Department of Computer Science |
Research and teaching activities in the area of computer science at the Faculty of Science have a long history. The faculty computing centre was founded in 1967, just a few years after the founding of the faculty itself. As it gradually became clear that, in addition to providing computing services for the whole faculty, a considerable part of its activities was devoted to research and, increasingly, to education of other faculty members and students, there was a growing tendency to make these two aspects independent. This led to the formation (in 1981) of the Institute of Computing Technology to provide the services while the research and to an increasing degree teaching activities were delegated to the newly section for theoretical cybernetics and computer science within the Department of Mathematical Analysis. In 1985 the Department of Theoretical Cybernetics and Computer Science was formed and later became the present Computer Science Department (1991).
The department is charged with the teaching of special subjects dealing with computing technology and computer science, such as operation systems, theory of algorithms, programming etc. The full list of courses offered by the department appears here. It is in charge of teaching computer related courses for future teachers, undergraduate and bachelor level specialist students as well as in-service training of high-school teachers who wish to become qualified to teach computer science.
The Faculty of Science has accreditation to grant PhD degrees in, among others, the following fields: 11-01-9 Mathematical logic and foundations of mathematics and 11-17-9 Theory of teaching of mathematics. It is also accredited to grant doctorates (RNDr degrees) in, among other fields, the field of Computer science.
In all these cases the Department of Computer Science is charged with administering the corresponding examinations and granting the degrees. Professor Lev Bukovsky is the chairman of the relevant examination committees.
Computer science as a field of research is characterized by the wide scope with the efforts of individual researchers take. This leads to a situation in which significantresults obtained at one university are imparted to students at that particular university but (prior to their dissemination in the whole scientific community) remain largely unknown to teachers and students at other universities. Among other things this reduces the number of researchers able and willing to work in these new directions. The main purpose of the proposed CEEPUS network is to provide a remedy of this situation by providing teachers with new ideas to present their results to a wider audience and at the same time give the students of participating universities the possibility of getting acquainted with new ideas at close range.
| Institution | Proposed research areas |
|---|---|
| P. J. Šafarik U. of Košice |
Fractal geometry Fuzzy sets Neural networks Use of logic in database design |
| Slovak Technical U. of Bratislava |
Genetic algorithms Evolution strategies Taboo search and hill climbing Simulated annealing |
| Faculty of Mathematics and Physics Charles U., Prague |
Recent developments in database theory Compilers |
| Lajos Kossuth U., Debrecen, Hungary |
Recent developments in database theory, dag-interpretations Automated reasoning in non-classical logics |
Department is also involved in another CEEPUS H-66 where the main coordinator is Prof. Jozsef Szabo, Lajos Kossuth University, Debrecen, Hungary.
The research topics cover a broad collection of soft computing and related areas and are focused on model development and implementation of methods for extracting knowledge from uncertain and vague data contained in large and possibly distributed databases and flexible quering. The topics include fuzzy logic (Vojtas), neural networks (Andrejkova), database analyssis and design (Vinar), computer networks (Jirasek), computer simulation methods and operations research in general (Kyselovic), design of information systems (Semanisin), problems of combinatorical structures (Geffert), or hypermedia (Vinar).
Fuzzy logic programming and flexible querying. The results obtained so far cover soundness and completeness of various fuzzy logic programming and resolution systems, especially with arbitrary finite approximations of connectives, fuzzy abduction for descision making coupled with linear programming for cheapest solution. Flexible querying is covered by results on various types of fuzzy unification including second-order logic which is used to extend this theory to meta-data and at the end to extend the flexibility of our query system to include the capability of providing answers to natural language queries and finding the appropriate access methods even through the user does not know about them. Work done on structural complexity and combinatorial optimization where many fundamental results have already been obtained may conceivably be useful in considering the efficiency of various query optimization algorithms.
Database design and analysis. This involves knowlwdge of database and CASE tools and programming languages and interface and communication standards. The language of logic is used to provide the possibility of quickly constructing trial versions of sophisticated query analysis, implementation and optimization systems to test the basic approaches before creating the final version. Models for detection of similar records in distributed databases are studied too.
Multimedia, Hypermedia. Hypermedia is commonly thought of only in the context of web pages, teaching software etc. Some preliminary results suggest the possibility of using them in a neglected area of making the output of software systems more user friendly by providing the option to produce output in hypermedia from linked to "resident textbooks".
Artificial neural networks. Predictions capability of neural networks (NN) was studied together with the topology of NN depending on the solved problem and from the research point of view very interesting problem of their topological adaptability (it means, the tpopogy of NN is adapted according to soslved problems). Incremental topology can be used for feed-forward NN, genetic algorithms for recurrent NN.
Recently there was worked on the following research projects that obtained grant support: