Veranstaltungsverzeichnis

Department of Computing Science

• English course offer for exchange students :
• School of Computing Science, Business Administration, Economics and Law
• Department of Computing Science

Winter semester 2024/2025 28 Seminars

VAK Course Number	Title	Type Lecture Preliminary studies Advanced courses Practical course Colloquium Research group Workgroup Project group Council conference Internship Language course Subject didactics Excursion Tutorial Committee	SWS Semester weekly hours	Teachers	Degree
2.01.176	Social Computing Dates on Friday, 08.11.2024, Friday, 06.12.2024, Friday, 17.01.2025 10:00 - 16:00, Friday, 07.02.2025 10:00 - 12:00 Description: In this module, students will critically explore technology as a way for people to observe and interact with others, in particular social media and purpose-built online forums. We will focus on understanding who uses social media to consume and communicate material as well as what topics are discussed how and for what purposes. Throughout, we will discuss the implications for designing new technologies. As part of this module, we will review core skills for conducting and documenting a study that are vital for successful completion of a Masters thesis. Skills include how to review the literature, how to read a paper in human-computer interaction, how to formulate a research question, how to design a qualitative study, and how to conduct and present qualitative data analysis. In the first part, we will work on lecturer provided data sets, in the second part, students are expected to curate their own. In this module, students will critically explore technology as a way for people to observe and interact with others, in particular social media and purpose-built online forums. We will focus on understanding who uses social media to consume and communicate material as well as what topics are discussed how and for what purposes. Throughout, we will discuss the implications for designing new technologies. As part of this module, we will review core skills for conducting and documenting a study that are vital for successful completion of a Masters thesis. Skills include how to review the literature, how to read a paper in human-computer interaction, how to formulate a research question, how to design a qualitative study, and how to conduct and present qualitative data analysis. In the first part, we will work on lecturer provided data sets, in the second part, students are expected to curate their own.	Seminar	-	Dr. phil. Maria Wolters Tobias Lunte Mikolaj Wozniak	Master
2.01.-CAUSE	Seminar of the RTN CAUSE Wednesday: 10:00 - 12:00, weekly (from 06/11/24) Description: The seminar is dedicated to the ongoing scientific exchange and cooperation of the memvers of the RTN CAUSE at Oldenburg. The seminar is dedicated to the ongoing scientific exchange and cooperation of the memvers of the RTN CAUSE at Oldenburg.	miscellaneous	-	Prof. Dr. Sebastian Lehnhoff Prof. Dr. Verena Klös Prof. Dr. Heike Wehrheim Prof. Dr. Martin Georg Fränzle
2.01.591	Verification of Distributed AI Systems Friday: 10:00 - 12:00, weekly (from 18/10/24) Description:	Lecture	2	Prof. Dr. Astrid Nieße Jens Sager	Master
2.01.5124	Research Project Digitalised Energy Systems The course times are not decided yet. Description:	Practical training	-	Prof. Dr.-Ing. habil. Andreas Rauh Prof. Dr. Sebastian Lehnhoff Prof. Dr. Astrid Nieße Jörg Bremer	Master
2.01.900-F	Project Group "AIM - Artificial Intelligence for Multimodal & Multisensor Applications" The course times are not decided yet. Description: - Lecturers: Aliki Anagnostopoulou, Michael Barz, Prof. Dr. Daniel Sonntag - Contact: Aliki Anagnostopoulou - Teaching language: English - Lecturers: Aliki Anagnostopoulou, Michael Barz, Prof. Dr. Daniel Sonntag - Contact: Aliki Anagnostopoulou - Teaching language: English	Practical training	-	Prof. Dr. Daniel Sonntag	Master
2.01.5458	Applied AI - Multimodal-Multisensor Interfaces 2: Signal Processing, Architectures, and Detection of Emotion and Cognition The course times are not decided yet. Description: We begin with multimodal signal processing, architectures, and machine learning. It includes recent deep-learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces. We discuss real-time multimodal analysis of emotion and social signals from various modalities and perception of affective expression by users. Then we discuss multimodal processing of cognitive state using behavioral and physiological signals to detect cognitive load, domain expertise, deception, and depression. This collection of chapters provides walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology, and tutorial support for mastering this rapidly expanding field. Finally, we look at experts' exchange views on the timely and controversial challenge topic of multimodal deep learning. The discussion focuses on how multimodal-multisensor interfaces are most likely to advance human performance during the next decade. This seminar is most appropriate for graduate students and of primary interest to students studying computer science and information technology, human-computer interfaces, mobile and ubiquitous interfaces, affective and behavioral computing, machine learning, and related multidisciplinary majors. Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition - Volume 2" (https://dl.acm.org/doi/book/10.1145/3107990). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Hannes Kath, hannes.kath@uni-oldenburg.de We begin with multimodal signal processing, architectures, and machine learning. It includes recent deep-learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces. We discuss real-time multimodal analysis of emotion and social signals from various modalities and perception of affective expression by users. Then we discuss multimodal processing of cognitive state using behavioral and physiological signals to detect cognitive load, domain expertise, deception, and depression. This collection of chapters provides walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology, and tutorial support for mastering this rapidly expanding field. Finally, we look at experts' exchange views on the timely and controversial challenge topic of multimodal deep learning. The discussion focuses on how multimodal-multisensor interfaces are most likely to advance human performance during the next decade. This seminar is most appropriate for graduate students and of primary interest to students studying computer science and information technology, human-computer interfaces, mobile and ubiquitous interfaces, affective and behavioral computing, machine learning, and related multidisciplinary majors. Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition - Volume 2" (https://dl.acm.org/doi/book/10.1145/3107990). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Hannes Kath, hannes.kath@uni-oldenburg.de	Seminar	-	Rida Saghir Ilira Hiller Hannes Kath Prof. Dr. Daniel Sonntag	Master
2.01.5452	Current Topics in Interpretable/Explainable AI (XAI) Monday: 08:00 - 10:00, weekly (from 14/10/24) Description: This seminar will cover different aspects of interpretable/explainable AI (XAI) ranging from inherently interpretable models over perturbation-based methods, such as Shapley values, to gradient-/decomposition-based approaches and their quantitative evaluation. Going beyond conventional single-feature attribution methods, we will also discuss current concept-based attribution methods, ways to assess feature interactions and connections to causality. This seminar will cover different aspects of interpretable/explainable AI (XAI) ranging from inherently interpretable models over perturbation-based methods, such as Shapley values, to gradient-/decomposition-based approaches and their quantitative evaluation. Going beyond conventional single-feature attribution methods, we will also discuss current concept-based attribution methods, ways to assess feature interactions and connections to causality.	Seminar	2	Prof. Dr. Nils Strodthoff	Master
2.01.420-a	Introduction to IT-Security (Ü) Wednesday: 16:00 - 18:00, weekly (from 30/10/24) Description:	Exercises	2	Marvin Büchel Prof. Dr. Andreas Peter	Bachelor Master of Education Master
2.01.809	Selected Topics in IT-Security Thursday: 14:00 - 16:00, weekly (from 17/10/24) Description: /// Goals of the course /// At the end of the course, students will be able to * analyze the technical merits of specific developments within the field of IT-security, * substantiate their analyses using existing and scientific documented knowledge, * clearly write up those analyses in a concise scientific report, and * further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing a specific topic in the field of IT-security. There will be multiple topics, and each topic is to be tackled by an individual student. Students will be handed out material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. In the start of the course, all available topics will be introduced to the students so that they can pick a for them suitable topic. /// Assessment /// Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …) /// Topics /// * Malicious software * Security operations centers and their performance * Weaknesses of the RSA cryptosystem * Zero-knowledge proofs (ZKPs) * Online tracking methods and countermeasures * Privacy in instant messaging * Privacy metrics and ways to achieve certain privacy levels * Your own topic More details will follow. /// Goals of the course /// At the end of the course, students will be able to * analyze the technical merits of specific developments within the field of IT-security, * substantiate their analyses using existing and scientific documented knowledge, * clearly write up those analyses in a concise scientific report, and * further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing a specific topic in the field of IT-security. There will be multiple topics, and each topic is to be tackled by an individual student. Students will be handed out material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. In the start of the course, all available topics will be introduced to the students so that they can pick a for them suitable topic. /// Assessment /// Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …) /// Topics /// * Malicious software * Security operations centers and their performance * Weaknesses of the RSA cryptosystem * Zero-knowledge proofs (ZKPs) * Online tracking methods and countermeasures * Privacy in instant messaging * Privacy metrics and ways to achieve certain privacy levels * Your own topic More details will follow.	Seminar	2	Prof. Dr. Andreas Peter	Bachelor
2.01.5456	Applied AI - Multimodal-Multisensor Interfaces 1: Foundations, User Modeling, and Common Modality Combination The course times are not decided yet. Description: We look at relevant theory and neuroscience foundations for guiding the development of high-performance systems. We discuss approaches to user modeling, interface design that supports user choice, synergistic combination of modalities with sensors, and blending of multimodal input and output. We also highlight an in-depth look at the most common multimodal-multisensor combinations- for example, touch and pen input, haptic and non-speech audio output, and speech co-processed with visible lip movements, gaze, gestures, or pen input. A common theme throughout is support for mobility and individual differences among users-including the world's rapidly growing population of seniors. This seminar would be most appropriate for graduate students, and of primary interest to students studying computer science and information technology, human–computer interfaces, mobile and ubiquitous interfaces, and related multidisciplinary majors. Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition - Volume 1" (https://dl.acm.org/doi/book/10.1145/3015783). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Ilira Troshani, ilira.troshani@uni-oldenburg.de We look at relevant theory and neuroscience foundations for guiding the development of high-performance systems. We discuss approaches to user modeling, interface design that supports user choice, synergistic combination of modalities with sensors, and blending of multimodal input and output. We also highlight an in-depth look at the most common multimodal-multisensor combinations- for example, touch and pen input, haptic and non-speech audio output, and speech co-processed with visible lip movements, gaze, gestures, or pen input. A common theme throughout is support for mobility and individual differences among users-including the world's rapidly growing population of seniors. This seminar would be most appropriate for graduate students, and of primary interest to students studying computer science and information technology, human–computer interfaces, mobile and ubiquitous interfaces, and related multidisciplinary majors. Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition - Volume 1" (https://dl.acm.org/doi/book/10.1145/3015783). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Ilira Troshani, ilira.troshani@uni-oldenburg.de	Seminar	-	Rida Saghir Ilira Hiller Hannes Kath Prof. Dr. Daniel Sonntag	Master
2.01.962b	CS4Science - Tutorial B Wednesday: 16:00 - 18:00, weekly (from 16/10/24), Location: V02 0-003 Dates on Friday, 29.11.2024 09:00 - 10:00, Friday, 29.11.2024 10:15 - 11:15, Location: A03 2-209 Description: This cource introduces basic concepts of Computer Science and also a short introduction to programming in the programming language Python. This cource introduces basic concepts of Computer Science and also a short introduction to programming in the programming language Python.	Exercises	2	Dr. Ute Vogel-Sonnenschein	Bachelor Master
2.01.5126	Digitalised Energy System Cyber-Resilience Wednesday: 12:00 - 14:00, weekly (from 16/10/24) Description:	Seminar	2	Prof. Dr. Sebastian Lehnhoff Jörg Bremer René Kuchenbuch	Master
2.01.5408a	Applied Deep Learning in PyTorch (Ü) Wednesday: 16:00 - 18:00, weekly (from 16/10/24) Description:	Exercises	2	Prof. Dr. Nils Strodthoff Tiezhi Wang	Master
2.01.420-b	Introduction to IT-Security (Ü) Thursday: 14:00 - 16:00, weekly (from 24/10/24) Description:	Exercises	2	Marvin Büchel Prof. Dr. Andreas Peter	Bachelor Master of Education Master
2.01.5408b	Applied Deep Learning in PyTorch (Ü) Friday: 10:00 - 12:00, weekly (from 18/10/24) Description:	Exercises	-	Zahra Mansour Prof. Dr. Nils Strodthoff Juan Lopez Alcaraz	Master
2.01.368	Microrobotics Selected Topics Tuesday: 16:00 - 18:00, weekly (from 15/10/24) Description: This seminar is an addition to the main lecture series “2.01.208 Mikrorobotik und Mikrosystemtechnik” Topics - Swimming MR - Flying MR - MR for surface locomotion - Gecko MR - Soft MR - On-chip MR - In-vivo MR - Bacteria- and Cell-MR - MR Swarms - Molecular MR This seminar is an addition to the main lecture series “2.01.208 Mikrorobotik und Mikrosystemtechnik” Topics - Swimming MR - Flying MR - MR for surface locomotion - Gecko MR - Soft MR - On-chip MR - In-vivo MR - Bacteria- and Cell-MR - MR Swarms - Molecular MR	Seminar	2	Prof. Dr. Sergej Fatikow	Master
2.01.5110	Practical Course (Energy Informatics) Wednesday: 14:00 - 16:00, weekly (from 16/10/24) Wednesday: 16:00 - 18:00, weekly (from 16/10/24) Description:	Practical training	4	Prof. Dr.-Ing. habil. Andreas Rauh Prof. Dr. Sebastian Lehnhoff Prof. Dr. Astrid Nieße Jörg Bremer Jens Sager Anand Narayan Marit Lahme	Master
2.01.815	Machine Learning in Security The course times are not decided yet. Description: // Goals of the course /// At the end of the course, students will be able to * analyze the technical merits of specific developments regarding the topic of machine learning in the field of IT-security, * substantiate their analyses using existing and scientific documented knowledge, * clearly write up those analyses in a concise scientific report, and * further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing selected methods in machine learning in an IT-security context. Each available topic is to be tackled by an individual student. For this purpose students will be provided with material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Finally, students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. At the beginning of the course, all available topics will be introduced to the students so that they can pick a topic suitable for them. /// Assessment /// Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …) /// Topics /// More details on the topics will follow. // Goals of the course /// At the end of the course, students will be able to * analyze the technical merits of specific developments regarding the topic of machine learning in the field of IT-security, * substantiate their analyses using existing and scientific documented knowledge, * clearly write up those analyses in a concise scientific report, and * further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing selected methods in machine learning in an IT-security context. Each available topic is to be tackled by an individual student. For this purpose students will be provided with material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Finally, students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. At the beginning of the course, all available topics will be introduced to the students so that they can pick a topic suitable for them. /// Assessment /// Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …) /// Topics /// More details on the topics will follow.	Seminar	-	Marvin Büchel Prof. Dr. Andreas Peter	Master
2.01.535	Evolution Strategies Dates on Monday, 17.02.2025 - Friday, 21.02.2025, Monday, 24.02.2025 - Tuesday, 25.02.2025 08:00 - 18:00 Description: The lecture on "Evolution Strategies" offers an in-depth exploration of optimization techniques that are pivotal in solving complex problems. It begins by introducing basic optimization concepts, setting the stage for more advanced strategies. The lecture delves into the (1+1)-ES, a simple evolution strategy that evolves solutions using one parent and one offspring per generation, illustrating the foundational mechanism of this approach. It further discusses the 1/5 success rule, a method for adapting the step size based on a target success rate, which helps maintain efficient progress. The concept of restarts is explored, emphasizing strategies to escape local optima and improve solution diversity. More complex is the (μ+λ)-ES, which involves multiple parents and offspring, enhancing the robustness and convergence rate of the strategy. Self-adaptation is highlighted as a crucial feature, allowing the algorithm to dynamically adjust its parameters to better suit the problem landscape. The lecture also covers the adaptation of the covariance matrix, a sophisticated technique that helps the algorithm learn and adapt to the shape of the optimization landscape. Experimental results are presented to showcase the practical applications and effectiveness of these strategies. Finally, benchmark functions described in the appendix serve as a standard for evaluating and comparing the performance of evolution strategies. In practical exercises, participants are introduced to Python and all algorithms are programmed to facilitate hands-on learning and application. The course is worth 6 ECTS. The lecture on "Evolution Strategies" offers an in-depth exploration of optimization techniques that are pivotal in solving complex problems. It begins by introducing basic optimization concepts, setting the stage for more advanced strategies. The lecture delves into the (1+1)-ES, a simple evolution strategy that evolves solutions using one parent and one offspring per generation, illustrating the foundational mechanism of this approach. It further discusses the 1/5 success rule, a method for adapting the step size based on a target success rate, which helps maintain efficient progress. The concept of restarts is explored, emphasizing strategies to escape local optima and improve solution diversity. More complex is the (μ+λ)-ES, which involves multiple parents and offspring, enhancing the robustness and convergence rate of the strategy. Self-adaptation is highlighted as a crucial feature, allowing the algorithm to dynamically adjust its parameters to better suit the problem landscape. The lecture also covers the adaptation of the covariance matrix, a sophisticated technique that helps the algorithm learn and adapt to the shape of the optimization landscape. Experimental results are presented to showcase the practical applications and effectiveness of these strategies. Finally, benchmark functions described in the appendix serve as a standard for evaluating and comparing the performance of evolution strategies. In practical exercises, participants are introduced to Python and all algorithms are programmed to facilitate hands-on learning and application. The course is worth 6 ECTS.	Lecture	-	Jill Baumann Prof. Dr. Oliver Kramer	Master
2.01.AM-2	Oberseminar Applied Artificial Intelligence Tuesday: 08:00 - 10:00, weekly (from 15/10/24) Description: Your Advisor and Your Committee In order to write a bachelor’s or master’s thesis you must find a member of the IML lab who is willing to be your thesis advisor. You propose your thesis topic together with your advisor to Prof. Sonntag as the first reviewer in your committee. How Long Should it Be? How Long Does it Take? A bachelor’s thesis is generally 20-40 pages, not including the bibliography. A master’s thesis is generally 40-80 pages, not including the bibliography. However, the length will vary according to the topic and the method of analysis, so the appropriate length will be determined by you, your advisor, and your committee. Students who write a master’s thesis generally do so over two semesters, bachelor’s one semester. More information: https://iml.dfki.de/teaching/writing-a-thesis/ Your Advisor and Your Committee In order to write a bachelor’s or master’s thesis you must find a member of the IML lab who is willing to be your thesis advisor. You propose your thesis topic together with your advisor to Prof. Sonntag as the first reviewer in your committee. How Long Should it Be? How Long Does it Take? A bachelor’s thesis is generally 20-40 pages, not including the bibliography. A master’s thesis is generally 40-80 pages, not including the bibliography. However, the length will vary according to the topic and the method of analysis, so the appropriate length will be determined by you, your advisor, and your committee. Students who write a master’s thesis generally do so over two semesters, bachelor’s one semester. More information: https://iml.dfki.de/teaching/writing-a-thesis/	Seminar	2	Michael Barz, M. Sc. Ilira Hiller Prof. Dr. Daniel Sonntag Hannes Kath	Bachelor Master of Education Master
2.01.814-A	Computing on Encrypted Data The course times are not decided yet. Description: // Goals of the course /// At the end of the course, students will be able to * analyze the technical merits of specific developments regarding secure computation methods on encrypted data, * substantiate their analyses using existing and scientific documented knowledge, * clearly write up those analyses in a concise scientific report, and * further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing a specific method of secure computation on encrypted data. Each available topic is to be tackled by an individual student. For this purpose students will be provided with material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Finally, students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. At the beginning of the course, all available topics will be introduced to the students so that they can pick a topic suitable for them. /// Assessment /// Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …) /// Topics /// * Hardware Acceleration for Homomorpic Encryption; * Funtional Encryption; * NTRU-based Homomorphic Encryption; * Threshold and Multiparty Homomorphic Encryption; * Hybrid Homomorphic Encryption; * Functional Secret Sharing; * Your Own Topic; More details on the topics will follow. // Goals of the course /// At the end of the course, students will be able to * analyze the technical merits of specific developments regarding secure computation methods on encrypted data, * substantiate their analyses using existing and scientific documented knowledge, * clearly write up those analyses in a concise scientific report, and * further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing a specific method of secure computation on encrypted data. Each available topic is to be tackled by an individual student. For this purpose students will be provided with material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Finally, students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. At the beginning of the course, all available topics will be introduced to the students so that they can pick a topic suitable for them. /// Assessment /// Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …) /// Topics /// * Hardware Acceleration for Homomorpic Encryption; * Funtional Encryption; * NTRU-based Homomorphic Encryption; * Threshold and Multiparty Homomorphic Encryption; * Hybrid Homomorphic Encryption; * Functional Secret Sharing; * Your Own Topic; More details on the topics will follow.	Seminar	-	Valentin Reyes Häusler Prof. Dr. Andreas Peter	Master
2.01.341	Robust Control and State Estimation in Digitalised Energy Systems Monday: 08:00 - 10:00, weekly (from 14/10/24) Tuesday: 16:00 - 18:00, weekly (from 15/10/24) Tuesday: 18:00 - 20:00, weekly (from 22/10/24) Description:	Lecture	6	Prof. Dr.-Ing. habil. Andreas Rauh Marit Lahme Dr.-Ing. Friederike Bruns	Master
2.01.5460	Applied AI - Multimodal-Multisensor Interfaces 3: Language Processing, Software, Commercialization, and Emerging Directions The course times are not decided yet. Description: This third seminar takes the contents of the first two seminars—namely, the motivations, foundational concepts, basic modality combinations, component analyses, and recognition and fusion techniques—to the next level. MMI 3 discusses how to design and build functioning multimodal-multisensor systems that can sustain real-world use. This seminar is most appropriate for graduate students and of primary interest to students studying computer science and information technology, human-computer interfaces, mobile and ubiquitous interfaces, affective and behavioral computing, machine learning, and related multidisciplinary majors. It is very useful if you want to work on practical applications, transfer of AI technology to application domains such as medicine and healthcare, and industrial prototypes. Afterward, students might engage in a hands-on project in which they design, build, and evaluate the performance of a multimodal system in our project group MMI II (https://elearning.uni-oldenburg.de/dispatch.php/course/details?sem_id=098bd500a63e723551364c7f921755b5&again=yes). Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging Directions - Volume 3" (https://dl.acm.org/doi/book/10.1145/3233795). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Rida Saghir, rida.saghir@uni-oldenburg.de This third seminar takes the contents of the first two seminars—namely, the motivations, foundational concepts, basic modality combinations, component analyses, and recognition and fusion techniques—to the next level. MMI 3 discusses how to design and build functioning multimodal-multisensor systems that can sustain real-world use. This seminar is most appropriate for graduate students and of primary interest to students studying computer science and information technology, human-computer interfaces, mobile and ubiquitous interfaces, affective and behavioral computing, machine learning, and related multidisciplinary majors. It is very useful if you want to work on practical applications, transfer of AI technology to application domains such as medicine and healthcare, and industrial prototypes. Afterward, students might engage in a hands-on project in which they design, build, and evaluate the performance of a multimodal system in our project group MMI II (https://elearning.uni-oldenburg.de/dispatch.php/course/details?sem_id=098bd500a63e723551364c7f921755b5&again=yes). Central part of the seminar is the reference book "The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging Directions - Volume 3" (https://dl.acm.org/doi/book/10.1145/3233795). At the beginning there will be an introduction to the subject. Everyone will receive a chapter, for which a presentation (30 min. + 30 min. discussion) and a written elaboration (5-10 pages) are to be prepared. Contact: Rida Saghir, rida.saghir@uni-oldenburg.de	Seminar	-	Rida Saghir Ilira Hiller Hannes Kath Prof. Dr. Daniel Sonntag	Master
2.01.490	Logical Methods in AI Verification The course times are not decided yet. Description:	Seminar	-	Prof. Dr. Heike Wehrheim	Master
2.01.963	Fundamentals of Psychology: Cognition Friday: 12:00 - 14:00, weekly (from 06/12/24), Location: A07 0-031 Dates on Friday, 29.11.2024 12:00 - 18:00, Friday, 31.01.2025 12:00 - 16:00, Location: A14 1-114, V03 0-M018 Description:	Exercises	2	Prof. Dr.-Ing. habil. Christoph Siegfried Herrmann, Dipl.-Ing. Dr. Seonghun Park	Master
2.01.5130	Socio-technical Energy Systems Wednesday: 10:00 - 12:00, weekly (from 16/10/24) Description:	Seminar	2	Prof. Dr. Sebastian Lehnhoff Jörg Bremer	Master
2.01.180	Smart Data Interoperability Tuesday: 10:00 - 12:00, weekly (from 15/10/24) Description:	Seminar	2	Florian Schmalriede Prof. Dr. Andreas Winter Dr. rer. nat. Christian Schönberg	Master
2.01.814	Advances in Security & Privacy The course times are not decided yet. Description: // Goals of the course /// At the end of the course, students will be able to * analyze the technical merits of specific developments within the field of IT-security, * substantiate their analyses using existing and scientific documented knowledge, * clearly write up those analyses in a concise scientific report, and * further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing a specific topic from the fields of security and/or privacy. There will be multiple topics, and each topic is to be tackled by an individual student. Students will be handed out material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. In the start of the course, all available topics will be introduced to the students so that they can pick a for them suitable topic. /// Assessment /// Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …) /// Topics /// Explainable Machine Learning in Security Attacks on Searchable Encrypted Databases and Countermeasures (Semi-)Automated Security Event Handling in Security Operations Centers Post-Quantum Encryption Algorithms Interplay of Safety and Security Mobile-App Fingerprinting on Encrypted Network Traffic Biometric Template Protection Automated Extraction of Tactics, Techniques, and Procedures from Cyber Threat Reports Your Own Topic More details on the topics will follow. // Goals of the course /// At the end of the course, students will be able to * analyze the technical merits of specific developments within the field of IT-security, * substantiate their analyses using existing and scientific documented knowledge, * clearly write up those analyses in a concise scientific report, and * further develop an attitude in which being able to clearly explain matters is geared to optimize the quality of feedback. /// Course contents /// The course contents consist of studying and assessing a specific topic from the fields of security and/or privacy. There will be multiple topics, and each topic is to be tackled by an individual student. Students will be handed out material such as scientific articles to help them understand the topic at hand. Part of the course consists of discovering additional material. Students will dig deep into the selected topic. Students will present their analyses and findings in two ways: in a concise scientific report as well as in a 20 min. presentation, which is followed by a discussion and a round of feedback. In the start of the course, all available topics will be introduced to the students so that they can pick a for them suitable topic. /// Assessment /// Students will be assessed on the basis of their written scientific report (high weight), their presentation and consequent discussion (medium to high weight), and the general process (low weight; includes: independence, planning, active involvement, …) /// Topics /// Explainable Machine Learning in Security Attacks on Searchable Encrypted Databases and Countermeasures (Semi-)Automated Security Event Handling in Security Operations Centers Post-Quantum Encryption Algorithms Interplay of Safety and Security Mobile-App Fingerprinting on Encrypted Network Traffic Biometric Template Protection Automated Extraction of Tactics, Techniques, and Procedures from Cyber Threat Reports Your Own Topic More details on the topics will follow.	Seminar	-	Valentin Reyes Häusler Prof. Dr. Andreas Peter	Master
28 Seminars