CS Bachelor Project and Thesis
About
- Course: Project Computer Science (CA10-320305)
- Course: Thesis Computer Science (CA10-320306)
- Semester: Fall 2020
- Semester: Spring 2021
- 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
Timeline
Activity | Deadline |
---|---|
Project topic/supervisor selection (campus track) | 2020-09-18 (Friday) |
Project topic/supervisor selection (world track) | 2021-02-08 (Monday) |
Project and thesis kickoff meeting | 2021-02-08 (Monday) |
Presentations | 2021-05-10 (Monday) |
Presentations | 2021-05-11 (Tuesday) |
Bachelor thesis submission | 2021-05-17 (Monday) |
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.
Materials
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).
Reading Material
- Justin Zobel: Writing for Computer Science, Springer, 3rd edition, 2015
- Writing a Bachelor Thesis in Computer Science
- Repeatability in computer systems research
- Benchmarking Crimes: An Emerging Threat in Systems Security
Research Groups and Topics
- Large-Scale Information Services (Peter Baumann)
- Robotics (Andreas Birk) Some BSc thesis ideas and guidelines can be found on Andreas Birk's web page.
- 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, Operating Systems, and Secure and Dependable Systems. Group work (2-3 students) is encouraged during the project phase. Topics will be related to Internet technology, operating systems, with a special focus on topics related to computer and cyber security. Good students are given opportunities to contribute to publications.
- Machine Learning and High Performance Computing (Peter Zaspel)
Project Course
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 <j.schoenwaelder@jacobs-university.de> so that we can track things.
Students must submit project reports at a deadline defined by the supervisor.
Bachelor Thesis
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
- technical correctness
- completeness (topic fully addressed)
- 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)?
- time management
- answers to questions
Bachelor Thesis Presentations
Bachelor thesis presentations are 15 minutes + 5 minutes discussion. The schedule has 20 minutes for each presentation, hence it is important to be efficient with changing laptops (make sure you have tested all necessary equipment before the actual presentation time). 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.
Monday, 2021-05-10
No | Time | Student | Major | Supervisor | Topic |
---|---|---|---|---|---|
M01 | 08:15 | Xu, Chenhui | CS | Maurelli, Francesco | Machine Learning Approaches towards Semantic Visual SLAM in Dynamic Scenes |
M02 | 08:35 | Held, Lena | IMS | Maurelli, Francesco | AUV Mission Control based on Petri Nets |
M03 | 08:55 | Checiu, Eliza-Alexandra | CS | Maurelli, Francesco | Computer Vision Techniques for Detection of Underwater Volcanic Carbon Dioxide Emissions |
M04 | 09:15 | Krishan, Harit | IMS | Maurelli, Francesco | Computer Vision Assisted Navigation Pipeline for Autonomous Vehicles |
– | 09:35 | BREAK | |||
M05 | 09:45 | Turcuman, Horia Ionut | CS | Mallahi-Karai, Keivan | Affine permutation codes |
M06 | 10:05 | Rota, Lorenzo Dominique Fiorentino | CS | Mallahi-Karai, Keivan | Private Information Retrieval Schemes based on Reed-Muller and Multiplicity Codes |
M07 | 10:25 | Perez Oteiza, Luis Alberto | IMS | Hu, Fangning | Image Detection Implementation by FPGA Board |
M08 | 10:45 | Ajmal, Zohair | CS | Hu, Fangning | Multi-Class Image Classification with Deep Residual Neural Network Learning |
– | 11:05 | BREAK | |||
M09 | 11:15 | Singh, Keshav Kumar | CS | Hu, Fangning | Object detection using a secure cross-platform mobile application powered by machine learning |
M10 | 11:35 | Petcan, Miruna Elena | CS | Hu, Fangning | Optimization of installations and service tours using Machine Learning and Graphhopper |
M11 | 11:55 | Khan, Farzan Ali | IMS | Hu, Fangning | Use of GNSS and Machine Learning to Track Human and Animal Movements |
M12 | 12:15 | Spirkoski, Kristijan | CS | Zaspel, Peter | Predicting excitation energies using machine learning |
– | 12:35 | BREAK | |||
M13 | 14:15 | Agbakpe, Edwin Makafui Kwame | CS | Kosov, Sergey | Facial Emotion Detection |
M14 | 14:35 | Fuentes, María Lucía | CS | Birk, Andreas | Digitalization of Construction Plans of Submarine Bunker Valentin. |
M15 | 14:55 | Sabir, Otmane | CS | Kosov, Sergey | Accelerated Ray Tracing of Constructive Solid Geometry |
M16 | 15:15 | Ud Din, Ibad | IMS | Birk, Andreas | Deep Learning for Object Detection with Sonar Data from an Acoustic Camera |
– | 15:35 | BREAK | |||
M17 | 15:45 | Amir, Anisha | CS | Zaspel, Peter | Coupling of Computational and Machine Learning models for Fluid Simulation |
M18 | 16:05 | Maharjan, Drishti | CS | Zaspel, Peter | Introducing data-driven filters in Paraview |
M19 | 16:25 | Sharma Acharya, Aayush | CS | Zaspel, Peter | Coupling of Computational method with Data-driven method in Fluid Simulation |
M20 | 16:45 | Wun Pyae, Eaindra | CS | Zaspel, Peter | Parallel Principal Component Analysis For Data Compression In Fluid Flows |
– | 17:05 | BREAK | |||
M21 | 17:15 | Liu, Ailin | IMS | Maurelli, Francesco | On the effect of real-person remote control for the trust of the social robot system |
M22 | 17:35 | Karki, Samundra | CS | Zaspel, Peter | GPU-based parallel preconditioner for Approximated Krylov-based kernel ridge regression training |
M23 | 17:55 | Doci, Flori | CS | Birk, Andreas | Detecting Cracks in Underwater Images (using Classic Vision) |
M24 | 18:15 | ||||
– | 18:35 | BREAK | |||
M25 | 18:45 | Martinez, Sergio | IMS | Maurelli, Francesco | Analysis of LoRa networks for multivehicle cooperation |
M26 | 19:05 | Messan, Peter-Newman Giffa | IMS | Maurelli, Francesco | Comparison of Computer Networking Approaches focused on Interaction with Remote Robotics Systems |
M27 | 19:25 | Bandukwala, Aliasgar | IMS | Maurelli, Francesco | Sequential Structure from Motion pipeline |
M28 | 19:45 | Bo, Xianfeng | CS | Maurelli, Francesco | Underwater 3D Mapping Using Multibeam Sonar Sensors |
– | 20:05 | END |
Tuesday, 2021-05-11
No | Time | Student | Major | Supervisor | Topic |
---|---|---|---|---|---|
T01 | 08:15 | Dermaku, Dion | CS | Kosov, Sergey | Intelligent Character Recognition for Handwritten Digits |
T02 | 08:35 | Shandro, Jovan | CS | Birk, Andreas | Photogrammetry on Dissimilar Images |
T03 | 08:55 | Blaceri, Romelda | CS | Birk, Andreas | Simulation of an Imaging Sonar |
T04 | 09:15 | Ishaq, Yousaf | CS | Birk, Andreas | Artificial Underwater Image Streams |
– | 09:35 | BREAK | |||
T05 | 09:45 | Hammad, Neeha | CS | Zaspel, Peter | Machine Learning for Non-Experts |
T06 | 10:05 | Dedaj, Fjolla | CS | Kosov, Sergey | Omnidirectional Camera |
T07 | 10:25 | Kabadzhov, Ivan | CS | Schönwälder, Jürgen | Hash-Based Signature Schemes Comparison on ARM Cortex-M4 |
T08 | 10:45 | Kandel, Dipak | CS | Schönwälder, Jürgen | Microarchitectural Attacks on Trusted Executed Environments |
– | 11:05 | BREAK | |||
T09 | 11:15 | Chen, Tianyao | CS | Birk, Andreas | Deep Learning for Detecting Amphoras in Ancient Shipwrecks |
T10 | 11:35 | Adhikari, Ashray | CS | Birk, Andreas | Underwater Image Enhancement |
T11 | 11:55 | Naeem, Ayesha | CS | Baumann, Peter | Extend an Open-Source Geo Library |
T12 | 12:15 | Beria, Luka | CS | Baumann, Peter | Semantics Assistance for WCPS Visual Query Editor |
– | 12:35 | BREAK | |||
T13 | 14:15 | Bendo, Albrit | CS | Schönwälder, Jürgen | Survey and Classification of Covid-19 Apps |
T14 | 14:35 | Begeyev, Taiyr | CS | Schönwälder, Jürgen | Decentralized versus Centralized Approaches for Contact-Tracing |
T15 | 14:55 | Balani, Eglis | CS | Schönwälder, Jürgen | Distributed Denial of Service Detection Survey |
T16 | 15:15 | Kunwar, Digdarshan | CS | Schönwälder, Jürgen | OpenWrt RESTCONF (ORC) Extension to Support Operations |
– | 15:35 | BREAK | |||
T17 | 15:45 | Sterjo, Kristian | CS | Zaspel, Peter | Multi-fidelity ML by MultiTask Gaussian Processes in Quantum Chemistry |
T18 | 16:05 | Basaula, Subid | CS | Zaspel, Peter | Low-rank approximate machine learning training in kernel ridge regression on GPU |
T19 | 16:25 | von Seggern, Katrin | IMS | Birk, Andreas | Machine Recognition of Interesting Underwater Video Sequences |
T20 | 16:45 | Koranteng, Felix Kwabena | CS | Maurelli, Francesco | Naive Algorithms for Cell Image Analysis |
– | 17:05 | BREAK | |||
T21 | 17:15 | Koirala, Ankit | CS | Schönwälder, Jürgen | Comparison of Software Update Mechanisms Supported by Embedded Operating Systems |
T22 | 17:35 | Percov, Arsenij | CS | Schönwälder, Jürgen | Comparison of TrustZone-M Support by Different Embedded Systems |
T23 | 17:55 | Rafey, Raja Abdur | CS | Schönwälder, Jürgen | Remote Attestation of Network Devices using TPM 2.0 and YANG |
T24 | 18:15 | Hashim, Huzaifa | CS | Schönwälder, Jürgen | Challenge-Response Based Remote Attestation with TPM 2.0 |
– | 18:35 | BREAK | |||
T25 | 18:45 | Hassan, Syed Roshaan | IMS | Maurelli, Francesco | Development of a Versatile Hexapod for Autonomous Exploration |
T26 | 19:05 | Chaudhary, Ayush Kumar | CS | Maurelli, Francesco | Trajectory optimization of a 6-legged robot |
T27 | 19:25 | Zafar, Muhammad Ammar | IMS | Maurelli, Francesco | Retinal Blood Vessels Segmentation using Deep Learning U-Net Architectures |
T28 | 19:45 | Bagh, Khaled | IMS | Maurelli, Francesco | Underwater 3D reconstruction using stereo vision |
– | 20:05 | END |
Wednesday, 2021-05-12
No | Time | Student | Major | Supervisor | Topic |
---|---|---|---|---|---|
W09 | 11:15 | He, Peiyan | IMS | Wicaksono, Hendro | Forecasting Wind Power using Gated Recurrent Units |
W10 | 11:35 | Jinga, Aron Teodor | IMS | Maurelli, Francesco | Comparison between T-REX and MOOS-IVP Adaptive Mission Planning |
W11 | 11:55 | Chachanidze, Giorgi | CS | Maurelli, Francesco | SLAM in Challenging Environments |
W12 | 12:15 | Mamo, Yonatan Girma | CS | Kosov, Sergey | Depth of Field Rendering using Distributed Ray Tracing |
Tuesday, 2021-06-08
No | Time | Student | Major | Supervisor | Topic |
---|---|---|---|---|---|
15:30 | Mushede, Gwinyai Kuzivakwashe | CS | Zaspel, Peter | Distributed Simulations for Wind Turbine Load Analysis |