Viera Rozinajova

• Professor
• researcher at Kempelen Institute of Intelligent Technologies

Automatic question generation is one of the most challenging tasks of Natural Language Processing. It requires "bidirectional" language processing: firstly, the system has to understand the input text (Natural Language Understanding) and it then has to generate questions also in the form of text (Natural Language Generation). In this article, we in...

This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern mining, anomaly detection, time series indexing and other data mining tasks. However, the disadvantage of this method is, that it...

Automatic question generation is one of the most challenging tasks of Natural Language Processing. It requires “bidirectional” language processing: first, the system has to understand the input text (Natural Language Understanding), and it then has to generate questions also in the form of text (Natural Language Generation). In this article, we int...

The huge amount of data generated on a daily basis in many areas of our lives predetermines data science to be one of the most significant current IT research areas. Thanks to the recent technological trends, such as internet of things (IoT), the data streams constitute a majority of currently created data. This chapter aims to provide one possible...

This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern mining, anomaly detection, time series indexing and other data mining tasks. However, the disadvantage of this method is, that it...

The Symbolic Aggregate approXimation algorithm (SAX) is one of the most popular symbolic mapping techniques for time series. It is extensively utilized in sequence classification, pattern mining, anomaly detection and many other data mining tasks. SAX as a powerful symbolic mapping technique is widely used due to its data adaptability. However this...

Prediction of photovoltaic (PV) energy is an important task. It allows grid operators to plan production of energy in order to secure stability of electrical grid. In this work we focus on improving prediction of PV energy using nature-inspired algorithms for optimization of Support Vector Regression (SVR) models. We propose method, which uses diff...

Cartesian Genetic Programming (CGP) is a type of Genetic Programming, which uses a sequence of integers to represent an executable graph structure. The most common way of optimizing the CGP is to use a simple evolutionary strategy with mutations, which randomly changes the integer values of integer sequence. We propose an alternative genotype-pheno...

This paper presents a comparison of the impact of various unsupervised ensemble learning methods on electricity load forecasting. The electricity load from consumers is simply aggregated or optimally clustered to more predictable groups by cluster analysis. The clustering approach consists of efficient preprocessing of data obtained from smart mete...

Nowadays, the electricity load profiles of customers (consumers and prosumers) are changing as new technologies are being developed, and therefore it is necessary to correctly identify new trends, changes and anomalies in data. Anomalies in load consumption can be caused by abnormal behavior of customers or a failure of smart meters in the grid. Ac...

In this paper, we explore how the modified Dynamic Weighted Majority (DWM) method of ensemble learning can enhance time series prediction. DWM approach was originally introduced as a method to combine predictions of multiple classifiers. In our approach, we propose its modification to solve the regression problems which are based on using differing...

This paper proposes a recommendation system for load shifting of energy consumption for residential consumers. The main goal is to provide to customer a set of energy consumption strategies, which would span from maximum cost saving strategy, to maximum comfort preserving strategy. The discomfort of user caused by load shifting is expressed here as...

The paper deals with the prediction of electricity demand, using data from smart meters obtained in defined time steps. We propose the modification of ensemble learning method called Dynamic Weighted Majority (DWM). The data are represented by data streams. According to our experiments, the proposed solution offers favorable alternative to current...

In this paper, we introduce an interactive approach to generation of factual questions from unstructured text. Our proposed framework transforms input text into structured set of features and uses them for question generation. Its learning process is based on combination of machine learning techniques known as reinforcement learning and supervised...

Smart grid, an integral part of a smart city, provides new opportunities for efficient energy management, possibly leading to big cost savings and a great contribution to the environment. Grid innovations and liberalization of the electricity market have significantly changed the character of data analysis in power engineering. Online processing of...

Accurate power demand forecasts can help power distributors to lower differences between contracted and demanded electricity and minimize the imbalance in grid and related costs. Our forecasting method is designed to process continuous stream of data from smart meters incrementally and to adapt the prediction model to concept drifts in power demand...

There are many endeavors aiming to offer users more effective ways of getting relevant information from web. One of them is represented by a concept of Linked Data, which provides interconnected data sources. But querying these types of data is difficult not only for the conventional web users but also for experts in this field. Therefore, a more c...

The paper presents an improvement of incremental adaptive power load forecasting methods by performing cluster analysis prior to forecasts. For clustering the centroid based method K-means, with K-means++ centroids initialization, was used. Ten various forecasting methods were compared in order to find the most suitable ones to combine with cluster...

Ensemble learning is one of the machine learning approaches, which can be described as the process of combining diverse models to solve a particular computational intelligence problem. We can find the analogy to this approach in human behavior (e.g. consulting more experts before taking an important decision). Ensemble learning is advantageously us...

This paper presents a novel approach to the area of automated factual question generation. We propose a template-based method which uses the structure of sentences to create multiple sentence patterns on various levels of abstraction. The pattern is used to classify the sentences and to generate questions. Our approach allows to create questions on...

The complexity of certain problems causes that classical methods for finding exact solutions have some limitations. In this paper we propose an incremental heterogeneous ensemble model for time series prediction where biologically inspired algorithms offer a suitable alternative. Ensemble learning techniques are advantageously used for improving pe...

The efforts of the European Union (EU) in the energy supply domain aim to introduce intelligent grid management across the whole of the EU. The target intelligent grid is planned to contain 80% of all meters to be smart meters generating data every 15 minutes. Thus, the energy data of EU will grow rapidly in the very near future. Smart meters are s...

SOA brought significant progress in information systems development, but the full exploitation of its advantages is sometimes hindered by specific problems. One of them is concerned with paradigm of Model Driven Development (MDD), where it is necessary to control correctness of the whole design within many models on different levels of abstraction....

In this paper we deal with the interoperability of digital libraries concerned with identification of hidden or invisible relationships within various data sources. By means of semantic processing and reasoning techniques we attempt to find the answers to sophisticated questions which are sometimes difficult also for human experts. Our initial inte...

Service oriented architecture (SOA) is nowadays one of the dominant styles in developing new information systems. These information systems often have complex models, which can contain mistakes, or are described by informally. In order to minimize mistakes and to create formal models, patterns as components of software development could be used - a...

In this paper we deal with design and creation of web mashups which represent one of the important Web 2.0 application approaches. The main goal of this work is to describe an approach where a recommendation about existing and cooperating data sources is made to the user, to analyze various existing methods and tools for creating mashups, and to in...

Acquiring information from the Web is a demanding task and currently subject of a world-wide research. In this paper we focus on research of methods, and experience with development of software tools designed for retrieval, organization, presentation of information in heterogeneous data source spaces such as the Web. We see the Web as a unique evol...

There are various approaches to service composition - some of them mainly based on AI planning. Although they offer relatively good results, in some cases they are not sophisticated enough to solve real problems. We identified some problems with automatic composition. Our belief is that the natural way to improve these techniques is to include user...

Most of the Internet sources are in unstructured HTML format, which is often difficult to process further. Content extraction from such sources is usually carried out by wrappers. There are various approaches to wrapper construction. The method proposed in this paper utilizes valued Document Object Model (DOM) trees. For each tag in the DOM tree a...

Composition of workflows of computational tasks, grid jobs, or even web services is not a new topic. Many papers and research projects have tackled this problem in the past, in recent years also using semantic description of resources. Most of the proposed or developed solutions deal only with the composition of the functional part of the workflow,...

A new approach to web search that is based on a bee hive metaphor is presented. We proposed a modified model of a bee hive. Our model comprises of a dance floor, an auditorium, and a dispatch room. We have shown that the model is a true model of a bee hive in the sense it simulates several kinds of its typical behaviour. However, more importantly i...

The most popular development methodologies in the last decade are based on object-oriented techniques. The goal of this paper is to investigate the possibilities of extending the object-oriented methodology of information systems development with a service-oriented approach and to examine the benefits of this extension. We propose an augmentation t...

This paper discusses applying the social behaviour of bees to the Web search. We proposed an on-line search of the user's predefined group of pages. In particular, this approach is based on our model of a bee hive being augmented by a model of the behaviour of bees outside the hive and by the method of assigning the page quality. With regard to the...

This paper describes aims, progress and some results of a research conducted in a project that is aimed at devising ways of processing of information and knowledge in a heterogeneous environment, in particular at acquiring, organising and presenting information and knowledge from the web. Important part of the project are pilot applications. Their...

We propose a model of searching semantic web that allows to incorporate data semantics into the searching process.. We concentrated to devising a searching module. Its inputs are two ontologies, describing supply and demand. Its output are supply offers that correspond to demand requests, ordered by relevance to the request. In the next step, we at...

Aiming at a qualitatively new model of processing information in a heterogeneous environment such as the current and the future Web, it is desirable to focus among others on investigating new ways of working with information and knowledge in a heterogeneous environment, especially with imperfect and vague information from perhaps dubious sources. T...

The paper investigates possibilities of writing programs with having the relevant knowledge on programing available in explicity form, in order to perform experiments, a knowledge base was built which codes some of the knowledge related to the problem of selecting a proper data type in the process of program formation. The base is presented in pape...

The paper gives a short overview of the area of knowledge based programming. Research direction toward an intelligent support to software development is identified as an important aim. The paper concentrates on an original work in the area of knowledge based programming. The work reported in the paper relates to a tool to assist trainee programmers...

The paper deals with the question of how to write programs with having the relevant knowledge on programming available in explicit form. A knowledge base was built which codes some of the knowledge related to the problem of selecting a proper data type in the process of program formation. The base is presented in this paper along with several exper...