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Modern Issues of Information and Computer Science

Code – 230100   Information and computer science; section: Software of computer equipment and automation systems

Qualification (degree) – Master

Description of course modules (sections, subjects):

1. Intelligent systems.

2. Metadata and ontology languages.

3. Evolutionary computations.

4. Data coding and compression.

5. Synergetics.

6. Conceptual design of systems.

7. Integration of automation systems.

8. Development of hardware for automation systems.

Syllabus of the course:

Knowledge presentation techniques. Introduction to knowledge management. Data mining. Text information processing challenges. Classification. Clusterization. Nearest neighbor analysis. Hierarchy analysis method. Ontologies. Ontology construction tools.

Semantic Web. Metadata. RDF metadata model. RDFS. Dublin core. Ontology languages. OWL. Web-2.

Evolutionary methods. Simple genetic algorithm.  Genetic programming. Heuristics combination method. Examples of application of genetic methods.

Information. Information entropy. Information coding. Shannon's theorems. Codes for text documents. Instant codes. Data compression. Data compression methods and data formats. MPEG methods. Wavelets. Wavelet transformation.

Theory of evolution. Dynamic systems. Thermodynamic entropy. Chaotic systems Chaotic systems. Bifurcations.  Fractals. Self-organization. Theory of catastrophes.

Development of enterprise management systems. Business process management systems. Architectural design of systems. Object oriented programming. Component oriented technologies. Network services. Service oriented architecture. Development managed by models. Engineering patterns. IDEF0. IDEF3. UML. Meta-Object Facility. UML based IT design techniques. Structural UML 2.0 diagrams. Behavioral UML 2.0 diagrams.

Integrated application development environment. Integration of applications. Techniques for integration of information systems. WorkFlow. SOAP technology. UDDI standard. WSDL. ESB Enterprise service bus and BPEL. Integration of subsystems of the automatic process control system.

Supercomputers of the 21st century. IPv6 protocol. Internet 2. Wavelength division multiplexing (WDM). Grid technologies. Grid architectures.

Teaching methods:

  • lectures,
  • classroom group training directed by a teacher,
  • students’ independent work performed according to the teacher’s task in classrooms and on an extracurricular basis, including use of technical training aids (compulsory),
  • tutorials.

Total hours – 108.

Total credits – 3.

Laboratory work – not applicable.

Form of interim assessment – exam.

Modern High-Performance Computer Equipment

Code – 230100   Information and computer science; section: Software of computer equipment and automation systems

Qualification (degree) – Master

Description of course modules (sections, subjects):

  1. Classification of high-performance computer equipment by applications.
  2. General requirements set for modern computers.
  3. Performance assessment of computing systems.
  4. Fundamentals of multithreaded programming by the example of Threads (Win API).
  5. Parallel programming using OpenMP technology.
  6. Multiprocessing systems.

Syllabus of the course:

Definition of high-performance computer equipment. Personal computers and workstations. X-terminals. Servers. Mainframes. Cluster architecture.

Price / performance ratio. Reliability and failure tolerance. Scalability. Software compatibility and mobility.

General comments. Comparison of compilers of modern high-level programming languages and their impact on PC performance. MIPS. MFLOPS. SPECint92, SPECfp92. SPECrate_int92, SPECrate_fp92. TPC-A, TPC-B, TPC-C. AIM.

Generation of, waiting for, and stop of threads. Critical sections. Semaphores. Conditional variables.

Basic model of multithreaded programming using OpenMP. Program structure. Serial, parallel and critical sections. Sharable and local thread variables. Barriers.

Classification of parallel data processing systems. Multiprocessing systems with shared and local memories. High-availability systems and fault-tolerant systems. Key definitions. High-availability external memory subsystems. Requirements set for high-availability systems.

Teaching methods:

  • lectures,
  • classroom group training directed by a teacher,
  • students’ independent work performed according to the teacher’s task in classrooms and on an extracurricular basis, including use of technical training aids (compulsory),
  • tutorials.

Total hours – 72.

Total credits – 2.

Laboratory work – not applicable.

Form of interim assessment – exam.

Management of a Social Structure and an Economic System

Code – 230100   Information and computer science; section: Software of computer equipment and automation systems

Qualification (degree) – Master

Description of course modules (sections, subjects):

  1. Definition of a system, system components and classification of systems.
  2. Economic system and its types.
  3. Social structure and its types.
  4. System management.
  5. Educational system as a subject of management.

Syllabus of the course:

Definition of a system. Classification of systems.

Economic system. Types of economic systems and their classification.

Social structure. Types of social interactions between subsystems. Educational system.

Teaching methods:

  • lectures,
  • laboratory work
  • students’ independent work performed according to the teacher’s task in classrooms and on an extracurricular basis, including use of technical training aids (compulsory),
  • tutorials.

Total hours – 108.

Total credits – 3.

Laboratory work – not applicable.

Form of interim assessment – pass/fail exam.

Intelligent Systems

Code – 230100   Information and computer science; section: Software of computer equipment and automation systems

Qualification (degree) – Master

Description of course modules (sections, subjects):

  1. Key principles of artificial intelligence, state space representation, decomposable production system.
  2. Different strategies of managing a search in a state space representation (depth-first search, breadth-first search, heuristic search).
  3. Expert systems (architecture, data bases, forward and backward chaining).
  4. Approximate reasoning schemes, expert systems with fuzzy inference.

Syllabus of the course:

State space representation – formalism for representation of artificial intelligence tasks. Production system. Decomposable production system – generalization of the state space representation. AND-OR tree – graphic presentation of the decomposable production system.

Uninformed search management strategies. Depth-first search. Неинформированные Uninformed search management strategies. Breadth-first search. Informed search management strategies. A algorithm. A* algorithm. Heuristics with small and large heuristic force.

Different knowledge presentation models. Forward and backward chaining in production systems. Expert system architecture based on rules. Expert system architecture based on facts. Knowledge bases. Expert system architecture based on facts.

Reasoning in case of uncertain and incomplete information. Approximate reasoning schemes. Multi-stage reasoning. Inference net. Expert system with fuzzy inference.

Teaching methods:

  • lectures,
  • classroom group training directed by a teacher,
  • students’ independent work performed according to the teacher’s task in classrooms and on an extracurricular basis, including use of technical training aids (compulsory),
  • tutorials.

Total hours – 108.

Total credits – 3.

Laboratory work – not applicable.

Form of interim assessment – pass/fail exam.

Information Systems in Metallurgy

Code – 230100   Information and computer science; section: Software of computer equipment and automation systems

Qualification (degree) – Master

Description of course modules (sections, subjects):

  1. Concept of modern information systems and processes applied in metallurgy.
  2. Architecture of software and information systems of iron and steelmaking processes.
  3. Key concepts of database management systems.
  4. Key concepts of communications technologies.
  5. Key principles of construction and implementation of information systems in metallurgy.

Syllabus of the course:

Structure of an information system of a metallurgical plant.  Information technologies of information collections, processing, storage, management and transfer.

System software. Application software packages. Particular features of software for technological processes. Software packages of operators’ process workstations (SCADA). Application packages for process management. Generalized scheme of a level automation information system.

Construction principles of a modern automation information system as an example of a blast furnace process. Distributed system of databases in sintering and blast furnace processes. Expert systems in a blast furnace process.

Teaching methods:

  • lectures,
  • laboratory work,
  • students’ independent work performed according to the teacher’s task in classrooms and on an extracurricular basis, including use of technical training aids (compulsory),
  • tutorials.

Total hours – 108.

Total credits – 3.

Laboratory work – 1/36.

Form of interim assessment – pass/fail exam.

HR Management

Code – 230100   Information and computer science; section: Software of computer equipment and automation systems

Qualification (degree) – Master

Description of course modules (sections, subjects):

  1. HR management in a modern management system.
  2. Organizational context of HR management.
  3. Key approaches to HR management.
  4. Concept of “human capital”.
  5. HR policy.
  6. Methods for setting up a structure of an organization.
  7. Methods for maintaining personnel’s working capacity.

Syllabus of the course:

Paradigms of HR management in the 20th century. Evolution of forms of joint activities and formation of HR management. Key types of a professional culture of HR management. Key occupational roles of an HR manager. Business ethics in work of an HR manager.

Elements of an organization. Life stages and cycles of the organization.

Economic approach. Organic approach. Humanistic approach. Organizational cultures as a subject of management.

Theory of human capital. Concept “Analysis of human resources”. Measurement of an employee’s individual value. Stochastic position model.

Types of HR policies. Stages of developing HR policy. HR actions and HR strategy. Development conditions of HR policy.

Setting up a structure of an organization. Evaluation of personnel requirements. Analysis of HR situation in a region. Analysis of activities. Recruitment of candidates. Appraisal of applicants for a job. Competitive recruitment of staff. Staff adaptation.

Increase in labor efficiency and labor rate setting. Labor appraisal. Staff performance appraisal. Talent pool management. Career planning. Development of benefit programs. Personnel training.

Teaching methods:

  • lectures,
  • classroom practical work directed by the teacher,
  • students’ independent work performed according to the teacher’s task in classrooms and on an extracurricular basis, including use of technical training aids (compulsory),
  • tutorials.

Total hours – 108.

Total credits – 3.

Laboratory work – not applicable.

Form of interim assessment – pass/fail exam.

High-Performance Systems

Code – 230100   Information and computer science; section: Software of computer equipment and automation systems

Qualification (degree) – Master

Description of course modules (sections, subjects):

  1. Concept of high-performance systems.
  2. Real-time operating systems.
  3. Programming of real-time applications to be executed in QNX Neutrino.
  4. Application of equations of mathematical physics and particular features of their mathematical description.

Syllabus of the course:

Concept of high-performance systems. General requirements set for high-performance systems. Classification of operating systems.

Definition of terminology. Review of architectures of real-time operating systems. Functional requirements for real-time operating systems.

Key tools of timer services of QNX Neutrino RTOS. Their use in practice. Key processing techniques for interruptions of QNX Neutrino RTOS and their use in practice.

Teaching methods:

  • lectures,
  • laboratory work
  • students’ independent work performed according to the teacher’s task in classrooms and on an extracurricular basis, including use of technical training aids (compulsory),
  • tutorials.

Total hours – 144.

Total credits – 4.

Laboratory work – 0.5/18.

Form of interim assessment – exam.

Functional Analysis

Code – 230100   Information and computer science; section: Software of computer equipment and automation systems

Qualification (degree) – Master

Description of course modules (sections, subjects):

  1. Concept of an infinite-dimensional linear space.
  2. Techniques for introduction of a metric and a norm in an infinite-dimensional space.
  3. Linear operator in infinite-dimensional spaces.
  4. Adjoint spaces and adjoint operators.
  5. Key principles of a linear functional analysis.
  6. Convexity and a geometrical approach to theorems on prolongation of functionals.
  7. Optimization problems, Kuhn-Tucker theorems in finite-dimensional and infinite-dimensional spaces.

Syllabus of the course:

Objective of the course: introduction of students to key concepts and results of the functional analysis, application of functional analysis methods and algorithms based on such methods to solutions of scientific and application problems.

Tasks of the course

The course is aimed at:

  • study of fundamental mathematical and applied concepts  of infinite-dimensional linear space, topological structures, operators and optimization problems in an infinite-dimensional space;
  • acquiring of skills to put problems of classical mathematical analysis into an abstract form allowing to represent and solve them in functional and analytical terms;
  • acquiring of skills to apply a geometric approach to problems of finite-dimensional and infinite-dimensional functional analysis by identification of their geometrical character;
  • introduction to application of methods of functional analysis to problems of natural science and engineering.

The course in included in optional courses of a general scientific cycle attributed to the master’s degree educational standard.

As a result of studying the course a student should:

  • know main concepts of functional analysis;
  • be able to apply in practice results of functional analysis in mathematical problems;
  • have skills of application of functional analysis methods to problems of natural science and engineering;
  • show ability and readiness to solving problems of functional analysis as applied to various subject areas.

Teaching methods:

  • lectures,
  • laboratory work,
  • students’ independent work performed according to the teacher’s task in classrooms and on an extracurricular basis, including use of technical training aids (compulsory),
  • tutorials.

Total hours – 108.

Total credits – 3.

Laboratory work – 1/36.

Form of interim assessment – pass/fail exam.

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