Learning Outcomes
Within the framework of the course, students will be able:
- To understand information and systems security problems for public and private bodies
- To realise the necessity of information security management system ISMS according to ISO 27001:2022
- To conduct risk management actions according to ISO 27005:2022, starting with risk assessment process and continue with risk treatment process, using software tools
- To select suitable technological controls, organizational controls, physical controls and people controls, according to ISO 27002:2022
- To effectively design ISMS and information security polices
- To understand the challenges posed by the evolving dynamics of the combination of the cognitive fields of cyber security, privacy protection, and Artificial Intelligence and the way they create social, cultural, political, and financial issues, as well as ethical issues in modern societies
- To possess state-of-the-art specialized scientific knowledge in the subjects of the course as a basis for original thinking and research activities.
Course Contents
- Information and systems security terminology and ISO 27000:2018
- Information Security Management System – ISMS: Basic principles and ISO 27001:2022
- Controls/safeguards: technological controls, organizational controls, physical controls, and people controls according to ISO 27002:2022
- Information Security Risk management and ISO 27005:2022: assets, threats, vulnerabilities, controls
- Risk assessment: risk identification, risk analysis, risk evaluation
- Risk treatment: risk modification, risk retention, risk avoidance, risk sharing
- Statement of applicability
- Software for conducting the risk management process
- Information security organizational framework: Security policies, policies hierarchy, thematic policies, policies life cycle, responsibilities for the development of security policies
Suggested Bibliography
- R. Anderson, Security Engineering, J. Wiley & Sons, 3rd edition, 2020
- D. Gollmann, Computer Security, J. Wiley & Sons, 3rd edition, 2011
Scientific Journals
Learning Outcomes
The course’s material includes mathematical definitions, fundamental concepts, results and reasoning methodologies that pertain to basic multivariate calculus and fourier theory objects and models underlying the foundations and applications of computer science. Moreover the relevant connections of the fourier analysis to several more specific branches of computer science are presented. The course directly supports most of the curriculum subjects and lessons: Note that during the lesson, specific examples of application are discussed using new technologies with the help of programs such as Matlab, Octave and R.
Upon successfully completion of the course the students will be in position to:·
- Know, describe and handle basic knowledge of mathematical analysis of many variables (indicatively: Gamma and Beta Function, Laplace Transformation, Multiple Variable Functions (Derivation, Integration), Sequences and Function Sequences, Fourier Series, Fourier Integral, Fourier Integral)·
- Choose the appropriate mathematical concepts and be able to model the particular IT problem that it is called upon to solve. In addition to develop mathematical thinking and being able to analyze and adapt acquired knowledge to applications of computer science. ·
- Combine the essentials of Multivariate Computation and Fourier theory to solve more complex mathematical problems.·
- Know, handle and understand Matlab, Octave and R programs on applications of mathematical analysis of multiple variables and fourier theory in computer science.
Course Contents
- Improper Integrals, Improper Integrals Depending on a Parameter. Gamma and Beta Functions.
- Laplace transformations.
- Vectors in the level and in the space.
- Vector interrelations. Applications: laws of Kepler.
- Functions of Two Variables. Differentiation of Functions of Two Variables. Interrelations of multiple variables (definition, graphic representation, limits, constantly, certain derivative, derivative as for direction, total differential gear, very little – biggest).
- Precise differential equations.
- Double and triple integrals, change of coordinates – applications.
- Sequences and Series of Functions
- Fourier Series and Integrals. Fourier transformations.
- Completion of vector fields (bent – divergence – turn, curve integrals, surface integrals, theorems green, gauss, stokes).
Recommended Readings
- M. Filippakis, Applied Analysis and Fourier Theory, Tsotras Publications, Athens 2017, 2nd Edition.
- Zygmund A. (2003): Trigonometric Series, 3rd Edition, Cambridge University Press.
- Teaching Notes.
Learning Outcomes
The course’s material includes mathematical definitions, results and reasoning methodologies that pertain to basic mathematical analysis and linear algebra objects and models underlying the foundations and applications of computer science. Moreover the relevant connections of mathematical analysis and linear algebra to several more specific branches of computer science are presented. The course directly supports most of the curriculum subjects and courses. Note that during the course specific examples of application are discussed using new technologies with the help of programs such as Matlab, and Octave.
Upon successfully completion of the course the students will be in position to:·
- Know and understand basic analysis method of mathematical analysis and linear algebra (indicatively: sequences, series, functions of one variable (differentiation, integration), differential equations, matrices, determinants, linear systems, inner product, characteristics values, orthogonal diagonalization)·
- Choose the appropriate mathematical concepts and representations for each problem at hand. In addition to develop mathematical thinking and be able to analyze and adapt acquired knowledge to applications of computer science·
- Combine the basic components of calculus and algebra to solve more complex mathematical problems.·
- Know, handle and understand Matlab and Octave programs on applications of mathematical analysis and linear algebra to computer science.
Course Contents
- Sequences. Series.
- Functions of One Variable.
- Differentiation.
- Iterative Methods for Solving Equations.
- Polynomial Approximation of Functions.
- Numerical Differentiation.
- Indefinite Integral. Differential Equations.
- Definite Integral.
- Algebraic Structures: Groups. Rings.
- Fields. Vector Spaces.
- Basic Linear Algebra: Matrices. Determinants. Linear Systems.
- Characteristics of a Matrix: Eigenvalues. Eigenvectors. Diagonalization of Matrices. Bilinear Forms.
- Inner Product: Orthonormalization. Gram-Schmidt Algorithm.
- Linear Transformation Matrices: Change of bases matrix. Matrix of a linear transformation.
Recommended Readings
- M.Filippakis, Applied Analysis and Linear Algebra, Tsotras Publications, Athens 2017, 2nd Edition.
- Χ. Moisiadis, Advanced Mathematics.
- Teaching Notes.
Learning Outcomes
At the end of the course, students will be equipped with fundamental knowledge in first order mathematical logic, which allows the critical deepening in the scientific domain of mathematical logic, including higher order systems.
Students will be able to model and process real-life problems using tools of mathematical logic. The obtained skills include abilities such as the semantic analysis and precise modeling of real-life problems in the typical systems of the propositional and first order predicate logic, the use of proof systems and logic evaluation of arguments. Furthermore, students will be able to implement small scale expert system and artificial intelligence applications using the Prolog programming language.
The systematization of logic, which is strongly addressed by the course, will help students understand better and more deeply a plethora of scientific topics included in their program of studies and efficiently determine complex logic calculations both in the system hardware level and during application development in all practiced programming languages.
Course Contents
The content of the course is to introduce:
- the language and semantics of propositional and first order predicate logic
- the study of logic arguments
- the understanding and use of proof systems for propositional and predicate logic (tableaux, direct mathematical argumentation, equivalences, natural deduction, Beth analysis)
- the translation between logic and natural language
- the Prolog programming language for Artificial Intelligence applications
Recommended Readings
- Metakides G. (1992): Logic, Logic Programming and Prolog,Kardam;itsa Publishing (in Greek).
- Marakakes M. (2016): Prolog: Logic Programming for Artificial Intelligence. New Technologies Publishing (in Greek).
- S. Russel, P. Norvig (2009): Artificial Intelligence: A Modern Approach. Pearson.
- Tzouvaras Α. (1998): Mathematical Logic Elements. Ziti Publishing (in Greek).
- Portides D., Psyllos S., Anapolitanos D. (2007): Logic: The Argument Structure (in Greek).
- P.D. Magnus: forallx: An Introduction to Formal Logic
- Mendelson E. (1997): Introduction to Mathematical Logic, 4th Edition, Chapman & Hall.
Learning Outcomes
The purpose of the course is to highlight the particular requirements and features of operating system multiprocessor systems, multi-computers, distributed systems and multimedia systems. Also it covers operating system’s security issues as well as the basic design principles. At the same time, emphasis is placed on the UNIX operating system (use and programming).
In this context, the learning outcomes of the course, after its successful completion, are that the students will be able:
- to understand the basic concepts of operating systems for multiprocessing, multi-computing and distributed systems.
- to know the basic security mechanisms (authentication, authentication, access control, etc.) that an operating system implements and the basic principles of designing an operating system.
- to analyse, evaluate and justify alternative technologies / mechanisms of operating systems.
- to design scripts for implementing specific functionalities at the operating system level.
Course Contents
- Operating Systems for Multiprocessors, Multi-Computers and Distributed Systems.
- Multimedia Operating Systems: Multimedia Files, Video Compression.
- Time scheduling of Multimedia Files.
- Security of Operating Systems: Threats, Attacks, User Identity Certification, Access Control Mechanisms.
- Design principles of Operating Systems.
- Smart Card Operating Systems: Multi-application support from a service provider, multi-application support, JAVA cards.
- UNIX History and Basic Concepts: File System Navigation, UNIX shell, Utilities, The Kernel Structure.
- UNIX Processes.
- Memory management in UNIX.
- Input – Output in UNIX.
- The UNIX File System.
- The Bourne shell: Use, shell Environment Adaptation, Redefining Input and Output.
- Shell Programming: Variables, Flow Control, Regular Expressions, Signals.
- System Management, User and Group Management, Disk Management and File Systems, Software Installation and Management.
Recommended Readings
- Andrew S. Tanenbaum, Herbert Bos (2018), Modern Operating Systems, 4th American Edition.
- Wrightson K. & Merlino J. (2001): Mastering UNIX.
Learning Outcomes
The purpose of the course is to familiarize the students with the basic concepts of operating systems, their design principles, the themes they manage and the impact of their various variants on the operation of systems.
In this context, the learning outcomes of the course, after its successful completion, are that the students will be able:
- to understand the basic concepts of operating systems as well as the key areas / operating difficulties of a computational system that they are required to manage.
- to know the basic mechanisms that an operating system implements in order to serve multiple processes, to manage memory and manage the file system.
- to analyse, evaluate and justify alternative technologies / mechanisms of operating systems.
Course Contents
- Basic Concepts and History of Operating Systems.
- Operating Systems Structure.
- Processes: Process Properties and Implementation, Interprocess Communication, Process time sharing and scheduling.
- Thread Use and Implementation, Emerging threads, Conversion of a single thread code to Multi-Threaded code, Thread Schedule.
- Deadlocks: Ostrich’s Algorithm, Detection, Recovery, Avoidance, Prevention.
- Memory Management: Virtual Memory, Design and Implementation of paging Mechanisms, Page Replacement Algorithms, Segmentation.
- Input / Output: Input / Output Hardware and Software, Disks, Terminals.
- File Systems: Files and Directories, Implementation.
Recommended Readings
- Andrew S. Tanenbaum, Herbert Bos (2018), Modern Operating Systems, 4th American Edition.
- Silberschatz, Galvin, Gagne (2013), Operating Systems Concepts.
Learning Outcomes
The aim of this course is to support the students in learning the principles, concepts and applications of Information Theory. Information theory is a discipline in applied mathematics involving the quantification of data with the goal of enabling as much data as possible to be reliably stored on a medium or communicated over a channel. The measure of information, known as information entropy, is usually expressed by the average number of bits needed for storage or communication.
Course Contents
- Concepts of entropy and information; channel capacity; channel coding; the Shannon’s theorem; error correction codes and decoding methods.
- Basic definitions of probabilities.
- Source coding.
- Channel capacity.
- Channel coding.
- The Shannon’s theorem.
- Error connection codes and decoding methods.
Recommended Readings
- Teaching Notes.
- Thomas M. Cover & Joy A. Thomas (2006): Elements of Information Theory, Second Edition, Wiley, ISBN: 0-471-24195-4.
- MacKay D.J.C. (2003): Information Theory, Inference, and Learning Algorithms, Cambridge University Press.
Learning Outcomes
The course’s material includes mathematical definitions, fundamental concepts, results and reasoning methodologies that pertain to basic Probability Theory objects and models underlying the foundations and applications of computer science. Moreover the relevant connections of probability theory to several more specific branches of computer science are presented. The course directly supports most of the curriculum subjects and lessons: Note that during the lesson, specific examples of application are discussed in some of the above curricular subjects such as applications in telecommunication systems, cryptography, digital services such as e- learning, e-health using new technologies with the help of programs such as Matlab, Octave, SPSS and R.
Upon successfully completion of the course the students will be in position to:·
- Know, describe the basic knowledge of Probability Theory (indicatively: probability definition, combinatorial analysis, random variables, discrete and continuous distributions, two-dimensional probabilistic generators and generators, etc.) basic values and the basic tools (probability mass and density functions, etc.) of Probability Theory.·
- Calculate the probabilities of possible non-trivial probability problems using probability theory, combinatorial and calculus.·
- Identify and know well-known probabilistic models in Computer Science problems.·
- Choose the appropriate mathematical method so that it can model the problem that it is called upon to solve and compile new probabilistic models for problems and systems that appear in computer science using simpler probabilistic models.·
- Know, handle, and understand Matlab, Octave, SPSS, and R programs on probability theory applications in computer science.
Course Contents
- Accidental experiment, samples and possibilities.
- Definitions of possibilities.
- Finite samples with results of equal possibilities.
- Provisions, combinations, binomial theorem.
- Committed probability.
- The multiplicative theorem.
- Total probability and Bayes theorem.
- Independent trials.
- Random variables, probability distributions.
- Parameters of distributions, interrelation of distribution accidental variables.
- One-dimensional distributions.
- Continuous distributions.
- Discrete distributions
- Generators of proneness; probabilities generators.
- Two-dimensional distributions.
- Marginal distributions distributions, Average-Committed distributions
- Transformations of random variables-Stochastic independence of two random variables.
Recommended Readings
- M. Filippakis, Probability Theory and elements of statistics analysis, applications with python, matlab, R, Spss, Tsotras Publications, Athens 2019, 1rst Edition.
- M. Filippakis-T. Papadoggonas, Probability Theory and Business Statistics, Tsotras Publications, Athens 2017, 1rst Edition.
- M. Koutras, Probability Theory and applications, tsotras publications, Athens 2015.
- Durrett R. (2004): Probability: Theory and Examples, 3rd Edition, Duxbury Press.
- Teaching Notes.
Learning Outcomes
This course is designed to promote a fundamental understanding of the various theories of learning that have been developed to help us explain how people learn, emphasizing on the effects on the learner, the learning process, and the learning situation in educational environments, especially digital ones.
On completion of the course, the students will be able:
- to demonstrate a critical understanding of a wide range of theoretical perspectives of learning which have been developed to understand human behavior.
- to analyze these perspectives in terms of the nature of learning in technology enhanced learning environments (TELE).
- to show ability to match knowledge to how human learns through designing lessons/case studies etc.
- to synthesise and create knowledge into an understanding of the implications of the learning theories for every day practice in TELE
- to demonstrate knowledge and appreciate of social diversity in solving problems in the classroom through individual projects and assignments, and through work in groups/community classrooms.
Course Contents
- Psychological Learning theories: i) Behavioral Learning Theories. ii) Social Cognitive Learning theories (self- efficacy & self-regulation). iii) Cognitive Learning theories and tools. iv) Social Constructivism: Vygotsky’s Theory (open-ended learning environments).
- Neuroscience & Information Processing: information processing models, memory, perception, attention, mnemonic devices & strategies, chunking, metacognitive strategies, problem solving, critical thinking, hyperlearning, applications in TELE.
- Social Constructivism (Vygotsky’s Zone of Proximal Development, Situated Learning, Cognitive Apprenticeship, Communities of Practices): principles, conditions, restrictions, applications in TELE.
- Critical approaches of learning theories based on digital learning environments: learning and applications in TELE (software tools, open learning environments, communication and collaborative tools).
- Applications in different conditions of schooling (primary-secondary), tertiary educational programs.
- LLL programs for professional development, job training, and business settings (marketing, sales, advertising, health).
Recommended Readings
- Elliott S, Kratochwill T, Littlefield-Cook J, Travers J. (2000). Educational Psychology: Effective Teaching, Effective Learning, Brown & Benchmark Pub.
- Slavin R., E. (2008). Educational Psychology: Theory and Practice, 9th ed., Allyn & Bacon.
- Snowman, J. Biehler, R. (2008). Psychology Applied to Teaching, 12th Edition, Hougton & Mifflin.
- Woolfolk, A. (2010). Educational Psychology, 11th ed., Allyn & Bacon.
- Roblyer M.D. (2009). Integrating Educational Technology into Teaching, 5th ed., Allyn & Bacon.
- Mayer, R. C., Richard, E. (2007). e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning, Pfeiffer.
- Reggie, K., Fox, R., Chan F. T., Tsang P. (2008). Enhancing Learning Through Technology: Research on Emerging Technologies and Pedagogies. World Scientific
- Beetham, H., Sharpe, R. (2007). Rethinking Pedagogy for a Digital Age: Designing and Delivering E-Learning, Routledge.
- Sawyer, R. K. (2006). The Cambridge Handbook of the Learning Sciences, Cambridge University Press.
