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.

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.

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

Wechselnd, aufbauend auf der Vorlesung TI II: Rechnerarchitektur

Wird mit der Ankündigung bekannt gegeben.

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

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

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.

This course teaches agile methods (Scrum and XP) and open source tools using a single semester-long project. It takes place online and across multiple universities.

Topics covered are:

• Agile methods and related software development processes
• Scrum roles, process practices, including product and engineering management
• Technical practices like refactoring, continuous integration, and test-driven development
• Principles and best practices of open source software development

The project is a software development project in which each student team works with an industry partner who provides the idea for the project. This is a practical hands-on experience. Students take on the role of a software developer. In this role, they estimate and evaluate the effort of requirements and implement them in the project.

Students will be organized into teams of 7-8 people, combining product owners with software developers. An industry partner will provide requirements to be worked out in detail by the product owners and to be realized by the software developers. The available projects will be presented in the run-up to the course.

The course consists of 90min lectures (participation voluntary) followed by a 90min team meeting (participation mandatory).
 

The schedule as well as further information about the course can be viewed via the following Google Spreadsheet: https://amos.uni1.de - here you will also find the registration link for the course.

http://goo.gl/5Wqnr7

Educational objectives and competencies

• Students learn about software products and software development in an industry context
• Students learn about agile methods, in particular Scrum and Extreme Programming
• Students learn about open source software development and its underlying principles
• Students gain practical hands-on experience with a Scrum process and XP technical practices

Target group

Students of computer science (and related fields). If you want to play the software developer role, you should have had practical programming experience. This is not a course to learn programming.

Language

English (lectures in English, team meeting German or English by choice of student team)

Other

• SWS: 4 SWS (2 SWS lecture, 2 SWS team meeting)
• Semester: Every winter semester
• Modality: Online, across multiple universities
• Tags: Scrum

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.

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This software project aims to make machine learning more user-friendly. The aim is that machine learning can also be used by people who are not experts in the field. To achieve this goal, machine learning processes should be set up, adapted and visualized via a GUI. In addition, usability studies are planned and carried out to ensure user-friendliness.

In addition to regular meetings, the work takes place in independent group work.

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.

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 recent years, data science has developed rapidly, primarily due to the progress in machine learning. This development has opened up new opportunities in a variety of social, scientific, and technological areas. From the experience of recent years, however, it is becoming increasingly clear that the concentration on purely statistical and numerical aspects in data science fails to capture social nuances or take ethical criteria into account. The research area Human-Centered Data Science closes this gap at the intersection of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Human Computation, and the statistical and numerical techniques of Data Science.

Human-Centered Data Science (HCDS) focuses on fundamental principles of data science and its human implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness, and accountability; data provenance, curation, preservation, and reproducibility; user experience design and research for big data; human computation; effective oral, written, and visual scientific communication; and societal impacts of data science.

At the end of this course, students will understand the main concepts, theories, practices, and different perspectives on which data can be collected and manipulated. Furthermore, students will be able to realize the impact of current technological developments may have on society.

This course curriculum was initially developed by Jonathan T. Morgan, Cecilia Aragon, Os Keyes, and Brock Craft. We have adapted the curriculum for the European context and our specific understanding of the field.

Aragon, C. M., Hutto, C., Echenique, A., Fiore-Gartland, B., Huang, Y., Kim, J., et al. (2016). Developing a Research Agenda for Human-Centered Data Science. (pp. 529–535). Presented at the CSCW Companion, New York, New York, USA: ACM Press. http://doi.org/10.1145/2818052.2855518

Baumer, E. P. (2017). Toward human-centered algorithm design:. Big Data & Society, 4(2), 205395171771885. http://doi.org/10.1177/2053951717718854

Kogan, M., Halfaker, A., Guha, S., Aragon, C., Muller, M., & Geiger, S. (2020, January). Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices. In Companion of the 2020 ACM International Conference on Supporting Group Work (pp. 151-156).

[link HCC-Webseite aktuelles Semester]

The lecture is part II of the cycle Cybersecurity I-IV (data protection, explainability, robustness/attacks, certification) and includes:

1. Introduction, Motivation, Definitions
   Taxonomy and assumptions, brief overview of ideas from different categories, representation of explanation results, saliency maps, feature importance, first applications
2. Black-box (model-agnostic) explanations
   Additive feature attribution method and  properties, LIME and other variants, SHAP, Shapley values, from local explanation to global understanding Implementation details regarding neighborhood construction (on-manifold explanations), risks of random perturbations
3. White-box (model-specific) explanations
   LRP, DTD, DeepLIFT, Grad-CAM, Counterfactual explanations
4. Information-theoretic explanation methods
   Information decomposition, causality, theory of representation learning
5. Application and Implementation
   Debugging, model extraction, challenges, trade-off (e.g., explainability vs. privacy)

Die Übungen bestehen im Wesentlichen aus PRAKTISCHEN Implementierungsaufgaben.

The course is held in cooperation with Prof. Sabine Ammon (TU Berlin).

Content:

Students will learn to critically assess the relationship between technology and society and to analyze the interactions between technology and society from an ethical perspective. Furthermore, students will deal with the deconstruction of the concept of neutrality of technology and learn to critically assess it. At the same time, the environment will be taken as a stakeholder in its own right in order to consider the impact of technological applications from a sustainability perspective. The course will provide students with the necessary theoretical foundations stemming from both Computer Science (in particular AI and digital technologies) and Ethics. This knowledge will be put into practice and deepened through case-based projects carried out in interdisciplinary groups.

Knowledge:

• Acquiring an understanding of foundational concepts of Philosophy and Ethics of Artificial Intelligence (AI) and their application in research and practice in the field;
• acquiring an overview of the current ethical challenges in AI;
• transdisciplinary perspectives on these said challenges (including sociological, ecological, political, economic, cultural, historical issues, etc.).

Skills:

• Critical discussion and evaluation of various current perspectives among ethical debates in Philosophy and Ethics of AI;
• development of own argumentative positions based on the fundamental concepts of Philosophy and Ethics of AI (articulation of logical reasoning supported by examples);
• drafting of current and practical case studies in relation to contemporary societal challenges and assessment of these case studies through interdisciplinary perspectives.

Competencies:

• Ability to apply methods of interdisciplinary cooperation, specifically at the intersection of humanities/social sciences and natural/technical sciences;
• ability to discuss and integrate critical feminist, queer and anti-racist perspectives on science, i.e., how sociotechnical problems surrounding race, gender, sexuality, status, class, ability etc. relate to institutional structures of power and domination;
• effective self-management in a collaborative group setting.

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.

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 recent years, data science has developed rapidly, primarily due to the progress in machine learning. This development has opened up new opportunities in a variety of social, scientific, and technological areas. From the experience of recent years, however, it is becoming increasingly clear that the concentration on purely statistical and numerical aspects in data science fails to capture social nuances or take ethical criteria into account. The research area Human-Centered Data Science closes this gap at the intersection of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Human Computation, and the statistical and numerical techniques of Data Science.

Human-Centered Data Science (HCDS) focuses on fundamental principles of data science and its human implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness, and accountability; data provenance, curation, preservation, and reproducibility; user experience design and research for big data; human computation; effective oral, written, and visual scientific communication; and societal impacts of data science.

At the end of this course, students will understand the main concepts, theories, practices, and different perspectives on which data can be collected and manipulated. Furthermore, students will be able to realize the impact of current technological developments may have on society.

This course curriculum was initially developed by Jonathan T. Morgan, Cecilia Aragon, Os Keyes, and Brock Craft. We have adapted the curriculum for the European context and our specific understanding of the field.

Aragon, C. M., Hutto, C., Echenique, A., Fiore-Gartland, B., Huang, Y., Kim, J., et al. (2016). Developing a Research Agenda for Human-Centered Data Science. (pp. 529–535). Presented at the CSCW Companion, New York, New York, USA: ACM Press. http://doi.org/10.1145/2818052.2855518

Baumer, E. P. (2017). Toward human-centered algorithm design:. Big Data & Society, 4(2), 205395171771885. http://doi.org/10.1177/2053951717718854

Kogan, M., Halfaker, A., Guha, S., Aragon, C., Muller, M., & Geiger, S. (2020, January). Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices. In Companion of the 2020 ACM International Conference on Supporting Group Work (pp. 151-156).

[link HCC-Webseite aktuelles Semester]

The lecture is part II of the cycle Cybersecurity I-IV (data protection, explainability, robustness/attacks, certification) and includes:

1. Introduction, Motivation, Definitions
   Taxonomy and assumptions, brief overview of ideas from different categories, representation of explanation results, saliency maps, feature importance, first applications
2. Black-box (model-agnostic) explanations
   Additive feature attribution method and  properties, LIME and other variants, SHAP, Shapley values, from local explanation to global understanding Implementation details regarding neighborhood construction (on-manifold explanations), risks of random perturbations
3. White-box (model-specific) explanations
   LRP, DTD, DeepLIFT, Grad-CAM, Counterfactual explanations
4. Information-theoretic explanation methods
   Information decomposition, causality, theory of representation learning
5. Application and Implementation
   Debugging, model extraction, challenges, trade-off (e.g., explainability vs. privacy)

Die Übungen bestehen im Wesentlichen aus PRAKTISCHEN Implementierungsaufgaben.

The course is held in cooperation with Prof. Sabine Ammon (TU Berlin).

Content:

Students will learn to critically assess the relationship between technology and society and to analyze the interactions between technology and society from an ethical perspective. Furthermore, students will deal with the deconstruction of the concept of neutrality of technology and learn to critically assess it. At the same time, the environment will be taken as a stakeholder in its own right in order to consider the impact of technological applications from a sustainability perspective. The course will provide students with the necessary theoretical foundations stemming from both Computer Science (in particular AI and digital technologies) and Ethics. This knowledge will be put into practice and deepened through case-based projects carried out in interdisciplinary groups.

Knowledge:

• Acquiring an understanding of foundational concepts of Philosophy and Ethics of Artificial Intelligence (AI) and their application in research and practice in the field;
• acquiring an overview of the current ethical challenges in AI;
• transdisciplinary perspectives on these said challenges (including sociological, ecological, political, economic, cultural, historical issues, etc.).

Skills:

• Critical discussion and evaluation of various current perspectives among ethical debates in Philosophy and Ethics of AI;
• development of own argumentative positions based on the fundamental concepts of Philosophy and Ethics of AI (articulation of logical reasoning supported by examples);
• drafting of current and practical case studies in relation to contemporary societal challenges and assessment of these case studies through interdisciplinary perspectives.

Competencies:

• Ability to apply methods of interdisciplinary cooperation, specifically at the intersection of humanities/social sciences and natural/technical sciences;
• ability to discuss and integrate critical feminist, queer and anti-racist perspectives on science, i.e., how sociotechnical problems surrounding race, gender, sexuality, status, class, ability etc. relate to institutional structures of power and domination;
• effective self-management in a collaborative group setting.

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.

The lecture aims at a deeper understanding of cryptographic algorithms, especially which design criteria have to be considered for the development of secure encryption algorithms. For that purpose we will get to know and evaluate different cryptanalytic methods for symmetrical and asymmetrical encryption techniques – e.g. linear and differential cryptanalysis on block ciphers, correlation attacks on stream ciphers and algorithms to solve the factorization problem and the discrete logarithm problem. Weaknesses in the implementation, e.g. to exploit side-channel attacks, will be discussed only peripherally.

This class is a continuation of "Advanced Algorithms". We will see advanced notions and methods from the theory of algorithms and from computational complexity theory.

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 class is a continuation of "Advanced Algorithms". We will see advanced notions and methods from the theory of algorithms and from computational complexity theory.

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.

The lecture aims at a deeper understanding of cryptographic algorithms, especially which design criteria have to be considered for the development of secure encryption algorithms. For that purpose we will get to know and evaluate different cryptanalytic methods for symmetrical and asymmetrical encryption techniques – e.g. linear and differential cryptanalysis on block ciphers, correlation attacks on stream ciphers and algorithms to solve the factorization problem and the discrete logarithm problem. Weaknesses in the implementation, e.g. to exploit side-channel attacks, will be discussed only peripherally.

• linear algebra:
  • vector space, basis and dimension;
  • linear map, matrix and rank;
  • Gauss-elimination and linear systems of equations;
  • determinants, eigenvalues and eigenvectors;
  • euclidean vector spaces and orthonormalization;
  • principal component transformation;
  • Applications of linear algebra in affine geometry, statistics, and coding theory  (linear codes)

• Klaus Jänich: Lineare Algebra, Springer-Lehrbuch, 10. Auflage 2004
• Dirk Hachenberger: Mathematik für Informatiker, Pearson 2005
• G. Grimmett, D. Welsh: Probability - An Introduction, Oxford Science Publications 1986
• Kurt Meyberg, Peter Vachenauer: Höhere Mathematik 1, Springer-Verlag, 6. Auflage 2001
• G. Berendt: Mathematik für Informatiker, Spektrum Akademischer Verlag 1994
• Oliver Pretzel: Error-Correcting Codes and Finite Fields, Oxford Univ. Press 1996

The sign-up for the tutorial sessions will be announced in due time.

Content:
This is the first part of a three semester introduction into the basic mathematical field of Analysis. Differential and integral calculus in a real variable will be covered. Topics:

1. fundamentals, elementary logic, ordered pairs, relations, functions, domain and range of a function, inverse functions (injectivity, surjectivity)
2. numbers, induction, calculations in R, C
3. arrangement of R, maximum and minimum, supremum and infimum of real sets, supremum / infimum completeness of R, absolute value of a real number, Q is dense in R
4. sequences and series, limits, cauchy sequences, convergence criteria, series and basic principles of convergence
5. topological aspects of R, open, closed, and compact real sets
6. sequences of functions, series of functions, power series
7. properties of functions, boundedness, monotony, convexity
8. continuity, limits and continuity of functions, uniform continuity, intermediate value theorems, continuity and compactness
9. differentiability, concept of the derivative, differentiation rules, mean value theorem, local and global extrema, curvature, monotony, convexity
10. elementary functions, rational functions, root functions, exponential functions, angular functions, hyperbolic functions, real logarithm, inverse trigonometric functions, curve sketching
11. beginnings of integral calculus

Literature:

• Bröcker, Theodor: Analysis 1, Spektrum der Wissenschaft-Verlag.
• Forster, Otto: Analysis 1, Vieweg-Verlag.
• Spivak, Michael: Calculus, 4th Edition.

Viele Analysis Bücher sind auch über die Fachbibliothek der FU Berlin elektronisch verfügbar.

Bei Schwierigkeiten mit den Grundbegriffen Menge, Abbildung etc. ist die folgende Ausarbeitung empfehlenswert:

• Scheerer, Hans: Brückenkurs, Skript FU Berlin 2006.

Content:

• Basic terms/concepts: sets, maps, equivalence relations, groups, rings,
• fields
• Linear equation systems: solvability criteria, Gauss algorithm
• Vector spaces: linear independence, generating systems and bases, dimension,
• subspaces, quotient spaces, cross products in R3
• Linear maps: image and rank, relationship to matrices, behaviour under
• change of basis
• Dual vector spaces: multilinear forms, alternating and symmetric bilinear
• forms, relationship to matices, change of basis
• Determinants: Cramer's rule, Eigenvalues and Eigenvectors

Prerequisites:

Participation in the preparatory course (Brückenkurs) is highly recommended.

• Siegfried Bosch, Lineare Algebra, 4. Auflage, Springer-Verlag, 2008;
• Gerd Fischer, Lernbuch Lineare Algebra und Analytische Geometrie, Springer-Verlag, 2017;
• Bartel Leendert van der Waerden, Algebra Volume I, 9th Edition, Springer 1993;

Zu den Grundlagen

• Kevin Houston, Wie man mathematisch denkt: Eine Einführung in die mathematische Arbeitstechnik für Studienanfänger, Spektrum Akademischer Verlag, 2012

Content

The module focuses on the role of empirical evaluation and its impact on the knowledge gain in computer science research and practice and then generically introduces the approach of empirical evaluations (with the following phases: definition of the problem, choice of method(s), study design, execution, evaluation, report/presentation).

Starting from this basic knowledge and by means of the central quality terms of reliability (especially inner reliability) and relevance (especially outer validity) different methodic classes (e.g. controlled experiments, quasi experiments, surveys etc.) will be dealt with and illustrated respectively by real case studies: suitability and contraindications; strengths and weaknesses; approach and pitfalls.

In the tutorial the use of software for data evaluation is learned and a small empirical study carried out as a project starting with the conceptualization and ending with a presentation.

• Jacob Cohen: The Earth Is Round (p > .05). American Psychologist 49(12): 997003, 1994. Darrell Huff: How to lie with statistics, Penguin 1991.
• John C. Knight, Nancy G. Leveson: An Experimental Evaluation of the Assumption of Independence in Multi-Version Programming. IEEE Transactions on Software Engineering 12(1):9609, January 1986.
• John C. Knight, Nancy G. Leveson: A Reply to the Criticisms of the Knight and Leveson Experiment. Software Engineering Notes 15(1):24-35, January 1990.
• Audris Mockus, Roy T. Fielding, James D. Herbsleb: Two Case Studies of Open Source Software Development: Apache and Mozilla. ACM Transactions of Software Engineering and Methodology 11(3):309-346, July 2002.
• Timothy Lethbridge: What Knowledge Is Important to a Software Professional? IEEE Computer 33(5):44-50, May 2000.
• David A. Scanlan: Structured Flowcharts Outperform Pseudocode: An Experimental Comparison. IEEE Software 6(5):28-36, September 1989.
• Ben Shneiderman, Richard Mayer, Don McKay, Peter Heller: Experimental investigations of the utility of detailed flowcharts in programming. Commun. ACM 20(6):373-381, 1977.
• Lutz Prechelt, Barbara Unger-Lamprecht, Michael Philippsen, Walter F. Tichy: Two Controlled Experiments Assessing the Usefulness of Design Pattern Documentation in Program Maintenance. IEEE Transactions on Software Engineering 28(6):595-606, 2002.
• Lutz Prechelt. An Empirical Comparison of Seven Programming Languages: Computer 33(10):23-29, October 2000.
• Lutz Prechelt: An empirical comparison of C, C++, Java, Perl, Python, Rexx, and Tcl for a search/string-processing program. Technical Report 2000-5, March 2000.
• Tom DeMarco, Tim Lister: Programmer performance and the effects of the workplace. Proceedings of the 8th international conference on Software engineering. IEEE Computer Society Press, 268-272, 1985.
• John L. Henning: SPEC CPU2000: Measuring CPU Performance in the New Millennium. Computer 33(7):28-35, July 2000.
• Susan Elliot Sim, Steve Easterbrook, Richard C. Holt: Using Benchmarking to Advance Research: A Challenge to Software Engineering. Proceedings of the 25th International Conference on Software Engineering (ICSE'03). 2003.
• Ellen M. Voorhees, Donna Harman: Overview of the Eighth Text REtrieval Conference (TREC-8).
• Susan Elliott Sim, Richard C. Holt: The Ramp-Up Problem in Software Projects: A case Study of How Software Immigrants Naturalize. Proceedings of the 20th international conference on Software engineering, April 19-25, 1998, Kyoto, Japan: 361-370.
• Oliver Laitenberger, Thomas Beil, Thilo Schwinn: An Industrial Case Study to Examine a Non-Traditional Inspection Implementation for Requirements Specifications. Empirical Software Engineering 7(4): 345-374, 2002.
• Yatin Chawathe, Sylvia Ratnasamy, Lee Breslau, Nick Lanham, Scott Shenker: Making Gnutella-like P2P Systems Scalable. Proceedings of ACM SIGCOMM 2003. April 2003.
• Stephen G. Eick, Todd L. Graves, Alan F. Karr, J.S. Marron, Audris Mockus: Does Code Decay? Assessing the Evidence from Change Management Data. IEEE Transactions of Software Engineering 27(1):12, 2001.
• Chris Sauer, D. Ross Jeffrey, Lesley Land, Philip Yetton: The Effectiveness of Software Development Technical Reviews: A Behaviorally Motivated Program of Research. IEEE Transactions on Software Engineering 26(1):14, January 2000.

Language

The course language is German, but the actual slides and practice sheets are in English.

The exam will be formulated in German, but answers may be given in English, too.

Homepage

http://www.inf.fu-berlin.de/w/SE/VorlesungEmpirie

Lecture contents

1. Preliminary meeting: organization – attendance issues – exercises – examination regulations.
2. Introduction – imaging modalities – image properties – computers in medical imaging systems.
3. Image quality: image contrast – spatial resolution – signal noise – digitalisation of analog signals / storage in numerical form – sampling rate / aliasing.
4. Interaction between electromagnetic radiation and matter – particles – electromagnetic radiation – absorption of energy – dosage – radiobiology.
5. X-ray imaging: X-ray tube assembly – radiation generation – projection method – magnification – scattering – detectors – dual energy method – applications.
6. Computed tomography: technical development – radiation source – detectors – image reconstruction – Hounsfield values – image quality – image artefacts – applications.
7. Methods of nuclear medicine: functional imaging – radioactive decay modes – coincidence – generation of radionuclides in cyclotrons – tracers – detectors – image reconstruction – quality assurance – applications.
8. Magnetic resonance imaging I: magnetism – nuclear spin – excitation – signal acquisition – relaxation mechanisms – signal reconstruction in 1D – applications of 1D techniques.
9. Magnetic resonance imaging II: magnetic field gradients – spatial resolution – spin echo – gradient echo – image reconstruction – pulse sequences.
10. Magnetic resonance imaging III: image contrast – advanced imaging techniques – image artefacts – applications – patient exclusion criteria.
11. Demonstration MRI-Scanner (venue location: Charité-Universitätsmedizin Berlin).
12. Ultrasound imaging: properties of longitudinal waves – generation / detection of ultrasound waves – acoustic impedance – reflection – scattering – data acquisition modes – Doppler methods – image artefacts – applications.
13. Demonstration ultrasound imaging (portable scanner).
14. Consolidation of the course contents: presentation of recent medical imaging related research projects of the Institutes of Medical Informatics and Radiology.

• The Physics of Medical Imaging (Medical Science Seriesvon S. Webb, Steve Webb (Herausgeber) Institute of Physics Publishing (1988)
• The Essential Physics of Medical Imaging von Jerrold T. Bushberg (Herausgeber)Lippincott Williams & Wilkins (2002)Bildgebende Verfahren in der MedizinOlaf DösselSpringer Verlag (2000)

Target audience

Students in the master’s program

Conditions of participation

Bachelor degree

Homepage

http://medinfo.charite.de

Contents

Advanced topcis in algorithm design with a changing focus. The topic is determined newly in each semester. For example, we might consider algorithms for problems on graphs, such as connectivity, shortest paths, or network flows.

Target audience

Masters students in computer science and mathematics.

Recommended prerequisites

"Advanced algorithms" or a similar class.

Spezialliteratur aus Zeitschriften

Content

The module focuses on the role of empirical evaluation and its impact on the knowledge gain in computer science research and practice and then generically introduces the approach of empirical evaluations (with the following phases: definition of the problem, choice of method(s), study design, execution, evaluation, report/presentation).

Starting from this basic knowledge and by means of the central quality terms of reliability (especially inner reliability) and relevance (especially outer validity) different methodic classes (e.g. controlled experiments, quasi experiments, surveys etc.) will be dealt with and illustrated respectively by real case studies: suitability and contraindications; strengths and weaknesses; approach and pitfalls.

In the tutorial the use of software for data evaluation is learned and a small empirical study carried out as a project starting with the conceptualization and ending with a presentation.

• Jacob Cohen: The Earth Is Round (p > .05). American Psychologist 49(12): 997003, 1994. Darrell Huff: How to lie with statistics, Penguin 1991.
• John C. Knight, Nancy G. Leveson: An Experimental Evaluation of the Assumption of Independence in Multi-Version Programming. IEEE Transactions on Software Engineering 12(1):9609, January 1986.
• John C. Knight, Nancy G. Leveson: A Reply to the Criticisms of the Knight and Leveson Experiment. Software Engineering Notes 15(1):24-35, January 1990.
• Audris Mockus, Roy T. Fielding, James D. Herbsleb: Two Case Studies of Open Source Software Development: Apache and Mozilla. ACM Transactions of Software Engineering and Methodology 11(3):309-346, July 2002.
• Timothy Lethbridge: What Knowledge Is Important to a Software Professional? IEEE Computer 33(5):44-50, May 2000.
• David A. Scanlan: Structured Flowcharts Outperform Pseudocode: An Experimental Comparison. IEEE Software 6(5):28-36, September 1989.
• Ben Shneiderman, Richard Mayer, Don McKay, Peter Heller: Experimental investigations of the utility of detailed flowcharts in programming. Commun. ACM 20(6):373-381, 1977.
• Lutz Prechelt, Barbara Unger-Lamprecht, Michael Philippsen, Walter F. Tichy: Two Controlled Experiments Assessing the Usefulness of Design Pattern Documentation in Program Maintenance. IEEE Transactions on Software Engineering 28(6):595-606, 2002.
• Lutz Prechelt. An Empirical Comparison of Seven Programming Languages: Computer 33(10):23-29, October 2000.
• Lutz Prechelt: An empirical comparison of C, C++, Java, Perl, Python, Rexx, and Tcl for a search/string-processing program. Technical Report 2000-5, March 2000.
• Tom DeMarco, Tim Lister: Programmer performance and the effects of the workplace. Proceedings of the 8th international conference on Software engineering. IEEE Computer Society Press, 268-272, 1985.
• John L. Henning: SPEC CPU2000: Measuring CPU Performance in the New Millennium. Computer 33(7):28-35, July 2000.
• Susan Elliot Sim, Steve Easterbrook, Richard C. Holt: Using Benchmarking to Advance Research: A Challenge to Software Engineering. Proceedings of the 25th International Conference on Software Engineering (ICSE'03). 2003.
• Ellen M. Voorhees, Donna Harman: Overview of the Eighth Text REtrieval Conference (TREC-8).
• Susan Elliott Sim, Richard C. Holt: The Ramp-Up Problem in Software Projects: A case Study of How Software Immigrants Naturalize. Proceedings of the 20th international conference on Software engineering, April 19-25, 1998, Kyoto, Japan: 361-370.
• Oliver Laitenberger, Thomas Beil, Thilo Schwinn: An Industrial Case Study to Examine a Non-Traditional Inspection Implementation for Requirements Specifications. Empirical Software Engineering 7(4): 345-374, 2002.
• Yatin Chawathe, Sylvia Ratnasamy, Lee Breslau, Nick Lanham, Scott Shenker: Making Gnutella-like P2P Systems Scalable. Proceedings of ACM SIGCOMM 2003. April 2003.
• Stephen G. Eick, Todd L. Graves, Alan F. Karr, J.S. Marron, Audris Mockus: Does Code Decay? Assessing the Evidence from Change Management Data. IEEE Transactions of Software Engineering 27(1):12, 2001.
• Chris Sauer, D. Ross Jeffrey, Lesley Land, Philip Yetton: The Effectiveness of Software Development Technical Reviews: A Behaviorally Motivated Program of Research. IEEE Transactions on Software Engineering 26(1):14, January 2000.

Language

The course language is German, but the actual slides and practice sheets are in English.

The exam will be formulated in German, but answers may be given in English, too.

Homepage

http://www.inf.fu-berlin.de/w/SE/VorlesungEmpirie

Lecture contents

1. Preliminary meeting: organization – attendance issues – exercises – examination regulations.
2. Introduction – imaging modalities – image properties – computers in medical imaging systems.
3. Image quality: image contrast – spatial resolution – signal noise – digitalisation of analog signals / storage in numerical form – sampling rate / aliasing.
4. Interaction between electromagnetic radiation and matter – particles – electromagnetic radiation – absorption of energy – dosage – radiobiology.
5. X-ray imaging: X-ray tube assembly – radiation generation – projection method – magnification – scattering – detectors – dual energy method – applications.
6. Computed tomography: technical development – radiation source – detectors – image reconstruction – Hounsfield values – image quality – image artefacts – applications.
7. Methods of nuclear medicine: functional imaging – radioactive decay modes – coincidence – generation of radionuclides in cyclotrons – tracers – detectors – image reconstruction – quality assurance – applications.
8. Magnetic resonance imaging I: magnetism – nuclear spin – excitation – signal acquisition – relaxation mechanisms – signal reconstruction in 1D – applications of 1D techniques.
9. Magnetic resonance imaging II: magnetic field gradients – spatial resolution – spin echo – gradient echo – image reconstruction – pulse sequences.
10. Magnetic resonance imaging III: image contrast – advanced imaging techniques – image artefacts – applications – patient exclusion criteria.
11. Demonstration MRI-Scanner (venue location: Charité-Universitätsmedizin Berlin).
12. Ultrasound imaging: properties of longitudinal waves – generation / detection of ultrasound waves – acoustic impedance – reflection – scattering – data acquisition modes – Doppler methods – image artefacts – applications.
13. Demonstration ultrasound imaging (portable scanner).
14. Consolidation of the course contents: presentation of recent medical imaging related research projects of the Institutes of Medical Informatics and Radiology.

• The Physics of Medical Imaging (Medical Science Seriesvon S. Webb, Steve Webb (Herausgeber) Institute of Physics Publishing (1988)
• The Essential Physics of Medical Imaging von Jerrold T. Bushberg (Herausgeber)Lippincott Williams & Wilkins (2002)Bildgebende Verfahren in der MedizinOlaf DösselSpringer Verlag (2000)

Target audience

Students in the master’s program

Conditions of participation

Bachelor degree

Homepage

http://medinfo.charite.de

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.

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.

Content

The module focuses on the role of empirical evaluation and its impact on the knowledge gain in computer science research and practice and then generically introduces the approach of empirical evaluations (with the following phases: definition of the problem, choice of method(s), study design, execution, evaluation, report/presentation).