Experiment Based on Student Data

This paper is focused on the detection of communities in social networks. We propose and describe a novel method for detecting local communities. We have used this method in an experiment on student social networks in order to prove our hypothesis about the nature of student communities. The results of the experiment rationalized our hypothesis and confirmed the effectiveness of the described method of local community detection.

Improving Rule Selection from Robot Soccer Strategy with Substrategies

Robot Soccer is a very attractive platform in terms of research. It contains a number of challenges in the areas of robot control, artificial intelligence and image analysis. This article presents a method to improve the description of the strategy by creating substrategies in strategy and thus ensuring smoother implementation of actions defined by this strategy. In presented method we have extracted sequences of game situations from the log of a game played in our simulator, as they occurred during the game. Afterwards, these sequences were compared by methods for sequence comparison and thus we are able to visualize relations between the sequences of game situations and clusters of similar game situations in a graph. This output seems to be very helpful feedback for further strategy development.

Autotuning and adaptivity approach for energy efficient Exascale HPC systems: The ANTAREX approach

The main goal of the ANTAREX 1 project is to express by a Domain Specific Language (DSL) the application self-adaptivity and to runtime manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to the Exascale level. Key innovations of the project include the introduction of a separation of concerns between self-adaptivity strategies and application functionalities. The DSL approach will allow the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management.

Time-Dependent Route Planning for the Highways in the Czech Republic

This paper presents an algorithm for dynamic travel time computation along Czech Republic highways. The dynamism is represented by speed profiles used for computation of travel times at specified time. These speed profiles have not only the information about an optimal speed, but also a probability of this optimal speed and the probability of the speed which represents the possibility of traffic incident occurrence. Thus, the paper is focused on the analysis of paths with the uncertainty created by traffic incidents. The result of the algorithm is the probability distribution of travel times on a selected path. Based on these results, it is possible to plan a departure time with the best mean travel time for routes along the Czech Republic highways for a specified maximal acceptable travel time. This method will be a part of a larger algorithm for dynamic traffic routing.