Course: Project Computer Science (CA10-320305)
Course: Thesis Computer Science (CA10-320306)
Semester: Fall 2019
Semester: Spring 2020
Instructor: Peter Baumann
Instructor: Andreas Birk
Instructor: Horst Karl Hahn
Instructor: Sergey Kosov
Instructor: Kinga Lipskoch
Instructor: Francesco Maurelli
Instructor: Jürgen Schönwälder
Instructor: Peter Zaspel
Prerequisites: Two CS core modules passed
|Project topic/supervisor selection (campus track)||2019-09-20 (Friday)|
|Project topic/supervisor selection (world track)||2020-02-03 (Monday)|
|Project and thesis kickoff meeting||2020-02-10 (Monday)|
|Bachelor thesis submission||2020-05-15 (Friday)|
We expect that our students take the initiative and drive the process. How self-organized students work is part of the assessment. In terms of effort, please note that 1 CP equals ~25 hours.
Doing research in computer science usually starts with a lot of reading and learning. In order to do research that is significant, it is crucial to pick a tractable topic and it is essential to understand the state of the art as well as any algorithms and tools that are relevant. While the details differ depending on the area of computer science, reading about the state of the art is essential for all of them. To find relevant literature, it is good to be aware of systems such as:
IEEE Xplore (digital library provided by the IEEE)
ACM Digital Library (digital library provided by the ACM)
IFIP Digital Library (digital library provided by the IFIP)
Scopus (commercial research publication indexing system)
dblp (open computer science publication indexing system)
Semantic Scholar (academic search engine by the Allen Institute of AI)
The project phase is essentially a way into your specific bachelor thesis topic. During the project phase, you should pick up and deepen the necessary knowledge, you should develop a good understanding of the state of the art, and you should get familiar with any programs or tools or datasets that are essential for carrying out a little research project during the bachelor thesis course.
LaTeX is widely used as the typesetting system for research papers in computer science. Hence, we expect that project and thesis reports are written in LaTeX. Below are some LaTeX templates that you are expected to use for typesetting the project report and later the thesis. Please do not change or improve the format, it is usually far better to spend your brain cycles on the content instead of the format (and we really appreciate a common format).
Justin Zobel: Writing for Computer Science, Springer, 3rd edition, 2015
Research Groups and Topics
Large-Scale Information Services (Peter Baumann)
Robotics (Andreas Birk)
The prerequisite for carrying out the project and bachelor thesis module on a robotics topic are good coding skills, i.e., a passing grade of the programming labs of 2.0 or better. Having successfully taken the IMS choice module, especially the Introduction to IMS lecture, and/or the robotics lecture is recommended but not required - but good math knowledge/interest is needed. Group work (2-3 students) is allowed during the project phase. Topics will be related to underwater robotics, especially underwater perception (e.g., object recognition) and mapping. Good students are given opportunities to contribute to publications in high-ranking conferences and journals.
Medical Image Processing (Horst Hahn)
Graphics and Machine Learning (Sergey Kosov)
Marine Systems and Robotics (Francesco Maurelli)
Some BSc thesis ideas are on Francesco Maurelli's web page. Feel free to propose your own idea.
Computer Networks and Distributed Systems (Jürgen Schönwälder)
The prerequisite for carrying out the project and bachelor thesis module on a topic related to computer networking and distributed systems is a passing grade at least as good as 3.0 in the courses Computer Networks and Operating Systems. Group work (2-3 students) is encouraged during the project phase. Topics will be related to software defined networks, to large-scale Internet measurements, the Internet of Things, edge computing or cyber security. Good students are given opportunities to contribute to publications.
Machine Learning and High Performance Computing (Peter Zaspel)
The project is the entry door to a subsequent bachelor thesis. The project course introduces to a specific area of research. After obtaining the necessary understanding of the chosen area of research, you select a topic for your bachelor thesis. An important part of the project will be to familiarize yourself with the state of the art in a certain area of computer science.
The project phase includes, among others (and obviously somewhat also depending on the particular topic): familiarization with the topic; elaborating background through literature work; detailed study of related work.
The project may lead to a project report. The project report needs to contain at least these elements (again, to be confirmed with your supervisor): motivation; overview of the state of the art, description of research questions; discussion of the relevance of the research questions ("how will the world be better once the research questions have been answered?"); a discussion of any experimental setups that may be necessary to answer research questions, possibly including a realistic time plan for addressing research questions.
Students must select the project topic and supervisor beginning of September (see the timeline above) if the project is done in the Fall semester and begining of January (see the timeline above) if the project is done in the Spring semester. The choosen topic and supervisor must be communicated by email to Jürgen Schönwälder <email@example.com> so that we can track things.
Students must submit project reports at a deadline defined by the supervisor.
Experience has shown that it is crucial to start work on the bachelor thesis topic as soon as possible. It may be very useful to use time during intersession, in particular if still a number of credits need to be earned during the last semester. Starting work on the bachelor thesis end of April clearly is too late to achieve good results and in particular to deal with any unforseen problems.
The bachelor thesis must be submitted electronically via Moodle. The submission deadline is a hard deadline. Failure to submit the thesis in time will lead to an incomplete course grade or to a fail. Faculty will ensure that a bachelor thesis submitted by the deadline will be graded by the grade submission deadline for graduating students. Note that faculty availability for thesis supervision during the summer break may be limited.
The grade of the bachelor thesis will be determined using the following criteria:
Technical Work (weight 50%)
understanding of the subject
completeness (topic fully addresses)
originality and independence
work organization (sustained work pace, regular progress reporting)
Writing and Thesis (weight 40%)
proper and concise abstract
"research" questions clearly formulated and motivated
survey of the state of the art
clear methodology (e.g., experiment design, algorithm design…)
presentation and interpretation of results
reflection about limitations of the work
proper references and citations
proper scientific writing
Presentation (weight 10%)
clarity of the slides
clarity of the presentation
motivation and flow of the presentation
technical clarity (proper use of notations etc.)
demo included (where feasible)?
answers to questions
Bachelor Thesis Presentations
Bachelor thesis presentations are 15 minutes + 5 minutes discussion. The schedule has 25 minutes for each presentation to allow for time to change laptops etc. In addition, we have scheduled breaks to recover our minds and to makeup any schedule quirks should they arise (we hope not).
Time slots are assigned on a first-come-first-served basis. To apply for a time slot, contact Jürgen Schönwälder and send him your preferred list of time slots, the name of your supervisor, and the title of your talk. Before submitting the list, make sure that the time slots fit the schedule of your supervisor.
|1||10:10||Teams||Silaj, Kevin||Birk, Andreas||Detecting Cracks in Underwater Images (using Classic Vision)|
|2||10:35||Teams||Anifowoshe, Shalom-David Oluwatofunwa||Mallahi Karai, Keivan||Geometry of a Ball in the Baumslag-Solitar Group BS(1,2)|
|3||11:15||Teams||Shala, Ardit||Schönwälder, Jürgen||Service Dependency Discovery on Microservice Architectures|
|4||11:40||Teams||Al-Wardi, Osama Zaid Saleem||Schönwälder, Jürgen||Performing Service Dependency Discovery on Web Application Clients|
|5||12:05||Teams||Mana, Irsida||Schönwälder, Jürgen||Network Service Dependency Discovery|
|6||14:15||Teams||Gjorgoski, David||Baumann, Peter||3D Display of Datacube Tiling|
|7||14:40||Teams||Anil Kumar, Aadil||Wilhelm, Adalbert||Epidemiological Estimation of COVID-19|
|8||16:10||Teams||Kim, Minji||Maurelli, Francesco||Leader Vehicle Detection and Tracking with Computer Vision and Wireless Communication|
|9||16:35||Teams||Adilbish, Ganbold||Zaspel, Peter||Parallel Hyperparameter Optimization|
|10||17:15||Teams||Ali, Mark Ray (IMS)||Bode, Mathias||Predictive Maintenance: Predicting Chaos|
|11||17:40||Teams||Hou, Mingchi (IMS)||Godde, Ben; Zaspel, Peter|
|12||08:40||Teams||Villeda Tosta, Jose Diego||Baumann, Peter||Accessing Array Databases with Python|
|13||09:05||Teams||Ramilev, Maksat||Kosov, Sergey||Predicting Sales using Weather Forecast|
|14||09:45||Teams||Imran, Hammad||Birk, Andreas||Deep Learning for Object Detection with Sonar Data from an Acoustic Camera|
|15||10:10||Teams||Ciurezu, Bogdan||Zaspel, Peter||Parallel Text Classification|
|16||10:35||Teams||bin Tahir, Sanan||Birk, Andreas||Detection of Underwater Surface Cracks using Deep Learning|
|17||11:15||Teams||Tsvetkov, Lyubomir Rosenov||Schönwälder, Jürgen||OpenWRT LuCI Support for the LMAP Daemon|
|18||11:40||Teams||Terzikj, Dushan||Schönwälder, Jürgen||Integration of a Network Measurement Daemon with a Remote Configuration Daemon|
|19||12:05||Teams||Deng, Yiping||Schönwälder, Jürgen||DDoS Open Threat Signaling on OpenWRT Devices|
|20||14:15||Teams||Hanna Nasralla, Brian Sherif||Schönwälder, Jürgen||Secure Update Mechanisms on Constrained IoT Devices|
|21||14:40||Teams||Wolf, Melvin||Schönwälder, Jürgen||Applications of Metamorphic Testing to Security|
|22||15:05||Teams||Huynh, Dung Tri||Schönwälder, Jürgen||Sonification of Network Intrusion Detection Systems|
|23||15:45||Teams||Dayinta, Jasmine||Baumann, Peter||Security Graph Visualization and Interaction|
|24||16:10||Teams||Cornea, Iulia Ana-Maria||Schönwälder, Jürgen||Clustering and Community Detection in Cyber Threat Intelligence|
|25||16:35||Teams||Gora, Takundei Makwara||Kosov, Sergey||Global Illumination in OpenRT|
|26||17:15||Teams||Seifu, Henok Hailu||Zaspel, Peter||Optimization of Kernel Ridge Regression in Distributed Systems|
|27||17:40||Teams||Tazi, Fatine||Kosov, Sergey||Probabilistic Graphical Methods for Stereo Disparity Estimation|
|28||18:05||Teams||Kamov, Dragi||Baumann, Peter||Datacube Visualization|
|29||15:00||Teams||Bhattarai, Ananta||Zaspel, Peter||Scalable Optimal Variable Selection|
|30||10:10||Teams||Yau, Lap Man (IMS)||Maurelli, Francesco||Real-time Removal of Points in a Map belonging to the Mapping Vehicle|
|31||10:35||Teams||Dreger, Hendrik (IMS)||Maurelli, Francesco||Development of a Robotic Prototype for the Exploration of Lunar Lava Tubes|
|32||11:00||Teams||Seghouani, Nesrine||Birk, Andreas||??|
|33||11:25||Teams||Manana, Fezile Lindokuhle||Maurelli, Francesco||Optimising Minimisation Algorithms for a Self-Calibrating Sensor Signal Conditioner|
|34||15:15||Teams||Bhattarai, Prajwal||Maurelli, Francesco||A Deep Learning Approach for Underwater Bubble Detection|
|35||15:40||Teams||Shiferaw, Leul||Maurelli, Francesco||Comparative study of micro-controllersand microprocessors on ROS|
|36||16:05||Teams||Jubakhanji, Alaa (IMS)||Maurelli, Francesco||Anomaly-based Network Intrusion Detection using Machine Learning Techniques|
|37||16:30||Teams||Paudel, Rohit||Maurelli, Francesco||Automated sensor data extraction pipeline and Subsystems visualization for EDENISS Project|
|38||15:00||Teams||Shiferaw, Leul Abiy||Maurelli, Francesco||Comparative Study of Micro-Controllers and Microprocessors on ROS|
|39||15:25||Teams||Nishaant, Yash||Maurelli, Francesco||Semi-Supervised Video Object Segmentation using Capsule Networks|