studies

The course presents topics related to the study, design and implementation of modern distributed systems including cloud computing, content delivery networks and vehicular networks. The course offers a comprehensive study of key concepts related to distributed computing hardware and software. Emphasis is placed on the communication among the various components of the system as well as on task management, ontology naming and security. The course provides an in-depth examination of cloud computing architectures as well as emerging models that expand their capabilities (Network Function Virtualization - NFV, Software Defined Networking - SDN, Edge Cloud και Fog/Edge Computing). In addition, technologies and architectures of content delivery and vehicular networks will be studied. Relative composition models will be explored including heterogeneity, scaling, dynamic workflow representation techniques, quality assurance, classification of parameters and requirements, and fault tolerance techniques.

• Tables, operations with tables, definitions, inverse and inverse of a table. Linear equations, methods of solving linear systems, Gauss elimination, Cramer's rule. eigenvalues and eigenvectors. probability theories, random distributions of basic discrete and continuous distributions.

• Data cleaning, transformation. Measures of similarity, distance. Linear and accounting regression. Introduction to Matlab Programming, Programming in mat lab. Introduction to R.

• Linear Multiple Linear Regression, Logistic Regression, Probit Regression, ANOVA-MANOVA. Exploratory factor analysis. The R language environment, Syntax, Basic Structures and Functions, Linear regression and Accounting regression applications, with R.

• Basic types of categorization. Statistical classification. Discriminant function analysis. Criteria for evaluating categorization methods. Database mining and advanced forecasting techniques.

• Βασικά είδη κατηγοριοποίησης. Στατιστική ταξινόμηση. Ανάλυση συνάρτησης διάκρισης (Discriminant function analysis). Κριτήρια αξιόλογησης μεθόδων κατηγοριοποίησης. Εξόρυξη από βάση δεδομένων και προηγμένες τεχνικές πρόβλεψης.εφαρμογές συσταδοπόίησης και νευρωνικών με matlab και R.

The course introduces the main challenges, solutions and application fields of machine-to-machine communications. As an emerging networking paradigm, machine-to-machine communications spans all communication processes that do not involve only humans and which are designed to pursue tasks of automation in the most general sense. This enables completely new application areas but introduces several novel and severe challenges, especially in the Smart Cities and IoT world. Many Issues have been addressed by research industry and by researchers over the last years and new standardization activities have initiated. This course deals with these new insights and technologies and related them to the new emerging application fields in IoT and smart cities. Indicatively, the course includes traditional automation systems, connected world and networking, Internet-of-things, smart grid, vehicular networks communications and application scenarios.

Also, this course M2M (Machine-to-Machine) includes drivers and benefits, business trends, relationship of M2M with Machine Learning and the IoT (Internet of Things), Machine Learning algorithms, M2M standardization efforts, M2M ecosystem and applications, M2M network infrastructure technologies, M2M network planning and implementation in Smart Cities, and other key topics.

Additionally, the course includes the following subjects: Fundamentals of Wireless Communications and Networking, Physical Modeling of Wireless Channels, Transmission Fundamentals, Multiple Access Techniques and Wireless Protocols, Channels’ Capacity. Next Generation Networks (NGN) and Applications, NGN Architectures, principal characteristics and platforms. Satellite Communications, DVB-T platform and DVB-S 2+, analysis and design of satellite links. Multihop networks. Wireless LAN, IEEE 802.11, and Ad-hoc/Wireless Sensor Networks (WSNs), Power Control and Energy Efficiency, Routing, Resource Allocation. M2M Communication Fundamentals. Requirements. Services. Application Examples. Information communication technologies. Wired transmission. Wireless transmission. Analysis of popular reference models, e.g., Lora, SigFox, LTE-M. Data Transfer protocols.

The course introduces topics related to the design and development of mobile applications for smart city environments using Internet of Things (IoT) technologies (hardware and software), as well as programming in a cloud environment. Related apps within the area of Internet of Things include, but are not limited to: smart transport, smart cities, smart living, smart energy, smart health and smart learning. In addition, virtualization techniques of resources at the network edge involving Edge computing and fog computing are studied. Mobile Edge Computing (MEC) technology defines an innovative network architecture where cloud computing services are provided from the edge of the network, i.e. from smart base stations and network access points. The edge of the network is the part closest to the end user. Transferring the services to the edge, delays are reduced as the distance between the user and the service point is shorter. Further, design and development methodologies are discussed for 5G wireless networks, Ultra Dense Networks - UDN, Wireless Sensor Networks - WSN, Software Defined Networks – SDN and Vehicular Networks for smart cities environments.

The evolution of digital technologies, in particular the Internet of Things, raises a wealth of legal issues ranging from copyright law, privacy issues, consumer protection, free access to information, data rights regulation (in particular the Big Data issue) and the security of information systems (cybercrime issues). The course examines all of the foregoing and, more specifically, the legal aspects of artificial intelligence, machine learning, blockchain technology and smart contracts.

The purpose of the course is to detect and address the legal issues related to the rapid development of digital technologies focusing also to the new reality of the Internet of Things (IoΤ).

This is an introductory course on financial management and control. The aim of the course is to familiarize the students with the process by which managers insure that the effective and efficient administration of financial resources achieves a cultural organization’s public policy objectives. Cultural organizations include not only government agencies but also nonprofit organizations such as colleges, museums and arts organizations, and foundations.

The course provides the basic concepts, fundamental approaches and key tools for aspiring decision makers who do not necessarily hold financial positions or backgrounds. It will equip students with the state-of-the art tools, methodologies and ideas needed in making and analyzing the two key decisions in finance concerning Investments and Financing.

The widespread use of technology in addition to the simplification and automation of many of our day-to-day tasks exposes users and organizations to a host of risks. These risks can come from many factors such as incorrect system architecture, incorrect configuration or lack of necessary control mechanisms. The course will examine real public service computer systems with common security problems and their detection methodology, both manually and using tools, as well as identify key security problems in web and mobile applications.

Given the need for modern Public Service systems for decentralized and secure architectures, the course will analyze Blockchain technologies, their security, their applications as well as platforms for the development of such applications.

The subject of the course is the development of software that can be used in the context of smart cities. This software can be installed and run on both modern "smartphones" and other smart devices, that have appeared in recent years and use an operating system. The software that can be used in smart cities includes, among others, software for wearables, software for smart TVs, software for the new generation of smart cars, as well as software for smart boards "Android Things" The course analyzes the most popular IoT operating systems, as well as the application development tools in them, however the material mainly includes the use of object-oriented Java programming language, for the development of applications on mobile devices under the Android operating system, and software development on cloud computing platforms.

Principles of Geographic Information Systems, Selection of Spatial Data Models (vector / raster depending on the nature of the case study), basic concepts and cartographic techniques, basic principles of geographic database management systems, concepts and techniques of spatial analysis, mapping spatial data in regional and urban level.

Introduction to Semantics, Knowledge and Data Management, Web Information Retrieval, Semantic protocols (RDF, RDF Schema, OWL, SPARQL). Semantic tools, Ontologies, Modern Search Engines, Web 3.0 Technologies, Text Analytics, Text mining and Sentiment Analysis on the Web and Social Networks, Collective Intelligence, Crowdsourcing.