informatics autumn - susu.lt/bylos/tarptautiniai_rysiai/2015-2016_exchange/informatics_autumn... ·...

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INFORMATICS autumn

Course title ECTS Degree Course code Prerequisites Subject area

Computer Networks 6 Bachelor P170B144 Elements of computer‘s architecture and

programming

Computer Science

Artificial Intelligence 6 Bachelor P176B001 Programming, probabilities, algorithm

theory, discrete mathematics, graph theory.

Computer Science

Programming Languages 6 Bachelor P175B157 Programming Fundamentals Computer Science

Cryptography 3 Bachelor P170B407 Programming and higher mathematics. Computer Science

Object-oriented

programming

6 Bachelor P175B159 Programming fundamentals, structure-

oriented programming.

Computer Science

Human-computer

interaction

3 Bachelor P175B119 Basics of using computers (MS Word or

another similar editor, Internet, email)

Computer Science

Software design 6 Bachelor P175B171 Programming fundamentals, structure-

oriented programming, object-oriented

programming

Computer Science

Internet technologies 3 Bachelor P175B163 School Information Technology course. Computer Science

Software Engineering1 6 Bachelor P175B161 Programming, Object-oriented

programming.

Computer Science

Software Engineering2 6 Bachelor P175B636 Programming skills in several programming

languages

Informatics

Component-based

programming

3 Bachelor P175B125 Programming, Object-oriented programming

Computer Science

Programming for smart

devices

3 Bachelor P175B211 Programming, Object-oriented programming

Computer Science

Network programming 3 Bachelor P175B162 Elements of computer‘s architecture,

Programming, computer networks

Computer Science

Programmed control of

servers

3 Bachelor P170B014 Elements of computer‘s architecture,

programming, computer networks

Computer Science

Probability theory and

statistical analysis of

numerical data

3 Bachelor P170B014 Fundamentals of higher mathematics Computer Science

Computer and

Telecommunication

Networks

6 Bachelor T120B127 Information Technology, Computer

Elements, Computer Architecture, Operating

Systems

Computer

Technology

Management of

Information System

Projects

6 Bachelor T120B111 Information systems design, realization of

information systems technology

Computer

Technology

Data Security 6 Bachelor P175B622 Programming skils, data structure and

operating system knowledge

Informatics

Information Processing

Systems

6 Bachelor T120B619 Information management, discrete

structures, probability theory and

mathematical statistics, operational

computing

Computer

Technology

Business Information

Systems

6 Bachelor T120B012 Information management, discrete



computing

Computer

Technology

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Subject area: Computer Sciences

Status Course code: P170B144

Course title: COMPUTER NETWORKS

Taught by: Assoc. professor dr. Liudvikas Kaklauskas

Semester ECTS credits Languages Duration

Autumn or spring 6 English, Russian 1 semester

Study hours Assessment Prerequisites Examination

Lectures – 32 h

Seminars – 0 h

Homework – 32 h

Self-study – 96 h

10-point scale Elements of computer‘s architecture and

programming

Mid-term examination – 30%

Seminars – 0%

Homework – 50%

Final examination – 20%

Subject content Evolution of computer networks. Classification of computer networks, architectures, structure, standards. OSI model.

Hardwired and logical tools and services for local and remote communication. Protocols and their sets. TCP/IP.

Development of Web applications. Error indication, data compression. Addressing routes and inter-network

communication. Security of networks. Internet technologies and services. Network control, domains. Multi-

environment networks. Radio, mobile and other modern networks.

Learning Outcomes Students should acquire knowledge about various structures of computer networks, network hardware and software

and their usage opportunities. Also they should be able to choose appropriate tools for design and development of the

local (LAN) and global (WAN) computer networks. Students should learn to manage computer network more

effectively, use network protocols.

Subject area: Computer Science


Course title: ARTIFICIAL INTELLIGENCE

Taught by: Assoc. professor dr. Gražvydas Felinskas


Autumn or Spring 6 Lithuanian, English, Russian 1 semester


Lectures – 32 h

Seminars – 0 h

Homework – 32 h

Self-study – 96 h

10-point scale Accomplishment of modules, related to

programming fundamentals, probabilities,

algorithm theory, discrete mathematics, graph

theory, is necessary.


Homework, Reporting for laboratory work – 40%


Subject content Notion of artificial intelligence, history, philosophical aspects, components of artificial intelligence. Theoretical

branches of artificial intelligence. Agents, communicating agents. The review of search mechanisms, restrictions.

Knowledge representation and knowledge bases. Knowledge-based systems, data mining. Reasoning chains, decision

trees, semantic networks and frames. Expert systems. Probabilistic reasoning methods, fuzzy logic, coefficient of

confidence. Decision making, multi-criteria decisions, strategies, usefulness. Self-learning systems. Design of artificial

intelligence systems. Complex search, simulated annealing and genetic methods. AI applicability scope – recognition

theory, natural language analysis; usage in development of modern technologies.

Learning Outcomes Students should acquire knowledge about various branches of artificial intelligence, its application areas and modern

achievements. Also they should be able to choose appropriate tools for design and development of the artificial

intelligence-based system, to apply artificial intelligence methods in development of various types of software, to

choose appropriate methods for knowledge representation, to model various reasoning and search algorithms with

standard programming tools. Students should learn to program in logical programming language PROLOG, to

represent the facts and rules by the means of language structures, to form queries, to develop the prototype of

experimental system. Students should conceptualise the problem areas in finding solutions for the modern complex

practical tasks, the importance of application of various heuristic algorithms.

Literature 1. Nils Nilsson. Principles of artificial intelligence. Springer verlag, 1982.

2. Nils Nilsson. Artificial Intelligence: a new synthesis. Morgan Kaufmann Publishers, 1998. 513 p.

3. Stuart Russell, Peter Norvig. Artificial intelligence: a modern approach. Second edition, Prentice Hall, 2003.

1132 p. http://aima.cs.berkeley.edu.

4. George Luger. Artificial intelligence: structures and strategies for complex problem solving (fifth ed.),

Addison-Wesley, 2005. 928 p. http://www.cs.unm.edu/~luger/

5. Mark Stefik. Introduction to knowledge systems. Morgan Kaufmann Publishers, 1995. 871 p.

6. Michael Wooldridge. An introduction to multiagent systems. John Wiley, UK, 2002. 348 p.

http://aima.cs.berkeley.edu/

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Course title: PROGRAMMING LANGUAGES

Taught by: Lector Lina Tankelevičienė


Autumn or Spring 6 English 1 semester


Lectures – 32 h

Seminars – 32 h

Self-study – 96 h

10-point scale Programming Fundamentals Reporting for laboratory work – 30%

Homework – 30%

Exam – 20%

Non-traditional tasks (in Moodle environment) –

20%

Subject content Conception of programming language. History of programming languages. Syntax and semantic of programming

language. BNF. Main elements in programming language (concerning the example of programming language for

imperative programming). Classifications of programming languages. Programming paradigms. Code translation.

Comparative anglysis of chosen programming languages.

Learning Outcomes Students will acquire knowledge on classifications of programming languages, their common principles, main

language elements: types, objects, names, expressions, functions, parameters, and other constructions. They will able to

read and understand source code, to manage programming languages constructions, to compare them and to use for

practical purposes.

Literature 1. R.W. Sebesta. Concepts of Programming Languages. 5ed. Addison Wesley, 2001.

2. D. P. Friedman, M. Wand. Essentials of Programming Languages, 3ed. MIT Press, 2008.

3. B. W. Kernighan, D.Ritchie, D. M. Ritchie. The C Programming Language, 2ed. Prentice Hall PTR, 1988.

4. T. Crawford, P.Prinz. C: In a Nutshell. O'Reilly, 2005, 618 p.

5. Material in virtual learning environment MOODLE, prepared by lect. Lina Tankeleviciene.



Course title: CRYPTOGRAPHY

Taught by: Lector Mindaugas Stoncelis


Autumn or Spring 3 Lithuanian, English, Russian 1 semester


Lectures – 16 h

Seminars – 16 h

Homework – 0 h

Self-study – 48 h

10-point scale Elements of and programming and higher

mathematics.

Reporting for laboratory work – 50%

Seminars – 0%

Homework – 20%


Subject content Basics from elementary numbers theory (divisibility, Euclidian algorithm, finite corps). Basics from complexity theory.

Classical cryptosystems. Concept of public key cryptosystem. RSA and other cryptosystems. The problem of discrete

time. Algorithms for finding prime numbers and factorising the natural numbers. Digital signatures.

Learning Outcomes This module covers cryptographic primitives like symmetric and public key encryption schemes, digital signatures

(based on RSA); also heir security issues are discussed. Besides that, necessary mathematical background is provided.

Literature 1. A. Buchmann. Introduction to Cryptography, Springer, 2001

2. A. Menezes. Handbook of Applied Cryptography, CRC Press, 2001 (http://www.cacr.math.uwaterloo.ca/hac)

3. Cryptography Pointers (http://www.cs.ut.ee/~helger/crypto/)

4. Ron Rivest’s Security Links (http://theory.lcs.mit.edu/rivest/crypto-security.html)

5. David Wagner’s Crypto Links (http://www.cs.berkeley.edu/~daw/crypto.html)

6. Cryptography Research, Inc. (http://www.cryptography.com/resources/

7. International Association for Cryptologic Research (http://www.iacr.org)

8. NIST Cryptographic Toolkit (http://csrc.nist.gov/CryptoToolkit/)

9. AES page (http://csrc.nist.gov/encryption/aes/)

10. National Security Agency (http://www.nsa.gov)

11. Bruce Schneiers news letter CRYPTOGRAM(http://www.counterpane.com/crypto-gram.html)

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Subject area: Computer Sciences


Course title: OBJECT-ORIENTED PROGRAMMING

Taught by: Assoc. professor Kęstutis Žilinskas




Lectures – 32 h

Seminars – 0 h

Homework – 32 h

Self-study – 96 h

10-point scale Programming fundamentals, structure-

oriented programming.

Homework – 50%


Subject content Evolution of programming paradigms. Object-oriented programming paradigm. Evolution of object-oriented

programming languages. Concepts of class and object. Classes and subclasses. Abstraction. Information hiding,

encapsulation. Inheritance, class hierarchy. Polymorphism. Overloaded methods. Virtual functions. Class libraries.

Program design, development, testing, and debugging. Hierarchical data structures.

Learning Outcomes To gain basic knowledge about programming paradigms, object-oriented programming paradigm; to analyse and

select tools for object-oriented programming; to know main types of tasks which can be solved with tools of object-

oriented programming; to gain knowledge about display, development and adjustment of diagrams of classes and

objects; to learn main constructs of object-oriented language; to learn to design dynamic data structures; to prepare

program documentation; to gain practical skills about object-oriented programming.

Literature 1. Bjarne Stroustrup. The C++ Programing Language.

2. Grady Booch. Object-Oriented Analysis and Design with C++ Applications.

3. Clark Dan. Beginning C# Object-Oriented Programming.

4. Farrell J. Microsoft Visual C# 2010. Introduction to Object-Oriented Programming.

5. Johnson B. Professional Visual Studio 2012.

ubject area: Computer Science


Course title: HUMAN-COMPUTER INTERACTION

Taught by: Lector Lina Tankelevičienė




Lectures – 16 h

Seminars – 32 h

Self-study – 112 h

10-point scale Basics of using computers (MS Word or

another similar editor, Internet, email)

Reporting for laboratory work – 30%

Individual homework – 20%

Exam – 30%

Paper work – 20%

Subject content The components of the process of human-computer interaction. The styles of user interaction. Human factors, that

influence human-computer interaction. The types of software user interfaces. Design and development of user oriented

user interface. Graphical and browser-oriented user interfaces. Standards, principles and guidance for user interfaces.

The types and tools for human interaction support. The tendencies of user interfaces.

Learning Outcomes Students will acquire fundamental knowledge, skills and understanding allowing them to analyse, evaluate, design

and develop user interfaces, considering appropriate standards and principles, using appropriate techniques and

procedures. Essentially, students will be able to define user goals, to develop layout of user interface and design

interaction.

Literature 1. Alan Dix, Janet E. Finlay, Gregory D. Abowd, Russell Beale. Human-Computer Interaction. 3ed, Prentice Hall,

2003.

2. Ghaoui C. (Editor). Encyclopedia of Human Computer Interaction. Idea Group Inc. 2006.

3. Mike Gunderloy. Developer to Designer: GUI Design for the Busy Developer. Sybex, 2005.

4. Wilbert O. Galitz. The Essential Guide to User Interface Design: An Introduction to GUI Design Principles and

Techniques, 2 ed, John Wiley & Sons, 2002.

5. Proctor R. W., Vu K. P. L. (Eds.). Handbook of human factors in web design. Lawrence Erlbaum associates,

London, 2005.


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Course title: SOFTWARE DESIGN

Taught by: Lector Lina Tankelevičienė, lector Mindaugas Stoncelis




Lectures – 32 h

Seminars – 32 h

Self-study – 96 h

10-point scale Programming fundamentals, structure-

oriented programming, object-oriented

programming

Reporting for laboratory work– 35%

Individual homework – 35%

Exam – 30%

Subject content Structure and features of complex systems. Object-oriented decomposition. Abstractions and hierarchies. Methods of

design of complex systems. Requirements from business and developer perspectives. Object-oriented model. Static and

dynamic models. Physical and logical models. Classes and objects. Concepts of object and class and relations between

them. Classification. Identification of classes and objects. Object-oriented analysis. Modelling of systems. Mark-up

system and languages of modelling. UML standards. UML syntax and diagrams. Peculiarities of usage of UML.

Learning Outcomes After successful completion of the module, a student will understand how to design software according to an object-

oriented methodology. They will master UML as a notation to support this design.

Literature 1. Howard Podeswa. UML For The IT Business Analyst. Cengage Learning PTR; 2 edition, 2009.

2. Martin Fowler. UML Distilled: A Brief Guide to the Standard Object Modeling Language (3rd Edition),

Addison Wesley, 2003.

3. Kevin Lano. UML 2 Semantics and Applications. Wiley, 2009.




Course title: INTERNET TECHNOLOGIES

Taught by: Lector Mindaugas Stoncelis


Autumn or spring 3 English 1 semester


Lectures – 16 h

Seminars – 0 h

Homework – 32 h

Self-study – 32h

10-point scale School Information Technology course. Reporting for laboratory work – 50%

Seminars – 0%

Homework – 20%


Subject content Hardware and software. Addresses of Internet nodes. Protocols for accessing the Web. Transferring files. Ways of

connection to remote system. Search. Planning Web page. Netiquette, maintaining Web pages, copyright. HTML

language, structure of HTML document. Main elements. Tables. Graphics in HTML document. Additional symbols.

Graphical maps and their usage for linking. Frames, connecting several documents. Design of forms and their usage

for communicating with user. HTML editors. Programming in JavaScript ant PHP possibilities. Generating Web pages

(Wizard and CMS).

Learning Outcomes To introduce students with modern internet technologies, protocols, services and most popular tools for development

of Web pages. To provide with abilities to store information on internet.

Literature 1. Dunaev C. Intranet - technologiji. Web dbc. Cgi. Cobra 2.0. Netscape. Suite. Borland. Intrabuilder. Java. Javascript

livewire. 1997m

2. Meloni, Julie C. PHP, MySQL ir Apache. 2007

3. Patrick Carey, New Perspectives on HTML and CSS, 6th Edition, 2013

4. Bruce Lawson and Remy Sharp, Introducing HTML 5, 2011

5. Rob Larsen, Beginning HTML and CSS, 2013

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Subject area:


Course title: Software Engineering2

Taught by: assoc. professor Asta Slotkienė


Autumn 6 Lithuanian, English 1 semester


Lectures – 32 h

Laboratory work – 32 h

Self-study – 96 h

10-point scale Programming skills in several

programming languages

Reporting for labaratory work – 25 %

Colloquium – 10 %

Course Project – 15 %

Exam – 50 %

Subject content Students are introduced to the module, the system life cycle, software development and systems theory, object-

oriented modeling language UML basics. They are trained to specify, design, test and install the software and

documentation, the value of the works, and software quality. Students gain the skills and system development process

execution and management.

Learning Outcomes Software life cycle phases and their methods;

Requirements-capture techniques and features;

Software design methods and strategies;

Testing strategies, software quality assessment methods;

Software documentation of the types and uses.

Literature 1. Michael Blaha, James Rumbaugh. Object-oriented modeling and design with UML™

2. Ian Sommerville. Software engineering

3. Brian Berenbach. Software & systems requirements engineering : in practice

4. Alan Dennis, Barbara Haley Wixom, David Tegarden. Systems analysis and design with UML version 2.0 : an object-

oriented approach

Subject area: Computer Science Status Course code: P175B161

Course title: SOFTWARE ENGINEERING1 Taught by: Assoc. professor dr. Vaidas Giedrimas

Semester ECTS credits Languages Duration Autumn or spring 6 English, Russian 1 semester Study hours Assessment Prerequisites Examination Lectures – 32 h Seminars – 16 h Homework – 32 h Self-study – 80 h

10-point scale Programming, Object-oriented programming

Mid-term examination – 0% Seminars – 0% Homework – 50% Final examination – 50%

Subject

content Software Engineering concept. SE components, PSP and TSP, Project management. Reuse and Code Generation: API and

libraries, Design patterns, Reverse engineering, Code generation. Software life cycles. Domain analysis and conceptual

modelling. Requirements engineering Software architecture styles. Software design. Software validation. Testing.

Documenting. User support. Agile methods. Learning

Outcomes In this course the foundations of the software engineering are presented, having in focus importance of each stage of the

software lifecycle. Students acquire knowledge required to big software project management. Students get the skills of the

code generation, reverse engineering, software testing and documenting. Literature L. A. Maciaszek, B. L. Liong Practical software engineering : a case study approach. Pearson/Addison Wesley. 2005

I. Sommerville. Software Engineering. Addison Wesley. 2008

O. Pastor, J.C. Molina Model-driven architecture in practice: a software production environment based on conceptual

modeling Springer, 2007

K. Beck Extreme Programming Explained: Embrace Change. Addison-Wesley 1999

K. Czarnecki Generative Programming: Methods, Tools, and Applications. Addison-Wesley 2000

A. Endres, D. Rombach. A Handbook of Software Systems Engineering 2003

J. W. Moore The Road Map to Software Engineering: A Standards-Based Guide. Wiley-IEEE Computer Society Pr 2006

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Subject area: Computer Science Status Course code: P175B125

Course title: COMPONENT-BASED PROGRAMMING Taught by: Assoc. professor dr. Vaidas Giedrimas

Semester ECTS credits Languages Duration Autumn or

spring 3 English, Russian 1 semester

Study hours Assessment Prerequisites Examination Lectures – 16 h Seminars – 0 h Homework –

16 h Self-study – 48

h



Subject

content The component-oriented paradigm. The lifecycle of component-based software. Component concept. Interface of the

component. , .NET, EJB, CORBA /CCM component models. Web-services and RESTfull services. Other component models.

The tools of CBSE. Learning

Outcomes Students get knowledge about the singularities of component-oriented programming, get familiar with various component

models, get skills to design and implement components as well as component-based systems Literature I. Crnkovic, M. Larsson Building Reliable Component-based Software Systems. Artech House 2002

G. T. Heineman, W. T. Councill Component-based software engineering : putting the pieces together. Addison Wesley 2001

J. Löwy. Programming .NET Components. O'Reilly 2005

C. Szyperski. Component software. Addison-Wesley, 2002

A. Ju , W. Kai Qian. Component-oriented programming. Wiley 2006

J. Cheesman, J. Daniels. UML Components: A Simple Process for Specifying Component-Based Software. 2002, Addison-

Wesley J.


Status Course code: P175B211 Course title: PROGRAMMING FOR SMART DEVICES

Taught by: Assoc. professor dr. Vaidas Giedrimas Semester ECTS credits Languages Duration Autumn or

spring 3 English, Russian 1 semester

Study hours Assessment Prerequisites Examination Lectures – 16 h Seminars – 0 h Homework –

16 h Self-study – 48

h



Subject

content The module is designed for anyone wanting to learn how to build applications for most popular smartphones and tablets. At

theoretical lectures students acquire knowledge about these devices and their OS architecture. At laboratory work acquires

the ability to use specialized for these devices programming and software quality management tools. Doing the individually

home work creates applications for specific subject area. The core topics are: smart device concept, its architecture; major

smart device operating systems; the programming of Apple devices as well as devices with Android OS fundamentals; data

storage means (SQLite, CoreData, iCloud, etc.); the market of smart devices applications, their distribution capabilities.. Learning

Outcomes Students will know the architecture of Smart devices and its OS and they will be able to develop smart-devices-oriented

applications and to choose optimal tools for the development. Literature 1. Ali, M. (2010). Advanced iOS 4 Programming: Developing Mobile Applications for Apple iPhone, iPad, and

iPod touch. Wiley

2. Deitel, P. J. (2013). Android : how to program. Pearson.

3. Harwani, B.M. (2011). Core Data iOS Essentials. Packt publishing.

4. Rogers, R. et al. (2009). Android Application Development. O'Reilly

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Course title: NETWORK PROGRAMMING





Lectures – 32 h

Seminars – 0 h

Homework – 32 h

Self-study – 96 h

10-point scale Elements of computer‘s architecture,

Programming, computer networks


Seminars – 0%

Homework – 50%


Subject content Interfaces of applications and protocols. Client and server software design means and their algorithms. Main types of

servers. Single process parallel servers. Multi-protocol TCP and UDP servers. Unifies and effective management of

parallel server processes. Parallel client processes.

Learning Outcomes The student will be able to design simplest programs for work in network. The student will be able to select the

required technological solution.

Literature 1. Embedded ethernet and internet complete : designing and programming small devices for networking / Jan

Axelson. Madison, WI : Lakeview Research LLC, 2003. XIV, 482 p.

2. Java TM network programming / Elliotte Rusty Harold. Beijing, : O'Reilly, 2000. 731, [2] p

3. W3 Schools. Interactyve: http://www.w3schools.com/.

4. Open Merchant Account Ltd. Network Programming in .NET. Interactyve: http://webtropy.com/

5. Buyya R., Selvi T., Chu X. Object Oriented Programming with Java: Essentials and Applications, 2009, Tata

McGraw-Hill Education



Course title: PROGRAMMED CONTROL OF SERVERS





Lectures – 32 h

Seminars – 0 h

Homework – 32 h

Self-study – 96 h

10-point scale Elements of computer‘s architecture,

programming, computer networks


Seminars – 0%

Homework – 50%


Subject content Interface software control commands integrated into network operation systems. Script development and their

application for server control. Control automation tools. Ruby, Perl and other programming language, which are best

suitable for control of server processes. Development, running and debugging of scripts. Variable types, arrays and

their usage for operating system control. Types of operations, their input and output. Control structures. String

processing, working with files and directories. Functions, subroutines, packages, modules. Analysis of user and system

data, reports.

Learning Outcomes To learn how to perform complex management of a server‘s work using network operating system interface (Bash, Sh,

cmd, etc.) commands, and capabilities of modern programming languages (Ruby, Perl, etc.).

Literature 1. The Ruby, A Programmer's Best Friend. Interactyve: https://www.ruby-lang.org/en/

2. The Perl Programming language. Interactyve: http://www.perl.org/

3. Jean Ross and Greg Stemp. The Windows PowerShell Owner’s Manual: Version 2.0. Microsoft Communications

Server UA. Interactyve: http://allunifiedcom.files.wordpress.com/2010/07/powershell_v2_owners_manual.pdf.

4. Mike G. BASH Programming - Introduction HOW-TO. Interactyve: http://tldp.org/HOWTO/Bash-Prog-Intro-

HOWTO.html. n.

5. Rockwood B. The Cuddletech Guide to SNMP Programming. Interactyve:

http://cuddletech.com/articles/snmp/snmp_paper.pdf.

http://allunifiedcom.files.wordpress.com/2010/07/powershell_v2_owners_manual.pdf

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Course title: PROBABILITY THEORY AND STATISTICAL ANALYSIS OF NUMERICAL DATA

Taught by: Lector dr. Kristina Vaitkuvienė




Lectures – 32 h

Seminars – 16 h

Laboratory works

– 16 h

Self-study – 96 h

10-point scale Fundamentals of higher mathematics Mid-term examination – 20%

Reporting for laboratory works – 50%

Final examination - 30%

Subject content 1. Overview of the main combinatorial formulas. Concept of probability (definitions of probability,

properties of probabilities, application examples of probabilities properties). Conditional probability (definition of

conditional probability, properties of conditional probability, formula of full probability, Bayes theorem). Independent

random events (definition of independent random event, Bernstein example, properties of independent random

events, Bernoulli experiments). Random variable (notion of random variable, probability distribution of random

variable, discrete and continuous random variables, definition of independent random variables, examples, numerical

characteristics of random variables: mean, dispersion, covariance, correlation). Generators of random values

(generating computer methods of uniformly distributed randoma variables, transformation method, acceptance-

rejection method and its applications, two-dimensional Gaussian random vectors and its values generators). Markov

chains (definition of Markov chain, examples, Markov chains appilcations for web-pages rating). Random sample

(random sample, realization of random sample, data search online, empirical characteristics of random sample,

calculation of empirical characteristics of sample using statistical packages). Statistical hypothesis testing (concept of

statistical hypothesis, first and second type of errors, level of criterion significance, statistical testing methodology of

random variables values generator, check of hypotheses about distribution parameters, verification of independence,

testing of statistical hypothesis using statistical packages). Linear regression (linear regression model and its

importance in addressing real challenges, regression line, analysis of residual errors, prediction in regression analysis,

overview of multiple regression models, regression applications in network resources allocation, autoregressive

process, autoregression aplications in LAN type networks problematique, checking of regression dependencies using

statistical packages).

Learning Outcomes To introduce the students of informatics with main concepts in probability theory. To show the importance of

mathematical statistics in statistical analysis with real data.

Literature 1. Feller W., An Introduction to Probability Theory and Its Applications. John Wiley & Sons. Inc.

(http://alg.csie.ncnu.edu.tw/~ykshieh/b1.pdf)

2. Devroye L., Non-Uniform Random Variate Generation, New York, Springer-Verlag, 1986

(http://luc.devroye.org/rnbookindex.html).

3. Ching W.-K., Ng M.K., Markov Chains: Models, Algorithms and Applications, New York, Springer science and

Business Media Inc., 2006

4. Cheng L. and Marsic I., Lightweight Models for Prediction of Wireless Link Dynamics in Wireless/Mobile Local

Area Networks, Proceedings of IEEE 2002,pp. 98-101, 2002

5. Lowekamp B., O'Hallaron D. and Gross T., Direct Queries for Discovering Network Resource Properties in a

Distributed Environment, Cluster Computing, 4(3), pp. 281-291, 2000

6. Press W.H., Teukolsky S.A., Vetterling W.T. and Flannery B.P., Numerical Recipes in C, Cambridge, Cambridge

University Press, 1992, http://www.nrbook.com/a/bookcpdf.php

Subject area:

Status Course code: T120B127

Course title: Computer and Telecommunication Networks

Taught by: assoc. professor dr. Egidijus Paliulis




Lectures – 32 h


Self-study – 96 h

10-point scale Information Technology, Computer

Elements, Computer Architecture,

Operating Systems

Paper – 25 %

Colloquium – 25 %

Exam – 25 %

Reporting for laboratory work – 25 %

Subject content Introduction to computer network architecture, topology and classification. Familiarizing with network management

and its components, communication tools and their characteristics, circuit and packet switching, the OSI network

reference model. Analysing Application, Transport, Network, Link layers and the corresponding protocols (HTTP,

FTP, DNS, E-mail protocols, UDP, TCP, IP). Familiarizing with error detection and correction methods. Analysing

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network technology standards, hardware and software, addressing and routing. Providing knowledges about LAN

technologies and network reliability enhancement and protection measures. Familiarizing with network management

capabilities.

Learning Outcomes Internet and computer network technology, the basics of design and application of optimality

Network structure, packet and circuit switching, characteristics

Principles of network applications and protocols

Transport, Network and Link layer services in the basic principles and protocols

Network reliability, security principles and their application in practice

Literature 1. Kurose, James F. Computer networking : a top-down approach.

2. John R.Vacca. Wireless broadband networks handbook : 3G, LMDS & wireless Internet.

3. Kurose J.F., Ross K.W. Computer Networking: A Top-Down Approach 5/E, Addison-Wesley, 2010.

4. Stallings W. Data and Computer communications. 6th edition. Prentice-Hall, 1999.

5. Andrew Tannenbaum. Computer Networks, 3rd Edition, New Jersey: Prentice Hall PTR, 1996.

6. Annabel Z. Dodd. Essential Guide to Telecommunications, The, 5/E, Prentice Hall, 2012.

7. Mischa Schwartz. Telecommunication Networks: Protocols, Modeling and Analysis, Prentice Hall, 1987.

8. Lillian Goleniewski. Telecommunications Essentials, Second Edition: The Complete Global Source, 2/E, Addison-

Wesley Professional, 2007.

Subject area:


Course title: Management of Information System Projects

Taught by: assoc. professor dr. Asta Slotkiene




Lectures – 16 h


Self-study – 96 h

10-point scale Information systems design, realization of

information systems technology

Individual Homework – 25 %

Defence of laboratory work – 25 %

Exam – 50 %

Subject content Students are introduced to information systems planning, management and documentation. They learn how to prepare

the system development project, human resource and work plans paskistymo, is able to apply different control

strategies and the ability to develop the system design documentation. Students learn how to use project management

tools.

Learning Outcomes • Information systems design, realization of information systems technology

• Acquire skills to develop project plans and prepare project reports

• Able to calculate the cost of the project, monitor and evaluate the activities of the project risks

• The ability to select project management approach and effective use of project management tools.

Literature 1. Biafore, Bonnie. Successful project management : applying best practices and real-world techniques with

Microsoft Project / Bonnie Biafore. Sebastopol *Calif.+ : O’Reilly, 2011. xxiii, 433 p

Aditional literature

1. McManus, John. Information systems project management : methods, tools and techniques / John McManus,

Trevor Wood-Harper. Harlow : Financial Times/Prentice Hall, 2003. XVI, 294 p.

11

Subject area:


Course title: Data Security

Taught by: assoc. professor dr. Asta Slotkienė




Lectures – 48 h


Self-study – 96 h

10-point scale Programming skils, data structure and

operating system knowledge


Paper – 20 %

Exam – 50 %

Subject content Stdents learn about the information security objectives and the attacks against the security of information types.

During the semester they go deep into cryptography, its types, use of specific security objectives they possible

solutions to it. Students also learn about computer network security problems and their solutions, analyze the

applicable operating systems security solutions and their practical application, go deep into a computerized data

storage management and reproduction strategies. The unit has also trained with the European Union legal regulations

and standards governing security systems and criteria for evaluation.

Learning Outcomes -Information security targets, attacks against them and the types of solutions;

-Existing security standards and their uses;

-Cryptography type;

-The personal and public-key cryptography, hash functions and the use of the substance;

-Specific cryptographic algorithms, performance and security analysis;

-Social engineering attacks, and tips on how you should behave while surfing the Internet;

-Web security problems and their solutions;

-Internet security systems, gaps and opportunities for the exploitation of counter-measures used;

-Security source operating system problems and their solution methods and a tool for management.

Literature 1. Layton, Timothy P. Information security : design, implementation, measurement, and compliance / Timothy P.

Layton. Boca Raton, Fla. : Auerbach Publications, 2007

2. Whitman, Michael E. Hands-on information security: lab manual / Michael E. Whitman, Herbert J. Mattord, Dave

M. Shackleford. 2nd ed. Boston (Mass.) : Thomson, 2006

3. Shema, Mike. Hacking web apps *elektroninis išteklius+ : detecting and preventing web application security

problems / Mike Shema. Waltham, MA : Syngress, c201

Subject area:


Course title: Information Processing Systems

Taught by: assoc. professor dr. Egidijus Paliulis




Lectures – 32 h


Self-study – 96 h

10-point scale Information management, discrete



computing


Colloquium – 30 %

Exam – 20 %

Subject content Introducing to the characteristics of information, laws, classification and operations with the information. Consider

continuous and discrete information processing, its particularities and processing principles. Analyse discretization

(sampling), quantization, coding, filtering and correlation procedures. Introducing to the transformations of

information. Consider media information processing principles. Introducing to the architecture of information

processing system. Trained to understand working principles of continuous and discrete systems. Knowledge about

mathematical description of systems and its modeling capabilities. Introduction to artificial neural networks. Ability to

analyze and design information processing systems.

Learning Outcomes Description of continuous and discrete information, its visualization and presentation techniques;

Procedures of discretization (sampling), quantization, coding, filtering and correlation, its purpose and

performance;

Media formation methods, filtering, encryption, exclusion and identification.

Image transformation, segmentation and interpretation;

Components of basic information processing systems and its properties.

Artificial neural networks and its usage in information processing systems;

Design stages of information processing system, its techniques and particularity.

Literature 1. E. C. Ifeachor, B. W. Jervis. Digital signal processing : a practical approach

2. V. K.Ingle, J. G. Proakis. Digital signal processing using MATLAB

12

3. Diniz, E. Da Silva, S. Netto Digital Signal Processing.- Cambridge University Press

4. S. W. Smith The Scientist and Engineer's Guide to Digital Signal Processing.- California

5. Technical Publishing.

6. C.S.Burrus, J.H. McCleallan, A.V.Oppenheim, T.W.Parks, R.W. Schafer, H.W.Schuessler Computer-based

exxercises for signal processing using MATLAB. Pretice-Hall.

Subject area:


Course title: Business Information Systems

Taught by: lector Asta Drukteinienė




Lectures – 32 h


Self-study – 80 h

10-point scale Computer Networks, Databases, modeling

and development of web systems


Paper – 10 %

Exam – 30 %

Subject content Students are introduced to the possibilities of e-business shortcomings and advantages, analyzes the modules and their

application possibilities. Trained to select the appropriate hardware and software to address the challenges of e-

business. Familiarized with the specific software for the organization's supply chain management and customer

resource management, analysis of groupware systems. Deepened working with a database - an introduction to the

transactions and their characteristics. Also introduced to the business intelligence technologies. Students are trained to

design and implement the selected measures of e-business solutions through the transactions and data protection.

Learning Outcomes - E-business and e-commerce concept, models;

- Online payment methods;

- E-business architecture elements, graphical user interface design principles;

- Internet technologies for e-commerce;

- Transaction concept and properties;

- Business intelligence concept and technologies.

Literature 1. Jonathan Groucutt and Paul Griseri. Mastering e-Business.

2. Ian H. Witten, Eibe Frank. Data mining: practical machine learning tools and techniques.

3. Dave Chaffey. E-business and e-commerce management: strategy, implementation and practice

4. 5. IBM Redbook. SG24-6248-00. http://ibm.com/redbooks.6.

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