The registration for all computer science seminars will open in September.
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 14th 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 17th. Please note that the assignment cannot be optimal for all students if you drop the assigned seminar.
Please note the following:
The assignment will be automatically performed by a constraint solver on October 17th, 2019. You will be added to the respective seminars automatically and be notified about this shortly thereafter.
This seminar is about algorithms that can handle very large amounts of data.
The amount of available data oftentimes vastly exceeds the size of the available fast memory.
Examples are data collected in scientific experiments from biology of physics such as, for instance, data recorded by large scale telescopes.
Further examples are streams of data that have to be analyzed by routers or firewalls.
In order to process these data, we cannot simply apply our usual algorithms.
Instead of storing all data in fast memory, we have to either process the data right away without storing them long term or to store them in slow memory such as for example disks or tapes.
We then access some of the data (i.e, we copy them into fast memory).
The seminar will cover topics including hashing, Bloom filters, probabilistic counting, and principal component analysis. We intend to cover both the practical applications and the theoretical foundation of the concepts and algorithms.
The seminar is organized as block-seminar. Further details are available at
Requirements: There are no formal requirements for participation.
For many topics, a good understanding of probabilities is useful.
In this seminar, we will focus on the particular challenges of user-centered design for Human-Machine Interfaces in the Automotive Domain for driver and passengers, taking into account recent progress made in sensor and presentation tech available in a vehicle. You will read about these technologies and develop an in-car information or assistance system by applying one or more of the following Artificial Intelligence (AI) techniques:
- Multimodal Interaction Design
- Dialogue Systems
- User Adaptation
- Machine Learning
For further information, please visit https://umtl.cs.uni-saarland.de/teaching/winter-2019/2020/automotive-hmi-seminar.html .
Requirements: The course is intended for students in Computer Science and Media Informatics who like challenges that come with working on practical projects with frameworks and devices they have never used before. Very good programming skills and proactivity are essential.
Deep learning is the predominant machine learning paradigm in natural language processing (NLP). This approach not only gave huge performance improvements across a large variety of natural language processing tasks. It also allowed for a much easier integration of real world knowledge and visual information into NLP systems. However, a big problem of deep learning is the need for massive amounts of training data. Therefore in some of the topics you have to look into methods that can cope with this issue, e.g. by automatically creating noisy training data.
For a list of topics see: https://www.lsv.uni-saarland.de/block-seminar-machine-learning-for-natural-language-processing-spring-2020/
In this practical seminar, you will learn how to program and use robots of various kinds, e.g. humanoid robots (RethinkRobotics Baxter and Softbank Pepper), telepresence robots (Double Robot), and even drones (DJI Tello). Students will form teams and develop small demonstrations that should address cognitive behaviour (by implementing AI methods) and/or human-robot collaboration. In both cases additional sensors and interaction hardware will also be available, e.g. Microsoft HoloLens, HTC Vive, RGBD cameras, Smart Watches, Eyetrackers (and even a Brain Computer Interface (BCI) hardware).
The seminar main-page is at https://cms.sic.saarland/cocooro/
Requirements: self-motivated interest in robotics and AI
creativity and high team-working skills
basic understanding of AI algorithms (not only neural networks)
With manufacturing going digital (e.g. 3D printers), there is an increasing need for computational algorithms. In this seminar, we will explore together the emerging and exciting field of computational design and manufacturing: creating novel manufacturing solutions and products with the help of computational thinking and methods. Your main task in this Seminar is to develop a Wikipedia page for a topic related to computational design and fabrication. We identify together the topics which are not well-represented, or occasionally mis-represented. You will develop a new topic from scratch, or improve an existing one. You will work in a group of two and keep the class informed about your progress.
This year, between November 19th and 22nd we will give a one-day visit to FormNext, the biggest 3D printing industrial show in Europe which will be held in Frankfurt (the travel costs are covered).
Seminars will be on Fridays 10am-12pm with kick off session on Friday October 18, in Room 0.08 of MMCI (E1 7).
Requirements: A basic knowledge in visual computing is recommended, but not required.
The goal of the seminar is to provide a broad overview of the theoretical underpinnings of concurrency. Prospective participants should have enjoyed part "T" of the lecture "Nebenläufige Programmierung".
Starting off from there, we will discuss popular process calculi, including the pi calculus and mobile ambients. Further topics include input/output automata, modal transition systems and Petri nets as well as causality-based models of concurrency. In addition we will look at various concurrent automata models which in different senses extend finite automata. With these, one can reason about timed behaviour of real-time systems; describe behaviour of reactive systems using infinite words; or reason about randomness using probabilistic automata.
We have selected a number of papers for each of these areas. Participation in the seminar includes writing a report providing the theoretical background of the selected topic together with motivation, examples, and links to relevant literature. Furthermore, each participant gives a formal presentation (approx. 45 minutes) to explain the topic to the audience.
Requirements: Prospective participants should have enjoyed part "T" of the lecture "Nebenläufige Programmierung".
If you have taken the Seminar"Advanced Concurrency Theory" before, do not sign up since the two seminars are too close to count as different.
In this seminar we discusss "crazy" ideas from big data management. By "crazy" we mean that those ideas seemed non-obvious, totally out-of-the-box or even nonsense at the time but actually turned out to be creative, fresh, or even outrageous solutions.
There will be a weekly meeting during the semester. Every week a paper will be discussed by all students. One of the students is a discussion leader.
Requirements: You should have successfully passed Prog2, Informationssysteme 2019 or alternatively our core lecture Database Systems. Other courses in the area of systems are considered a plus, e.g. operating systems, networking, and software engineering.
The development of ICT has resulted in an unprecedented amount of data available. The 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.
The goal of this seminar is to (1) understand usable security challenges based on user studies from scientific literature, (2) identify the design space based on these studies and (3) propose potential solutions for user-friendly security and privacy technology.
In the first part of the seminar, we will focus on user studies published at top tier conferences to identify a design space for new usable security and privacy technology. The second part of the seminar will be focused on design methods from human computer interaction. We will search for relevant papers on design methods and discuss their applicability to security, and, in particular, the problems we identified in the first part of the seminar. You will also write a seminar paper on a usable security challenge of your choice consisting of a literature review of user studies and design methods, and your own reasoning about potential solutions. The goal of this seminar is to use interdisciplinary methods to understand and address hard usable security problems. You will be encouraged to be creative and think outside the box to find innovative and unconventional solutions.
Requirements: Foundations of Cybersecurity I and II, Usable Security (Advanced Lecture)
Model-based approaches to AI are well suited to explainability in principle, given the explicit nature of their world knowledge and of the reasoning performed to take decisions. AI Planning in particular is relevant in this context as a generic approach to action-decision problems. Indeed, explainable AI Planning (XAIP) has received interest since more than a decade, and has been taking up speed recently along with the general trend to explainable AI. This seminar provides an overview of the area, covering its major work lines pertaining to contrastive explanation, model reconciliation, hierarchical planning with user interaction, and the explanation of unsolvability.
Requirements: Ideally, participants should have completed the AI Planning course. Completion of an introductory AI course covering AI Planning is an absolute requirement.
Many real-world phenomena like rumor spreading, contagions in financial markets, epidemic outbreaks, or cognitive processes can be described by complex networks. The seminar explores dynamical processes on such networks which are linked to networked structures in intriguing ways. In particular, we aim at understanding the complex interplay between the topology and the emerging dynamics of a network. We analyze brain networks or online social networks and explore various techniques for their analysis and classification ranging from diffusion models and stochastic simulations to deep learning approaches.
Requirements: There are no prerequisites to take the seminar but a basic understanding of linear algebra and probability theory will be useful.
Engineering of secure systems is an arms race between attackers and system designers. In recent years, hardly a week goes by without the discovery of a new attack, and system designers scrambling to plug the holes. Formal methods are a means to break out of this arms race by ruling out entire classes of attacks once and for all.
In this seminar, students will learn to present, discuss, and summarize papers in different areas of formal methods for security. The seminar is split into two parts. The first part will take the form of reading sessions, where we lay the foundations of the topic. For the second part, each student is assigned a recent paper from the research area. Students will present their paper and will write a seminar paper on the topic assigned to them, taking into account connections to the topics discussed in the seminar.
Requirements: Basic knowledge of computer security and formal logic is assumed. More detailed knowledge about formal security properties and verification/automated reasoning is beneficial, but not necessary.
Description: Nearly everyone uses a messaging app, many of which claim that they offer secure messaging. However, it is not always exactly clear what this exactly means. When is a messaging app secure? How do we know it is secure? Can we prove anything about their security? The focus on this course is on modern ways to mathematically specify what various levels of messaging security actually mean, and the methodologies that can be used to prove that this is indeed the case. This will give both a good insight in the state-of-the-art as well as the scientific questions that are still unanswered.
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 must have basic knowledge about cryptography and protocols (e.g., through Foundations of Cybersecurity I and II) and basic knowledge about either: verification, cryptography, computational logic or concurrent systems (e.g., through the respective lectures).
This seminar is concerned with selected hybrid 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 a few selected methods and systems for hybrid learning and reasoning, and discuss their strengths and weaknesses. Participants will present and explain assigned topics to the audience (up to 45 minutes). In addition, each topic also gets two other participants assigned who will drive the discussion (up to 45 minutes) about it with the topic presenter.
Kick-off meeting with topic assignment will take place on October 29, 2019, at 16:00, in DFKI (campus building D3.2), Room +2.30 “Turing 1” (New Building).
More information on the HyLEAR seminar available at:
Requirements: Ideally, participants should have completed and enjoyed an introductory course on AI, machine learning or deep learning, and automated reasoning, or know some basics of thereof from other sources.
Augmented reality and virtual reality have been a topic of intense research for the last decades, but mostly restricted to research labs. In the past few years, massive advances in affordable consumer hardware and accessible software frameworks are now bringing AR and VR to the masses. A myriad of devices and applications are now finding their way to the end-users, suggesting AR and VR will be the next big platform for interactive computing. For instance, powerful head-mounted displays are getting affordable for mainstream users, such as the Oculus Rift or Microsoft Hololens; smartphones feature increasingly powerful cameras and on-board visual processing; and tracking systems that can be easily deployed in the living room feature a quality that long was restricted to high-end lab systems.
Indeed, augmented and virtual reality interfaces bring unique benefits for interaction, including a high degree of immersion, opportunities for more natural interactions, or seamless integration within a physical context. Prominent recent examples comprise AR games, such as Pokemon Go, , or computer-augmented lego bricks for children.
The objective of this seminar is to acquire basic conceptual, technical and practical skills in developing AR/VR applications. We will address the unique challenges of AR/VR on those three levels.
The seminar will cover the following topics:
Conceptual basics of AR/VR interfaces: The VR/AR continuum
How to design great AR/VR interfaces
Understanding and applying tracking technologies and implementing them using state-of-the-art APIs
Visual interfaces for AR/VR
Haptic interfaces for VR/AR
Designing and building interactions for AR applications
Applications of AR/VR
This seminar combines conceptual basics and theory with a practical team project:
To make you familiar with important conceptual and technical foundations, the seminar will involve three reading activities, done individually by each student. For every reading activity, you will read one or more seminal research papers and hand in a brief written report that discusses these papers. Reports should summarise the key aspects, and more importantly, should include original and critical thought that show you have acquired a meta level understanding of the topic.
In the team project, you will work together with other students on developing your own AR or VR system or application. In the project, you will gain practical experience with important technologies for AR/VR, you will learn how to design of your own innovative AR/VR interface, and you will gain implementation experience by realizing a working solution. Creativity, ideation and experimentation will be essential aspects of the project phase. We will present a list of potential project topics during the kick-off session. Students can choose from these or propose their own ideas for projects. Our experience is that developing your own solution within a team of students can be a lot of fun and lead to impressive results; some of our past student projects have gained remarkable attention on the web and even have received awards (see for instance Interactive Jacket for Cyclists (http://www.instructables.com/id/Interactive-Jacket-for-Cyclists/), Interactive Dancing Socks (http://www.instructables.com/id/Interactive-Dancing-Socks/) , Ambient Flower (http://www.instructables.com/id/Ambient-Flower/) , Sprinter (http://www.instructables.com/id/Sprinter-Two-Player-Jumping-Game-Using-Arduino-And)). We’ll help you to realize a great project.
Bei dem interdisziplinären Seminar werden unter anderem folgende Themen angeboten:
1. Predictive Policing
2. Nutzung von Analysemethoden Künstlicher Intelligent zur Strafverfolgung
3. Telekommunikationsüberwachung in Mobilfunknetzen
In this seminar, students will learn to present, discuss, and summarize papers in different areas of Web security. The seminar is taught as a combination of a reading group with weekly meetings and a regular seminar, where you have to write a seminar paper. Specifically, each student will get a single topic assigned to them, consisting of two papers (a lead and follow-up paper).
For the weekly meetings, all students have to have read the lead paper and must state at least three questions before the meeting. In the meeting, the assigned student will present the follow-up paper. Afterward, the entire group will discuss both papers.
Moreover, each student will write a seminar paper on the topic assigned to them, which covers at least the two papers given for the topic.
Requirements: Knowledge about the topic of Web security generally helps
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 (Robo Judge)
3. Automatische Verschlagwortung
[This seminar exists as a Proseminar and Seminar]
Cars have ceased to be purely mechanical devices since their computerised counterparts are usually cheaper to manufacture and provide more functionalities. Even in the entry-level segment, modern cars feature at least ten different computers, so-called Electronic Control Units (ECUs). These ECUs pose a risk to the security and privacy of passengers. In this block seminar, you are required to work in teams of 2 students and write a seminar report on a topic you choose.
- Data collection in modern cars
- Pay-as-you-drive Insurance tariffs
- Driver identification based on behaviour
- European Emergency Call (eCall)
- Telematic Control Units
- Sensor spoofing (GPS, Radar, RDS, ...)
- Security risks of connected devices (OBD, charging stations and protocols)
- Vehicle-to-Vehicle (V2V) communication & Vehicle to Infrastructure comm.
- In-vehicle networks between ECUs
- Autonomous driving verification
- Embedded Systems / ECU fuzzing
- Dynamic Homologation
You can then vote for up to three topics from above and an algorithm will fairly assign each student a topic such that most needs are met.
Then, we introduce personas to make your analysis more realistic and interesting. A Persona is a simple tool to create your product with a specific target user in mind rather than a generic one. It’s a representation of the real target audience and helps you to tailor your thoughts to a specific use case/user.
During the semester, you have to write a report and create a presentation (30 min) about a topic that you got.
The content shall be about the topic with respect to the presented personas (i.e. how people actually use cars, taking into account that components may fail, that electronics might be destroyed in an accident and so on...)
The report/presentation should be about the same content.
At the end of semester, each of the 6 groups has to give a short presentation (30 min + 10 min Q&A) about their topic. We will choose a week in which 2 groups will present per day -- so in total there will be three (mandatory) presentation days. Additionally, you have to hand in the seminar paper which will be graded together with the presentation.
Requirements: Basic knowledge in security and interest in vehicles (cars, bikes, ...) and privacy concerns.
Optimization problems occur everywhere. In this seminar, we will investigate this claim from the perspective of an entrepreneur. Starting from an idea for an optimization problem, we will discuss all aspects relevant for successfully founding a viable company. That is, we will discuss not only useful mathematical methods from the optimization toolbox but also economic and legal facets of enduring enterprises.
Requirements: core course Optimization recommended but not required
Reinforcement Learning (RL) is a popular subdiscipline of Machine Learning for problems that require strategies to solve complex tasks such as board games, scheduling problems or other discrete optimization problems.
This seminar will discuss the theoretical basis of RL as well as popular RL-algorithms such as Deep Q-Learning. In the final phase of the seminar, the participants will apply these algorithms to the well-known game Connect 4.
The seminar will include short presentations by the participants, a programming project that will be solved in small groups as well as a write-up.
The seminar has 12 slots of which 6 will be given to bioinformatics students that register via https://mcms.cs.uni-saarland.de/r4l/.
The kick-off meeting will take place on October 18 at 10 am in E1.3 Room 328.
Requirements: A solid background in statistics and python programming is useful. Ideally, participants passed the lecture "Probabilistic modeling and data analysis".
The pivotal role of software in our modern world imposes strong requirements on quality, correctness, and reliability of software systems. The ability to understand program code plays a key role for programmers to fulfill these requirements. Despite significant progress, research on program comprehension has had a fundamental limitation: program comprehension is a cognitive process that cannot be directly observed, which leaves considerable room for (mis)interpretation, uncertainty, and confounding factors. Thus, central questions such as “What makes a good programmer?” and “How should we program?” are surprisingly difficult to answer based on the state of the art.
Recently, researchers began to lift research on program comprehension to a new level. The key idea is to leverage recent methods from cognitive neuroscience to obtain insights into the cognitive processes involved in program comprehension. Opening the “black box” of human cognition will lead to a breakthrough in understanding the why and how of program comprehension and to a completely new perspective and methodology of measuring program comprehension, with direct implications for programming methodology, language design, and education.
In this seminar, we will review and discuss the past, current, and future developments in this area.
Kick-off and topic assignment will take place on October 24, at 12:15 in E1 1 Room 206.
Requirements: Basic knowledge on software engineering and programming.
Fair division of resources is a well studied problem in Economics and Computer Science. Typically the goal is to distribute a set of resources (or goods) among agents (or people) in a "fair manner". Typical day to day applications include rent division, property division, splitting taxi fare and dividing tasks (or chores). The website Spliddit (www.spliddit.org) is a very popular site dedicated to fair-division services and theory. They have even got more than 60,000 users so far. There are other websites developed in the same spirit such as Fair Outcomes, Inc. (http://www.fairoutcomes.com) that offer similar services. In this seminar we would read fundamental and also more recent papers about different notions of ''fairness", their existential and computational aspects and their mutual relations.
The seminar is open for all interested students and postdocs. Students aiming to get credit points must give a regular talk and write a short summary about the paper. We will give a short overview of all the papers in the first meeting. The presentation needs to be discussed with us at least one week before your scheduled talk.
Requirements: You should bring a good background in algorithms. This is an advanced seminar. The papers are challenging and a proper preparation of your talk will require some effort. The target audience of this seminar are master students, PhD students, as well as postdocs.