The central registration for all computer science seminars will open on September 22nd.
This system is used to distribute students among the available actual seminars. To register for any of the other seminars that are offered by the computer science department, you have to register here until October 26th 23:59 CET. You can select which seminar you would like to take, and will then be automatically assigned to one of them on October 30th.
Please note the following:
The assignment will be automatically performed by a constraint solver on October 30th. You will be added to the respective seminars automatically and be notified about this shortly thereafter. Please note that the assignment cannot be optimal for all students if you drop the assigned seminar, i.e., make only serious choices to avoid penalty to others.
In this seminar, we will explore how artificial intelligence (AI) and interactive technologies in the area of Human-Computer-Interaction (HCI) can augment on- and offline shopping experiences in furniture retail. This seminar will be conducted in collaboration with the large furniture retailer Möbel Martin. During the course of the winter semester you will identify problems within one of several broader topics using user research methods, develop a concept and implement a working prototype that can be showcased to potential users (customers as well as employees/management of Möbel Martin).
Please see the web page for further details such as timeline, required hand-ins etc.:
Requirements: You should have passed Programming 2 or have sufficient knowledge about programming from elsewhere. Experience in user-centered design, human-computer-interaction and / or artificial intelligence is a benefit.
All meetings must be attended (potentially virtual, depending on the format of the event, exceptions require an official document, e.g. a doctor’s certificate), otherwise, you won't pass the seminar.
Planning is concerned with finding a step-by-step description on how to accomplish a task at hand (a plan). This is done based on a declarative model describing the environment and how it can be changed.
Models are usually defined in a first-order language enabling the use of variables in the problem definition (a lifted representation). This leads to a compact representation, but the majority of planners first generate a variable-free model in a process called grounding.
In this seminar, we will have a look at techniques reasoning about the different representations, e.g., extracting information from the lifted representation, finding concise groundings as basis for other systems, or those systems that find plans based on the lifted representation.
Requirements: Ideally, participants should have completed the AI Planning course. Completion of an introductory AI course covering AI Planning is an absolute requirement.
Commonsense knowledge (CSK) is critical for building versatile intelligent applications. In delineation from encyclopedic knowledge, which is centered on named entities like Trump, Paris, or FC Barcelona, commonsense is used to refer to properties, traits and relations between general concepts, such as elephants, universities, or painters. Machine-readable collections of CSK are crucial to enable question answering and natural conversation about the world, e.g., by enabling the agent to proactively communicate, identify likely answers, and detect implausible statements and conditions. In this seminar we will study foundational and recent topics around commonsense knowledge extraction and consolidation.
Requirements: Familiarity with basic concepts of data management and information extraction, as can be acquired for example in courses like IRDM or SNLP.
The goal of the seminar is to provide a broad overview of the foundational underpinnings of concurrency in all its facets.
If you have enjoyed part "T" of the lecture "Nebenläufige Programmierung", this seminar is for your. Starting off from there, we will discuss popular behavioural properties and process calculi, input/output automata, interface automata, modal transition systems, session types as well as causality-based models of concurrency. In addition we will look at various models which in different senses add expressiveness. With these, one can cover timed behaviour, can reason about randomness, and can describe reactivity using infinite words.
More details: https://dcms.cs.uni-saarland.de/concaws20/
Requirements: You should have enjoyed part "T" of the lecture "Nebenläufige Programmierung". If this lecture has not been on your menue yet, you are expected to associate positive emotions with the word "bisimulation".
The development of ICT has resulted in an unprecedented amount of data available. Big data, on the one hand, bring many benefits to society, on the other hand, raises serious concerns about people's privacy. In this seminar, students will learn, summarize, and present state-of-the-art scientific papers in data privacy. Topics include social network privacy, machine learning privacy, and biomedical data privacy. The seminar is organized as a reading group. Every week, one student will present her/his assigned papers on a certain topic, followed by a group discussion. All students are required to read the papers carefully and prepare a list of questions for discussion. Each student will write a summary of her/his assigned papers providing a general overview of the field.
Requirements: Students are required to have basic knowledge of data mining and machine learning.
Deep Neural Networks (DNNs) are very powerful. However, they are often considered "opaque", in the sense that it is not always easy to see how they come to a particular decision. Mathematically, of course, DNNs are fully transparent: they are just a mix of matrix algebra operations and non-linearities. But they are not always easy to interpret in terms of concepts that are broadly accessible to humans. The seminar will focus on methods that can help explain how neural networks "reason" while performing certain tasks, what they learn and which information they use for making predictions. The aim is to examine general methods for interpreting neural network based models, with a focus on methods that can be applied to NLP tasks.
Please see the course website for further details: https://sites.google.com/view/emnn-ws-2020/home
Requirements: Familiarity with neural networks (feedforward, recurrent, convolutional), backpropagation, calculus, and linear algebra
The rapid progress in artificial intelligence and machine learning has lead to the deployment of AI-based systems in a number of areas of modern life, such as manufacturing, transportation, and healthcare. However, serious concerns about the safety and trustworthiness of such systems still remain, due to the lack of assurance regarding their behavior. To address this problem, significant efforts in the area of formal methods in recent years have been dedicated to the development of rigorous techniques for the design of safe AI-based systems.
In this seminar, we will read and discuss research papers that present the latest results in this area. We will cover a range of topics, including the formal specification and verification of correctness properties of AI components of autonomous systems, and the design of reinforcement learning agents that respect safety constraints.
Each participant will give a presentation of an assigned paper, followed by a group discussion. All students are expected to read each paper carefully and to actively participate in the discussions. Each student will write a summary of the paper they have presented, including a general overview of the topic and reflecting the group discussion. Additionally, each student will be required to submit for 3 of the papers discussed in the seminar a short one-page review, describing the paper's strengths and weaknesses. Reviews are expected to be turned in at the beginning of the class during which the paper will be presented.
Requirements: There are no formal prerequisites for this seminar. However, participants are expected to have strong interest in formal verification and/or machine learning, and knowledge in these areas would be helpful.
While the Internet has started as a research effort, it has consistently evolved throughout the decades into the largest commercial network. Today, many research efforts focus on understanding the Internet's structural trends, optimizing its packet delivery, and laying the foundations for future developments. In this seminar, you will receive a closer look at bleeding-edge research published at the top conferences in our domain. Discussing state-of-the-art approaches and recent findings from a broad range of network-related topics with your peers and instructors will provide you with a deep understanding of your assigned topic. Preparing the accompanying research survey and topic presentation will not only strengthen your academic writing but also help you to present your future work in a more accessible and structured way.
For further information please refer to our course page:
Requirements: Participants should have successfully participated in "Data Networks" or an equivalent course.
This seminar is concerned with selected hybrid intelligent systems that combine techniques from machine learning with symbolic techniques from knowledge representation and reasoning. Both types of AI techniques have their strengths and limitations. While deep learning systems have been quite successfully applied to, for example, pattern recognition, image interpretation, speech recognition and translation, they can be characterized as overly data hungry, susceptible to adversarial attacks, opaque (non-interpretable by humans), and not informed by general principles such as causality or common-sense and domain knowledge. The successes of symbolic reasoning techniques (“good old fashioned AI”) are in such applications as automated (human-understandable, traceable) planning, diagnosis, design tasks, and question answering by cognitive virtual assistants but are often quite limited by the need of expensive, explicit knowledge acquisition and modelling, inefficient logic-based reasoning, and instability in the presence of noisy data. There is a consensus in the AI community that symbiotic, profound integration or combination of machine learning and reasoning is essential for human-level AI in general.
In this seminar, we will take a closer look at selected techniques and systems for hybrid learning and reasoning, and discuss their strengths and weaknesses. The seminar type is classic in the sense that registered participants will present assigned topics and discuss the strength and weaknesses of presented approaches. In addition, there will be two dedicated opponents for each presentation of an assigned topic.
Seminar website: http://www.dfki.de/~klusch/HyLEAR-seminar-ws20
Due to the Corona-crisis, the seminar will be held virtually in the Web.
The first seminar session (kick-off / introduction meeting) with topic assignment will be held on Thursday, November 5, 2020, 4:15pm – 6pm. Virtual meeting room link will be provided to assigned/registered participants via email in time.
IMPORTANT NOTE: Due to the tremendously increasing interest in the future-oriented AI research area of hybrid learning and reasoning, we decided to offer more places for students by the twin seminars HyLEAR (Hybrid Learning and Reasoning) and HMLA (Hybrid Machine Learning Approaches and Applications) with complimentary topics in this area. We highly recommend all interested particpants to register for both twin seminars in the SIC seminar assignment system (you will be assigned effectively to one of both but can visit the other twin). More info on our twin seminars:
Requirements: This seminar aims primarily at advanced master students in Computer Science who preferably hold a B.Sc. degree in this or related field.
Good knowledge in AI (introductory course on AI covering symbolic knowledge representation, machine or deep learning, automated reasoning, AI planning) is required.
Knowledge and Data together form the foundation of most AI systems: Symbolic or Semantic knowledge forms concepts and relations describing structures in a logical and human-interpretable way, allowing queries, reasoning and inference. Sub-symbolic or Syntactic Data is little or non-structured sensor data, such as images or audio, that has high volume and is harder to interpret or program against by humans in their raw form.
In this seminar, we are going to review
- the current state of Symbolic and sub-symbolic representation and learning methods
- hybrid learning approaches where sub-symbolic training can be improved by symbolic knowledge and vice versa
- models that merge symbolic and subsymbolic parts
- applications where hybrid learning provides benefits.
The seminar will be "online-only" due to the COVID-19 situation.
IMPORTANT NOTE: Due to the tremendously increasing interest in the future-oriented AI research area of hybrid learning and reasoning, we decided to offer more places for students by the twin seminars HyLEAR (Hybrid Learning and Reasoning) and HMLA (Hybrid Machine Learning Approaches and Applications) with complimentary topics in this area. We highly recommend all interested participants to register for both twin seminars in the SIC seminar assignment system (you will be assigned effectively to one of both but can visit the other).
More info on our twin seminars:
Requirements: Basic knowledge of and experience with at least one of the AI subtopics of (1) Machine Learning (incl. Deep Learning) or (2) Knowledge-based Modeling / Reasoning Systems.
In Human-Computer Interaction, robotics has been a topic of intense research, initially mostly restricted to research labs due to the difficulty of building such devices. In the past few years, the wide availability of accessible hardware platforms for prototyping (such as Arduino) and digital fabrication tools made it easier to develop interactive robotic systems. A variety of robotic devices and applications are now finding their way to end-users, and robots have the potential to enhance human performance and bring unique new opportunities for interaction with our environment and with other people. The objective of this seminar is to acquire conceptual, technical and practical skills in developing interactive robotics. We will address the unique challenges of interactive robotics on those three levels.
Requirements: For the registration, we require a brief motivation statement on why you want to take this class.
Knowledge of Hardware programming is preferred. Basic knowledge in hardware programming (e.g. some experience with Arduino) will be helpful, but is not a prerequisite for taking this course. In this seminar you will build functional tangible robotic prototype. Hence, you must be willing to use or learn digital fabrication (3D printing).
Bei dem interdisziplinären Seminar werden unter anderem folgende Themen angeboten:
1. Einsatzmöglichkeiten von Blockchain-Anwendungen im deutschen Recht
2. Legal Tech-Anwendungen bei gerichtlichen Entscheidungen
3. Automatische Verschlagwortung
Requirements: Es wird erwartet, dass Teilnehmer in der Lage sind, Vorträgen in deutscher Sprache zu folgen.
Light Fields other than photographic camera imagery contain several views (rays) stemming from the same 3D scene point. This enables innovative post-processing techniques but comes with a very data-volume.
In recent years different representation formats and compression schemes have been invented to enable practical usage of light fields.
We will discuss Shearlet-Transforms, Fourier Disparity Layers, JPEG PLENO and MPEG-I coding and the fundamentals of light fields.
Requirements: * Digital Signal Processing (e.g. from our lecture DTSP)
* Image Processing and Coding (from our lecture MT of from other Visual Computing courses)
* Some experience with MATLAB (for demonstration purposes) and the usage of MATLAB Toolboxes
Spectre, Meltdown, and other microarchitectural attacks have been in the limelight in recent years. These attacks exploit subtle timing and behavioral differences of processors that are caused by microarchitectural optimizations such as caches and speculative execution to gain access to secret information.
In this seminar, we are going to study
- prominent microarchitectural attacks,
- hardware-based countermeasures,
- software-based countermeasures, and
- formal methods to characterize vulnerabilities and to rigorously analyze hardware- and software-based countermeasures,
with a focus on speculative-execution attacks.
Due to COVID-19, the seminar will be conducted virtually, with a possibility to move to a hybrid setting during the semester. If that is the case, it will still be possible to attend the seminar virtually.
Each participant will give a presentation of an assigned paper, followed by a group discussion. All students are expected to read each paper carefully and to actively participate in the discussions. Each student will write a summary of the paper they have presented, including a general overview of the topic and reflecting the group discussion.
[This is a combined proseminar and seminar with a total of 12 seats.]
Requirements: Basic knowledge of computer architecture (e.g. due to Systemarchitektur) is required. Knowledge of security and formal methods is a plus.
The core idea of Monte Carlo (MC) methods is to perform computer simulations of a real-world system based on pseudo-random numbers. MC is applied in a large variety of research areas: physical sciences, computer sciences, engineering, statistics, finance, etc. Moreover, ideas that have originally been developed in the context of MC are meanwhile also in use for sampling problems in the area of machine learning.
Although, being a simple and very direct approach to analyze a system, MC methods come with a lot of challenges, in particular, when sampling rare events or when systems have multiple time scales.
In this seminar, we take a computer science perspective on Monte Carlo and cover different MC algorithms to tackle these challenges.
All seminar meetings will take place online (no physical meetings).
Requirements: You should have successfully passed MfI3, the Statistics Lab, or a comparable course covering probability theory/statistics.
The course will cover the state of the art research in Multi-agent Reinforcement Learning. More details about the course will be added on the website: https://multiagent-rl.mpi-sws.org/
The course schedule is as follows:
(1) Writeups: Each student will be assigned a total of 6 research papers. Students will have to write a short report for each paper. These reports will be due during the semester, roughly 2 reports per month.
(2) Presentation: At the end of the semester, students will give a 20 mins presentation for one of the papers.
This course is offered as a block seminar, and there will be no weekly classes. We will schedule designated office hours where students can receive feedback on their reports and presentation slides.
Requirements: There are no formal requirements, however, please note the following points:
(1) Preference will be given to students who have already taken courses covering topics such as machine learning, artificial intelligence, reinforcement learning, and probabilistic graphical models.
(2) We are requesting you to provide a short motivation letter (about 5-10 lines) explaining the reasons why you are interested in taking this seminar. Please also mention the relevant courses that you have taken, including your grades. Please provide this information in the text box below.
A digital signature is a basic cryptographic building block which ensures authenticity (who signed) and integrity (what is signed) of messages.
In this seminar we will learn about different types of digital signatures that relax those properties to increase the privacy in some applications. Among others we will cover topics like:
- ring signatures (Monero cryptocurrency),
- pseudonymous signatures (German eID),
- blind signatures (e-Cash and anonymous credentials).
Every week we will discuss one paper on a certain topic which will be presented by an assigned students. All students are required to read the papers carefully and prepare a list of questions for discussion.
Requirements: Students are required to have basic knowledge of cryptography.
Program analysis is a mature research area at the intersection of programming languages, formal methods and software engineering. One of its main applications is automatic vulnerability detection. However, the complexity of modern systems is overwhelming and the vulnerabilities to be detected are increasingly sophisticated. To account for these particularities, many recent approaches advocate for lightweight program analysis techniques or for hybrid methods, i.e., static and dynamic analysis. This seminar explores the trade offs involved in designing a program analysis that scales to analyzing the security of real systems.
In this seminar we will discuss recent research papers in the area in a reading group format. Each week, one student will present papers covering a given topic, followed by a discussion. All participants are expected to actively participate in the discussion by asking questions.
You can find more details about the seminar and the list of topics to be discussed on the seminar's page: https://cms.cispa.saarland/pa4vd/
Requirements: The students are expected to have good software engineering skills and a familiarity with the most important classes of vulnerabilities, e.g., buffer overflow, XSS.
We read and discuss the text book
Michael A. Nielsen, Isaac L. Chuang: Quantum Computing and Quantum Information, Cambridge University Press 2010
The goal is to understand Shor's factoring quantum algorithm and its background.
Requirements: MfI 123, Complexity theory
In this seminar course, we will cover topics around automated question answering (QA) systems over knowledge graphs, text corpora, and heterogeneous sources. The last few years have seen an explosion of research on the topic of QA, spanning the communities of information retrieval, natural language processing, and artificial intelligence. Through this seminar, students will be able to describe and critique state-of-the-art approaches for question answering. More generally, they will gain experience in analyzing relevant scientific literature.
We will compile a set of references that cover the key topics of active interest in the community, like complex QA, heterogeneous QA, and conversational QA. Topics will also include addressing key dimensions like user feedback, interpretability, unanswerability, and efficiency. Each student will be assigned a topic (typically comprising two research papers) that they have to review. At the end of the course, the student is expected to give a presentation and write a short report on their understanding of the assigned topic.
There will be one introductory lecture where the various sub-topics will be introduced. The presentations by the students will be at the end of the course, as a block seminar.
Requirements: Information Retrieval, Natural Language Processing, Deep Learning. The course on Question Answering Systems (Summer Semester 2020) is recommended but not mandatory.
With the rising size and complexity of software projects, good engineering practices become more and more important, especially for major tech companies such as Google, Microsoft, Facebook, and co. In this seminar, we discuss different topics from software engineering, viewed from the perspective of big tech companies, and we relate them to current research topics and open research problems.
We focus on topics like:
* How to work well in developer teams
* How manual techniques such as code review shape software development
* Handling testing and infrastructure at a large scale
* How tools can help in making software engineering at a large scale better manageable with automation
* Running continuous integration and delivery with millions of customers
Due to the current situation with the SARS-CoV-2 virus, this course will be held online. The topic assignment will take place on Thursday, November 05, 12:15 PM. Further information will be provided via e-mail after the registration.
Requirements: Basic knowledge on software engineering
Unlike traditional mobile networks, space and satellite networks (SSNs) are challenged by high delay due to signal propagation, and disruptions due to planet rotation and resource limitations. Store-carry-and-forward data handling is a perfect fit in this context, but needs to incorporate the available information on forthcoming connectivity of the SSN to get the best out of very expensive and complex space systems. In this seminar we explore state-of-the-art models, algorithms and artificial intelligence approaches enabling the realization of future SSN including mega-constellations and deep-space missions.