Κατεύθυνση: Major in Software & Data Systems

Student Placement

Students can choose it only once during undergraduate studies (either the 7th or the 8th semester).

Privacy on the Internet

Learning Outcomes

Within the framework of the course, students will be able:

  • To fully understand the concepts of privacy, territorial privacy, privacy of the person and especially informational privacy
  • To realise the privacy threats environment and related requirements
  • To understand the concept of privacy framework
  • To realise the legal requirements of privacy by design and privacy by default
  • To conduct data protection impact assessment surveys for public and private bodies
  • To study all privacy issues rised in modern public clouds
  • To know critical technological tools for privacy enhancement
  • To understand national and European regulations regarding informational privacy protection and personal data protection
  • 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

  • Privacy: The citizens and public & private bodies viewpoint. Territorial privacy, privacy of the person, informational privacy
  • Personally identifiable information PII and personal data
  • Threats and privacy requirements
  • The privacy paradox
  • Legal and regulatory frameworks for personal data protection: The EU GDPR General Data Protection Regulation
  • Privacy framework and ISO 29100:2024
  • Controls and best practices for privacy protection according to ISO 29151:2017
  • Privacy by design and ISO 31700-1: 2023
  • Privacy information management system and ISO 27701:2019
  • Data protection impact assessment and ISO 29134:2023
  • Privacy in public clouds and ISO 27018:2019
  • Privacy Enhancing Technologies: Data obfuscation tools (anonymization, pseudonymization, synthetic data, differential privacy, zero knowledge proofs), Encrypted data processing tools (homomorphic encryption, multiparty computation, trusted execution environments), Federated and distributed analytics (federated learning, distributed analytics), Data accountability tools (accountable systems, threshold secret sharing, personal information management systems)
  • Privacy protection and AI systems: The Artificial Intelligence Act

Suggested Bibliography

  • Acquisti, S. Gritzalis, C. Lambrinoudakis, S. De Capitani di Vimercati (Eds) (2008), Digital Privacy, Theory, Technology and Practices, Auerbach Publications
  • Tamo-Larrieux (2018), Designing for Privacy and its Legal Framework: Data Protection by Design and Default for the Internet of Things, Springer
  • Bart van der Sloot, A. de Groot, (2018) The Handbook of Privacy Studies, Amsterdam University Press

Scientific Journals

Educational Technology

With the completion of the course, the student will be able:

  • to know and understand the key concepts of educational design for technology-supported and technology-enhanced educational innovations (including flipped classroom for blended learning and Massive Open Online Courses).
  • to analyse and critique the basic elements of the ADDIE educational design process when applied to design, develop and evaluate technology-supported and technology-enhanced educational innovations (including flipped classroom for blended learning and Massive Open Online Courses).
  • to design and implement pedagogically grounded technology-supported and technology-enhanced educational innovations focusing to flipped classroom for blended learning and Massive Open Online Courses.

Course Contents

  • Educational Design for Technology-supported and Technology-enhanced Educational Innovations
    • What is Educational design? Definitions – Basic Principles – Models
    • The ADDIE Model: Analysis of each Phase
    • Analyse Learners and Learning Context
    • Educational Objectives and Assessment of Learning and/or Performance
    • Strategies for Teaching and Learning
  • Digital Media in Education and Training
    • Educational Videos
    • Interactive Digital Textbooks
    • Educational Games and Gamification
    • Educational Mobile Apps
    • Educational Web 2.0 Application
    • Educational Augmented Reality and 3D Virtual Worlds in Education & Training
  • Case Studies
    • Blended Learning: the Flipped Classroom model
    • Massive Open Online Courses (MOOCs)

Recommended Readings

  • Textbook in Greek (provided for free)
  • Additional Open Access Educational Resources available through the course management system

Assessment in Digital Learning

Goal and Learning Outcomes

The course seeks to both conceptualize and redefine the purpose and objectives of the 21st century assessment for learning, as well as to outline modern and easy-to-use techniques and tools used by teachers in modern formal and informal learning environments.

After the successful completion of the course, the students will be able to:

  • State the modern forms of educational evaluation, utilizing educational technologies
  • Understand methodological issues regarding the organization and conduct of quality assessment of the educational process.
  • Utilize modern tools and techniques to evaluate trainees’ performance.
  • Apply a variety of techniques for systematic evaluation of instructional process.

 

Contents

  1. Basic principles of evaluating the quality of educational interventions
  • Formative
  • Holistic-Summative
  1. Principles of Monitoring and Assessing Students’ Performance
  2. Known techniques for evaluating the performance of trainees
  3. Categories of assessment tools
  • Quiz-tests
  • Rubrics
  • Mindmaps
  • Learning Analytics
  • Computational Thinking Assessment tools
  1. Quality Evaluation Techniques for Learning Resources and Learning Systems
  2. Process of creation and application of evaluation techniques and tools.

Suggested Reading

  1. A. Tomlinson & T. R. Moon (2013). Assessment and Student Success in a Differentiated Classroom, Association for Supervision & Curriculum Development, ISBN 1416616179 (ISBN13: 9781416616177)

  2. Cowie, B., Moreland, J. and Otrel-Cass, K. (2013). Expanding Notions of Assessment for Learning. Dordrecht: Springer.

Compilers

Learning Outcomes

This course provides an introduction to the principles of analysis and design of programming languages as well as to the ways these principles are applied in modern programming languages. The successful completion of this course will allow students:

  • to understand the basic and important features of the design, implementation and analysis of compiler systems for modern programming languages.
  • to know the basic features of the tools and the development techniques for the creation of modern programming languages.

Course Contents

  • Introduction – Overview of Modern Programming Languages.
  • Language Definition and Design (Regular Expressions – Automata – Context-Free Grammars).
  • Programming Language Structure (Variables, Types and Scoping, Control Flow and Evaluation of Expressions, Subroutines, Iterative and Recursive Processes, Memory Management and Communication).
  • The Compiling/Interpretation Process (Lectical Analysis, Syntactic Analysis, Code Production & Optimization, Linking).

Recommended Readings

  • Scott, M. L., Programming Language Pragmatics, 2nd edition, Morgan Kaufmann, 2009
  • Instructor Notes

Digital Signal Processing

Learning Outcomes

The course introduces students to the design theory for continuous and discrete time linear systems. Based on this theory, students will be able to design analog and digital filters based on specifications in the frequency domain.

Upon successful completion of the course the student will be able to:

  • Use algorithms to design Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) digital filters.
  • Obtain transfer function of prototype analog filters based on required frequency response.
  • Obtain IIR and FIR filter implementation methods: serial and parallel structures.
  • Design FIR and IIR filters using the Matlab software tool.

Course Contents

  • Frequency response of discrete time signals
  • Finite impulse response (FIR) digital filters with linear phase.
  • FIR filter design using the window method.
  • FIR filter design using the frequency sampling method.
  • FIR filter design using the optimal method.
  • Prototypes of analogue lowpass filters: Butterworth polynomials and Chebyshev polynomials.
  • Frequency translation of ptototype analogue filters for creating analogue filters with arbitrary frequency response.
  • Design of digital infinite impulse response (IIR) filters using bilinear transformation.
  • Frequency transformation of digital filters.
  • Implementation issues and techniques for IIR and FIR digital filters
  • Telecommunication filters raised cosine.

Recommended Readings

  • Vinay Ingle & John Proakis, (2012) Digital Signal Processing using Matlab, 3rd edition, Cengage Learning
  • C. Ifeachor & B.W. Jervis, (2002): DSP A Practical Approach, 2nd edition, Prentice Hall, ISBN 0201-59619-9
  • J. Proakis & D. Manolakis, (2007): Digital Signal Processing: Principles, Algorithms and Applications, 4th Edition, Prentice Hall.

Digital Image Processing

Learning Outcomes

Digital image processing is used for two distinct purposes: (1) improving the appearance of the image so that it is easier for an observer to interpret and (2) digitally analyzing the image for the purpose of describing, identifying and interpreting the content of an image. image. The course will present the basic algorithms and methodologies for both purposes in the field of space and in the field of frequencies.

Students, upon successful completion of the course, will be able to:

A) Understand basic methodologies and basic knowledge of designing and developing image processing systems

B) Know the stages of digital image processing and analysis (optical sensors, image capture, digitization, transformation, coding, compression, transmission, segmentation, recognition)

C) Analyze problems across different application areas and select the right mechanisms for managing and processing digital images

D) Evaluate digital image processing systems and algorithms

Course Contents

  • Introduction to Digital Image Processing
  • 2-D Signals and Systems – Background Information
  • Sampling and Digitization Issues
  • Image Enhancement and Restoration
  • Binary Image Processing – Morphological Operators
  • Image Segmentation – Edge Detection
  • Image Transformations (Fourier, DCT, Hadamard, etc.)
  • Analysis in the frequency domain
  • Digital Image Compression
  • Digital Image Analysis – Computer Vision
  • Texture Analysis – Region of Interests
  • Other areas: eg Watermarking, Information Retrieval, etc.

Recommended Readings

  • Rafael C. Gonzalez & Richard E. Woods Digital Image Processing CRC Press 4th Edition

Educational Digital Systems

Learning Outcomes

With the completion of the course, the student will be able:

  • to know and understand the key concepts of exploiting digital technologies in teaching, learning and assessment of learning in K12 School Education.
  • to analyse, assess, select and justify a pedagogically appropriate educational technologies to support different teaching strategies in K12 School Education.
  • to design and create pedagogically grounded technology-supported teaching and learning scenarios for the K12 education.

The learning objectives of the course are aligned to the Greek State qualification framework for a teaching licence in K12 school education.

Course Contents

  • 1. Technology-Supported and Technology-Enhanced Teaching and Learning in School Education: Theoretical Underpinnings
  • 2. Integrating Technology in School Education (teaching, learning and assessment of learning): Models and Practice
  • 3. Taxonomy of Educational Technologies in School Education
  • 4. Educational Technologies for supporting different teaching and learning strategies
    • 4.1. Tutorials
    • 4.2. Drill and Practice
    • 4.3. Problem solving
    • 4.4. Modeling
    • 4.5. Virtual Labs and Simulations
    • 4.6. Inquiry-based Learning
    • 4.7. Collaborative Learning
    • 4.8. Assessment of Learning
    • o 4.9. Educational Games
  • 5. Digital School Infrastructure
    • 5.1. Interactive Boards
    • 5.2. ICT School Laboratory

Recommended Readings

  • Textbook in Greek (provided for free)
  • Additional Open Access Educational Resources available through the course management system

Telemedicine

Learning Outcomes

The course is introducing students in telemedicine systems and applications that improve the quality of life and provide remote electronic health services. The curriculum includes background knowledge in the areas of coding and processing of biomedical data, analyzes the design and implementation issues of telemedicine systems and discusses the next generation telemedicine systems, which include context awareness and computational intelligence as additional features. During the course case studies will be presented and there will be project assigned to students.

Students, upon successful completion of the course, will be able to:

A) Understand basic methodologies of design and development of telemedicine systems

B) Be familiar with the main techniques for the coding and processing of biomedical signals and data

C) Know the coding standards for medical information

D) Design telemedicine systems according to special requirements and the type of medical information exchanged

E) Evaluate telemedicine systems

Course Contents

  • Introduction to Telemedicine
  • Biomedical Data Coding and Compression
  • Biomedical Data Processing for Telemedicine Applications
  • Video Communication for Telemedicine Applications
  • Telemedicine Networks
  • Home Care Systems
  • Context Aware Telemedicine Systems
  • Wireless Telemedicine and Ambient Assisted Living
  • Wearable Systems
  • Clinical Applications of Telemedicine
  • Security in Telemedicine systems
  • Case Studies – Project Assignments

Recommended Readings

  • Medical Informatics, e-Health: Fundamentals and Applications (Health Informatics) Softcover reprint of the original 1st ed. 2014 Edition by Alain Venot (Editor), Anita Burgun (Editor), Catherine Quantin (Editor)
  • Telemedicine Handbook, Pompidou Alain, Apostolakis I, Α., Ferrer – Roca Olga, Sosa – Iudicissa Marcelo, Allaert Francois, Della Mea Vincenzo, Kastania Anastasia N.