1. http://www.omg.org
2. Ian Sommerville, Software Engineering, Addison-Wesley 2005
3. Anneke Kleppe, Wim Bast, Jos B Warmer, MDA Explained, the Model Driven Architecture, Addison-Wesley 2003
4. Scott Berkun, The Art Of Project Management, O Reilly 2005
Work on project.
Written and oral examination
Slovak or English
Content prerequisities: Software engineering, UML, OOP
System, information system, information pyramid. Conceptualisation of information systems. ISO model of the architecture of an information system. Introduction to MDA, software development life cycle based on MDA. Model, metamodel, modelling language. Model transformation and marking models. Entity types. Relationship types. Cardinality constraints. Integrity constraints. Taxonomies. Domain events. Use cases. State transition diagrams.
To provide an overview of the modern methodologies of information system development. To introduce the fundamental principles of conceptual modelling of information systems.
1. SZELISKI, Richard. Computer Vision: Algorithms and Applications. London: Springer, 2010. Texts in computer science. ISBN 978-1-84882-934-3.
2. ŠONKA, MIlan, HLAVÁČ, Václav a Roger BOYLE: Image Processing, Analysis, and Machine Vision. Cengage Learning, 2014. ISBN 978-1-133-59360-7.
3. ŠONKA, Milan a Václav HLAVÁČ. Počítačové vidění: první česká kniha o zpracování digitalizovaných obrazů ; rozpoznávání objektů v obrazech ; analýza trojrozměrných a pohybujících se objektů ; příklady aplikací počítačového vidění. Praha: Grada, 1992. Nestůjte za dveřmi (Grada).
4. ŠIKUDOVÁ, Elena. Počítačové videnie: detekcia a rozpoznávanie objektov. Praha: Wikina, [2014]. ISBN 978-80-87925-06-5.
5. NAYAR, Shree. First Principles of Computer Vision. [online: https://fpcv.cs.columbia.edu/]
Rules of the final examination: two parts of the final exam - theoretical oral exam and disscussion on the practical assignment.
Rules to pass the subject: Get at least 50% from both parts of the final exam. The grade will be calculated based on the result from the final exam and assignments during semester.
Slovak, English.
1. Introduction to computer vision.
2. Color, grayscale, binary image. Thresholding, histogram, histogram equalisation. Mathematical morphology.
3. Noise, noise removal. Filtering, convolution.
4. Frequency domain filtering, Fourier transformation, convolution theorem, sinusoid, sampling, aliasing. Method of least squares, RANSAC. Hough transform for line and circle detection.
5. Edge detection, gradient, Laplacian, Canny edge detector, corner detection.
6. Image segmentation. Clustering (k-means, meanshift). Grabcut. Active contour method. Textures.
7. Features. Blob detection. SIFT detector and descriptor. Geometric transformations.
8. Recognition. Machine learning and neural networks in computer vision. Image preprocessing, image whitening, data augmentation. Face detection, Haar features.
9. Object tracking in image sequences, mixture of Gaussians, template matching, tracking.
10. Image formation - pinhole camera. Projection from 3D to 2D, external and internal matrix, camera calibration, epipolar geometry, depth of image.
To examine selected computer vision methods. To get an ability to implement chosen solutions and evaluate them on practical problems.
J. Barwise ed., Handbook of Mathematical Logic, North Holland 1977S. C. Kleene, Introduction to the Metamathematics, Van Nostrand 1952, ruský preklad Moskva 1957.
E. Mendelson, Introduction to Mathematical Logic, Van Nostrand 1963, ruský preklad Nauka Moskva 1976.
M. Davis, Hilbert's Tenth Problem is Unsolvable, Amer. Math. Monthly,1973, 233--269.
Ju.V. Matijasevič, Diofantovy Množestva, Usp. Mat. Nauk,<b> 27</b> (1972), 185--222
L. Bukovský, Algoritmicky neriešiteľné problémy, učebný text v elektronickej forma na sieti Novel, PF UPJŠ, Košice, 2003
Satisfiable understanding of basic concepts.
Slovak or English
1.--3. Axiomatic theories of natural numbers.
4.--6. Definibality of recursive functions.
7.--8. Tarski theorem on undefinability of truth in formalized arithmethic.
9. Godel incompletness theorem.
10. Algorithmic unsolvability of particular mathematical problems.
11. Non-existence of an algorithm for deciding the existence of a solution of Diophantine equations.
12. Reduction of problems and degrees of unsolvability.
To introduce the student into most important results about non-existence of an algorithm for solving given problem.
1) LPIC-1 Exam 102. LPI [online]. Canada: The Linux Professional Institute, 2021 [cited 2021-9-22]. Available from: https://learning.lpi.org/en/learning-materials/102-500/,
2) Linux - Documentation Project [online]. 4. Prague: Computer Press, 2007 [cited 2021-9-22]. Available from: https://i.iinfo.cz/files/root/k/LDP_4.pdf,
3) The LPIC2 Exam Prep [online]. Sue B.V. - Open Sourced, 2021 [cited 2021-9-26]. Available from: https://lpic2book.github.io/src/
To complete the course, students must successfully complete a project focused on configuring network services.
Slovak or English
Content prerequisites: Understanding basic concepts of operating systems, computer networks, and basic knowledge of the Linux shell (e.g., Bash) and PowerShell.
The course is not organized every year.
1) Linux Operating System Management (basic system tools for troubleshooting, system startup, network configuration),
2) File systems (general overview, RAID, LVM),
3) Container Management (Docker),
4) Web hosting services I (basic concept, APACHE),
5) Web hosting services II (SQL, HTTPS, security, NGINX),
6) File services (SAMBA, NFS, FTP),
7) Virtualization platforms (VMware, Proxmox),
8) Local network Management (routing, DHCP, firewall),
9) Remote device Management Automation (Ansible),
10) VPN, SSH, and Proxy,
11) Windows OS and Windows domain management,
12) Linux kernel,
13) Logging in Linux OS and Windows OS.
The learning outcome is to understand the theoretical and practical background of Windows and Linux operating systems and selected network services.
Hromkovič, J.: Algorithmics for Hard Problems, Introduction to Combinatorial Optimization, Randomization, Approximation, and Heuristics, Springer=Verlag 2004.
Hromkovič, J.: Communication Protocols - An Exemplary Study of the Power of Randomness. In: Handbook on Randomized Computing, P.Pardalos, S.Rajasekaran, J.Reif, J.Rolim, Eds., Kluwer Publ., 2001.
Hromkovič, J.: Design and analysis of ranodmized algorithms. Springer-Verlag, 2005.
Hromkovič, J.: Einführung in die algorithmischen Konzepte der Informatik, Teubner, 2001.
Motwani R. and Raghavan P.: Randomized Algorithms. Cambridge University Press 1995.
Mitzenmacher M. and Upfal P.: Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press 2005.
Continuous assessment is awarded on the basis of the quality of homework given in lectures and continuous written test.
Oral final exam.
Slovak or English
content prerequisites: basics of probability, basics of algorithms and data structures
1. Basic notions of Probability Theory.
2. Basic randomized computing models and its characterisations.
3. Las Vegas algorithms.
4. One sided error Monte Carlo algorithms.
5. Two sided bounded error Monte Carlo algorithms.
6. Two sided unbounded error Monte Carlo algorithms.
7. Classes of randomized algorithms with polynomial time complexity and relationships between them.
8. Optimisation problem, approximation algorithm, relative error, approximation ratio.
9. Special optimisation problems and approximation solutions.
10. Classification of optimisation problems based upon their approximations.
11. FPTAS.
12. PTAS.
13. TSP problem and its relaxations.
14. Unapproximability.
To learn basic conceptions of randomized algorithms and to classify the algorithms due to their error probability.
1. Forshaw, James. Windows Security Internals: A Deep Dive into Windows Authentication, Authorization, and Auditing. No Starch Press, 2024.
2. Yosifovich, Pavel, et al. Windows Internals, Part 1: System Architecture, Processes, Threads, Memory Management, and More. 7th ed., Microsoft Press, 2017. ISBN 978-0735684188.
3. Allievi, Andrea, et al. Windows Internals, Part 2. 7th ed., Microsoft Press, 2021. ISBN 978-0135462409.
4. Stallings, William. Computer Security: Principles and Practice. 4th ed., Pearson, 2017. ISBN 978-0134794105.
5. Tevault, Donald A. Mastering Linux Security and Hardening: A Practical Guide to Protecting Your Linux System from Cyber Attacks. 3rd ed., Packt Publishing, 2023. ISBN 978-1837630516.
To complete the course, students must:
1. Participate in seminars (20% of the total score).
2. Complete assignments (30% of the total score).
3. Pass a written exam (50% of the total score).
Slovak or English
Content Prerequisites:
Students are expected to have a basic understanding of operating systems, Windows and Linux operating systems, and database fundamentals.
The course is not offered every year.
1. Introduction to security of computer systems and data, Security concepts and principles. Threats, attacks, and assets. Security functional requirements.
2. Windows operating system security. Security configuration settings. Encryption. Technologies to prevent malware execution.
3. Introduction to active directory. Kerberos protocol. Best practices for secure Active Directory management. Group policies.
4. Standard attacks on Active Directory. Credential theft attacks (Pass-the-Hash, Kerberoasting). Attacks exploiting Kerberos vulnerabilities (Golden Ticket, Silver Ticket). Lateral movement using Active Directory (Pass-the-Hash, Pass-the-Ticket).
5. Linux operating system security. Encryption (GPG, LUKS). User account security. Kernel security. Process isolation.
6. Access control in Linux operating systems. Discretionary Access Control (DAC), access control lists. Mandatory Access Control (MAC), implementing SELinux and AppArmor.
7. Public Key Infrastructure (PKI). Certificate Authorities. Certificates (X.509 standard) and their lifecycle. Pretty Good Privacy (PGP).
8. Database system security. Concepts and security of relational databases. Data integrity in database systems. Access control.
9. Cloud data security. Analysis of the Microsoft Azure cloud environment. Security incident response in Azure.
10. Monitoring and logging events in Windows OS. Log structure and sources. Types of events.
11. Monitoring and Logging Events in Linux OS. Syslog. Journald.
12. Data Analysis in SIEM (Security Information and Event Management). Data collection and storage. Normalization, aggregation, and correlation of data.
Students will familiarize themselves with the concepts, methods, and tools to ensure the confidentiality, integrity, and availability of data and computer systems. The course provides detailed insights into securing data in Windows and Linux operating systems as well as in cloud environments. Upon completing the course, students will acquire the knowledge necessary for designing and implementing data and system security, including analyzing security events.
1. Kizza, Joseph Migga. Guide to Computer Network Security. 6th ed., Springer, 2024.
2. Van Oorschot, Paul C. Computer Security and the Internet: Tools and Jewels from Malware to Bitcoin. Springer, 2020.
3. Andress, Jason. Cyber Operations: Building, Defending, and Attacking Modern Computer Networks. Apress, 2019.
The requirements for completing the course are:
1. Active participation in exercises (20% of the total score),
2. Homework assignments (30% of the total score),
3. Written exam (50% of the total score).
Slovak or English
Content prerequisites: To complete the course, knowledge from the courses ÚINF/PSIN/15 - Computer network Internet and ÚINF/UIB1/21 - Introduction to information security is assumed.
The course is not organized every year.
1. Introduction to network security. Network situational awareness.
2. Data transmission security at the data link layer of the communication model, data flow management in local networks, switching, STP, virtualization, MACsec, multiprotocol switching.
3. Security of wireless networks and transmissions, WLAN networks, authentication mechanisms for WDS, data transmission over mobile networks (GSM, LTE).
4. Remote access to local networks, EAP authentication, RADIUS protocol, trust management, certificate usage, certification process, tasks of a certification authority.
5. Security of IPv4 and IPv6 network protocols, potential attacks and protection, IPsec protocol, security associations and policies, cryptographic information exchange.
6. Security of transport protocols TCP and UDP, TLS protocol, securing data in TLS sessions, tunneling, VPN.
7. Security aspects of Internet application-layer protocols, DNSSEC.
8. Security gateway architecture (firewall), demilitarized zone, filtering rules.
9. Security information and event management. Analysis and aggregation of network data.
10. Intrusion detection and prevention, honeypots. Approaches to data analysis.
11. Network monitoring. Flow analysis.
12. Analysis and prediction of situational awareness.
https://ics.upjs.sk/~jirasek/ops/
Understand the nature and importance of network security threats and methods for securing computer networks. Be able to identify security vulnerabilities and implement security measures, including the use of standard network security technologies such as firewalls, intrusion detection and prevention systems, and honeypots. Understand the principles and risks of security protocols like SSL and IPsec and know how to use them. Be capable of collecting and analyzing network security data and logs from network security devices.
1. BUZKOVÁ, K. 2006. Fitness jóga. Praha: Grada. 167 s.
2. ČECHOVSKÁ, I., MILEROVÁ, H., NOVOTNÁ, V. Aqua-fitness. Praha: Grada. 136 s.
3. EVANS, M ., HUDSON, J., TUCKER, P. 2001. Umění harmonie: meditace, jóga, tai-či, strečink. 192 s.
4. JARKOVSKÁ, H., JARKOVSKÁ, M. 2005. Posilováni s vlastním tělem 417 krát jinak. Praha: Grada. 209 s.
5. KOVAŘÍKOVÁ, K. 2017. Aerobik a fitness. Karolium, 130 s.
Completion: passed
Condition for successful course completion:
- active participation in line with the study rule of procedure and course guidelines
- effective performance of all tasks- aerobics, water exercise, yoga, Pilates and others
Slovak language
Brief outline of the course:
1. Basic aerobics – low impact aerobics, high impact aerobics, basic steps and cuing
2. Basics of aqua fitness
3. Basics of Pilates
4. Health exercises
5. Bodyweight exercises
6. Swimming
7. Relaxing yoga exercises
8. Power yoga
9. Yoga relaxation
10. Final assessment
Students can engage in different sport activities offered by the sea resort – swimming, rafting, volleyball, football, table tennis, tennis and other water sports in particular.
Content standard:
The student demonstrates relevant knowledge and skills in the field, which content is defined in the course syllabus and recommended literature.
Performance standard:
Upon completion of the course students are able to meet the performance standard and:
- perform basic aerobics steps and basics of health exercises,
- conduct verbal and non-verbal communication with clients during exercise,
- organise and manage the process of physical recreation in leisure time
1) Sokol, Pavol, Ladislav Bačo, and Tomáš Bajtoš. Digital Forensic Analysis I. Pavol Jozef Šafárik University in Košice, 2020. ISBN 978-80-8152-916-0.
2) Arnes, André. Digital Forensics. 1st ed., Wiley, 2017. ISBN 978-1119262381.
3) Fortuna, Andrea. The Little Handbook of Windows Memory Analysis: Just Some Thoughts About Memory, Forensics, and Volatility!. 1st ed., 2019. ISBN 978-1798027400.
4) Carrier, Brian. File System Forensic Analysis. 1st ed., Addison-Wesley Professional, 2005. ISBN 978-0321268174.
5) Carvey, Harlan. Investigating Windows Systems. 1st ed., Academic Press, 2018. ISBN 978-0128114155.
The conditions for completing the course are:
1) Participation in exercises (20% of the total score),
2) Completion of a project – digital forensic analysis of a specific case (40% of the total score),
3) Written theoretical test (40% of the total score).
Slovak or English
Content prerequisites: Understanding basic concepts of operating systems, computer networks, basic knowledge of the Linux shell (e.g., Bash) and PowerShell.
The course is not organized every year.
1) Introduction to digital forensic analysis. Forensic investigation and the process of digital forensic analysis.
2) Legal and ethical aspects of forensic analysis, digital traces, expert activities.
3) Incident response and live forensic analysis.
4) Identification and securing of digital traces.
5) Extraction of digital traces and working with forensic images.
6) Windows Operating System Analysis I (basic aspects, system registry, logs).
7) Windows Operating System Analysis II (forensic file system artifacts, forensic program execution artifacts).
8) Windows Operating System Analysis III (user data, forensic artifacts from web browsers and email clients).
9) Linux Operating System Analysis.
10) Forensic analysis of system memory.
11) Network forensic analysis.
12) Introduction to forensic analysis of mobile devices.
13) OSINT, evaluation and presentation of digital trace analysis.
https://csl.science.upjs.sk/#/predmety/
The learning outcome is to understand the technical, legal, and procedural methods and processes in digital forensic analysis, from identifying and securing digital traces to their use in addressing security incidents or in civil or criminal proceedings.
- Recommended literature that is included in the diploma thesis assignments
- Regulations for diploma thesis preparation
- Template for diploma thesis
regular consultaions with diploma thesis supervisor about the progress of diploma project development accornig to agreed schedule, regular consultations, study of available resources connected with the diploma thesis assignments, first results
Slovak or English
The subject is tied to the diploma thesis. The evaluation is based on student's approach to the diploma thesis and the partially achieved results.
Student understands the methods of investigation and he/she gains first results.
- Recommended literature that is included in the diploma thesis assignments
- Regulations for diploma thesis preparation
- Template for diploma thesis
regular consultaions with diploma thesis supervisor about the progress of diploma project development accornig to agreed schedule, regular consultations
Slovak or English
The subject is tied to the diploma thesis. The evaluation is based on student's approach to the diploma thesis and the partially achieved results.
Student has enough knowledge to prepare a theoretical part of the diploma thesis and for practical part based on the problem analysis and drawing conclusions.
The recommended literature is determined individually in accordance with the topic of the diploma thesis.
The diploma thesis is the result of the student's own work. It must not show elements of academic fraud and must meet the criteria of good research practice defined in the Rector's Decision no. 21/2021, which lays down the rules for assessing plagiarism at Pavol Jozef Šafárik University in Košice and its components. Fulfillment of the criteria is verified mainly in the process of supervision and in the process of thesis defense. Failure to do so is reason for disciplinary action.
Slovak and optionally English.
1. Elaboration of the diploma thesis in accordance with the instructions of the supervisor.
2, Presentation of the results of the diploma thesis before the examination commission.
3. Answering questions related to the topic of the diploma thesis within the discussion.
The diploma thesis demonstrates mastery of extended theory and professional terminology of the field of study, acquisition of knowledge, skills and competencies in accordance with the declared profile of the graduate of the study program, as well as the ability to apply them creatively in solving selected field problems. Student demonstrates the ability of independent professional work in terms of content, formal and ethical. Further details on the diploma thesis are determined by Directive no. 1/2011 on the basic requirements of final theses and the Study Regulations of UPJŠ in Košice for the 1st, 2nd and combined 1st and 2nd degree.
BILASOVÁ, V. – ANDREANSKÝ, E.: Epistemológia a metodológia vedy. Prešov: FF PU 2007.
FAJKUS, B.: Filosofie a metodologie vědy. Praha: Academia 2005.
BEDNÁRIKOVÁ, M. Úvod do metodológie vied. Trnavská univerzita: Trnava 2013.
DÉMUTH, A. Filozofické aspekty dejín vedy. Trnavská univerzita: Trnava 2013.
FEYERABEND, P.: Proti metodě. Prel. J. Fiala. Praha: Aurora 2001.
KUHN, T. S.: Štruktúra vedeckých revolúcií. Prel. Ľ. Valentová. Bratislava 1982.
Attendance: A student may have one unexcused absence in seminar at the most. Absence in more than one seminar must be reasoned and substituted by consultations. Conditions of continuous and final control: during the semester a student is continuously checked and assessed according to his/her activity. To be awarded the credits, a student must pass a test from knowledge obtained in the lectures and seminars. Results of the test will make up the final grade.
Slovak
• Falsificationism and critical realism by K. R. Popper.
• Development and critique of the Popper’s concept.
• Understanding the science development in the work by T. S. Kuhn.
• Methodology of scientific research programmes of I. Lakatos.
• Methodological anarchism of P. Feyerabend.
• W.V.O. Quine – the issue of relation between theory and empiricism.
The course is aimed at getting familiar with the basic issues of methodology and philosophy of science. Significant part will be devoted to presenting the main concepts of the philosophy of science in the 20th century and this aim will be achieved by reading the source and interpretive texts.
"- Schell, Jesse. The Art of Game Design: A Book of Lenses. 3. vydanie (Third Edition). Taylor & Francis Ltd, 2019.
ISBN-13: 9781138632059
- Wolverson, Herbert. Hands-on Rust Effective Learning Through 2D Game Development and Play. Pragmatic Programmers, LLC, 2021.
ISBN-13: 9781680508161
- Salen, Katie; Zimmerman, Eric (2004). Rules of Play: Game Design Fundamentals. Cambridge, MA: MIT Press.
ISBN-13: 9780262240452
- Nystrom, Robert (2014). Game Programming Patterns. Genever Benning.
ISBN-13: 9780990582908"
Demonstrating adequate mastery of the content standard of the subject during the interim and final assessment, the ability to formulate a problem in the acquired terminology and solve it within the framework of a project. Assignment during the semester. Project and oral defense.
slovak
1. Introduction to Game Development.
2. Fundamentals of Game Design.
3. Introduction to the Rust Programming Language for Games.
4. Working with the Bevy Game Engine.
5. Game Loop and Time Management.
6. Graphics and Rendering in Games.
7. Game User Interface.
8. Movement and Physics in Games.
9. Game Logic and Game States.
10. Audio and Visual Effects.
11. Working with Game Data.
12. Procedural Content Generation.
13. Game Testing and Debugging.
14. Final Project – Development of a Custom Game.
During the course, the student will master game design and development techniques through the iterative implementation of a turn-based game.
1. BERMAN,G.P., DOOLEN,G.D., MAINIERI, R., TSIFRINOVIC, V.I. Introduction to Quantum Computers. World Scientific, 2003.
2. GRUSKA, J. Quantum Computing. McGraw-Hill, 1999.
3. JOHNSON, G. A Shortcut Through Time: The Path to the Quantum Computer, Knopf 2003.
4. KITAEV, A.Y., SHEN, A.H., VYALYI, M.N. Classical and Quantum Computation. American Mathematical Society, 2002.
5. NIELSEN, M.A., CHUANG, I.L. Quantum Computation and Quantum Information. Cambridge University Press, 2000.
6. HIRVENSALO, M., Quantum Computing, Springer 2004
Successful completion of the subject is conditioned by proper acquisition of basic concepts, algorithms and models and demonstrating the ability to apply them creatively. The acquisition of knowledge takes place:
- continuously during the semester in the form of partial assignments,
- a written test during the semester,
- a written test at the exam,
- oral exam.
In order to receive an evaluation, it is necessary to obtain at least 50% of points from each of the three parts (assignments during the semester, written part of the exam, oral part of the exam). The detailed evaluation method is published in the AIS.
Slovak or english
Content prerequisites:
Linear algebra, Group theory, Probability theory, Theory of algorithms, Introduction to quantum computers.
1. Introduction to quantum quantum computers. Basics of classical complexity theory.
2. Boolean circuits and their basic properties.
3. Probability algorithms.
4. BPP class and probability testing.
5. Basic properties of circuits and Fermat's test.
6. Miller - Rabin's test and the position of the BPP class in the hierarchy of complexity models.
7. Introduction to quantum computing and mathematical foundations of quantum theory.
8. Spectral representation of self-adjoint operators.
9. Quantum states and Hilbert vector spaces.
10. Basic quantum operators and basic quantum algorithms.
11. Quantum teleportation, superdense coding and Grover's algorithm.
12. Fourier transformation.
13. Shor's algorithm.
By completing the subject, the student will get:
- knowledge of the classification and design of probabilistic algorithms,
- basic knowledge of the principles of quantum computers and their differences compared to classical computing models,
- knowledge and skills about the design and functioning of quantum computing and become familiar with the most well-known algorithms,
= basic quantum computer programming skills.
1. D. Salomon: Data Compression, The Complete Reference, Springer, 2004.
2. K. Sayood: Introduction to Data Compression, Morgan Kaufmann, 2012.
Homeworks, active participation in laboratory exercises, midterm test.
Final written exam, oral examination.
Slovak or English
1. Formal model of coding and information transfer, compression ratio, criteria of uniquely decodable codes, block and prefix lossless codes.
2. Coding with known distribution of probabilities of occurrences of input characters, relation to entropy, Huffman construction, adaptive variants.
3. Arithmetic coding, integer, binary, adaptive versions, advantages and disadvantages of statistical codes.
4. Context coding, prediction methods, JBIG, JPEG-LS standards, PPM.
5. Dictionary compression methods, LZ77, LZW, use of transformations, BWT, ACB, dynamic Markov chains.
6. Principles of lossy compression, RD function, probabilistic and physiological models for efficient compression. Uniform and non-uniform scalar quantization, adaptive versions.
7. Vector quantization, optimization according to distribution function, compressors and expanders.
8. Differential techniques, prediction methods, adaptive quantization with prediction, DPCM method, use in audio and video coding.
9. Transformations in lossy coding, orthonormal representations, component analysis, two-dimensional transformations.
10. Discrete Fourier transform, use in image compression, JPEG encoder.
11. Subband filters, signal decomposition, signal synthesis from subbands, use in sound compression, psychoacoustic models, MP3, AAC coding.
12. Wavelet transforms, EZW encoder, use in audio and video coding.
13. Video compression, MPEG standards, adaptive algorithms for streaming and video conferencing.
https://ics.upjs.sk/~jirasek/kmu/
Understand the principles of lossy compression algorithms. Be able to apply different methods of quantization, prediction and difference procedures in lossy image and sound compression algorithms. Understand the JPEG and MPEG compression standards.
1. G. Chartrand, O.R. Oellermann: Applied and Algorithmic Graph Theory, McGraw-Hill, Inc. New York 1993.
2. J.L. Gross, J. Yellen: Graph Theory and Its Applications, Chapman & Hall/CRC 2006.
3. D. Jungnickel: Graphs, Networks, and Algorithms, Springer-Verlag Berlin 2005.
4. J. Plesník: Grafové algoritmy, Veda Bratislava 1983.
The evaluation consists of projects (30 points) and an exam (70 points). The semester projects consist of the elaboration of computer programs that return the optimal solution or acceptable approximations of the optimal solutions, of selected graph problems given by suitable representations.
Slovak
Basic notions from graph theory.
Introduction to algorithms and complexity. Basic types of algorithms - sorting algorithms, search algorithms, greedy algorithms. NP-completeness.
Trees, spanning trees and rooted trees. Depth first search, breadth first search. Generating of all spanning trees of a graph, number of spanning trees. Minimum spanning tree problem (Kruskal, Prim, and Boruvka´s algorithms).
Distance in graphs. Shortest path problem in (non)oriented (weighted) graphs (various types of algorithms) and other variations of this problem.
Introduction to network analysis, critical path method.
Flows in networks, the max-flow min-cut theorem and related concepts.
Matchings, maximum matchings in bipartite and general graphs, finding a matching with maximum weight in bipartite graphs.
Location of centers in graphs, finding a center, absolute center, and a median of a graph.
Eulerian graphs and Chinese postman's problem.
Hamiltonian graphs, Travelling salesman problem and approximation algorithms for TSP.
Understanding of basic graph algorithm, the close connection between the theoretical and algorithmic aspects of discrete mathematics, ability to understand how selected algorithms can be derived from mathematical statements, ability to prove the correctness of algorithms.
1. JUNGER, J. et al. Turistika a športy v prírode. Prešov: Fakulta humanitných a prírodných vied PU v Prešove. 2002. 267s. ISBN 80-8068-097-3.
2. PAVLÍČEK, J. Člověk v drsné přírodě. 3. vyd. Praha: Práh. 2002. ISBN 8072520598.
3. WISEMAN, J. SAS: příručka jak přežít. Praha: Svojtka & Co. 2004. 566s. ISBN
8072372807.
Completion: passed
Condition for successful course completion:
- active participation in line with the study rule of procedure and course guidelines,
- effective performance of all the tasks defined in the course syllabus
Slovak language
Brief outline of the course:
1. Principles of conduct and safety in the movement in unfamiliar natural environment
2. Preparation and guidance of a hike tour
3. Objective and subjective danger in the mountains
4. Principles of hygiene and prevention of damage to health in extreme conditions
5. Fire building
6. Movement in the unfamiliar terrain, orientation and navigation
7. Shelters
8. Food preparation and water filtering
9. Rappelling, Tyrolian traverse
10. Transport of an injured person, first aid
Content standard:
The student demonstrates relevant knowledge and skills in the field, which content is defined in the course syllabus and recommended literature.
Performance standard:
Upon completion of the course students are able to meet the performance standard and should:
- acquire knowledge about safe stay and movement in natural environment,
- obtain theoretical knowledge and practical skills to solve extraordinary and demanding situations connected with survival and minimization of damage to health,
- be able to resist and face situations related to overcoming barriers and obstacles in natural environment,
- be able implement the acquired knowledge as an instructor during summer sport camps for children and youth within recreational sport.
1. Colin Boyd, Anish Mathuria: Protocols for Authentication and Key Establishment, Springer, 2020
2. Douglas R. Stinson, Maura B. Paterson: Cryptography: Theory and Practice, Fourth Edition, Chapman & Hall/CRC, 2018
3. Paul C. van Oorschot: Computer Security and the Internet: Tools and Jewels, Springer, 2020
4. Peter Ryan, Steve Schneider: Modeling and Analysis of Security Protocols, Addison-Wesley, 2001
The conditions for completing the course are: active participation in exercises, homework assignments, presentation of a selected topic in the seminar, and a final test.
Slovak or English
Content prerequisities: understanding of fundamental cryptographic concepts and primitives (as taught in the course KRS/15 or in the scope of the textbook "Understanding Cryptography" by Christof Paar and Jan Pelzl).
The course is not organized annually.
1. Security goals and tools used in modern cryptography.
2. Overview of cryptographic tools, symmetric and asymmetric cryptography, cryptographic hash functions, digital signatures, certificates.
3. Authentication, authentication protocols, use of trusted centers, examples, and well-known attacks.
4. Formal model of protocol security, protocol idealization, analysis using modal logics (BAN, GNY), possibilities and limitations of proofs.
5. Key distribution protocols, possibilities for automatic error detection, formalization of protocols using process calculus, model verification in spi calculus.
6. Modeling attackers and their use in automated environments for security verification, Scyther and Tamarin tutorial.
7. Key agreements over unsecured channels, use of ephemeral keys in IKEv2 and TLS protocols, key agreement using passwords.
8. Key agreements among multiple participants, conference keys, key agreements using quantum cryptography.
9. Anonymous transfers, secret voting, interactive zero-knowledge proofs.
10-12. Seminar on current issues in cryptographic protocol security (electronic voting, RFID security, security in 3G, 4G, and 5G networks, electronic payments, digital money, blockchain, electronic auctions, secure protocols for social networks, etc.).
https://ics.upjs.sk/~jirasek/krp/
Understanding the challenges of designing secure cryptographic protocols for authentication and key management. Knowledge of methods to compromise them and the ability to apply proof techniques to verify their correctness. Proficiency with some tools for automated verification. Understanding and applying advanced cryptographic techniques in various application areas - signature schemes, electronic banking, electronic voting. Familiarity with current issues in the implementation of cryptographic protocols.
https://ics.upjs.sk/~krajci/skola/vyucba/ucebneTexty/LAD-presentation.pdf
Satisfiable understanding of basic concepts.
Slovak
1.-3. Basic concepts of logic – a symbol, a term, a formula, an interpretation
4. Formalization of a table and a database
5. Conjunctive queries
6. Conjunctive calculus
7. Relations between conjunctive calculus and conjunctive queries
8.-10. Relational algebra
11.-12. Relations of different models of databases
Ability to correctly formalize databases.
1. JUNGER, J. et al. Turistika a športy v prírode. Prešov: FHPV PU v Prešove. 2002. ISBN 8080680973.
Internetové zdroje:
1. STEJSKAL, T. Vodná turistika. Prešov: PU v Prešove. 1999.
Dostupné na: https://ulozto.sk/tamhle/UkyxQ2lYF8qh/name/Nahrane-7-5-2021-v-14-46-39#!ZGDjBGR2AQtkAzVkAzLkLJWuLwWxZ2ukBRLjnGqSomICMmOyZN==
Completion: passed
Condition for successful course completion:
- active participation in line with the study rule of procedure and course guidelines
- effective performance of all tasks: carrying a canoe, entering and exiting a canoe, righting a canoe, paddling
Slovak language
Brief outline of the course:
1. Assessment of difficulty of waterways
2. Safety rules for rafting
3. Setting up a crew
4. Practical skills training using an empty canoe
5. Canoe lifting and carrying
6. Putting the canoe in the water without a shore contact
7. Getting in the canoe
8. Exiting the canoe
9. Taking the canoe out of the water
10. Steering
a) The pry stroke (on fast waterways)
b) The draw stroke
11. Capsizing
12. Commands
Content standard:
The student demonstrates relevant knowledge and skills in the field, which content is defined in the course syllabus and recommended literature.
Performance standard:
Upon completion of the course students are able to meet the performance standard and:
- implement the acquired knowledge in different situations and practice,
- implement basic skills to manipulate a canoe on a waterway,
- determine the right spot for camping,
- prepare a suitable material and equipment for camping.
1. Company literature of SIEMENS. Available on-line: <http://www.siemens.com>
2. Company literature of SYNGO. Available on-line: <http://www.syngo.com>
Conditions for continuous evaluation: activity on excercises, homeworks, test
Conditions for the final evaluation: Oral and written exam
Slovak or English
Content prerequisities: foundations of software engineering
Introduction to medical informatics. Clinical workflow. Healthcare services. SW projects in the medical domain. Development methodologies in SW projects in the medical domain. Agile methods in medical projects, eXtreme programming, fast methods versus robust methods. Development tools in SW projects in the medical domain.
To present an application of computer science in medicine domain with emphasis on the specific conditions for so-called safety-relevant domain.
1. Krajči S., https://ics.upjs.sk/~krajci/skola/vyucba/ucebneTexty/logika-stromy.pdf
2. Goldstern M., Judah H.: The Incompleteness Phenomenon, A New Course in Mathematical Logic, A K Peters, Wellesley, Massachusetts, 1995
Knowledge of studied notions will be evaluated.
Slovak
1.--2. Boolean algebra
3.--4. Filters and ultrafilters
5.--6. Rasiowa-Sikorski's theorem
7. Safe substitution
8. Lindenbaum-Tarski's algebra
9.--11. Syntactical interpretation
12. Completeness
Understanding of basic concepts of mathematical logic.
Information sources recommended within individual profile subjects.
Appropriate knowledge and competencies from the profile subjects of the study program, demonstrating the ability to synthesize the acquired knowledge and procedures and apply them to the problems of computer science.
Slovak language or English language
The state exam is focused on one of the following areas:
1. Computer systems and networks, security of computer systems.
2. Information and knowledge systems.
3. Computational complexity, computational models.
4. Methods of artificial intelligence.
The examination may also include the basic principles and relationships between the topics of compulsory subjects and possible connections between these topics and the final thesis.
Verification of acquired student competencies in accordance with the graduate profile.
1. Y. Bengio: Learning Deep Architectures for AI, Foundations and Trends in ML, Vol. 2, No. 1 , 2009, pp. 1-127 ##
2. I. Goodfellow, Y. Bengio and A. Courville: Deep Learning, MIT Press book, 2016, ISBN-13: 978-0262035613
https://www.deeplearningbook.org/ ##
3. M. H. Hassoun: Fundamentals of artificial neural networks. MIT Press, Cambridge, 1995. ##
4. J. Hertz, A. Krogh, R.G. Palmer: Introduction to the theory of neural computation, Addison-Wesley, 1991. ##
5. V. Kvasnička a kol.: Úvod do teórie neurónových sietí, IRIS, Bratislava, 1997. ##
6. P. Sinčák, G. Andrejková: Neurónové siete. I. diel: Dopredné siete, II. diel: Rekurentné a modulárne siete, Košice, 1997. ##
7. J. Šíma, R. Neruda: Teoretické otázky neuronových sití, Matfyzpress, MFF UK, Praha, 1996. ##
8. F. Scarselli, M. Gori, Ah Ch. Tsoi, M. Hagenbuchner, and G. Monfardini: The Graph Neural Network Model. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 1, JANUARY 2009 ##
Successful realization of a project focused on the applications of neural networks. Successful completion of two written tests at 60% which are focused on various architectures of neural networks and the connections with other areas of computer science - automata, fuzzy logic. Demonstration of knowledge focused on neural network methods and their application in the exam.
Slovak or English
For ERASMUS students:
It is necessary to know a model of artificial neurons, its computation and its setting, layered neural networks and backpropagation training algorithm.
1. Motivational examples. Mathematical model of neuron and neural network. Perceptrons. Linear
separable objects, adaptation process (learning), perceptron convergence, multiple perceptrons.
2. Computational power of single input neural networks, neuromata. Simulation of automata using
neural networks.
3. Classical layer neural networks, hidden neurons, adaptation process (learning), feedback method
backpropagation and its variants.
4. Recurrent neural networks, algorithm for training recurrent networks. Examples of use.
5. Self-organization of neural networks and Kohonen neural networks, learning algorithm, use.
6. Networks with local neurons, RBF networks, networks with semi - local units. RBF approximations
networks.
7. Written test I. Neuromat for regular language. neural network to deterministic finite state automaton, recurrent backpropagation algorithm and its applications, Kohonen ane RBF neural networks.
8. Convolutional neural networks. Basic knowledge of convolution. Convolutional neural networks for image processing.
9. Deep neural networks and their use.
10. Graph neural networks, structure, learning and applications.
11. Deductive systems of fuzzy logic. Fuzzy neural networks and their use. Fuzzy controller.
12. Universal approximation using neural networks, Kolmogorov theorem. Approximation properties layered neural networks.
13. Solving practical problems using neural networks.
14. Written test II. Convolution and convolutional neural networks, deep neural networks, graph neural networks, constructtion of fuzzy regulator, Kolmogorov theorem and idea of its proof.
Knowledge of basic paradigms of neural networks. Knowledge about applications of neural networks in various fields. Ability to assess the applicability of neural networks in solving algorithmic problems.
- Jurafsky, D., Martin, J.H.: Speech and Language Processing.
- Goldberg, Y.: Neural Network Methods for Natural Language Processing.
- Vaswani, A. et al.: Attention Is All You Need (transformer paper).
- Wolf, T. et al.: Transformers for NLP: The Illustrated Guide.
- Mikolov, T.: Word2Vec Tutorial / rôzne články o embeddingoch.
Student assessment is based on a combination of continuous assignments and a practical project (50%).
slovak or english
1. Introduction to NLP, history and overview of NLP, traditional approaches vs. neural networks
2. Tokenization and preprocessing, basic techniques
3. Advanced tokenizers (BPE, WordPiece, SentencePiece)
4. Embeddings and contextual representations
5. Language models
6. Transformers: transformer architecture, self-attention mechanism, positional encoding
7. Training and fine-tuning of transformers
8. Training and fine-tuning of transformers II
9. Fine-tuning for specific tasks (transfer learning)
10. Datasets in NLP, creation of custom datasets
11. Big Data in NLP, processing of large datasets, use of IterableDataset and batching
12. Applications of transformers: text classification, question answering (Q&A), machine translation
After successful completion of the course, the student:
- understands the theoretical foundations of natural language processing and transformers,
- is able to prepare text data for NLP tasks (tokenization, normalization),
- is capable of creating and annotating datasets for NLP applications,
- can design a training pipeline and fine-tune transformer models for a specific problem,
- applies models to practical tasks such as text classification, question answering (QA), and machine translation.
To familiarize students with biologically and physically inspired optimization, simulation and prediction techniques. To expand students' creativity and programming skills by applying heuristic techniques in solving applied problems.
Hartmann, A. K., Rieger, H., Optimization Algorithms in Physics, Wiley, 2002
Reeves, C. R., Rowe, J. E., Genetic Algorithms: Principles and perspectives, Kluwer, 2003
Mitchell, M., Complexity. A Guided Tour, Oxford University Press, 2009
Solé, R. V., Phase Transitions, Princeton University Press, 2011
Ilachinski, A., Cellular Automata. A Discrete universe, World Scientific, 2002
Haykin, S., Neural Networks. A Comprehensive Foundation, Prentice-Hall, 1999
Actual literature and data related to problem sets
Monitoring progress in solving applied projects.
Oral examination (50%), results and quality of the
personal presentation of the projects (50%).
Monitoring progress in solving applied projects. From given set of problems, the student must pick 1 to 3 projects and develop functioning implementation of the solution in form of computer program. In case of more challenging problems, collaborative work of students is acceptable, but each student must be able to present her/his individual contribution.
English language is essential for students as "lingua franca" for the latest advancements and applications of optimization techniques.
The subject is taught using direct contact form. Should the epidemiological situation (or other relevant circumstances) mandate, the distant form will be used, preferentially using MS Teams learning environment.
1. Fundamentals terms and definitions of optimization theory. Physical laws as optimization tasks. Variational principle.
2. Model optimization problems. Basic types of objective functions. Classification of optimization methods. Computational scaling of optimization methods. Big O notation. Parallelization, Metcalf's law, Amdahl's bottleneck.
3. Exhaustive search, Gradient-based optimization techniques.
4. Evolutionary algorithms. Canonical Genetic algorithm. Genetic algorithms as Markov processes. Statistical Mechanics description of Genetic Algorithms.
5. Monte Carlo simulation and simulated annealing. Metropolis algorithm and statistics of sampling in solution space.
6. Swarm optimization. Ant algorithms.
7. Cellular Automata and their applications in simulations of complex systems.
8. data structures and representation of solution space and optimization problems. Compression of information and symmetry. Manifolds.
9. Generators. grammars and languages. Genetic programming. AST and operations on AST representation of programs.
10. Fractals. Lindenmayer systems. Life-like and agent-based models.
11. Evolutionary games. Evolution of cooperation.
12. Fundamentals of Neural Networks. Stochastic gradient optimization.
examination (50%), quality of the project (50%)
To familiarize students with biologically and physically inspired optimization, simulation and prediction techniques. To expand students' creativity and programming skills by applying heuristic techniques in solving applied problems.
Upon successful completion of course, student shall possess knowledge about most typical non-traditional optimization techniques, as well as practical experience of solving concrete problems.
examination
1.HARRISON G.: Next Generation Databases: NoSQL, NewSQL, and Big Data. Apress, 2015. ISBN 978-1-4842-1330-8.
2. HILLS T.: NoSQL and SQL Data Modeling: Bringing Together Data, Semantics, and Software. Technics Publications, 2016. ISBN 978-1-6346-2109-0
Conditions for continuous evaulation: Active attendance at seminars.
Conditions for the final evaluation: Implementation and defense of final project.
Slovak or English
Content prerequisities: programming at PAZ1c level - unrestanding of storage layer principles, besics of relationa databases (SQL language)
1. Big data, types of NoSQL databases.
2. Data representation formats
3. Key-value databases.
4. Column-oriented databases.
5. Graph databases.
6. Document-oriented databases.
https://nosql.ics.upjs.sk/
Know properties of different kinds of NoSQL databases, have an practical experience with given NoSQL databases (Redis, Cassandra, Neo4j, Mongo DB) from program code. Gain skills to identify the appropriate kind of NoSQL database for given purpose.
1. R. RAMAKRISHNAN, J. GEHRKE: Database Management Systems, McGraw Hill Higher Education, 2003
2. A. SILBERSCHATZ, H. F. KORTH, S. SUDARSHAN: Database system concepts, McGraw Hill Higher Education, 2006
Conditions for the final evaluation:
final test
Slovak or English
Content prerequisities: SQL language (DBS1a), basics of programming (PAZ1a)
1. Data representation, disk and file organization,
2. Tree-based indexing methods B+tree, R-tree,
3. Working with low-level classes to working with files
4. Creation of clustered and unclustered indexes
5. Hash-based indexing methods, external sorting,
6. Enumeration of relational operators, query optimization,
7. Case study: practical DB optimalization
8. Transaction management,
9. Crash recovery
10. Parallel databases, evaluation of relational operators in parallel databases
11. Distributed databases, evaluation of relational operators in distributed databases, database security and data consistency, recovery management in distributed database, distributed trasactions, distribution of table replicas
https://pdb.ics.upjs.sk/
To understand the principles of database management systems. To be able to use the knowledge when solving optimization problems over big data and managing parallel and distributed databases.
1. MEŠKO, D., KATUŠČÁK, D. Akademická príručka. 1. vyd. Vydavateľstvo Osveta : Martin, 2004. 316 s. ISBN 80-8063-150-6
2. ISO 690: 1987 Documentation - Bibliographic references. Content, form and structure.
3. ISO 2145: 1978 Documentation - Numbering of divisions and subdivisions in written documents.
4. Eco, U.: Jak napsat diplomovou práci, z taliančiny Come si fa una tesi di laures, Milano, 1977, Olomouc, Votobiax.
5. Professional and scientific literature related to the diploma thesis according to the recommendation of the thesis supervisor.
Evaluation of the student's paper with a focus on the issue of the diploma thesis.
Evaluation of the achieved results of the student during the semester on the diploma thesis on the basis of his / her report and the created diploma website.
Slovak or English
The seminar is oriented to problems prospective to preparations of Diploma theses.
https://ics.upjs.sk/~jirasek/dp/
To inform students about areas of informatics they are suitable to work in diploma theses. In the end of semester students have to prepared themes of diploma theses, goals and recommended study literature.
1. J. JáJá: An Introduction to Parallel Algorithms, Addison-Wesley, 1992, ISBN 0-201-54856-9
2. P. Sanders, K. Mehlhorn, M. Dietzfelbinger, R. Dementiev: Sequential and Parallel Algorithms and Data Structures, Springer, 2019
3. Sukumar Ghosh: Distributed Systems and Algorithms (Second Edition), CRC Press 2014
4. M. Raynal: Distributed Algorithms for Message-Passing Systems, Springer, 2013
5. Gerard Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2001
Home assignments, class project from tutorials, midterm written exam. Final written and oral exam.
Slovak or English
Content prerequisities: basic of concurrent programming, basic of operating system principles
Parallel architectures, parallel computational model, access to shared memory. Basic algorithms, scaling, optimality. Effective methods of parallel search and sorting. Working in a GPU environment. Distributed computational model, communication protocols, characteristics of distributed systems. Intercomputer communication, distributed synchronization algorithms, transactions, termination and deadlock detection. Consistency issues with distributed memory sharing. Distributed application environment. Reliable calculations in an environment with errors.
Understand the principles, basic problems and algorithms of parallel programming. Be able to implement synchronization procedures and manage and use interprocess communication. Master the basics of GPU programming. Understand the differences between parallel and distributed computational models. Master basic distributed algorithms and know how to implement them. Understand the problems of creating a distributed system environment and know how to solve them. Be able to use distributed environments in practical applications.
The study or technical literature is determined individually depending on the focus of the internship by the internship supervisor.
Conditions for continuous evaluation:
Active participation in the selected type of internship based on the instructions given by the internship supervisor.
Conditions for the final evaluation:
Evaluation of the student's approach to the internship and the work performed in the internship by the internship supervisor.
Slovak or English
The exact content of the internship is specified by the internship supervisor. Students choose from a menu of topics presented by the course administrator. Typical topics of practice are:
1. assistance in the realization of exercises for yunger studnets, providing feedback to students on submitted homeworks
2. assistance in the installation and maintenance of computer and network infrastructure at UPJŠ
3. realizations of courses for working with specific software
4. creation of overviews from freely available sources
Experiences with the implementation of a selected type of internship.
The study or technical literature is determined individually depending on the focus of the internship by the internship supervisor.
Conditions for continuous evaluation:
Active participation in the selected type of internship based on the instructions given by the internship supervisor.
Conditions for the final evaluation:
Evaluation of the student's approach to the internship and the work performed in the internship by the internship supervisor.
Slovak or English
The exact content of the internship is specified by the internship supervisor. Students choose from a menu of topics presented by the course administrator. Typical topics of practice are:
1. assistance in the realization of exercises for yunger studnets, providing feedback to students on submitted homeworks
2. assistance in the installation and maintenance of computer and network infrastructure at UPJŠ
3. realizations of courses for working with specific software
4. creation of overviews from freely available sources
Experiences with the implementation of a selected type of internship.
1. MURDOCH, Don. Blue Team Handbook: Incident Response Edition: A condensed field guide for the Cyber Security Incident Responder. South Carolina, United States: CreateSpace Independent Publishing Platform, 2014. ISBN 978-1500734756, 2. ANSON, Steve. Applied Incident Response. New York, United States: Wiley, 2020. ISBN 978-1119560265, 3. ROBERTS, Scott. Intelligence-Driven Incident Response: Outwitting the Adversary. Sebastopol, California, United States: O'Reilly Media, 2017. ISBN 978-1491934944.
The condition for passing the course are homeworks (50% of the total number of points) and the final practical task (50% of the total number of points).
Slovak or English
Content prerequisites: basic knowledge in the field of information security, basics of working with the Linux operating system, basic knowledge of computer networks.
1. Introduction to computer security incident hadling and response, 2. The process of handling and response to computer security incidents and computer security incident response teams, 3. Legal aspects of the computer security incidents handling, 4. Preparing for the security incidents handling and the first response, 5. Introduction to digital forensic analysis, 6. Incident handling and response to computer security incidents in the field of malware, 7. Incident handling and responseto computer security incidents in the field of email communication, 8. Incident handling and response to network security incidents I., 9. Incident handling and response to network security incidents II., 10. Incident handling and responseto computer security incidents in the field of web applications I., 11. Incident handling and responseto computer security incidents in the field of web applications II., 12. Incident handling and response to cloud security incidents, 13. Incident handling and responseto cyber security incidents in the field of insiders, 14. Final assignment.
The result of the education is an understanding of the basic approaches to solving computer security incidents from procedural and legal requirements to ways of identifying the security incident and the method of its technical solution.
According to the topic of diploma thesis.
Presentation of the analysis of the assignment and the proposal of the solution of the diploma thesis tasks, editing of the web page, written elaboration of the analysis and design of the solution.
Slovak or English
The seminar serves for control, public presentation and defense of partial results at DP. In order to be awarded the credits, it is necessary to successfully complete the presentation of the analysis of the assignment and the achieved results, including the proposal of specific steps of the further solution procedure, update the presentation of the diploma thesis on the network and prepare a written analysis and proposal for solving the assigned problem in the range of 15-20 pages.
https://ics.upjs.sk/~jirasek/dp/
Monitoring and public presentation of work done so fare on thesis preparation
According to the topic of diploma thesis
Presentation of achieved results on the diploma thesis, web page modification, written processing of results.
Slovak or English
Every thesis has a compulsory theoretical part and may also contain a software part. To gain recognition, the following is necessary: a detailed compilation of studied literature (a minimum of thirty pages) and at least twenty pages of text containing the candidate's own views of the problem area, possible research goals, own results are welcome (if the thesis is purely theoretical, this will be judged more strictly). For the SW part: a tested implementation (must conform to user requirements, help and user friendly user interface not necessary at this stage) and access to source texts.
For both parts there will be an oral presentation and discussion.
https://ics.upjs.sk/~jirasek/dp/
Monitoring and public presentation of work done so fare on thesis preparation
According to the topic of diploma thesis.
Presentation of the achieved results of the diploma thesis with a discussion. Final editing of the web page.
Slovak or English
The seminar serves for control, public presentation and defense of DP results. In order to be awarded the credits, it is necessary to complete a public presentation of the work associated with the discussion, together with the final presentation of the presentation on the Internet.
https://ics.upjs.sk/~jirasek/dp/
Monitoring and public presentation of work done so fare on thesis preparation
Seminar is connecte to the lecture UGR Introduction to computer graphics. In seminar form students presents actual theoretical and implementation problems. Main goal in interest is oriented to quick algorithms of computer graphics, geometric modelling and realistic drawing of scenes.
Knowledge from the lecture UGR and good programmers experience are supposed.
https://ics.upjs.sk/~rkb/?cmd=spg1
The sources of information depend on the selected project.
Active participation in the project. Participating in regular project team meetings. Presentation of the results achieved in solving a specific problem. Uploading a software work. Preparation of materials for the promotion of the final work.
Slovak or English
Content prerequisities: advanced programming skills.
The course is realized as part of "Živé projekty" (Live projects) in cooperation with the Technical University of Košice and several software companies. Students work in a team of 4-5 members to develop, test and present a software product under the guidance of a mentor from a university or a software company.
1. Team creation and project selection takes place at the beginning of October
2. Students meet with the project mentor on a weekly basis and continuously work on the creation of a software product
3. Around mid-January, students submit a video with a short presentation of the project
4. At the beginning of February, the project presentation takes place. The best teams are awarded with material prizes.
Learn how to work on a larger software part at all stages of its life cycle. Be able to analyze and explicitly express user requirements, precisely specify the task, design a solution and evaluate alternatives. Implement and test an effective and correctly designed solution. Learn to keep detailed documentation and present the results of the work in writing and in public. Learn to work together in a development team, share work effectively and exchange ideas.
The sources of information depend on the selected project.
Active participation in the project. Participating in regular project team meetings. Presentation of the results achieved in solving a specific problem. Uploading a software work. Preparation of materials for the promotion of the final work.
Slovak or English
Content prerequisities: advanced programming skills.
The course is realized as part of "Živé projekty" (Live projects) in cooperation with the Technical University of Košice and several software companies. Students work in a team of 4-5 members to develop, test and present a software product under the guidance of a mentor from a university or a software company.
1. Team creation and project selection takes place at the beginning of October
2. Students meet with the project mentor on a weekly basis and continuously work on the creation of a software product
3. Around mid-January, students submit a video with a short presentation of the project
4. At the beginning of February, the project presentation takes place. The best teams are awarded with material prizes.
Learn how to work on a larger software part at all stages of its life cycle. Be able to analyze and explicitly express user requirements, precisely specify the task, design a solution and evaluate alternatives. Implement and test an effective and correctly designed solution. Learn to keep detailed documentation and present the results of the work in writing and in public. Learn to work together in a development team, share work effectively and exchange ideas.
1. Scientific books and papers related to the selected field of computer science.
2. Book and online resources describing principles and use of selected software solutions
Presentation of scientific papers and software solutions in the selected field of computer science. Active participation in discussions about possible solutions to selected problems.
Slovak or English
Presentation of scientific papers from a selected field of informatics.
Practical presentation of current software solutions (libraries, frameworks) that are not included in study programs.
Discussions on possible solutions to selected problems in computer science.
The schedule of presentations will be published after the first meeting on the subject's website or other agreed location.
Student train the ability to study and present the principles and use of new software solutions to colleagues or to study and present the results of scientific results published in journals and conference papers.
1. Scientific books and papers related to the selected field of computer science.
2. Book and online resources describing principles and use of selected software solutions
Presentation of scientific papers and software solutions in the selected field of computer science. Active participation in discussions about possible solutions to selected problems.
Slovak or English
Presentation of scientific papers from a selected field of informatics.
Practical presentation of current software solutions (libraries, frameworks) that are not included in study programs.
Discussions on possible solutions to selected problems in computer science.
The schedule of presentations will be published after the first meeting on the subject's website or other agreed location.
Student train the ability to study and present the principles and use of new software solutions to colleagues or to study and present the results of scientific results published in journals and conference papers.
1. ANTHONY, Martin a Norman BIGGS. Computational Learning Theory, Cambridge University Press, 1997. ISBN 978-0521599221.
2. BROWNLEE, Jason. Machine Learning Mastery With Python. 2019.
3. WATT, Jeremy, Reza BORHANI a Aggelos K. KATSAGGELOS. Machine learning refined: foundations, algorithms, and applications. Cambridge: Cambridge University Press, 2016. ISBN 978-1-107-12352-6.
The realization of a project focused on the application of machine solution methods in solving practical tasks. Successful completion of two written tests based on machine learning, probabilistic learning, classification tasks. Successful completion of the written and oral part of the exam based on machine learning, probabilistic learning, classification tasks.
Slovak language or English language
1. Learning algorithms, concepts, hypotheses. Training and learning, learning by construction and numbering.
2. Boolean formulas and their representation. Learning algorithms for monocells. Hypothesis space representation.
3. Probabilistic learning. An estimate of the number of examples needed to achieve some accuracy and credibility.
4. Probabilistic learning and consistent algorithms.
5. Relationships between attribute sets and predicted variables. Regression. Linear modeling using the least squares method of deviations.
6. Linear modeling, generalization, nonlinear responses from a linear model, data validation.
Classification.
7. Linear modeling using probability theory and maximum confidence.
8. VC (Vapnik - Cervonenkis) dimension of its relation to perceptrons.
9. Bayesian approach to learning. SVM.
10. Clustering.
11. Hidden Markov models.
The result of education is an understanding of the basic principles of machine learning. The student will gain the ability to analyze data using selected methods of machine learning and artificial intelligence. Can work with a selected tool for modeling neural networks.
The recommended literature is specified individually by the student or research team in agreement with the consultant or the supervisor.
It is required to be registered for the participation on the Student Scientific Conference (ŠVK) in accordance to the Statute of the Student Scientific Conference at PF UPJŠ and the specific conditions for participation in a given year, which are announced by the dean of the faculty. Within one year of the ŠVK, a student or a research team can register in one track only. It is also possible to apply with a written work that is an integral part of a bachelor's or master's thesis or a result of a student support program. The written work at ŠVK is the result of the student's own work or the work of the research team. It must not show elements of academic fraud and must meet the criteria of good research practice defined in the Rector's Decision no. 21/2021, which lays down the rules for assessing plagiarism at Pavol Jozef Šafárik University in Košice and its components. Fulfillment of the criteria is verified mainly in the process of supervision and in the process of work presentation. Failure to do so is reason for disciplinary action. The condition for the evaluation is a successful presentation and defense of the work in the relevant track headed by a commission appointed by the dean of the faculty. The commission decides on the eligibility of credits and states its decision in the memorandum of the ŠVK.
Slovak or english
1. Analysis of the state of the art in the field.
2. Design and implementation of a solution to the researched problem.
3. Evaluation of achieved results.
4. Preparation of work annotation.
5. Processing the written work.
6. Preparation of results presentation.
7. Presentation and defense of the obtained results.
The student demonstrates mastery of extended theory and professional terminology of the field of study, acquisition of knowledge, skills and competences, the ability to apply them creatively in solving selected field problems, ability to present the results using appropriate presentation methods and tools and ability to actively participate in a professional discussion.
[1]ANTONIOU, Grigoris a Frank van HARMELEN. A semantic web primer. Cambridge: MIT Press, c2008. ISBN 978-0-262-01242-3.
[2] BAADER, Franz. The Description Logic Handbook. Theory,
Implementation and Applications. 2nd edition, Cambridge University Press, 2010. ISBN 978-0-521150118
[3] Project RDF4J. Available online: <http://www.openrdf.org/>
[4] Project Protege. Available online: <http://protege.stanford.edu/>
[5] Project Jena. Available online: <http://jena.sourceforge.net/>
[6] SPARQL langugae documantation. Available online: <http://www.w3.org/TR/rdf-sparql-query/>
Conditions for the final evaluation:
presentation of selected SW library or tool of semantic web in from of seminar for schoolmates
Slovak or english
Content prerequisities: basic programming in Java (PAZ1a), Foundations of first order logic (SLO1a), basics of databases (DBS1a)
1. Semantic web - motivation, problems, visions.
2. Structured web documents, XML, syntax, programming models DOM, SAX, StAX, namespaces in XML, XPath language, XQuery language. Examples of processing XML in Java.
3. Semantic web modelling languages: RDF, RDFS, OWL
4. Semantic web query language SPARQL, database RDF4J
5. Description logic
6. Creation of ontology in modelling tool Protege, reasoning
7. Topic Maps language, modelling in tool Ontopia
8. Jena linbrary
9. DBPedia, Google knowledge graph and thair usage in program
Understanding of semantic web languages RDF, RDFS, OWL, ability to use them in practical semantic web applications, experience with ontology modelling and communication with ontology databases.
- ISTQB CTFL Syllabus, available online: <https://istqb.org/wp-content/uploads/2024/11/ISTQB_CTFL_Syllabus_v4.0.1.pdf>
- ISTQB CT-TAE Sylabus, available online: <https://istqb.org/wp-content/uploads/2024/11/ISTQB-CT-TAE_Syllabus_v1.0_2016.pdf>
- Myers, G. (2011), The Art of Software Testing
- Lisa Crispin and Janet Gregory (2008), Agile Testing: A Practical Guide for Testers and Agile Teams,”
- Mark Fewster, Dorothy Graham(1999), Software Test Automation: Effective use of test execution tools
- Mark Fewster, Dorothy Graham(2012), Experiences of Test Automation: Case Studies of Software Test Automation
- Katarina Clokie (2017),A Practical Guid to Testing in DevOps“, available online: <https://leanpub.com/testingindevops>
- Activity during course
- Work on tasks/assignments
- Final exam
- Final evaluation based on collected points
Slovak, English
1. Software testing process, test strategy, test project, test management
2. Advanced test design techniques, context-driven testing, exploratory testing
3. Static testing, static code analysis, code review, mutation testing
4. Test automation strategies, design principles, mocking
5. Modern tools for web interface test automation
6. Modern tools for web services and API test automation
7. Introduction to testing and automation on mobile devices
8. Snapshot testing, visual validation, and other advanced approaches
9. Multi-level test automation solution within a mono-repo, integration into CICD
10. Performance testing using modern tools, performance testing strategy design
11. AI in testing, the future of software testing
12. Final exam, revision, practical examples
Acquaintance with advanced software testing principles and the use of testing strategy. Acquaintance with various test automation tools and their integration into software development. Expansion of skills in designing and developing automated tests.
1. D. Hankersson, G. Harris, P. Johnson: Introduction to Information Theory and Data Compression, CRC Pr., 1998.
2. J. Adámek: Kódovaní a teorie informace, Vydavatelství ČVUT, Praha 1994
3. J. Černý: Entrópia a informácia v kybernetike, Alfa 1981
Satisfiable knowledge of basic notions
Slovak
1. Word and language
2. Decodable codes
3. Prefix-free codes
4. Krafto-McMillan inequality
5.-7. Entropy
8.-9. Price of code sequence
10. Shannon's theorem
11. Fano's code sequence
12. Huffman's optimal code sequence
To understand principles of lossless coding and entropy and their mutual relationship.
BENCE, M. et al. 2005. Plávanie. Banská Bystrica: FHV UMB. 198s. ISBN 80-8083-140-8.
[online] Dostupné na: https://www.ff.umb.sk/app/cmsFile.php?disposition=a&ID=571
BUZKOVÁ, K. 2006. Fitness jóga, harmonické cvičení těla I duše. Praha: Grada. ISBN 8024715252.
JARKOVSKÁ, H, JARKOVSKÁ, M. 2005. Posilování s vlastním tělem 417 krát jinak. Praha: Grada. ISBN 9788024757308.
KAČÁNI, L. 2002. Futbal:Tréning hrou. Bratislava: Peter Mačura – PEEM. 278s. ISBN
8089197027.
KRESTA, J. 2009. Futsal.Praha: Grada Publishing, a.s. 112s. ISBN 9788024725345.
LAWRENCE, G. 2019. Power jóga nejen pro sportovce. Brno: CPress. ISBN 9788026427902.
SNER, Wolfgang. 2004. Posilování ve fitness. České Budějovice: Kopp. ISBN 8072322141.
STACKEOVÁ, D. 2014. Fitness programy z pohledu kinantropologie. Praha: Galén. ISBN 9788074921155.
VOMÁČKO, S. BOŠTÍKOVÁ, S. 2003. Lezení na umělých stěnách. Praha: Grada. 129s. ISBN
8024721743.
Min. 80% of active participation in classes.
Slovak language
Brief outline of the course:
The Institute of physical education and sport at the Pavol Jozef Šafárik University offers 20 sports activities aerobics; aikido, basketball, badminton, body-balance, body form, bouldering, floorball, yoga, power yoga, pilates, swimming, fitness, indoor football, SM system, step aerobics, table tennis, chess, volleyball, tabata, cycling.
Additionally, the Institute of physical education and sport at the Pavol Jozef Šafárik University offers winter courses (ski course, survival) and summer courses (aerobics by the sea, rafting on the Tisza River) with an attractive programme, sports competitions with national and international participation.
Sports activities in all their forms prepare university students for their professional and personal life. They have a great impact on physical fitness and performance. Specialization in sports activities enables students to strengthen their relationship towards the selected sport in which they also improve.
BENCE, M. et al. 2005. Plávanie. Banská Bystrica: FHV UMB. 198s. ISBN 80-8083-140-8.
[online] Dostupné na: https://www.ff.umb.sk/app/cmsFile.php?disposition=a&ID=571
BUZKOVÁ, K. 2006. Fitness jóga, harmonické cvičení těla I duše. Praha: Grada. ISBN 8024715252.
JARKOVSKÁ, H, JARKOVSKÁ, M. 2005. Posilování s vlastním tělem 417 krát jinak. Praha: Grada. ISBN 9788024757308.
KAČÁNI, L. 2002. Futbal:Tréning hrou. Bratislava: Peter Mačura – PEEM. 278s. ISBN
8089197027.
KRESTA, J. 2009. Futsal.Praha: Grada Publishing, a.s. 112s. ISBN 9788024725345.
LAWRENCE, G. 2019. Power jóga nejen pro sportovce. Brno: CPress. ISBN 9788026427902.
SNER, Wolfgang. 2004. Posilování ve fitness. České Budějovice: Kopp. ISBN 8072322141.
STACKEOVÁ, D. 2014. Fitness programy z pohledu kinantropologie. Praha: Galén. ISBN 9788074921155.
VOMÁČKO, S. BOŠTÍKOVÁ, S. 2003. Lezení na umělých stěnách. Praha: Grada. 129s. ISBN
8024721743.
active participation in classes - min. 80%.
Slovak language
Brief outline of the course:
The Institute of physical education and sport at the Pavol Jozef Šafárik University offers 20 sports activities aerobics; aikido, basketball, badminton, body-balance, body form, bouldering, floorball, yoga, power yoga, pilates, swimming, fitness, indoor football, SM system, step aerobics, table tennis, chess, volleyball, tabata, cycling.
Additionally, the Institute of physical education and sport at the Pavol Jozef Šafárik University offers winter courses (ski course, survival) and summer courses (aerobics by the sea, rafting on the Tisza River) with an attractive programme, sports competitions with national and international participation.
Sports activities in all their forms prepare university students for their professional and personal life. They have a great impact on physical fitness and performance. Specialization in sports activities enables students to strengthen their relationship towards the selected sport in which they also improve.
BENCE, M. et al. 2005. Plávanie. Banská Bystrica: FHV UMB. 198s. ISBN 80-8083-140-8.
[online] Dostupné na: https://www.ff.umb.sk/app/cmsFile.php?disposition=a&ID=571
BUZKOVÁ, K. 2006. Fitness jóga, harmonické cvičení těla I duše. Praha: Grada. ISBN 8024715252.
JARKOVSKÁ, H, JARKOVSKÁ, M. 2005. Posilování s vlastním tělem 417 krát jinak. Praha: Grada. ISBN 9788024757308.
KAČÁNI, L. 2002. Futbal:Tréning hrou. Bratislava: Peter Mačura – PEEM. 278s. ISBN
8089197027.
KRESTA, J. 2009. Futsal.Praha: Grada Publishing, a.s. 112s. ISBN 9788024725345.
LAWRENCE, G. 2019. Power jóga nejen pro sportovce. Brno: CPress. ISBN 9788026427902.
SNER, Wolfgang. 2004. Posilování ve fitness. České Budějovice: Kopp. ISBN 8072322141.
STACKEOVÁ, D. 2014. Fitness programy z pohledu kinantropologie. Praha: Galén. ISBN 9788074921155.
VOMÁČKO, S. BOŠTÍKOVÁ, S. 2003. Lezení na umělých stěnách. Praha: Grada. 129s. ISBN
8024721743.
min. 80% of active participation in classes
Slovak language
Brief outline of the course:
The Institute of physical education and sport at the Pavol Jozef Šafárik University offers 20 sports activities aerobics; aikido, basketball, badminton, body-balance, body form, bouldering, floorball, yoga, power yoga, pilates, swimming, fitness, indoor football, SM system, step aerobics, table tennis, chess, volleyball, tabata, cycling.
Additionally, the Institute of physical education and sport at the Pavol Jozef Šafárik University offers winter courses (ski course, survival) and summer courses (aerobics by the sea, rafting on the Tisza River) with an attractive programme, sports competitions with national and international participation.
Sports activities in all their forms prepare university students for their professional and personal life. They have a great impact on physical fitness and performance. Specialization in sports activities enables students to strengthen their relationship towards the selected sport in which they also improve.
BENCE, M. et al. 2005. Plávanie. Banská Bystrica: FHV UMB. 198s. ISBN 80-8083-140-8.
[online] Dostupné na: https://www.ff.umb.sk/app/cmsFile.php?disposition=a&ID=571
BUZKOVÁ, K. 2006. Fitness jóga, harmonické cvičení těla I duše. Praha: Grada. ISBN 8024715252.
JARKOVSKÁ, H, JARKOVSKÁ, M. 2005. Posilování s vlastním tělem 417 krát jinak. Praha: Grada. ISBN 9788024757308.
KAČÁNI, L. 2002. Futbal:Tréning hrou. Bratislava: Peter Mačura – PEEM. 278s. ISBN
8089197027.
KRESTA, J. 2009. Futsal.Praha: Grada Publishing, a.s. 112s. ISBN 9788024725345.
LAWRENCE, G. 2019. Power jóga nejen pro sportovce. Brno: CPress. ISBN 9788026427902.
SNER, Wolfgang. 2004. Posilování ve fitness. České Budějovice: Kopp. ISBN 8072322141.
STACKEOVÁ, D. 2014. Fitness programy z pohledu kinantropologie. Praha: Galén. ISBN 9788074921155.
VOMÁČKO, S. BOŠTÍKOVÁ, S. 2003. Lezení na umělých stěnách. Praha: Grada. 129s. ISBN
8024721743.
min. 80% of active participation in classes
Slovak language
Brief outline of the course:
The Institute of physical education and sport at the Pavol Jozef Šafárik University offers 20 sports activities aerobics; aikido, basketball, badminton, body-balance, body form, bouldering, floorball, yoga, power yoga, pilates, swimming, fitness, indoor football, SM system, step aerobics, table tennis, chess, volleyball, tabata, cycling.
Additionally, the Institute of physical education and sport at the Pavol Jozef Šafárik University offers winter courses (ski course, survival) and summer courses (aerobics by the sea, rafting on the Tisza River) with an attractive programme, sports competitions with national and international participation.
Sports activities in all their forms prepare university students for their professional and personal life. They have a great impact on physical fitness and performance. Specialization in sports activities enables students to strengthen their relationship towards the selected sport in which they also improve.
1. Russell S.J., Norvig P: Artificial Intelligence: A Modern Approach (2nd Edition), Prentice Hall, 2002, ISBN: 0137903952
2. Negnevitsky Michael: Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition), Addison Wesley, 2004, ISBN: 0321204662
3. Poeppel D., Mangun G., Gazzaniga M. (ed.): The Cognitive Neurosciences. 6th ed. MIT Press.
2020. ISBN-13: 978-0262043250
Home work and written tests.
Final exam - written or oral.
Slovak or english
Content prerequisities:
basic programing, neurobiology, cognitive psychology, or instructor's consent
1. Definition and goals of Artificial intelligence and Cognitive Science. Natural intelligence. Intlligence of a machine vs. humnan agent.
2. Knowledge representation in AI (semantic networks, frames), reasoning.
3. Problem solving in state space - uninformed vs informed search, depth-first vs. breadth-first search.
4. Planning and decision making, logic constraints programming, machine learning.
5. Computer vision - image recognition (feature vs structure scene analysis), preprocessing, representation and description of image, object recognition.
6. Natural language processing, artificial neural networks, knowledge systems (structure, characteristics, feedforward vs feedback propagatiion during inference).
7. Genetic algorithms and artificial life, distributed AI and multiagent stystems.
8. Visual perception and cognition.
9. Auditory perception and cognition.
10. Memory, learning and attention.
11. language, thinking and consciousness.
12. Emotions, motivation, attention.
13. Motor system and crossmodal interactions.
The goal of the course is to provide an overview of the extensive field of artificial intelligence and cognitive science. The student can opt to study individually a selected topic from the literature.
The SAP HANA reference guide is the main study and technical literature, it is an online source. There may occur some other refence guides as well, depending of the type of the particular task.
Conditions for continuous evaluation: Active participation in problem solving tasks during classes.
Conditions for final evaluation: Evaluation of student's approach and creativity on solutions of given tasks.
Communication: Slovak, English
Literature: English
1. Introduction to in memory computation
2. Comparison of in-memory and traditional SQL
3. HANA basics - administration, monitoring, data persistency, backup, update
4. HANA SQL language
5. HANA Eclipse Studio
6. Procedures, functions, scripts
7. Spatial data
8. HANA XS applications
9. advanced HANA XS applications
10. Streaming data analytics - notifications, patterns
11. Streaming data analytics - client - server application
12. Predictive analytics - machine learning
13. Predictive analytics - HANA libraries and tools
Experience with basic SAP HANA ecosystem, experience with system's modules and SAP UI5 application development for SAP HANA.
G. Birkhoff, S. MacLane: Prehľad modernej algebry, Alfa Bratislava, 1979
T. Katriňák a kol.: Algebra a teoretická aritmetika 1, Alfa Bratislava, 1985
Plesník, Dupáčová, Vlach: Lineárne programovanie, Alfa, Bratislava 1990
Riečan a kol.:Pravdepodobnosť a matematická štatistika, Alfa, Bratislava, 1984
Skřivánková V.: Pravdepodobnosť v príkladoch, UPJŠ, Košice, 2006
Awarded according to tests during semester (40 points), written exam (20 points), oral exam (40 points).
Slovak
Probability: classical definition, conditional probability, characteristics of random variables, geometrical probability.
Random processes, Markov chains.
Polynomials over a field. Decomposition into irreducible factors. Roots of polynomials.
Formulation of linear and integer programs. Graphic solution. Simplex method. Duality. Algorithm for integer programming.
Students learn the fundamentals of probability theory, random processes, algebra of polynomials, linear and integer optimalization. The emphasis is on practical applications.
1. KANDEL, E. R., SCHWARTZ, J. H. and JESSELL, T.M.: Principles of Neural Science. McGraw-Hill, 2021 ISBN-13: 978-1259642234
2. Dayan P and LF Abbott: Theoretical Neuroscience - Computational and Mathematical
Modeling of Neural Systems. MIT Press, 2005 ISBN-13: 978-0262541855
3. Thagard P: Mind: Introduction to Cognitive Science, 2nd Edition. Bradford Books. ISBN-13 :
978-0262701099
4. HERTZ, J., KROGH, A. and PALMER R. G.: Introduction to the theory of neural computation. Addison-Wesley 1991 ISBN-13: 978-0201515602
Midterm exam
Final exam consisting of written and/or oral part
Slovak or English
Content prerequisites:
basics of neurobiology, cognitive psychology, linear algebra and differential equations, programing, or instructor's consent
1. Intro: Cognitive psychology, neural modeling.
Theme 1: Topics in cognitive and neural science
2. Neural basis of vision
3. Visual object recognition and visual scene analysis
4. Auditory cognition. Echo suppression. Auditory scene analysis
5. Cortical sound processing.
6. Other topics in the study of brain and main: thinking, consciousness, emotions, motivation
Topic 2: Modeling in cognitive and neural science
7. Intro
8. Connectionism, STM and LTM modeling
9. Additive and shunting neural networks.
10. Learning rule Outstar.
11. Adaptive resonance theory.
12. Statistical and decision-theory modeling
Topic 3: Current research at UPJS
13. Invited lecture
https://pcl.upjs.sk/vkn_eng/
Advanced topics in computational and cognitive neuroscience, and in the tools used in neuroscience.
1. J.E. Hopcroft, R.Motwani, J.D. Ullman: Introduction to automata theory, languages, and computation, Addison-Wesley, 2007.
2. M. Sipser: Introduction to the Theory of Computation, Thomson, 2nd edition, 2006.
3. L.A.Hemaspaandra, M.Ogihara: Complexity theory companion, EATCS series, texts in computer science, Springer-Verlag, 2002.
4. S. Arora, B. Barak: Computational Complexity: A Modern Approach, Cambridge Univ. Pess, 2009. 5. G.Brassard, P.Bradley: Fundamentals of algorithmics, Prentice Hall, 1996.
6. D.P.Bovet, P.Crescenzi: Introduction to the theory of complexity, Prentice Hall, 1994.
7. C. Calude and J. Hromkovič: Complexity: A Language-Theoretic Point of View, in G. Rozenberg and A. Salomaa, Handbook of Formal Languages II, Springer, 1997.
Oral examination.
Slovak or english
Content prerequisities:
Basic notions from the theory of automata and formal languages.
Basic skills in programming and design of algorithms (in any programming language).
Basics knowledge in mathematical logic, set theory, and graph theory.
1: Introduction: the notion of computational complexity, computational time, computational model, example - the problem of sorting, computational complexity as an asymptotic function
2: Basic computational models: RAM and RASP computers, the cost of an elementary step on these computers, single-tape Turing machine, multi-tape Turing machine, nondeterministic variants of these computational models, transformations among these models with respect to the time complexity
3: The classes P and NP: basic definitions, presenting (un)undirected graphs on the input, 3COL – the set of all 3-colorable graphs is in NP, 2COL - the set of all 2-colorable graphs is in P, SAT – the set of satisfiable Boolean formulas is in NP, CNF-SAT - Boolean formulas in conjunctive normal form
4: Variants of P and NP: decision problem, the problem of finding a solution, optimization problem, polynomial conversions among different variants
5: NP-completeness: reducibility in polynomial time and its transitivity, definition of the NP-completeness and its basic properties
6: NP-completeness of SAT
7: Variants of SAT: 3CNF-SAT - satisfiability of Boolean formulas in 3-conjunctive normal form, kCNF-SAT, CNF-SAT - satisfiability in k-conjunctive (conjunctive) normal form, 2CNF-SAT is in P
8: 3COL and its variants: 3COL (the problem of coloring vertices of a graph with 3 colors) in NP-complete, consequently: for each k>3, kCOL (the problem of coloring with k colors) is NP-complete as well
9: Colorability of a planar graph with three colors: presenting a planar graph on the input, the proof of NP-completeness, coloring with a larger number of colors
10: Another NP-complete problems: Exact set cover, Clique, Vertex cover
11: Hamiltonian path: Hamiltonian path in a directed and in undirected graph
12: Subset-sum-like problems: Subset Sum - the problem of whether any subset of the integers sum to precisely a target sum, Partition - the problem of whether a given multiset of positive integers can be partitioned into two subsets with equal sums, a “more relaxed” version of Partition - achieving an approximate equality of the sums, distribution of tasks among K parallel processors
13: Beyond P a NP: a review of the basic complexity classes - L, NL, P, NP, PSpace , NPSpace, ExpTime, NExpTime, …, simulation of (non)deterministic space in (non)deterministic time, conversions in opposite directions
14: PSpace: QBF - true quantified Boolean formulas, prenex normal form, Pspace completeness of QBF, PSpace = NPSpace
To give students theoretical background in computational complexity and theory of NP-completeness.
1. Bělohlávek, R. (2002). Fuzzy Relational Systems: Foundations and Principles. New York: Kluwer Academic/Plenum Publishers.
2. Carpineto, C., & Romano, G. (2004). Concept Data Analysis: Theory and Applications. Hoboken, NJ: John Wiley & Sons, Inc.
3. Ganter, B., & Wille, R. (1999). Formal Concept Analysis: Mathematical Foundations. Berlin: Springer.
4. Guniš, J., Šnajder, L., Antoni, L., Eliaš, P., Krídlo, O., & Krajči, S. (2024). Formal Concept Analysis of Students’ Solutions on Computational Thinking Game. IEEE Transactions on Education. doi:10.1109/TE.2024.3442612.
5. Krídlo, O., Antoni, Ľ., & Krajči, S. (2022). Selection of appropriate bonds between L-fuzzy formal contexts for recommendation tasks. Information Sciences, 606, 21-37. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2022.05.047.
6. Krídlo, O., López-Rodríguez, D., Antoni, Ľ., Eliaš, P., Krajči, S., & Ojeda-Aciego, M. (2023). Connecting concept lattices with bonds induced by external information. Information Sciences, 648, 119498. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2023.119498.
7. Pitka, T., Bucko, Ľ., Šnajder, L., et al. (2024). Time analysis of online consumer behavior by decision trees, GUHA association rules, and formal concept analysis. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00274-y.
Test of theoretical knowledge in the middle of the semester.
Written and oral exam.
Slovak or English
content prerequisites: basics of logic, introduction to computer science
1. basic notions of Ordered sets and Formal concept analysis, motivation example
2. closure operator, closure system, Galois conection and concept lattice, example
3. basic notions of fuzzy logic, one-sided and fuzzy formal concept analysis
4. basic algorithms of Formal concept analysis
5. optimal decomposition of formal context, optimal factors, algorithms, example
6. intercontextual structures, bonds, direct products and selection of best bonds, relationship with factorisation
7. aplications on real data
The goal is to teach students some advanced applications of logic, fuzzy logic and basic clustering methods, especially in database and knowledge systems.