Contents

• foundations of computability:
  • universal register machines
  • syntax and operational semantics of imperative programming languages
• formal methods for the specification and verification of imperative programs:
  • assertions,
  • Hoare-calculus, partial correctness, termination
• imperative and object-oriented programming concepts (Java):
  • primitive and composite data types,
  • methords (procedures and functions). calling conventions, overloading
  • modules, classes, objects
  • class hierarchies, inheritance, polymorphism
  • abstract classes, interfaces
• programming methods:
  • stepwise refinement
  • divide and conquer
  • backtracking
• analysis of time and space requirements:
  • O-notation
  • recursion and iteration
  • analysis of algorithms for sorting and searching
  • algorithms, data structures, data abstraction

• Concepts of Programming Languages, Robert Sebesta, Pearson Education , 10th Edition, 2012, ISBN: 0131395319
• Data Structures & Problem Solving Using Java, Mark Allen Weiss, Addison Wesley, 4. Auflage, 2010, ISBN: 0-321-54140-5
• Cormen, Leiserson, Rivest: Introduction to Algorithms, 3. Auflage 2009,
• Bundle of algorithms in java, third edition, parts 1-5. Sedgewick Robert und Michael Schidlowsky. Addison-Wesley Longman, Amsterdam. 2003.

Prerequisites

background as taught in the class "Functional Programming".

Please also sign up for the class in our KVV.

Website: https://www.mi.fu-berlin.de/w/SE/VorlesungNichtseq_Vert_Prg2021

Contents:

Programming and synchronization of concurrent processes that share resources or interact through message passing.

• Non-Sequential programs and processes in their various forms, non-determinism, determinism
• Synchronization mechanisms: locks, monitors, guards, events, semaphores
• Non-Sequential program execution and object oriented systems
• Control flow, strategies selection, priorities, handling and avoiding deadlock
• Coroutines implementation, 
• - Multiprocessor systems
• Programming and Synchronisation of concurrent processes that interact through message passing
• Remote Calling Techniques
• Client-server, Peer-to-peer Networks
• Parallel computing over networks
• Concurrent and coordination languages
• Processing on the server and on the client.
• Middleware, structured communication, static and dynamic interfaces
• Event-based and stream-based processing
• Security of network applications
• Non-functional Aspects (time, memory, quality of service)

Literatur:

• Principles of Concurrent and Distributed Programming. M. Ben-Ari. Addison-Wesley. 
• Distributed Systems. Concepts and Design. Fifth Edition. George Coulouris, Jean Dollimore, Tim Kindberg, Gordon Blair. Pearson.

Website: https://www.mi.fu-berlin.de/w/SE/VorlesungNichtseq_Vert_Prg2021

Contents:

Programming and synchronization of concurrent processes that share resources or interact through message passing.

• Non-Sequential programs and processes in their various forms, non-determinism, determinism
• Synchronization mechanisms: locks, monitors, guards, events, semaphores
• Non-Sequential program execution and object oriented systems
• Control flow, strategies selection, priorities, handling and avoiding deadlock
• Coroutines implementation, 
• - Multiprocessor systems
• Programming and Synchronisation of concurrent processes that interact through message passing
• Remote Calling Techniques
• Client-server, Peer-to-peer Networks
• Parallel computing over networks
• Concurrent and coordination languages
• Processing on the server and on the client.
• Middleware, structured communication, static and dynamic interfaces
• Event-based and stream-based processing
• Security of network applications
• Non-functional Aspects (time, memory, quality of service)

Literatur:

• Principles of Concurrent and Distributed Programming. M. Ben-Ari. Addison-Wesley. 
• Distributed Systems. Concepts and Design. Fifth Edition. George Coulouris, Jean Dollimore, Tim Kindberg, Gordon Blair. Pearson.

The module operating and communication systems closes the gap between the hardware of a computer and the applications.

We will cover the following topics::

• I/O systems
• DMA/PIO
• Interrupt handling
• Buffers
• Processes/threads
• Virtual memory
• UNIX and Windows
• Shells
• Utilities
• Peripherals and networking
• Networks
• Media
• Media access
• Protocols
• Reference models
• TCP/IP
• The Internet

• Andrew S. Tanenbaum: Computerarchitektur, 5.Auflage, Pearson Studium, 2006
• English: Andrew S. Tanenbaum (with contributions from James R. Goodman):
• Structured Computer Organization, 4th Ed., Prentice Hall International, 2005.

Begleitveranstaltung zur Vorlesung 19300701

The module Mobile Communications covers many topics of mobile and wireless communication systems - an area of Computer Science that shows tremendous growth rates and that influences more and more areas of our society.

We put special emphasis on a system perspective throughout the whole module and give many references to real systems, international standards and up-to-date research results.

Topics coverd are:

• Basics of wireless transmission: frequencies, signals, antennas, signal propagation, multiplexing, modulation, spread spectrum, cellular systems;
• Media access: SDMA, FDMA, TDMA, CDMA;
• Wireless telecommunication systems: GSM, TETRA, UMTS, IMT-2000, LTE;
• Wireless local area networks: infrastructure/ad-hoc, IEEE 802.11/15, Bluetooth;
• Mobile networking: Mobile IP, ad-hoc networks;
• Mobile transport layer: traditional TCP, adapted TCP versions, additional mechanisms;
• Mobility support;
• Outlook: 5th generation mobile networks

Jochen Schiller, Mobilkommunikation, Addison-Wesley, 2.Auflage 2003

Alle Unterlagen verfügbar unter http://www.mi.fu-berlin.de/inf/groups/ag-tech/teaching/resources/Mobile_Communications/course_Material/index.html

Cluster computer are the prevailing type of high performance computers. They are built of custom off-the-shelf processor boards that are connected by a high speed interconnection network. Although usually locally integrated, they are conceptually distributed systems with local operating system images. Their enormous potential, however, can only be exploited, if program code and data are optimally distributed across the nodes. Cluster management mechanisms also need to be scalable to be employed in systems with thousands of nodes. The lecture course gives an overview of the architecture of cluster computers and the related management problems for which algorithmic solutions are presented.

• Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
• Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
• Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
• Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
• Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999

Target group

• Computer Science Master students

Requirements

• Experience with computers and software as well as programing skills.

Language

• The course language is German (or English if requested).
• The exam will be formulated in German, but answers may be given in English, too.

Credits & Exams

The criteria for gaining credits are

• active participation in the tutorials: regular preparation of assignements & presentation of results in the tutorials
• passing of the exam

Website

https://www.mi.fu-berlin.de/w/SE/VorlesungClusterComputing

• Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
• Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
• Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
• Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
• Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
• Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.

In this lecture, we will focus on the Internet core and the deployment of its applications. We will introduce methods to measure and assess network protocols and network architectures. The topics of this lecture are embedded in a practical context. For example, we will discuss how the current peering ecosystem works and why it is challenging to measure it. You will better understand how to quantify the deployment of common and upcoming protocols (e.g., IPv6, DNSSEc, HTTP/2). We will also discuss how to process large-scale Internet data sets efficiently. We will discuss important application fields of Internet measurements, with a special focus on network security and performance.

In addition to the lecture, we will offer lab exercises. In these exercises, you will conduct your own measurements and analyze existing data sets.

• Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle, The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research, ACM SIGCOMM Computer Communication Review, Vol. 49, No. 1, pp. 24--30, January 2019. Editorial note  PDF   BibTeX   Abstract  
   

Audience: All students who want to gain a better understanding of the Internet and its applications.

Requirements: Good understanding of computer networks and communication systems.

Cluster computer are the prevailing type of high performance computers. They are built of custom off-the-shelf processor boards that are connected by a high speed interconnection network. Although usually locally integrated, they are conceptually distributed systems with local operating system images. Their enormous potential, however, can only be exploited, if program code and data are optimally distributed across the nodes. Cluster management mechanisms also need to be scalable to be employed in systems with thousands of nodes. The lecture course gives an overview of the architecture of cluster computers and the related management problems for which algorithmic solutions are presented.

• Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
• Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
• Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
• Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
• Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999

Target group

• Computer Science Master students

Requirements

• Experience with computers and software as well as programing skills.

Language

• The course language is German (or English if requested).
• The exam will be formulated in German, but answers may be given in English, too.

Credits & Exams

The criteria for gaining credits are

• active participation in the tutorials: regular preparation of assignements & presentation of results in the tutorials
• passing of the exam

Website

https://www.mi.fu-berlin.de/w/SE/VorlesungClusterComputing

• Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
• Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
• Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
• Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
• Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
• Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.

In this lecture, we will focus on the Internet core and the deployment of its applications. We will introduce methods to measure and assess network protocols and network architectures. The topics of this lecture are embedded in a practical context. For example, we will discuss how the current peering ecosystem works and why it is challenging to measure it. You will better understand how to quantify the deployment of common and upcoming protocols (e.g., IPv6, DNSSEc, HTTP/2). We will also discuss how to process large-scale Internet data sets efficiently. We will discuss important application fields of Internet measurements, with a special focus on network security and performance.

In addition to the lecture, we will offer lab exercises. In these exercises, you will conduct your own measurements and analyze existing data sets.

• Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle, The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research, ACM SIGCOMM Computer Communication Review, Vol. 49, No. 1, pp. 24--30, January 2019. Editorial note  PDF   BibTeX   Abstract  
   

Audience: All students who want to gain a better understanding of the Internet and its applications.

Requirements: Good understanding of computer networks and communication systems.

Contents:

• models of computation
  • automata
  • formal languates
  • grammars and the Chomsky-hierarchy
  • Turing-machines
  • computabilty
• introduction to the complexity of computational problems

• Uwe Schöning, Theoretische Informatik kurzgefasst, 5. Auflage, Spektrum Akademischer Verlag, 2008
• John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman, Einführung in die Automatentheorie, Formale Sprachen und Komplexität, Pearson Studium, 3. Auflage, 2011
• Ingo Wegener: Theoretische Informatik - Eine algorithmenorientierte Einführung, 2. Auflage, Teubner, 1999
• Michael Sipser, Introduction to the Theory of Computation, 2nd ed., Thomson Course Technology, 2006
• Wegener, Kompendium theoretische Informatik - Eine Ideensammlung, Teubner 1996

Contents

The proseminar delves more deeply into topics covered in the basic classes taught by the theory group. During the winter semester, we consider advanced topics from the theory of computability and of formal languates (in continuation of "Theory of Computation"); during the summer semester, we talk about algorithms (in continuation of "Algorithms, Data Structures, and Data Abstraction").

Prerequisites

two semesters of computer science, successful completion of "Theory of Computation"

wird mit der Ankündigung bekannt gegeben

In this Proseminar students will present selected topics from programming languages. We will cover special topics in the theoretical and practical foundations of programming languages as well as the scientific basis for several less mainstream programming paradigms such as aspect orientation and the synchronous paradigm. 

Each presentation will be followed by detailed discussion amongst all participants. To help all participants prepare for these discussions, a writeup of the presentation should be made available to all students well ahead of the presentation.

Literatur wird selbst recherchiert

Target group:

• Students in the bachelor track (third semester or later)
• Students in the teaching bachelor track
• Students minoring in our department

Prerequisites:

• Functional and object oriented programming

Content

Database design with ERM/ERDD. Theoretical foundations of relational database systems: relational algebra, functional dependencies, normal forms. Relational database development: SQL data definitions, foreign keys and other integrity constraints, SQL as applicable language: essential language elements, embedding in programming language. Application programming; object-relational mapping. Security and protection concepts. Transaction subject, transactional guaranties, synchronization of multi user operations, fault tolerance features. Application and new developments: data warehousing, data mining, OLAP.

Project: the topics are deepened in an implementation project for student groups.

• Alfons Kemper, Andre Eickler: Datenbanksysteme - Eine Einführung, 5. Auflage, Oldenbourg 2004
• R. Elmasri, S. Navathe: Grundlagen von Datenbanksystemen, Pearson Studium, 2005

Requirements

• ALP 1 - Functional Programming
• ALP 2 - Object-oriented Programming
• ALP 3 - Data structures and data abstractions
• OR Informatik B

Contents

We choose a typical application area of algorithms, usually for geometric problems, and develop software solutions for it, e.g., computer graphics (representation of objects in a computer, projections, hidden edge and surface removal, lighting, raytracing), computer vision (image processing, filtering, projections, camera calibration, stereo-vision) or pattern recognition (classification, searching).

Prerequsitions

Basic knowledge in design and anaylsis of algorithms.

je nach Anwendungsgebiet

Programming for Internet Communication Along the Stack

In this course, students will develop solutions that relate to Internet communication. We will consider all aspects of the Internet ecosystem, including lower layers up to the application layer for the current Internet as well as the Internet of Things. Participants will work in smaller groups (3-4 students), where each group will focus on a specific builing block. This will allow to adapt to interests and expertises (e.g., web vs. embedded system programming).

Note that we intend to use the results of this software project to revamp the old super computers in the entrance area of the CS building (i.e., the PDP 11 and the Russian clone). We will extend both computers by network-capabale devices, sensors, screens, and your software (a) to demonstrate the Internet of Things and (b) to give insights into the current state of the Internet backbone.

Below you find some initial topics, which we will clarify during the first meeting.

Embedded / IoT Programming

You will develop applications for embedded devices. The software base is the open source operating system RIOT.

Internet Backbone Measurements

You will develop applications that process live BGP data and check if an attack is happening. The software base is the RTRlib and BGPMON/BGPStream.

Web Interface

You will develop a web interfaces that shows the state of the super computers to remote participants. Your web frontend should be suitable for mobile devices.

If you have questions in advance, feel free to contact Matthias Wählisch.

• A. S. Tanenbaum, Modern Operating Systems, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall Press, 2007.
• Shelby, Zach, and Carsten Bormann. 6LoWPAN: The wireless embedded Internet. Vol. 43. Wiley. com, 2011.
• A. Dunkels, B. Gronvall, and T. Voigt, "Contiki - a lightweight and flexible operating system for tiny networked sensors." in LCN. IEEE Computer Society, 2004, pp. 455-462.
• P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, D. Gay, J. Hill, M. Welsh, E. Brewer, and D. Culler, "TinyOS: An Operating System for Sensor Networks," in Ambient Intelligence, W. Weber, J. M. Rabaey, and E. Aarts, Eds. Berlin/Heidelberg: Springer-Verlag, 2005, ch. 7, pp. 115-148.
• Oliver Hahm, Emmanuel Baccelli, Mesut Günes, Matthias Wählisch, Thomas C. Schmidt, "RIOT OS: Towards an OS for the Internet of Things," in Proceedings of the 32nd IEEE International Conference on Computer Communications (INFOCOM), Poster Session, April 2013.
• M.R. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L.A. Grieco, G. Boggia and M. Dohler, "Standardized Protocol Stack For The Internet Of (Important) Things", IEEE Communications Surveys and Tutorials, December 2012.
• J. Wiegelmann, Softwareentwicklung in C für Mikroprozessoren und Mikrocontroller, Hüthig, 2009

In this course you will be expected to write code. The outcome of your software project should be a concrete contribution to the RIOT code base, and take the shape of one or more pull request(s) to the RIOT github (https://github.com/RIOT-OS/RIOT). Before you start coding, refer to the starting guide

https://github.com/RIOT-OS/RIOT/wiki#wiki-start-the-riot

Mixed groups of master and bachelor students will either implement an independent project or are part of a larger project in the area of semantic technologies. They will gain in-depth programming knowledge about applications of semantic technologies and artificial intelligence techniques in the Corporate Semantic Web. They will practice teamwork and best practices in software development of large distributed systems and Semantic Web applications. The software project can be done in collaboration with an external partner from industry or standardization. It is possible to continue the project as bachelor or master thesis.

Corporate Semantic Web

Corporate Semantic Web

Further information can be found on the course website

Content

Subject of the project: either development of software together with a company (in this case: 4­ weeks fulltime August/September) or we build a so called NoSQL system. Decision in March. Further information are published in the KVV.

Wird bekannt gegeben

Requirement

ALP I-V, Foundations of Datenbase Systems, good programming knowledge.

In this software project we will carry out the entire process of designing, implementing, and evaluating mobile devices (mainly Android) with a focus on usable security. We will work in small groups.

Tasks:

1. Further development of existing authentication methods and adaptation to smartphones.

2. Development of new authentication methods (some ideas already exist but new ones can also be incorporated).

3. E- mail encryption on mobile devices.

Requirements:

1. Knowledge of programming on Android devices (Alternatively, for iOS). There will be a brief preliminary test.

2. Interest in Security and Usability.

3. Participation in the planning meeting (end of summer term 2016).

Zielgruppe:

Dieses Software-Projekt richtet sich an Studenten (m/w/d) der Informatik (Computer Science) und der Datenwissenschaften (Data Science).

Programming for Internet Communication Along the Stack

In this course, students will develop solutions that relate to Internet communication. We will consider all aspects of the Internet ecosystem, including lower layers up to the application layer for the current Internet as well as the Internet of Things. Participants will work in smaller groups (3-4 students), where each group will focus on a specific builing block. This will allow to adapt to interests and expertises (e.g., web vs. embedded system programming).

Note that we intend to use the results of this software project to revamp the old super computers in the entrance area of the CS building (i.e., the PDP 11 and the Russian clone). We will extend both computers by network-capabale devices, sensors, screens, and your software (a) to demonstrate the Internet of Things and (b) to give insights into the current state of the Internet backbone.

Below you find some initial topics, which we will clarify during the first meeting.

Embedded / IoT Programming

You will develop applications for embedded devices. The software base is the open source operating system RIOT.

Internet Backbone Measurements

You will develop applications that process live BGP data and check if an attack is happening. The software base is the RTRlib and BGPMON/BGPStream.

Web Interface

You will develop a web interfaces that shows the state of the super computers to remote participants. Your web frontend should be suitable for mobile devices.

If you have questions in advance, feel free to contact Matthias Wählisch.

Automated methods for the recognition and analysis of behavior have been increasingly used to investigate individual and collective animal behavior. This includes applications such as tracking individuals in large groups (humans in crowds, animals in herds/swarms/flocks, cars in traffic), mapping observations of an individual to a behavioral space ("what is person X doing"), and learning about the structure of social interactions ("who is doing what with whom"). Recent methods allow processing large amounts of data resulting in surprisingly accurate results. We will look at a variety of supervised and unsupervised approaches with examples from our own research.

,,

,,

Content

This course motivates the need for computer security and introduces central concepts of computer security such as security objectives, threats, threat analysis, security policy and mechanism, assumptions and trust, and assurance.

We discuss authentication mechanisms, followed by various security models and show which security related questions can be answered in these models. The models we discussed include the Access Control Matrix Model, the Take-Grant Protection Model, the Bell-LaPadula and related models, the Chinese Wall Model, the Lattice Model of Information Flow.

Subsequently, we cover principles of security architectures and go through design approaches for secure systems e.g., capability based systems and hardware rotection mechanism concepts such as protection rings. Based on the learned, we may look at selected case studies of existing systems.

In the remainder of the course, we cover exploitation techniques for specific implementation vulnerabilities such as race conditions, stack and heap overflows, integer overflows, and return oriented programming. We continue with a discussion of insider threats, insider recruitment and social engineering attacks.

If time permits, we continue to look at the problems that arise when humans interface with security e.g., password habits and password entry mechanisms, human responses to security prompts, incentives and distractors for better security, or reverse Turing tests.

1. M. Gasser. Building Secure Computer Systems.
2. D. Denning. Cryptography and Data Security.
3. D. Kleidermacher, M. Kleidermacher. Embedded Systems Security.
4. M. Bishop. Computer Security Art and Practice.
5. Various research papers.

The lecture held by Dr. Andreas Wolf (from the Bundesdruckerei) He will give a broad overview of biometric processes and applications. He will also address the current issues with ePassports and new electronic identity cards.

The course aims to include:

• General structure of biometric systems
• Features of biometric modalities
• IT security and risk assessment
• Errors in biometric processes
• Fingerprinting
• Facial and iris recognition
• Speaker recognition and other modalities
• Standards
• ePassport

Next to the theoretical foundations of biometric modalities, the students are to develop the ability to assess the applicability of biometrics in various scenarios.

Die Lehrveranstaltung soll am Freitag den 12. April beginnen.

Das Vorlesungsskript liegt unter

https://drive.google.com/drive/folders/0B7NhYbv9QewkRkk2WVRuM2Rqd00?usp=sharing

Webex Link zu der Veranstaltung:

Meeting-Link: https://fu-berlin.webex.com/fu-berlin/j.php?MTID=m2cc50d96918fcaeb09f3c36a264f4f29

Meeting-ID: 121 079 7504

Meeting-Password: mCwDw274PS8

Cluster computer are the prevailing type of high performance computers. They are built of custom off-the-shelf processor boards that are connected by a high speed interconnection network. Although usually locally integrated, they are conceptually distributed systems with local operating system images. Their enormous potential, however, can only be exploited, if program code and data are optimally distributed across the nodes. Cluster management mechanisms also need to be scalable to be employed in systems with thousands of nodes. The lecture course gives an overview of the architecture of cluster computers and the related management problems for which algorithmic solutions are presented.

• Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
• Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
• Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
• Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
• Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999

Target group

• Computer Science Master students

Requirements

• Experience with computers and software as well as programing skills.

Language

• The course language is German (or English if requested).
• The exam will be formulated in German, but answers may be given in English, too.

Credits & Exams

The criteria for gaining credits are

• active participation in the tutorials: regular preparation of assignements & presentation of results in the tutorials
• passing of the exam

Website

https://www.mi.fu-berlin.de/w/SE/VorlesungClusterComputing

• Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
• Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
• Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
• Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
• Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
• Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.

In this lecture, we will focus on the Internet core and the deployment of its applications. We will introduce methods to measure and assess network protocols and network architectures. The topics of this lecture are embedded in a practical context. For example, we will discuss how the current peering ecosystem works and why it is challenging to measure it. You will better understand how to quantify the deployment of common and upcoming protocols (e.g., IPv6, DNSSEc, HTTP/2). We will also discuss how to process large-scale Internet data sets efficiently. We will discuss important application fields of Internet measurements, with a special focus on network security and performance.

In addition to the lecture, we will offer lab exercises. In these exercises, you will conduct your own measurements and analyze existing data sets.

• Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle, The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research, ACM SIGCOMM Computer Communication Review, Vol. 49, No. 1, pp. 24--30, January 2019. Editorial note  PDF   BibTeX   Abstract  
   

Audience: All students who want to gain a better understanding of the Internet and its applications.

Requirements: Good understanding of computer networks and communication systems.

The founding fathers of artificial intelligence viewed the mind as an information-processing machine that can be understood, modeled, and replicated. This computer metaphor of the brain had a profound influence not only on AI algorithms, but also on the study of the mind. In the previous five decades the so-called cognitive science emerged as an interdisciplinary field operating at the intersection of artificial intelligence, psychology, linguistics, neuroscience and philosophy. Its aim is to advance our understanding of, for example, how we think and feel, how we make complex decisions, and how we assess our experiences and learn from mistakes. In the age of hyped AI dreams and nightmares, it is more important than ever to study the mechanisms that underlie human cognition and emotion in order to reflect in a scientific way on the potentials and limits of artificial intelligence. In this course we will discuss topics as diverse as the mathematics of intuition, emotional intelligence, the relationship between language and intelligence, embodiment and consciousness, as well as modelling complex motives.

The instructors come from different academic backgrounds that cover various aspects of cognitives cience. Thus, this course should be accessible not only to computer science, but also to psychology, linguistics, neuroscience and philosophy students.

The seminar will present up to 12 papers.

Suggested reading

• Baars, B. (1989). A cognitive theory of consciousness. Cambridge, Mass.: Cambridge University Press.
• Bermúdez, J. (2014). Cognitive Science: An Introduction to the Science of the Mind. 2nd ed.
• Cambridge: Cambridge Univ. Press.
• Chalmers, D. J. (1996). The Conscious Mind: In Search of a Fundamental Theory. New York, Oxford: Oxford University Press
• Damasio, A. (2005). Descartes' Error: Emotion, Reason, and the Human Brain. New York: Penguin Books.
• Dennett, D. 1991. Consciousness Explained. Little, Brown
• Dennett, D. (1996). Kinds of minds: towards an understanding of consciousness. London: Weidenfeld and Nicholson.
• Donald M, Origin of the Modern Mind, Harvard University Press, Cambridge, 1991.
• Dörner, D. (2001). Bauplan für eine Seele. Zweite Auflage. Hamburg: Rowohlt Taschenbuch Verlag.
• Engeler, E. (2019). Neural algebra on “how does the brain think?”. Theoretical Computer Science, 777, pp. 296-307. doi:10.1016/j.tcs.2019.03.038
• Frith, C. (2007). Making up the mind. How the brain creates our mental world. London: Blackwell.
• Goleman, D. (1996). Emotional Intelligence: Why It Can Matter More than IQ. London: Bloomsbury Publishing.
• Haugeland, J. 1981. Semantic engines: An introduction to mind design. In Mind Design:
• Philosophy, Psychology, Artificial Intelligence, ed. J. Haugeland. MIT Press
• LeDoux, J. E. (1996). The Emotional Brain. New York: Simon & Schuster.
• Marblestone, A. H., Wayne, G., & Kording, K. P. (2016). Toward an Integration of Deep Learning and Neuroscience. Frontiers in computational neuroscience, 10, 94. doi:10.3389/fncom.2016.00094
• Penrose R, The Emperor’s New Mind: Concerning Computers, Mind, and the Law of Physics, Oxford: Oxford University Press, 1989.
• Pinker, S. (2009). How the Mind Works. New York: W. W. Norton & Company.
• Searle J. (1980). Brains and Programs, Behavioral and Brain Sciences, 3:417-57
• Searle J (1992). The Rediscovery of the Mind, the MIT Press, Cambridge, 1992
• Sloman, A. (1978). The Computer Revolution in Philosophy. Hassocks, Sussex: Harvester Press (and Humanities Press).
• 
  
  Wittgenstein, L, Philosophical Investigations, Basil Blackwell Ltd, 1953   It is also useful to consult the journal Cognitive Science, published on behalf of the CognitiveScience Society.

Nowadays, in our digital society, the question is no longer whether software should be used, but which software should be chosen, and how we, as computer scientists, should design our software for human users. In such decision-making processes, the usability of the software has a strong influence on its success (independently how success is defined). Excellent usability and positive user experience can only be achieved by knowing and understanding the user's goals, their hidden needs, and also their cognitive abilities. Thus, besides choosing the "right" technology, considering the users and their needs is fundamental for the later success of the service or product. However, usability is neither a characteristic inherent in software nor can usability, as a software feature, be developed separately at a certain point in time. Usability always stands in a particular usage context, and this context must be clearly understood. Thus, we need to ensure the usability of software for the respective target group despite probably existing complex software functionality. Thus, for improving a software's usability, the entire software development process must be adapted. Depending on the development phase, and the project situation, we can select and apply different principles and methods.  In the course "Human-Computer Interaction I", you learn and apply these principles and methods to create a positive user experience.

Shneiderman, Ben, et al. "Designing the user interface: strategies for effective human-computer interaction". Pearson, 2016.

Dix A, Finlay J, Abowd GD, Beale R "Human-Computer Interaction". Pearson Prentice Hall 2004.

Sharp, Helen, Yvonne Rogers, and Jenny Preece. "Interaction design: beyond human-computer interaction." 2007.

The lecture will be held online only during summer term 2021.

For further information: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/summer_term_2021/index.html

The lecture held by Dr. Andreas Wolf (from the Bundesdruckerei) He will give a broad overview of biometric processes and applications. He will also address the current issues with ePassports and new electronic identity cards.

The course aims to include:

• General structure of biometric systems
• Features of biometric modalities
• IT security and risk assessment
• Errors in biometric processes
• Fingerprinting
• Facial and iris recognition
• Speaker recognition and other modalities
• Standards
• ePassport

Next to the theoretical foundations of biometric modalities, the students are to develop the ability to assess the applicability of biometrics in various scenarios.

Die Lehrveranstaltung soll am Freitag den 12. April beginnen.

Das Vorlesungsskript liegt unter

https://drive.google.com/drive/folders/0B7NhYbv9QewkRkk2WVRuM2Rqd00?usp=sharing

Webex Link zu der Veranstaltung:

Meeting-Link: https://fu-berlin.webex.com/fu-berlin/j.php?MTID=m2cc50d96918fcaeb09f3c36a264f4f29

Meeting-ID: 121 079 7504

Meeting-Password: mCwDw274PS8

Cluster computer are the prevailing type of high performance computers. They are built of custom off-the-shelf processor boards that are connected by a high speed interconnection network. Although usually locally integrated, they are conceptually distributed systems with local operating system images. Their enormous potential, however, can only be exploited, if program code and data are optimally distributed across the nodes. Cluster management mechanisms also need to be scalable to be employed in systems with thousands of nodes. The lecture course gives an overview of the architecture of cluster computers and the related management problems for which algorithmic solutions are presented.

• Heiss, H.-U.: Prozessorzuteilung in Parallelrechnern, BI-Verlag, Mannheim, 1996
• Andrews, G. A.: Foundations of Multithreaded, Parallel and Distributed Programming, Addison-Wesley, 2000
• Pfister, G.: In Search of Clusters 2nd ed., Prentice Hall, 1998
• Zomaya, A.: Parallel and distributed computing handbook, McGraw Gill, 1995
• Buyya, R.: High Performance Cluster Computing, Vol. 1+2, Prentice Hall, 1999

Target group

• Computer Science Master students

Requirements

• Experience with computers and software as well as programing skills.

Language

• The course language is German (or English if requested).
• The exam will be formulated in German, but answers may be given in English, too.

Credits & Exams

The criteria for gaining credits are

• active participation in the tutorials: regular preparation of assignements & presentation of results in the tutorials
• passing of the exam

Website

https://www.mi.fu-berlin.de/w/SE/VorlesungClusterComputing

• Bull, D. R., “Communicating Pictures: A Course in Image and Video Coding,” Elsevier, 2014.
• Ohm, J.-R., “Multimedia Signal Coding and Transmission,” Springer, 2015.
• Wien, M., “High Efficiency Video Coding — Coding Tools and Specifications,” Springer 2014.
• Sze, V., Budagavi, M., and Sullivan, G. J. (eds.), “High Efficiency Video Coding (HEVC): Algorithm and Architectures,” Springer, 2014.
• Wiegand, T. and Schwarz, H., "Source Coding: Part I of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 4, no. 1–2, 2011.
• Schwarz, H. and Wiegand, T., “Video Coding: Part II of Fundamentals of Source and Video Coding,” Foundations and Trends in Signal Processing, Now Publishers, vol. 10, no. 1–3, 2016.

In this lecture, we will focus on the Internet core and the deployment of its applications. We will introduce methods to measure and assess network protocols and network architectures. The topics of this lecture are embedded in a practical context. For example, we will discuss how the current peering ecosystem works and why it is challenging to measure it. You will better understand how to quantify the deployment of common and upcoming protocols (e.g., IPv6, DNSSEc, HTTP/2). We will also discuss how to process large-scale Internet data sets efficiently. We will discuss important application fields of Internet measurements, with a special focus on network security and performance.

In addition to the lecture, we will offer lab exercises. In these exercises, you will conduct your own measurements and analyze existing data sets.

• Vaibhav Bajpai, Anna Brunstrom, Anja Feldmann, Wolfgang Kellerer, Aiko Pras, Henning Schulzrinne, Georgios Smaragdakis, Matthias Wählisch, Klaus Wehrle, The Dagstuhl Beginners Guide to Reproducibility for Experimental Networking Research, ACM SIGCOMM Computer Communication Review, Vol. 49, No. 1, pp. 24--30, January 2019. Editorial note  PDF   BibTeX   Abstract  
   

Audience: All students who want to gain a better understanding of the Internet and its applications.

Requirements: Good understanding of computer networks and communication systems.

The founding fathers of artificial intelligence viewed the mind as an information-processing machine that can be understood, modeled, and replicated. This computer metaphor of the brain had a profound influence not only on AI algorithms, but also on the study of the mind. In the previous five decades the so-called cognitive science emerged as an interdisciplinary field operating at the intersection of artificial intelligence, psychology, linguistics, neuroscience and philosophy. Its aim is to advance our understanding of, for example, how we think and feel, how we make complex decisions, and how we assess our experiences and learn from mistakes. In the age of hyped AI dreams and nightmares, it is more important than ever to study the mechanisms that underlie human cognition and emotion in order to reflect in a scientific way on the potentials and limits of artificial intelligence. In this course we will discuss topics as diverse as the mathematics of intuition, emotional intelligence, the relationship between language and intelligence, embodiment and consciousness, as well as modelling complex motives.

The instructors come from different academic backgrounds that cover various aspects of cognitives cience. Thus, this course should be accessible not only to computer science, but also to psychology, linguistics, neuroscience and philosophy students.

The seminar will present up to 12 papers.

Suggested reading

• Baars, B. (1989). A cognitive theory of consciousness. Cambridge, Mass.: Cambridge University Press.
• Bermúdez, J. (2014). Cognitive Science: An Introduction to the Science of the Mind. 2nd ed.
• Cambridge: Cambridge Univ. Press.
• Chalmers, D. J. (1996). The Conscious Mind: In Search of a Fundamental Theory. New York, Oxford: Oxford University Press
• Damasio, A. (2005). Descartes' Error: Emotion, Reason, and the Human Brain. New York: Penguin Books.
• Dennett, D. 1991. Consciousness Explained. Little, Brown
• Dennett, D. (1996). Kinds of minds: towards an understanding of consciousness. London: Weidenfeld and Nicholson.
• Donald M, Origin of the Modern Mind, Harvard University Press, Cambridge, 1991.
• Dörner, D. (2001). Bauplan für eine Seele. Zweite Auflage. Hamburg: Rowohlt Taschenbuch Verlag.
• Engeler, E. (2019). Neural algebra on “how does the brain think?”. Theoretical Computer Science, 777, pp. 296-307. doi:10.1016/j.tcs.2019.03.038
• Frith, C. (2007). Making up the mind. How the brain creates our mental world. London: Blackwell.
• Goleman, D. (1996). Emotional Intelligence: Why It Can Matter More than IQ. London: Bloomsbury Publishing.
• Haugeland, J. 1981. Semantic engines: An introduction to mind design. In Mind Design:
• Philosophy, Psychology, Artificial Intelligence, ed. J. Haugeland. MIT Press
• LeDoux, J. E. (1996). The Emotional Brain. New York: Simon & Schuster.
• Marblestone, A. H., Wayne, G., & Kording, K. P. (2016). Toward an Integration of Deep Learning and Neuroscience. Frontiers in computational neuroscience, 10, 94. doi:10.3389/fncom.2016.00094
• Penrose R, The Emperor’s New Mind: Concerning Computers, Mind, and the Law of Physics, Oxford: Oxford University Press, 1989.
• Pinker, S. (2009). How the Mind Works. New York: W. W. Norton & Company.
• Searle J. (1980). Brains and Programs, Behavioral and Brain Sciences, 3:417-57
• Searle J (1992). The Rediscovery of the Mind, the MIT Press, Cambridge, 1992
• Sloman, A. (1978). The Computer Revolution in Philosophy. Hassocks, Sussex: Harvester Press (and Humanities Press).
• 
  
  Wittgenstein, L, Philosophical Investigations, Basil Blackwell Ltd, 1953   It is also useful to consult the journal Cognitive Science, published on behalf of the CognitiveScience Society.

Nowadays, in our digital society, the question is no longer whether software should be used, but which software should be chosen, and how we, as computer scientists, should design our software for human users. In such decision-making processes, the usability of the software has a strong influence on its success (independently how success is defined). Excellent usability and positive user experience can only be achieved by knowing and understanding the user's goals, their hidden needs, and also their cognitive abilities. Thus, besides choosing the "right" technology, considering the users and their needs is fundamental for the later success of the service or product. However, usability is neither a characteristic inherent in software nor can usability, as a software feature, be developed separately at a certain point in time. Usability always stands in a particular usage context, and this context must be clearly understood. Thus, we need to ensure the usability of software for the respective target group despite probably existing complex software functionality. Thus, for improving a software's usability, the entire software development process must be adapted. Depending on the development phase, and the project situation, we can select and apply different principles and methods.  In the course "Human-Computer Interaction I", you learn and apply these principles and methods to create a positive user experience.

Shneiderman, Ben, et al. "Designing the user interface: strategies for effective human-computer interaction". Pearson, 2016.

Dix A, Finlay J, Abowd GD, Beale R "Human-Computer Interaction". Pearson Prentice Hall 2004.

Sharp, Helen, Yvonne Rogers, and Jenny Preece. "Interaction design: beyond human-computer interaction." 2007.

The lecture will be held online only during summer term 2021.

For further information: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/summer_term_2021/index.html

In this course we will study stochastic models commonly used to analyse the performance of dynamic systems. Markov models and queues are used to study the behaviour over time of a wide range of systems, from computer hardware, communication systems, biological systems, epidemics, traffic networks to crypto-currencies. We will take a tour of the basics of Markov modelling, starting from birth-death processes, the Poisson process to general Markov and semi-Markov processes and solution methods for those processes. Then we will look at queueing models and queueing networks with exact and approximate solution algorithms. If time allows we will finally study some of the foundations of discrete event simulation.

William Stewart. Probability, Markov Chains, Queues and Simulation. Princeton University Press 2009.

Qualification objectives: The students have a basic understanding of algebraic and computational methods for deep neural networks, their application scope and can practically build and train them with state-of-the-art software tools. They are familiar with typical deep learning structures and understand the relationship to their shallow counterparts.

Content:

- Perceptron

- Multilayer neural network and universal represenation theorem

- Backpropagation

- Deep feedforward networks

- Convolutional Neural Networks

- Autoencoder versus principal component analysis

- Time-autoencoder versus time-lagged independent component analysis

- Generative networks: Variational Autoencoders and Adversarial Generative Networks

- Active learning

This lecture/lab course is suitable for Master students of Mathematics, Computer Science, Computational Sciences and Physics.
Students of the Computational Sciences program can combine this lecture/lab course with 19234502 + 19234501 (Mathematical aspects in machine learning) to complete “complex algorithms A/B”

Physics students please contact the Exam Office, dmitrij.heinz(at)fu-berlin.de, indicating modul and matriculation number

This lecture gives an overview of methods for graph drawing (planarity, minimization of crossings, good layout)

Wird noch bekannt gegeben.

Target Audience

Masters students in Computer Science or Mathematics, advanced Bachelor students.

Prerequisites

"Advanced Algorithms" or a similar class

In this course we will study stochastic models commonly used to analyse the performance of dynamic systems. Markov models and queues are used to study the behaviour over time of a wide range of systems, from computer hardware, communication systems, biological systems, epidemics, traffic networks to crypto-currencies. We will take a tour of the basics of Markov modelling, starting from birth-death processes, the Poisson process to general Markov and semi-Markov processes and solution methods for those processes. Then we will look at queueing models and queueing networks with exact and approximate solution algorithms. If time allows we will finally study some of the foundations of discrete event simulation.

William Stewart. Probability, Markov Chains, Queues and Simulation. Princeton University Press 2009.

This lecture gives an overview of methods for graph drawing (planarity, minimization of crossings, good layout)

Wird noch bekannt gegeben.

Target Audience

Masters students in Computer Science or Mathematics, advanced Bachelor students.

Prerequisites

"Advanced Algorithms" or a similar class

In this course we will study stochastic models commonly used to analyse the performance of dynamic systems. Markov models and queues are used to study the behaviour over time of a wide range of systems, from computer hardware, communication systems, biological systems, epidemics, traffic networks to crypto-currencies. We will take a tour of the basics of Markov modelling, starting from birth-death processes, the Poisson process to general Markov and semi-Markov processes and solution methods for those processes. Then we will look at queueing models and queueing networks with exact and approximate solution algorithms. If time allows we will finally study some of the foundations of discrete event simulation.

William Stewart. Probability, Markov Chains, Queues and Simulation. Princeton University Press 2009.

Qualification objectives: The students have a basic understanding of algebraic and computational methods for deep neural networks, their application scope and can practically build and train them with state-of-the-art software tools. They are familiar with typical deep learning structures and understand the relationship to their shallow counterparts.

Content:

- Perceptron

- Multilayer neural network and universal represenation theorem

- Backpropagation

- Deep feedforward networks

- Convolutional Neural Networks

- Autoencoder versus principal component analysis

- Time-autoencoder versus time-lagged independent component analysis

- Generative networks: Variational Autoencoders and Adversarial Generative Networks

- Active learning

This lecture/lab course is suitable for Master students of Mathematics, Computer Science, Computational Sciences and Physics.
Students of the Computational Sciences program can combine this lecture/lab course with 19234502 + 19234501 (Mathematical aspects in machine learning) to complete “complex algorithms A/B”

Physics students please contact the Exam Office, dmitrij.heinz(at)fu-berlin.de, indicating modul and matriculation number