Seminar on Internet Technologies (Winter 2020 2021): Difference between revisions
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|credits=5 ECTS (BSc/MSc AI); 5 (ITIS) | |credits=5 ECTS (BSc/MSc AI); 5 (ITIS) | ||
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu] | |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu] | ||
|ta =Tingting Yuan [ | |ta =Tingting Yuan [tingting.yuan@cs.uni-goettingen.de], [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding] and [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao, Sripriya Srikant Adhatarao] | ||
|time=Nov 4th. Register on ecampus before Nov 8th.'''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=Nov 4th. Register on ecampus before Nov 8th.'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.''' | ||
|place=Through Zoom, waiting link | |place=Through Zoom, waiting link | ||
|univz=[https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=262017&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung] | |univz= [https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=262017&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung] | ||
}} | }} | ||
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==Schedule== | ==Schedule== | ||
* '''7th Nov. 2020 ''': Deadline for registration the course | |||
* ''' | * '''20th Jan. 2021 ''': Deadline for registration to attend the final presentation | ||
* '''Jan. | * '''28th Jan. 2021 (14:00-18:00)''' : Final Presentations online (waiting for the link) | ||
* ''' | * '''28th March 2021 (23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!). | ||
== Topics == | == Topics == | ||
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using neural networks in the research field of Physics is nowadays more | using neural networks in the research field of Physics is nowadays more | ||
and more significant. The student is expected to be interested in the | and more significant. The student is expected to be interested in the | ||
interdisciplinary subject of physics and computer science. | the interdisciplinary subject of physics and computer science. | ||
| Basic programming knowledge, Basic machine learning knowledge | | Basic programming knowledge, Basic machine learning knowledge | ||
| [Yunxiao Zhang, yunxiao.zhang@ds.mpg.de] | | [Yunxiao Zhang, yunxiao.zhang@ds.mpg.de] | ||
| Line 71: | Line 71: | ||
| [http://www.net.informatik.uni-goettingen.de/?q=people/sripriya-srikant-adhatarao Sripriya Adhatarao] | | [http://www.net.informatik.uni-goettingen.de/?q=people/sripriya-srikant-adhatarao Sripriya Adhatarao] | ||
| | | | ||
| | | No | ||
|- | |- | ||
| | | Graph neural network | ||
| In this topic, you will study | | In this topic, you will study graph neural networks (GNNs), which are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. | ||
| Basic programming knowledge, Basic machine learning knowledge | | Basic programming knowledge, Basic machine learning knowledge | ||
| [Tingting Yuan, tingt.yuan@hotmail.com] | | [Tingting Yuan, tingt.yuan@hotmail.com] | ||
|[https:// | |[https://arxiv.org/pdf/1812.08434.pdf?source=post_page] | ||
| | | No | ||
|- | |||
|AI painter | |||
| In this topic, you will study how AI has been used for painting. | |||
| Basic programming knowledge, Basic machine learning knowledge | |||
| [Tingting Yuan, tingt.yuan@hotmail.com] | |||
|[https://topten.ai/ai-painting-generators/] | |||
| No | |||
|- | |- | ||
| The maximum throughput problem in quantum entangle routing | | The maximum throughput problem in quantum entangle routing | ||
| In this topic, you will study entanglement routing problem in quantum network, which is a novel network built on quantum mechanics. | | In this topic, you will study the entanglement routing problem in a quantum network, which is a novel network built on quantum mechanics. | ||
| Basic programming knowledge, Basic mathematical programming knowledge | | Basic programming knowledge, Basic mathematical programming knowledge | ||
| [Bangbang Ren, bangbang.ren@cs.uni-goettingen.de] | | [Bangbang Ren, bangbang.ren@cs.uni-goettingen.de] | ||
| Line 95: | Line 102: | ||
|- | |- | ||
| Data augmentation with generative adversarial network (GAN) | | Data augmentation with generative adversarial network (GAN) | ||
| Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this | | Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this topic, you will learn to use GAN as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. | ||
| Familiar with machine learning and deep learning; image processing with using python; | | Familiar with machine learning and deep learning; image processing with using python; | ||
| [Yachao Shao, yachao.shao@cs.uni-goettingen.de] | | [Yachao Shao, yachao.shao@cs.uni-goettingen.de] | ||
| [https://arxiv.org/abs/1803.09655] | | [https://arxiv.org/abs/1803.09655] | ||
| Yes | | Yes | ||
|- | |||
| Passenger flow prediction with machine learning | |||
| You will study existing methods and algorithms used for the prediction of passenger flow in an urban area to determine the demand for buses, trams or trains. | |||
| Basic machine learning knowledge | |||
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de] | |||
| | |||
| No | |||
|- | |||
| Optimization of public transport schedules | |||
| You will study techniques and algorithms to optimize the schedules for public transport systems. | |||
| Basic machine learning knowledge, Basic mathematical knowledge (knowledge in mathematical optimization problems can be helpful, but is not mandatory) | |||
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de] | |||
| | |||
| No | |||
|- | |||
| Automatic Classification of Time Series (ACTS) | |||
| In this project you will apply machine learning techniques to identify differences and similarities in the evolution of real-world phenomena across different regions and countries, like the spread of the SARS-CoV2 virus. The student is expected to have prior knowledge in data science and programming skills. | |||
| Basic programming knowledge, basic machine learning knowledge | |||
| Pablo Gutierrez-Marques p.gutierrezmarques01@stud.uni-goettingen.de | |||
| [https://doi.org/10.1080/014311600210308] [https://doi.org/10.1109/ICDE.2017.68] | |||
| No | |||
|- | |- | ||
|} | |} | ||
| Line 145: | Line 173: | ||
The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.). | The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, 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=== | === 6. Course schedule=== | ||
Latest revision as of 15:22, 2 February 2021
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], Shichang Ding and Sripriya Srikant Adhatarao |
| 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
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 (12-15 pages) (LaTeX Template:[3]).
- This accounts for 40% of your grade.
- Please check the #Schedule and adhere to it.
Schedule
- 7th Nov. 2020 : Deadline for registration the course
- 20th Jan. 2021 : Deadline for registration to attend the final presentation
- 28th Jan. 2021 (14:00-18:00) : Final Presentations online (waiting for the link)
- 28th March 2021 (23:59) : Deadline for submission of the report (should be sent to the topic adviser!).
Topics
| Topic | Description | Prerequisites | Topic Advisor | Readings | Available |
| 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 experiment data, which is different from big data-driven machine learning. The idea of using neural networks in the research field of Physics is nowadays more and more significant. The student is expected to be interested in the the interdisciplinary subject of physics and computer science. |
Basic programming knowledge, Basic machine learning knowledge | [Yunxiao Zhang, yunxiao.zhang@ds.mpg.de] | [4] | Yes |
| Comparative study of video analytic platforms and algorithms using neural networks: Principles, Standard Algorithms, and Open issues | In this topic, you will study and analyze the existing video analysis platforms and standard machine learning and deep learning algorithms with small set of experiment data, especially the data from sensor networks. The student is expected to have prior knowledge/experience in data science and programming skills. | Basic programming knowledge, Basic machine learning knowledge | Sripriya Adhatarao | No | |
| Graph neural network | In this topic, you will study graph neural networks (GNNs), which are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. | Basic programming knowledge, Basic machine learning knowledge | [Tingting Yuan, tingt.yuan@hotmail.com] | [5] | No |
| AI painter | In this topic, you will study how AI has been used for painting. | Basic programming knowledge, Basic machine learning knowledge | [Tingting Yuan, tingt.yuan@hotmail.com] | [6] | No |
| The maximum throughput problem in quantum entangle routing | In this topic, you will study the entanglement routing problem in a quantum network, which is a novel network built on quantum mechanics. | Basic programming knowledge, Basic mathematical programming knowledge | [Bangbang Ren, bangbang.ren@cs.uni-goettingen.de] | [7] | Yes |
| Video Analytics | Artificial Intelligence has been and is going to be popular for many years. Static object detection, recognition technique has been studied for many years. However, how these techniques work in a dynamic environment (eg. Self-driving ) is not clear. In this topic, we want to reveal which kind of technique performs better in a video surveillance system with limited computing and network resources. Based on this, our goal is to develop a real Wireless Moving Video Surveillance System which including video analysis, wireless data delivery, and data compression and fusion. Fortunately, we already have some preliminary work. | Interested in this topic, willing to follow the advisor's guidance, patience and time for reading multiple papers. Interested in embedded development, we will use Raspberry Pi and NVIDIA Jetson Nano Developer Kit. Have Fun With This Project! | [Weijun Wang, weijun.wang@informatik.uni-goettingen.de] | [8] | Yes |
| Data augmentation with generative adversarial network (GAN) | Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this topic, you will learn to use GAN as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. | Familiar with machine learning and deep learning; image processing with using python; | [Yachao Shao, yachao.shao@cs.uni-goettingen.de] | [9] | Yes |
| Passenger flow prediction with machine learning | You will study existing methods and algorithms used for the prediction of passenger flow in an urban area to determine the demand for buses, trams or trains. | Basic machine learning knowledge | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de] | No | |
| Optimization of public transport schedules | You will study techniques and algorithms to optimize the schedules for public transport systems. | Basic machine learning knowledge, Basic mathematical knowledge (knowledge in mathematical optimization problems can be helpful, but is not mandatory) | [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de] | No | |
| Automatic Classification of Time Series (ACTS) | In this project you will apply machine learning techniques to identify differences and similarities in the evolution of real-world phenomena across different regions and countries, like the spread of the SARS-CoV2 virus. The student is expected to have prior knowledge in data science and programming skills. | Basic programming knowledge, basic machine learning knowledge | Pablo Gutierrez-Marques p.gutierrezmarques01@stud.uni-goettingen.de | [10] [11] | No |
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 the table of content, 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.