Seminar on Internet Technologies(Winter 2021/2022)

Details

Workload/ECTS Credits:	5 ECTS (BSc/MSc AI); 5 (ITIS)
Lecturer:	Prof. Xiaoming Fu
Teaching assistant:	Zhengze Li [zhengze.li@stud.uni-goettingen.de]
Time:	Nov 4th. Register on ecampus before Nov 8th.Please read this introduction slide [1]. If there is any question, please contact teaching assistants.
Place:	Through Zoom, waiting link
UniVZ	[2]

Announcement

No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.

Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.

Course description

This course covers selected topics on up-to-date Internet technologies and research. Each student chooses a topic, does a presentation, and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures, or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable the independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to the topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of the first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

Passing requirements

• There will be 2 milestones before the presentations where the students should pass before they register for the course.
  • Intro milestone where the adviser makes sure that the student starts to work on the topic and follows an accepted methodology.
  • Midterm milestone. (ex. programming tasks are done etc... )

• Actively and frequently participate in the project communication with the topic advisor
  • This accounts for 20% of your grade.
• Present the selected topic (20 min. presentation + 10 min. Q&A).
  • This accounts for 40% of your grade.
• Write a report on the selected topic (10-15 pages) (LaTeX Template:[3]).
  • This accounts for 40% of your grade.
• Please check the #Schedule and adhere to it.

Schedule

• 1th Nov. 2021 : Deadline for registration the course
• 10th Feb. 2022 : Deadline for registration to attend the final presentation
• 17th Feb. 2022  : Final Presentations online (Throught Meetings in StudIP)
• 22th March 2022 (23:59) : Deadline for submission of the report (should be sent to the topic adviser!).

Topics

Topic	Description	Prerequisites	Topic Advisor	Readings	Available
Event detection from microblog	In this topic, you will study how to detect events from microblog, like Twiter.	Basic programming knowledge, Basic machine learning knowledge	[Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]	[4],[5],[6],[7],[8]	Yes
Graph neural network	In this topic, you will study GNNs, like TCN and subgraph GNNs.	Basic programming knowledge, Basic machine learning knowledge	[Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]	[9][10] [11]	Yes
Submodel Federated learning	In this topic, you will study Submodel Federated learning.	Basic programming knowledge, Basic machine learning knowledge	[Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]	[12]	No
Running neural-network-based applications on mobile devices	In this topic, you will study how to partition application processing pipelines, e.g., scaling face recognition.	Basic programming knowledge, Basic machine learning knowledge	[Weijun Wang, weijun.wang@informatik.uni-goettingen.de]	[13]	Yes
Running neural network on Multiple CPUs/GPUs or heterogeneous hardware	In this topic, you will study how to fine-grained partition NN and schedule them on multiple hardware.	Basic programming knowledge, Basic machine learning knowledge	[Weijun Wang, weijun.wang@informatik.uni-goettingen.de]	[14][15]	Yes
Physics-informed neural networks: Principles, Case studies, and Prospects	In this project, you will be devoted to solving a specific problem using physics-informed neural networks with a small set of existing experimental data. The student is expected to be interested in the interdisciplinary subject of physics and computer science.	Basic programming knowledge, Basic machine learning knowledge	[Yunxiao Zhang, yunxiao.zhang@ds.mpg.de]	[16]	Yes
Change Detection in Satellite Image Time Series	In this topic, you will study methods to detect changes in land-use, vegetation etc. in Satellite Image Time Series.	Basic machine learning knowledge	[Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]		Yes
Satellite-based approaches for Flood Management	In this topic, you will study methods to predict and/or map floods by utilizing image data from satellites.	Basic machine learning knowledge	[Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]		Yes

Workflow

1. Select a topic

Each student needs to choose a topic from the list. You can start to work on your selected topic at any time. However, please make sure to notify the advisor of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

2. Get your work advised

Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor. It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

3. Approach your topic

• By choosing a topic, you will get a direction of elaboration.
• You may work in different styles, for example:
  • Survey: Basic introduction, an overview of the field; general problems, methods, approaches.
  • Specific problem: Detailed introduction, details about the problem, and the solution.
• Based on the research, you should have your own ideas on your topic.

4. Prepare presentation

• Present on your topic to the audience (in English).
• 20 minutes of presentation followed by 10 minutes discussion.

You need to present your topic to an audience of students and other interested people (usually the NET group members). Your presentation should include your general idea of your topic and highlight interesting problems and solutions. You must finish your presentation within a limited time. You have 20 minutes to present your topic followed by 10 minutes of discussion. It is highly recommended to send your slides to your topic advisor in advance, he/she will give you help for your presentation.

Hints for preparing the presentation: If your topic includes many aspects, and 20 minutes is too short for you to introduce them all, it is recommended to focus on one certain important aspect. Besides, you can discuss with your advisor, he/she will help you to reduce the content. Please make sure to finish your presentation in time.

Suggestions for preparing the slides: No more than 20 pages/slides. Get your audiences to quickly understand the general idea. Figures, tables, and animations are better than sentences. Don't forget a summary of the topic and your ideas.

5. Write a report

• Present the problem with its background.
• Detail the approaches, techniques, methods to solve the problem.
• Evaluate and assess those approaches (e.g., pros and cons).
• Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding bibliography, etc.). Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism. All quoted images and tables need to indicate their source.

6. Course schedule

There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the #Schedule to take appropriate actions.