Course: Project Computer Science (CA10-320305)
Course: Thesis Computer Science (CA10-320306)
Semester: Fall 2017
Semester: Spring 2018
Instructor: Peter Baumann
Instructor: Andreas Birk
Instructor: Horst Karl Hahn
Instructor: Herbert Jaeger
Instructor: Kinga Lipskoch
Instructor: Jürgen Schönwälder
Instructor: Michael Sedlmair
Prerequisites: Two CS core modules passed
|Project topic/supervisor selection (campus track)||2017-09-18 (Monday)|
|Project topic/supervisor selection (world track)||2018-02-02 (Friday)|
|Bachelor thesis submission||2018-05-16 (Wednesday)|
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)
Scopus (commercial research publication indexing system)
dblp (open computer science publication indexing system)
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).
Research Groups and Topics
Large-Scale Information Services (Peter Baumann)
Robotic (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.
Machine Learning (Herbert Jaeger) Prerequisites for joining Herbert Jaeger's Machine Learning team for the semester project: passing the 2nd year IMS course "Machine Learning" with a grade at least as good as 3.33, OR passing the 1rst year math courses all with at least 1.66. A good first impression of the computational methods that will be used for this project can be gotten by checking out the "echo state network" intro reading materials. Group project work (2-3 students joining forces) is encouraged. Update: Supervision capacity of Herbert Jaeger is exhausted for Spring 2018, no more requests can be accomodated.
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 3.33 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, or to the Internet of Things. Good students are given opportunities to contribute to publications.
Visualization and Human Computer Interaction (Michael Sedlmair) Topics will include (but are not limited to) the visualization of high-dimensional data, analyzing, interacting with, and visualizing machine learning models, as well as designing and running user experiments. Programming skills are necessary, data analysis and/or experimental skills are an asset. Michael Sedlmair <firstname.lastname@example.org> is joining Jacobs University in Spring 2018; please contact him if you are interested to do a BSc project under his supervision.
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 any 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 grader. 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||08:15||WH-3||Retevoi, Maria-Alexandra||Andreas Birk||Visual Servoing Methods applied on a ROV|
|2||08:40||WH-3||Mateen, Tayyab||Herbert Jaeger||Musical Key Recognition Using Echo State Networks|
|3||09:05||WH-3||Liu, Zhao||Michael Sedlmair|
|4||09:45||WH-3||Glontaru, Alexandru-Mihai||Herbert Jaeger||Comparison between Deep Bidirectional RNN's and Bidirectional ESN's on Protein Secondary Structure Prediction|
|5||10:10||WH-3||Borisov, Oleg (IMS)||Michael Sedlmair||Visual Analysis of Hotel Fake Reviews|
|6||10:35||WH-3||Xhelili, Orgest||Herbert Jaeger||Automatic Music Genre Classification using Ensemble Learning|
|7||11:15||WH-3||Maiereanu, Alexandru George||Herbert Jaeger||Practical applications of vector representation of words in the travel industry|
|8||11:40||WH-3||Barginda, Kayla||Herbert Jaeger||Computing the Commutator Matrix …|
|9||12:05||WH-3||Bhandari, Sabin||Herbert Jaeger||Exploring effects of different selections of inputs on accuracy of predictions using Echo State Networks|
|10||12:30||WH-3||Hernandez, Dylan (IMS)||Andreas Birk||Man-made Objects Detection with Underwater Vision|
|11||14:15||SRLH||Karkee, Shikhat||Herbert Jaeger||Lyrics Generation Using Echo State Networks|
|12||14:40||SRLH||Getahun, Natnael||Herbert Jaeger||Exploring the effect of time-aggregation on the prediction of Bitcoin price|
|13||15:05||SRLH||Pan, Yu||Herbert Jaeger||Classical Music Generation|
|14||15:45||SRLH||Singh, Lalit||Herbert Jaeger||Web Traffic Prediction using Echo State Networks|
|15||16:10||SRLH||Khanal, Ashish||Herbert Jaeger||Wind Speed Prediction using Echo State Networks|
|16||16:35||SRLH||Ulugbek Uulu, Temirlan||Michael Sedlmair||Twitter posts as indicator for future price of Bitcoin|
WH-3 = West Hall 3, SRLH = Seminar Room Reimar Luest Hall
|17||08:15||WH-2||Thapa, Bishwa Kranti||Jürgen Schönwälder||Robustness Testing of ONOS|
|18||08:40||WH-2||Kumar, Sagar||Peter Baumann||Performance Evaluation of Multi-Dimensional Compression in Array Databases|
|19||09:05||WH-2||Dikici, Berk||Jürgen Schönwälder||IP Forwarding Resilience in ONOS|
|20||09:45||WH-2||Hasanbega, Kamila||Jürgen Schönwälder||Robustness of Automated Software Updates of Browsers|
|21||10:10||WH-2||Ngwarai, Samuel Simbarashe||Jürgen Schönwälder||Robustness of Automated Software Updates of Operating Systems|
|22||10:35||WH-2||Cheema, Omar Saif (IMS)||Francesco Maurelli||Point Cloud registration in shore environments|
|23||11:15||WH-2||Kunwar, Sajit (IMS)||Francesco Maurelli||Lidar sensor modeling, simulation and object perception|
|24||11:40||WH-2||Sheth, Miraj Parin (IMS)||Francesco Maurelli||Object Detection in Point Clouds|
|25||12:05||WH-2||Tudor, George (IMS)||Francesco Maurelli||Research implications of 3D LiDAR sensors in Marine Robotics field: Visualization and Processing of a large-scale outdoor environment Point Cloud dataset|
|26||12:30||WH-2||Bhattacharjee, Aranya||Andreas Birk||Image Enhancement Methods for Underwater Vision|
|27||14:15||WH-2||Baumann Gomez, Anton||Michael Sedlmair||Exploring the use of different interactivity tools for data visualization in a 3-dimensional environment|
|28||14:40||WH-2||Zia, Taha||Michael Sedlmair||Sentiment Analysis on Twitter Movie Reviews|
|29||15:05||WH-2||Raykov, Nikolay||Michael Sedlmair||Visualization on 2nd Screen Devices|
|30||15:45||WH-2||Atanasov, Zapryan Milkov||Michael Sedlmair||Musical Instrument Digital Interface (MIDI) Data Visualization|
|31||16:10||WH-2||Rakipi, Frenci||Michael Sedlmair||Detection and Visual Analysis of Fake News|
|32||16:35||WH-2||Lazaj, Kejsi||Michael Sedlmair||Detecting Outliers and Inliers with Smooth Linking and Brushing|
WH-2 = West Hall 2