Secondary Major in Networks or Telecommunications: Optional Courses

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

ACM Transactions on Privacy and Security https://dl.acm.org/journal/tops
Security and Privacy, J. Wiley and Sons https://onlinelibrary.wiley.com/journal/24756725
International Journal of Information Security, Springer https://link.springer.com/journal/10207

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

Compulsory Course of the Secondary Major that has not been chosen as compulsory course of Secondary Major

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

Data Processing Techniques

Learning Outcomes

The objective of this course is to familiarize students with: (a) learning access methods for large data volumes for various data formats, as well as scalable writing, (b) efficient data storage and retrieval with appropriate indexing techniques, (c) the design and implementation of data processing algorithms aiming at the development of efficient applications that manage data.

Upon successful completion of the course, the students will be in position:

to develop data-centric applications with emphasis in efficiency and scalability
to use the most appropriate indexing methods for a given problem
to evaluate and improve the parts of data processing algorithms that incur high computational load
to apply the most suitable data processing techniques that match with data under analysis and for a given query workload
to develop efficient data processing algorithms

Course Contents

Operation of disk and main memory, serial and random access, cost and efficiency, data locality on disk and main-memory, direct and indirect access, main-memory data structures (arrays, priority queues, hashing)
Data access techniques for structured, semi-structured and unstructured data: relational DBs, XML, RDF, text documents, web pages, web APIs, social networks.
One-dimensional data and indexing, B-tree, variations (B+tree, B*tree), range queries, inverted indexes.
Spatial data, spatial data types, spatial queries, approximation in representation, distance measures, extensions for multidimensional data.
Spatial indexing techniques, grid file, spatial indexes (R-tree, QuadTree), space-filling curves (Hilbert, Z-order)
Similarity search, k-nearest neighbor search, branch-and-bound algorithms, locality sensitive hashing (LSH), approximate k-nearest neighbor.
Top-k search: algorithms based on pre-processing, online algorithms, Fagin’s algorithm, index-based algorithms.
Join queries, spatial joins, top-k joins.
Spatio-textual data, query types, indexing methods, processing algorithms.

Climate Change and Environment Systems and Policies

e-Learning Systems

Learning Outcomes

Upon successful completion of the course the students will be able:

to know and understand the key concepts of digital teaching and learning
to analyse, assess, select and justify pedagogically appropriate e-learning methods and tools for digital teaching and learning innovations.
to design and create pedagogically grounded online courses.

Course Contents

Online Teaching and Learning: Theoretical Underpinnings
Educational Design for Online Teaching and Learning
An hierarchical Open Access to Online Education framework: Elements (Open Educational Resources, Learning Activities and Lesson Plans, Online Courses, Digital Learning Spaces). Tools and Key Roles (Online Education Instructional Designers, e-Tutors, e-Learning Systems Administrators, Managers)
Open Educational Resources: Learning Objects, Educational Metadata, Repositories of Learning Objects. Case Studies: the National Repositories of Learning Objects
Learning Activities and Lesson Plans: Authoring Tools for Learning Activities and Lesson Plans, Repositories of Learning Activities and Lesson Plans. Case Studies: the National Repositories of Learning Activities and Lesson Plans
Design, Development and Delivery of Online Courses: Methodology for Designing Online Courses. Authoring Tools for Developing Online Courses. Course Management Systems. Case Study: Open edX, MOODLE
Digital Learning Spaces: 3D Virtual Classrooms and Laboratories

Ιntelligent Agents and Multiagent Systems

Learning Outcomes

Upon successful completion of this course, students should be able to

Know principles, paradigmatic architectures and methods for developing single agent and multi agent systems, have a critical and informed view of strengths and limitations regarding agents and multi agent systems, towards designing and delivering such systems.

Specifically, students know and acquire the abilities to develop

Architectures of single and multi-agent systems
Methods for agents coordination, collaboration and competition in specific settings and paradigmatic environments and problems
Agents’ communication methods and protocols

Via the critical view of agents technology and experience in developing such systems.

Course Contents

Agents: Principles, architectures and application examples
Deliberation vs Reaction: Architectures
Mental attitudes, states and their representation
Multi-agent Systems: Interactions and dependencies
Multiagent organizations and communication
Cooperation and collaboration
Agents communication
The JASON framework and Agent Speak language

Κατεύθυνση: Secondary Major in Networks or Telecommunications: Optional Courses

Student Placement

Energy Systems and Policies

Privacy on the Internet

Learning Outcomes

Course Contents

Suggested Bibliography

Scientific Journals

Educational Digital Systems

Learning Outcomes

Course Contents

Recommended Readings

Compulsory Course of the Secondary Major that has not been chosen as compulsory course of Secondary Major

Telemedicine

Learning Outcomes

Course Contents

Recommended Readings

Data Processing Techniques

Learning Outcomes

Course Contents

Recommended Readings

Climate Change and Environment Systems and Policies

e-Learning Systems

Learning Outcomes

Course Contents

Recommended Readings

Ιntelligent Agents and Multiagent Systems

Learning Outcomes

Course Contents

Recommended Readings

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