The Price of Anarchy and its variants. Analysis of the Price of Anarchy for both general and specific games e.
Distributed Multiple Description Coding Principles Algorithms and Systems by Bai & Huihui
Applications to realistic cases e. Content Introduction to security threats and attacks. Cryptographic and cryptanalysis techniques. Key exchange management PKI. Identification and authentication standards Kerberos, AAA. Systemsecurity Firewalls, IDS. Specific threats on end-systems viruses, worms, trojan horses, stack overflow, rootkits.
- Corporate Control and Enterprise Reform in China: An Econometric Analysis of Block Share Trades.
- Distributed Systems (3).
- Download Distributed Multiple Description Coding Principles Algorithms And Systems.
- Russian social democracy in the underground: A study of the RSDRP in the Ukraine, 1907-1914.
- How I Feel: A Book About Diabetes.
- CSCI 4510/6510 - Distributed Systems and Algorithms - Fall 12222.
Identification of security vulnerabilities in software and operating systems. Checking of networks and applications for vulnerabilities, introduction to computer systems forensics. Security policies.
Max-Planck-Institut für Informatik: Theory of Distributed Systems
Security management, ethical and legal issues in system security. Objectives: Understanding of the principles and the design of Web services, the programming of Web services and the protocols used. Understanding of the new architecture of microservices. Familiarization with knowledge representation via ontologies.
Understanding of representation mechanisms. Content: Introductory concepts. Relationship and difference between services and other related formalisms distributed systems, component-based systems, etc. The microservices architecture. Problems and challenges. LinkedData representation. Cooperative Information Systems and service composition.
Objectives: Often the question whether medicine is art or science is posed. The support of various medical tasks diagnosis, prognosis, therapy planning and patient monitoring through information systems, actively concerns a number of scientific communities artificial intelligence, databases, biomedical engineering, medical informatics , where the temporal dimension constitutes an integral aspect. Recently the focus has changed, from applications based on knowledge, to applications based on data, and from systems that advise to systems that inform.
The key challenge is no longer the mechanization of knowledge but the intelligent utilization of data. Medical problems and the deployment of medical data pose particular challenges, especially when the temporal dimension is a central component. The course aims to present from a critical perspective the various aspects of Temporal Information Systems in Medicine theoretical foundations, applications, open research topics where such systems can be defined as information systems that can store, manage and query time-oriented clinical data and can support various reasoning tasks with respect to such data.
Content: The significance of time in medicine. Modeling and reasoning with time models of time and temporal entities. Requirements, ontologies and temporal reasoning models. General theories of time from the perspective of the medical domain. Temporal databases and their extensions for clinical data. Temporal abstraction of medical data types of abstractions, time-oriented patient monitoring. Time and clinical diagnosis diagnostic concepts, example applications, abductive reasoning using time-objects, temporal constraints.
Automated support for clinical guidelines and protocols time-oriented modeling of clinical guidelines. Research challenges. Objectives: To introduce the student to the medical and clinical environment from the perspective of medical informatics and exploit the possibilities of using information technologies for modeling, prototyping, presenting and using the relevant data. To study and develop practical skills in building relevant intelligent information systems. Content: Information retrieval from medical databases, data, medical records, live signals, and data mining using intelligent techniques.
Study of application systems that are currently in use for managing medical data and suggest ways for better handling and building, medical knowledge bases, electronic health record, and decision support systems for the medical profession. Objectives: Understanding the usefulness of software reuse. Deepening in the different levels of reuse and understanding the differences between them. Use of software components in practice. Content: Levels of reuse. Best practices for reuse. Evolution of reuse.
Software repositories. Search and retrieval. Data extraction. Use of dedicated APIs. Design patterns. Object-oriented programming standards. Open source software.
Distributed Multiple Description Coding
Open source licensing and legal issues. Organization policies and open-source based development. License compliance. Model-Driven Engineering reuse. Reuse on service level. Objectives: Crash-course introduction to applied cryptography, systems and network security and thoroughly covering of several advanced topics in applied cryptography, software vulnerabilities and memory errors, side channels, attacks and defenses, mobile security, web security, network se-curity, privacy, and anonymity.
Content: Short introduction to security basics and then special topics are presented. Special topics include advanced cryptographic attacks in protocols, software exploi-tation through code-reuse return-oriented programming, jump-oriented programming, and call-oriented programming , heap exploitation, side channels, advanced software hardening, exploit-ing special network protocols DNS, NTP, etc. The course is seminar-based in part. Once the basics are introduced by the in-structor, students will study advanced papers in class and will have the opportunity to get a feel-ing of what are the important topics in modern security research.
Assessment: Final exam, midterm exam, student paper presentation and homework including laboratory assignments. Objectives: This course explores key challenges faced when one aspires to translate scientific and technological ideas into globally marketable products or services and embark on innovation-driven enterprise entrepreneurship, with a focus on software and Internet-based businesses.
Students will develop knowledge and skills to understand and be able to address a variety of issues faced by startup founders and chief technology officers who need to innovate at the boundaries of information technology and business by understanding both perspectives. Objectives: The objective of this course is to promote the research and technology culture in the graduate program of the Computer Science Department.
The course focuses on a specific topic of Computer Science. Content: The content of the course is according to the specific topic. Prerequisites: With consent of the Lecturer. Assessment: Final exam, midterm exam and homework laboratory exercises and additional exercises. Home Education Admissions Undergraduate Postgraduate. Degree Research Areas Requirements. Faculty Administrative Duties. Georgiou Objectives: Familiarization with fundamental concepts and principles of distributed systems in both breadth and depth.
Prerequisites: Bibliography: A. Tanenbaum and M. English Version: A. Goulouris, J.
Dollimore and T. Assessment: Final exam, midterm exam and homework programming assignments.
Table of contents
Dimopoulos and Chr. Christodoulou Objectives: This course covers specialized topics in Artificial Intelligence, such as modeling and solving constraint satisfaction and knowledge representation problems, symbolic learning, learning with various forms of neural networks, including deep learning and reinforcement learning.
Content: Introduction to Artificial Intelligence. Topics in Constraint Satisfaction. Satisfiability and Optimization in Logic. Answer Set Programming. Introduction to Artificial Neural Networks. Single layer and Multi layer Perceptrons. Backpropagation learning algorithm. Deep Learning and Convolutional Neural Networks. Recurrent Neural Networks. Self-organizing Maps. Radial-basis Function Networks.
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