Seminar on Internet Technologies (Summer 2022): Difference between revisions
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|ta = Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] | |ta = Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] | ||
|time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.''' | |time='''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.''' | ||
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=302541&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]}} | |||
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid= | |||
==Announcement== | ==Announcement== | ||
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk. | No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk. | ||
'''!! 25.06 deadline for registration on Flexnow''' | |||
==Course description== | ==Course description== | ||
| Line 28: | Line 27: | ||
==Passing requirements== | ==Passing requirements== | ||
*There will be 2 milestones before the presentations | *There will be 2 milestones before the presentations that 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. | **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... ) | **Midterm milestone. (ex. programming tasks are done etc... ) | ||
| Line 34: | Line 33: | ||
*Actively and frequently participate in the project communication with the topic advisor | *Actively and frequently participate in the project communication with the topic advisor | ||
**This accounts for 20% of your grade. | **This accounts for 20% of your grade. | ||
* Present the selected topic (20 min. | * Present the selected topic (20 min. presentations + 10 min. Q&A). | ||
** This accounts for 40% of your grade. | ** This accounts for 40% of your grade. | ||
* Write a report on the selected topic ( | * Write a report on the selected topic (6-8 pages) (LaTeX Template:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]). | ||
** This accounts for 40% of your grade. | ** This accounts for 40% of your grade. | ||
* Please check the [[#Schedule]] and adhere to it. | * Please check the [[#Schedule]] and adhere to it. | ||
==Schedule== | ==Schedule== | ||
* ''' | * '''24.06.2022 ''': Deadline for registration to attend the final presentation | ||
* '''19.07.2022 2PM-3PM''' : Final Presentations (IFI 1.101) | |||
* ''' | * '''12.08.2022 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!). | ||
* ''' | |||
== Topics == | == Topics == | ||
| Line 57: | Line 55: | ||
|{{Hl2}} |'''Available''' | |{{Hl2}} |'''Available''' | ||
|- | |- | ||
| | | Video analytics with deep reinforcement learning | ||
| In this topic, you will study | | In this topic, you will study deep reinforcement learning used in video analytics. | ||
| Basic programming knowledge, Basic machine learning knowledge | | Basic programming knowledge, Basic machine learning knowledge, need coding work | ||
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de] | | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de] | ||
| | | | ||
| Yes | | Yes | ||
|- | |- | ||
|- | |- | ||
| | |AI painter | ||
| In this topic, you will study | | In this topic, you will study how AI has been used for painting. e.g. GAN. | ||
| Basic programming knowledge, Basic machine learning knowledge | | Basic programming knowledge, Basic machine learning knowledge, need coding work | ||
| [Tingting Yuan, | | [Tingting Yuan, tingt.yuan@hotmail.com] | ||
|[https:// | |[https://topten.ai/ai-painting-generators/] | ||
| Yes | | Yes | ||
|- | |- | ||
| Line 118: | Line 108: | ||
| Basic machine learning knowledge | | Basic machine learning knowledge | ||
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de] | | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de] | ||
| | |||
| Yes | |||
|- | |||
|- | |||
| Social Media Comments Network | |||
| In this topic, you will study methods to crawl the dataset from social networks and utilizing social science network analysis in any topic you are interested in (science/education/politics…) to find out the network structure and compare the difference among different topics. | |||
| Basic programming knowledge | |||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | |||
| | |||
| No | |||
|- | |||
|- | |||
| Analysis of MOOC Discussion Forum | |||
| In this topic you will study methods to crawl the dataset from MOOCs and evaluate if the active users have more influence on overall forum activities and the evaluation of the course. | |||
| Basic programming knowledge | |||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | |||
| | |||
| No | |||
|- | |||
|- | |||
| Open topics | |||
| Topics regarding to computer science | |||
| | |||
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | |||
| | | | ||
| Yes | | Yes | ||
| Line 165: | Line 179: | ||
* Give a short outlook on potential future developments. | * Give a short outlook on potential future developments. | ||
The report must be written in English according to common guidelines for scientific papers, between | The report must be written in English according to common guidelines for scientific papers, between 6 and 8 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. | 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. | ||
Latest revision as of 14:01, 13 July 2022
Details
| Workload/ECTS Credits: | 5 ECTS (BSc/MSc AI); 5 (ITIS) |
| Lecturer: | Prof. Xiaoming Fu |
| Teaching assistant: | Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] |
| Time: | Please read this introduction slide [1]. If there is any question, please contact teaching assistants. |
| UniVZ | [2] |
Announcement
No open talk. You can contact your topic advisor to schedule a 1V1 meeting or talk.
!! 25.06 deadline for registration on Flexnow
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 that 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. presentations + 10 min. Q&A).
- This accounts for 40% of your grade.
- Write a report on the selected topic (6-8 pages) (LaTeX Template:[3]).
- This accounts for 40% of your grade.
- Please check the #Schedule and adhere to it.
Schedule
- 24.06.2022 : Deadline for registration to attend the final presentation
- 19.07.2022 2PM-3PM : Final Presentations (IFI 1.101)
- 12.08.2022 (23:59) : Deadline for submission of the report (should be sent to the topic adviser!).
Topics
| Topic | Description | Prerequisites | Topic Advisor | Readings | Available |
| Video analytics with deep reinforcement learning | In this topic, you will study deep reinforcement learning used in video analytics. | Basic programming knowledge, Basic machine learning knowledge, need coding work | [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de] | Yes | |
| AI painter | In this topic, you will study how AI has been used for painting. e.g. GAN. | Basic programming knowledge, Basic machine learning knowledge, need coding work | [Tingting Yuan, tingt.yuan@hotmail.com] | [4] | Yes |
| 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] | [5] | 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] | [6][7] | 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] | [8] | 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 | |
| Social Media Comments Network | In this topic, you will study methods to crawl the dataset from social networks and utilizing social science network analysis in any topic you are interested in (science/education/politics…) to find out the network structure and compare the difference among different topics. | Basic programming knowledge | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | No | |
| Analysis of MOOC Discussion Forum | In this topic you will study methods to crawl the dataset from MOOCs and evaluate if the active users have more influence on overall forum activities and the evaluation of the course. | Basic programming knowledge | [Zhengze Li, zhengze.li@cs.uni-goettingen.de] | No | |
| Open topics | Topics regarding to computer science | [Zhengze Li, zhengze.li@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 6 and 8 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.