NET Wiki - User contributions [en]

Seminar on Internet Technologies (Winter 2025/2026)

2025-09-02T07:57:45Z

Dwu1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''31.01.2026''': Deadline for registration to attend the final presentation
* '''10.02.2026''' : Final Presentations (Offline)
* '''27.02.2026(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Social Media Comments Network Analysis (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the dataset from social networks(e.g. Tiktok, X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Self-supervised Learning and Foundation Models for Remote Sensing Applications
| In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|Service Migration
|When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.
|Edge computing and Machine Learning.
|[yufei.liu@cs.uni-goettingen.de]
|
|Yes
|-
|-
|Task Offloading and Resource Allocation Optimization
|This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.
|Edge computing & Basic optimization algorithms.
|[dongkuo.wu@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2025/2026)

2025-09-02T07:55:11Z

Dwu1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''15.07.2025''': Deadline for registration to attend the final presentation
* '''12.08.2025''' : Final Presentations (Offline)
* '''25.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Social Media Comments Network Analysis (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the dataset from social networks(e.g. Tiktok, X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Self-supervised Learning and Foundation Models for Remote Sensing Applications
| In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|Service Migration
|When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.
|Edge computing and Machine Learning.
|[yufei.liu@cs.uni-goettingen.de]
|
|Yes
|-
|-
|Task Offloading and Resource Allocation Optimization
|This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.
|Edge computing & Basic optimization algorithms.
|[dongkuo.wu@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2025/2026)

2025-09-02T07:54:51Z

Dwu1: /* Details */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''15.07.2025''': Deadline for registration to attend the final presentation
* '''12.08.2025''' : Final Presentations (Offline)
* '''25.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Social Media Comments Network Analysis (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the dataset from social networks(e.g. Tiktok, X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Self-supervised Learning and Foundation Models for Remote Sensing Applications
| In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|Service Migration
|When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.
|Edge computing and Machine Learning.
|[yufei.liu@cs.uni-goettingen.de]
|
|Yes
|-
|-
|Task Offloading and Resource Allocation Optimization
|This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.
|Edge computing & Basic optimization algorithms.
|[dongkuo.wu@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2025/2026)

2025-09-02T07:51:54Z

Dwu1: /* Details */

Seminar on Internet Technologies (Winter 2025/2026)

2025-09-02T07:51:15Z

Dwu1: /* Details */

Seminar on Internet Technologies (Summer 2025)

2025-06-06T07:21:57Z

Dwu1: /* Schedule */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''15.07.2025''': Deadline for registration to attend the final presentation
* '''12.08.2025''' : Final Presentations (Offline)
* '''25.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Social Media Comments Network Analysis (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the dataset from social networks(e.g. Tiktok, X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Self-supervised Learning and Foundation Models for Remote Sensing Applications
| In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|Service Migration
|When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.
|Edge computing and Machine Learning.
|[yufei.liu@cs.uni-goettingen.de]
|
|Yes
|-
|-
|Task Offloading and Resource Allocation Optimization
|This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.
|Edge computing & Basic optimization algorithms.
|[dongkuo.wu@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Theses and Projects

2025-03-25T15:16:31Z

Dwu1:

== An introduction to the Computer Networks group ==

See a [https://wiki.net.informatik.uni-goettingen.de/w/images/5/5a/NETGroup_Poster-Jan2021.pdf poster] for a general overview, an [http://www.net.informatik.uni-goettingen.de/?q=research anchor] to our research activities, a list of [https://wiki.net.informatik.uni-goettingen.de/w/images/a/a3/Social_Computing_publications.pdf social computing related] or networking-related publications, and the
[http://www.net.informatik.uni-goettingen.de/?q=news/annual-report-2020-best-wishes-2021 annual report(s)] for our recent activities.

== Joint PhD Program with University of Sydney ==
From September 2024 on there will be the possibility to start a joint PhD with the University of Sydney (Australia). PhD students will stay in both Göttingen and Sydney for at least one year and can achieve two PhD degrees.
For more information, please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de].

In November/December 2023, Fabian visited research groups in Melbourne and Sydney. Impressions of his visit can be seen here: [[Media:australia.pdf | pdf]]

== Open Theses and Student Project Topics ==

The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].

* (B) Bachelor thesis
* (M) Master thesis
* (P) Student project

=== * '''New!''' Task Offloading and Resource Allocation Optimization===

With the continuous advancement of the next-generation wireless communication technologies and the population of mobile devices, a variety of Internet of Things (IoT) applications are emerging and seeking efficient task execution paradigms. This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency. We expect you have a background in edge computing, optimization algorithms, and programming skills.

Please contact Dongkuo Wu [dongkuo.wu@cs.uni-goettingen.de]

=== * '''New!''' Efficient Live Volumetric Video Streaming System===

The exponential growth of digital data and multimedia content necessitates robust and efficient systems to handle the streaming of high-resolution, three-dimensional volumetric videos. These videos offer a more immersive and realistic experience, making them increasingly used in various sectors such as virtual reality, augmented reality, and entertainment. The challenge here lies in creating a system that can handle the high-bandwidth and computation-intensive demands of live volumetric video streaming while ensuring the delivery of a seamless and high-quality user experience. This project conceptualizes the development and optimization of efficient algorithms and systems to handle volumetric video streams, mitigating bandwidth cost and latency issues. We expect you to have a background in video streaming technologies, computer vision, and programming skills.

Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]

=== * '''New!''' Edge-Cloud Orchestration for LiDAR-based Traffic Analysis===

The imminent era of smart cities and autonomous vehicles paves the way for the deployment and operation of advanced monitoring and processing systems. Among these, LiDAR technology stands out for its ability to provide high-resolution, three-dimensional traffic data, becoming an essential component for efficient traffic analysis and management. However, the computation-intensive and latency-sensitive nature of LiDAR data processing poses significant challenges and dictates the need for efficient orchestration between edge and cloud computing resources. Edge-Cloud Orchestration offers an innovative solution to this problem by bridging the gap between these two technologies, enabling the low-latency processing of complex LiDAR data. It would be good if you have a background in point cloud processing/cloud computing, K8s, and programming skills.

Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]

=== * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===

Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure. We need students for this topic. We expect you have a background in knowledge graph and programming skills in Python.

Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]

=== * '''New!''' Using LLM for Knowledge Graph Completion (B/M/P)===

Large language models (LLMs), such as ChatGPT and GPT-4 (OpenAI, 2023), have extensive internal knowledge repositories from their vast pretraining corpora, which can be used as an extra knowledge base to alleviate information scarcity for the long-tail entities in Knowledge Graphs. However, there is no effective workflow design for LLM on KGC tasks. How to leverage the LLM to perform reasoning on the KG Completion (KGC) task is a noteworthy and significant topic. We need students for this topic. We expect you to have a background in knowledge graph and LLMs, you'd better have a programming skill in Python.

Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]

=== * '''New!''' Context Specific Self-supervised Pre-Training for Remote Sensing Applications (Semantic Segmentation, Change Detection, Socio-Economic Indicator Estimation, ...) (B/M/P) ===

Satellite images in combination with Machine/Deep Learning models have shown to be an effective tool for analysis and monitoring tasks regarding disasters, deforestation, climate change, socio-economic estimation and others. The training of these models usually rely on labelled ground-truth data, which is labour intensive and therefore often scarcely available. To overcome this limitation, models are often trained in self-supervised approaches with unlabelled data, such as Contrastive Learning or Masked Autoencoders. However, these approaches are completely independent and not related to the intended downstream task. In this project/thesis the relationship between the pre-training task and the model performance on the downstream task will be explored and self-supervised training approaches tailored for a selected remote sensing downstream task (semantic segmentation of trees, tree crown share per pixel estimation, change detection of disasters, socio-economic estimation, ...) will be developed.

Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]

=== [Occupied] Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===

A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was (un)successful.

Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]

=== * '''New!''' Emotional Support Conversation Generation based on LLM (B/M/P)===

Emotional support conversation aims to reduce individuals' emotional distress through social interaction and help them understand and cope with the challenges they face. Using LLM to provide emotional support is a promising technology which can be used in customer service chats, mental health support and so on. We need students for this topic. We expect you have a background in dialogue generation and programming skills in Python.

Please contact Jing Li [jing.li@cs.uni-goettingen.de]

=== * '''New!''' Rumor control and detection method for social networks based on GCN===

In social networks, rumors spread quickly and have a wide impact. Through GCN, the complex relationships between nodes in the network can be effectively captured, and the detection and propagation paths of rumors can be modeled and controlled. The core of this method is to improve the ability to identify and control the spread of rumors by jointly learning user behavior, information content, and network topology by building an information propagation graph. We need students in this topic. We expect you have a background in rumor detection and programming skills in Python.

Please contact Fei Gao [fei.gao@cs.uni-goettingen.de]

=== [Occupied] Image-to-Image Translation of Different Nightlight Image Types (B/M/P) ===

Nightlight intensities have been proven to be a good indicator for socio-economic status. However, for long-term temporal analyses their use can be challenging, as different satellites for sensing nightlight intensities operated at different times (DMSP OLS 1992-2014 and VIIRS 2012-2023). Both types differ not only in resolution, but there is also a big discrepancy in the optical appearance and value ranges. To obtain consistent nightlight images for temporal analysis, Image-to-Image Translation methods shall be used in this project/thesis for the conversion between both types. Finally the performance of the translated and original nightlight images for a regression on socio-economic indicators shall be evaluated.

Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]

=== [Occupied] Satellite Image Indices and Machine Learning for Socio-economic Estimation (B/M/P) ===

There are several indices, which can be derived from satellite images. For example the Normalized Difference Vegetation Index (NDVI) indicates the presence and condition of vegetation, while the Normalized Difference Built-up Index (NDBI) indicates the presence of built-up areas such as buildings or roads. The distributions of these and other indices may have different explanatory power to estimate the socio-economic status of locations. Therefore in this project/thesis the regression performance of machine learning models - using statistics of these indices as features - to estimate socio-economic indicators shall be evaluated for the individual and also combined indices. Optionally, Convolutional Neural Networks (CNNs) can be applied additionally, which take the derived index images as input.

Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]

=== * 3D natural hazard simulator ===

The aim of the project is to simulate representative natural hazards for hazard response, such as flooding and forest fire. A natural hazard response simulator will be implemented for both visualization and performance validation. For example, we can visualize the flooding of 2021 in Germany, and then validate the performance of drone deployment in hazard sensing and emergency communication. Here, we introduce some related works in virtual 3D scene which may help you to understand this project, e.g., Agents Toolkit (ML-Agents) [1], DisasterSim [2] and Airsim [3].

[1] Unity Technologies.Unity ML-Agents Toolkit. Jan 26, 2021.URL:https://github.com/Unity-Technologies/ml-agents. (accessed: 21.11.2021)
[2] Wang, H., Liu, C. H., Dai, Z., Tang, J., & Wang, G. (2021, August). Energy-efficient 3D vehicular crowdsourcing for disaster response by distributed deep reinforcement learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3679-3687).
[3] S. Shah, D. Dey, C. Lovett, and A. Kapoor. “Airsim: High-fidelity visual and physical simulation forautonomous vehicles”. In:Field and Service Robotics. Springer. 2018, pp. 621–635.

Please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de](B/M/P)

=== * '''[Occupied]''' OCR (Optical Character Recognition) and Annotation Transfer ===

The aim of the project is to develop a tool/software that can convert a printed paper with annotations and text into electronic versions with text highlighting and annotations. The successful candidate will be responsible for developing this tool/software that can perform the following tasks:

1. Text Alignment: Develop algorithms to align the text in the electronic version with the original printed paper.

2. Annotation Recognition: Develop software that can recognize annotation areas in the printed paper and transfer them to the electronic version.

3. Transfer Annotations: Transfer annotations and highlighting from the paper-based article to the electronic version.

[1] https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143

[2] https://developer.adobe.com/analytics-apis/docs/2.0/guides/endpoints/annotations/

[3] https://developer.adobe.com/document-services/apis/pdf-services/

[4] https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs

Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P)

=== * Privacy-preserved Video Analytics===
This project/thesis topic focuses on the protection of privacy in video analytics.

The project involves three key tasks:

1) Implementation of a system utilizing YOLO and CycleGANs/DataGen for video analysis and processing. The code for this is already available for use.

2) Development of a privacy protection mechanism by adjusting the level of blur applied to the video, taking into account a trade-off between inference accuracy (e.g., detection by YOLO) and the level of privacy protection.

3) Optimize the blur level for Pan-tilt-zoom cameras to ensure that the system effectively captures key visual information while still preserving privacy.

Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] (B/M/P)

[1] Pecam: privacy-enhanced video streaming and analytics via securely-reversible transformation [https://dl.acm.org/doi/abs/10.1145/3447993.3448618].

=== * AI for networking adaption ===
In this project/theses topic, you will explore how to make AI meets networking requirements (e.g., fluctuating network states).
You will (1) deploy and test Genet[1]; (2)extend the Genet environment to multi-client environment (e.g., ABR); (3) deploy multi-agent algorithms on Genet and valid the performance.

[1] Genet: Automatic Curriculum Generation for Learning Adaptation in Networking [https://francisyyan.org/documents/fyy-genet-sigcomm22.pdf]

Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P)

=== * [Occupied] Video analytics with deep reinforcement learning ===

The proliferation of video analytics is facilitated by the advances of deep learning and the low prices of high-resolution network-connected cameras. However, the accuracy improvement from deep learning is at the high computational cost. Although the state-of-the-art smart cameras can support deep learning method, the deployed surveillance and traffic camera paint a much bleaker resource picture. For example, DNNCam that ships with a high-end embedded NVIDIA TX2 GPU costs more than $2000 while the price of deployed traffic cameras today ranges $40-$200; these cameras typically loaded with a single-core CPU only provide very scarce compute resource. Because of this huge gap, typical video analytics applications, e.g., traffic cameras stream live video to remote server for further analysis.
As a result, a natural question occurs: which video streaming configuration also server decoding configuration should we select to guarantee high analysis accuracy as well as not incur network congestion? To answer this question, we attempt to explore the performance of deep reinforcement learning under this scenario.

Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de], Weijun Wang [weijun.wang@informatik.uni-goettingen.de] (B/M/P)

=== * [Occupied] AI for Games ===

Can Artificial intelligence (AI) beat humans at games?
AI has played an increasingly prominent and productive role in the gaming world. Implemented in many different ways, AI is used to improve game behaviors and environments.
In this project, we will design AI algorithms (i.e., multi-agent reinforcement learning) for games (e.g., StarCraft: https://github.com/oxwhirl/smac). The main challenge here is to coordinate agents in achieving joint goals (i.e., win), such as by efficient communication.

[1]https://www.nature.com/articles/d41586-019-03298-6

Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] (B/M/P)

=== * '''[Closed]''' Socioecomonic analysis based on spatiotemporal and linguistic analysis on microblogging data ===

Identifying the socioeconomic status (SES) of users in social media like Twitter or Weibo is useful e.g., for digitized advertisements and social policies. This study aims to collect profiles of Twitter users on selected topics such as culture or foreign language learning, extract the temporal, spatial and linguistic features, and compare different classification algorithms (e.g., decision tree, random forest, na\"{i}ve Bayes, deep learning, and Gaussian processes classifier) to predict the socioeconomic status.

[1] Ren Y, Xia T, Li Y, Chen X. Predicting socio-economic levels of urban regions via offline and online indicators. PLoS One. 2019;14(7):e0219058. Published 2019 Jul 10. doi:10.1371/journal.pone.0219058
[2] Pappalardo L, Pedreschi D, Smoreda Z, Giannotti F. Using Big Data to study the link between human mobility and socio-economic development. In: IEEE International Conference on Big Data 2015. doi:10.1109/BigData.2015.7363835
[3] Vasileios Lampos, Nikolaos Aletras, Jens K. Geyti, Bin Zou and Ingemar J. Cox (2016). Inferring the Socioeconomic Status of Social Media Users based on Behaviour and Language. Proceedings of the 38th European Conference on Information Retrieval (ECIR '16), pp. 689-695. doi:10.1007/978-3-319-30671-1_54

Please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de](B/M/P)

=== [Closed] Super resolution technique for efficient video delivery ===

Super-resolution (SR) is one of the fundamental tasks in Computer vision. Video delivery on Internet or in WAN is important for various applications, eg., video analytics and video viewing. This project attempts to explore the potential of SR for video delivery. We expect you have Data Science and Computer Vision background, as well as programming skills like Python.

Please contact Weijun Wang [weijun.wang@informatik.uni-goettingen.de] (B/M/P)

=== [closed] Assessing city livability with big data ===

* City livability is related to a number of factors, such as quality of life, job satisfaction, environment (green space, CO2/PM2.5, schooling/health support etc), policy, commuting time, entertainment. We utilize different data sources to understand their relation to the city livability, and analyze the coherent features which offer an evaluation framework for a city's attractiveness and livability for different types of citizens. We expect you have some statistics and machine learning background, as well as programming skills like Python.

Please contact Prof. Xiaoming Fu (B/M/P).

=== Socioeconomic analysis on commuters ===

* '''New!''' Understanding the commuter behaviour and the factors that lead to commuting are more important today than ever before. With steadily increasing commuter numbers, the commuter traffic can be a major bottleneck for many cities. The increasing awareness of a good work-life balance leads to more people wanting shorter commuting distances. The commuter behaviour consequently plays an increasingly important role in city and transport planning and policy making. This topic aims to infer knowledge from commuter data, analyzing the influence of GDP, housing prices, family situation, income and job market on the decision to commute. We expect you have some statistics and machine learning background, as well as programming skills like Python.

Please contact Prof. Xiaoming Fu (B/M/P)

=== Socioeconomic Status and Internet Language Usage ===

* '''New!''' Numerous people write social media posts and exchange messages with colleagues, friends, acquaintances or even strangers on different platforms. We would like to understand how the underlying social class membership (socioeconomic status) affects Internet users' language use, by investigating the sociolinguistic features in users' posts/messages across a multitude of datasets and their relationship to their socioeconomic status. We expect you have some statistics and textual analysis/natural language processing background, as well as programming skills like Python.

Please contact Prof. Xiaoming Fu (B/M/P)

===[closed] Multimedia Resource Allocation for QoE Improvement by Deep Learning===

* Deep learning has been widely used in various real-time applications and systems. Dynamic resource allocation for multimedia (e.g. Video) to improve QoE is an interesting topic. We need three students for this topic. We expect you have a background in deep learning and computer network, as well as programming skills like Python and Go.

(1) one to realize and improve the system for video transmission and network configuration according to resource allocation policy;
* You will use QUIC [https://github.com/lucas-clemente/quic-go] protocol (Go language) to implement network allocation and place the server part on AWS/other clouds.
(2) one to implement the deep learning algorithm to design the controller for dynamic resource allocations.

(3) one student for the QoE model using deep learning.

Please contact Dr.Tingting Yuan [tingting.yuan@cs.uni-goettingen.de ] and Weijun Wang [weijun.wang@informatik.uni-goettingen.de](B/M/P)

== Ongoing Topics ==

== Completed Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''
|-
| OCR (Optical Character Recognition) and Annotation Transfer (Bachelor Project+Thesis)
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan Tingting Yuan]
|
|
| Assigned to Jiaying
|-
| AI for Games (Bachelor Project+Thesis)
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan Tingting Yuan]
|
|
| Completed by Jason
|-
| Neural video analytics(Master Thesis)
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan Tingting Yuan]
|
|
| Completed by Mai
|-
| Submodel Federated learning (Bachelor Project + Thesis)
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan Tingting Yuan]
|
|
| Completed by Zilin
|-
|Bio-Data analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-mayutan-arumaithurai Mayutan Arumaithurai]
|
|
| Assigned to Lindrit
|-
|Sentiment Analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Beatrice Kateule
|-
| Analysis of Business Transitions: A Case Study of Yelp (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Marcus Thomas Khalil
|-
| Understanding Group Patterns in Q&A Services (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Jonas Koopmann
|-
| COPSS-lite : Lightweight ICN Based Pub/Sub for IoT Environments (Master Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Haitao Wang
|-
| A ICN Gateway for IoT (Bachelor Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Janosch Ruff
|-
| Build a personalized context-aware recommender system for customers according to their own interest.
|
|
|
| Completed by Haile Misgna
|-
| Emotion Patterns Analysis in OSNs (Bachelor thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen]
|
| We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users.
| Completed by Stefan Peters
|-
| Implementation of a pub/sub system (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/jiachen_chen Jiachen Chen] [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to show how application layer intelligence cupled with network layer pub/sub can be beneficial to both users as well as network operators
| Completed by Sripriya
|-
| Large Scale Distributed Natural Language Document Generation System (Student project at IBM)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The work was done at IBM
| Completed by Eeran Maiti
|-
| Investigate real time streaming tools for large scale data processing (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to compare real time streaming tools.
| Completed by Ram
|-
| Software-Defined Networking and Network Operating System (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| SDN based ntwork operating system
| Completed by Rasha
|-
| GEMSTONE goes Mobile (BSc Thesis/Student Project)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of a Decentralized Online Social Network to the Android Platform
| Completed by Fabien Mathey and improved by Eeran Maiti
|-
| Transitioning of Social Graphs between Multiple Online Social Networks (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of friendship graphs between different Online Social Networks
| Completed by Kai-Stephan Jacobsen
|-
| Prevention and Mitigation of (D)DoS Attacks in Enterprise Environments (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of enterprise infrastructures and their vulnerarbility towards attacks from the outside.
| Completed by David Kelterer
|-
| Sybils in Disguise: An Attacker View on OSN-based Sybil Defenses (Student Project and MSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of fake detection approaches in social networks.
| Completed by Martin Schwarzmaier
|-
| Design and Implementation of a distributed OSN on Home Gateways (Student project and Master's Thesis)
|[http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
|
| Completed by Dieter Lechler
|-
|}



===QUIC or Multipath QUIC Design===
* '''New!''' Implement algorithms for improving QUIC or Multipath QUIC performance. (B/M/P, at least familiar with one programming language (eg. [https://github.com/devsisters/libquic C++], [https://github.com/lucas-clemente/quic-go go] or Python).) Please contact [http://134.76.18.81/?q=people/dr-yali-yuan Yali Yuan] (Finished)

===Segment Routing based SDN===
* '''<span style="color:#8B0000">NEW! Winter 2018/2019 </span>''' There are many topics opened for Master and Bachelor theses and projects. Please contact [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]

=== Software Defined Networks (SDN) ===
* '''New!''' Implementing more Gavel application by exploiting Graph algorithms. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Including a Graph Database engine into an SDN Controller. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' A graph database tuning. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]





=== Data Analysis with Bio data ===

* '''<span style="color:#8B0000">NEW! 2019 </span>' if you are interested in topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/?q=people/dr-mayutan-arumaithurai Mayutan Arumaithurai]

=== Data Crawling and analysis ===

* [[Large scale distributed Data crawling and analysis of a popular web service]] (B/M/P)

* '''New!''' [[Data crawling and analysis of Twitter]] (P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])

=== Massive Data Mining and Recommender System===

* [[Data Mining of the Web : User Behavior Analysis]] (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Building the Genealogy for Researchers]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Recommender System Design]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

=== Social Networking(finished) ===
* '''New!''' [[Goettingen Assistant: Android App Development (completed)]] (P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])
* [[Topic prediction in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* [[Mining emotion patterns in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* Mining human mobility pattern from intra-city traffic data (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])

* For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here].
</noinclude>

Theses and Projects

2025-03-25T15:16:10Z

Dwu1:

Seminar on Internet Technologies (Summer 2025)

2025-03-24T08:24:43Z

Dwu1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''03.07.2025''': Deadline for registration to attend the final presentation
* '''20.07.2025''' : Final Presentations (Offline)
* '''30.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Self-supervised Learning and Foundation Models for Remote Sensing Applications
| In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|Service Migration
|When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.
|Edge computing and Machine Learning.
|[yufei.liu@cs.uni-goettingen.de]
|
|Yes
|-
|-
|Task Offloading and Resource Allocation Optimization
|This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.
|Edge computing & Basic optimization algorithms.
|[dongkuo.wu@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2025)

2025-03-24T08:23:44Z

Dwu1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''03.07.2025''': Deadline for registration to attend the final presentation
* '''20.07.2025''' : Final Presentations (Offline)
* '''30.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Self-supervised Learning and Foundation Models for Remote Sensing Applications
| In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|Service Migration
|When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.
|Edge computing and Machine Learning.
|[yufei.liu@cs.uni-goettingen.de]
|
|Yes
|-
|-
|Task Offloading and Resource Allocation Optimization
|This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.
|Edge computing & Basic optimization algorithms.
|[dongkuo.wu@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2025)

2025-03-24T08:23:05Z

Dwu1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''03.07.2025''': Deadline for registration to attend the final presentation
* '''20.07.2025''' : Final Presentations (Offline)
* '''30.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Self-supervised Learning and Foundation Models for Remote Sensing Applications
| In this topic, you will study (and if desired, also apply) self-supervised learning methods and Foundation Models for remote sensing applications (e.g. semantic segmentation of satellite images, super-resolution, estimation of socioeconomic indicators by utilizing satellite images, change detection, disaster monitoring, etc.).
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|Service Migration
|When users or devices move, services are migrated among edge nodes to ensure low latency and high-quality service. This topic introduces edge architectures and the application of intelligent algorithms, catering to the popular fields of intelligent transportation and autonomous driving.
|Edge computing and Machine Learning.
|[yufei.liu@cs.uni-goettingen.de]
|
|Yes
|-
|-
Task Offloading and Resource Allocation Optimization
|This topic presents efficient joint task offloading and auction-based resource allocation mechanisms in edge computing, which not only expand the computational capabilities of mobile devices but also enhance the Quality of Service of IoT applications by significantly reducing latency.
|Edge computing & Basic optimization algorithms.
|[dongkuo.wu@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Teaching

2025-03-19T09:15:10Z

Dwu1: /* Summer Semester 2025 */

== Summer Semester 2025 ==
* [[AI-Empowered Networking and Mobile Communications(Summer 2025) | AI-Empowered Networking and Mobile Communications (Summer 2025)]] (MSc) (Fabian, ALL)

* [[Computer Networks (Summer 2025) | Computer Networks (Exam only!) (Summer 2025)]] (BSc) (Fabian)

* [[Seminar on Internet Technologies (Summer 2025) | Seminar on Internet Technologies(Summer 2025)]](MSc, BSc) (Tong,Dongkuo)

* [[Data Science in Smart City (Summer 2025) ]](MSc) (Zhengze, Yanlong)

* [[Advanced Topics in AI for Computing and Networking (Summer 2025) | Advanced Topics in AI for Computing and Networking (Summer 2025)]] (MSc, BSc) (Wenfang)

* [[Practical Course Networking Lab (Summer 2025) | Practical Course Networking Lab (Summer 2025) ]] (BSc) (Peichen, Yufei)

== Winter Semester 2024/2025 ==
* [[Computer Networks (Winter 2024/2025)| Computer Networks (Winter 2024/2025)]] (BSc) (Fabian)

* [[Seminar on Internet Technologies (Winter 2024/2025) | Seminar on Internet Technologies (Winter 2024/2025)]](MSc, BSc) (Tong)

* [[Data Science in Smart City (Winter 2024/2025) ]](MSc) (Zhengze, Yanlong)

* [[Advanced Topics in AI for Computing and Networking (Winter 2024/2025) | Advanced Topics in AI for Computing and Networking (Winter 2024/2025)]] (MSc, BSc) (Wenfang)

* [[Practical Course Networking Lab (Winter 2024/2025) | Practical Course Networking Lab (Winter 2024/2025) ]] (BSc) (Jin)

== Summer Semester 2024 ==
* [[AI-Empowered Networking and Mobile Communications(Summer 2024) | AI-Empowered Networking and Mobile Communications (Summer 2024)]] (MSc) (Fabian, ALL)

* [[Computer Networks (Summer 2024) | Computer Networks (Exam only!) (Summer 2024)]] (BSc) (Fabian)

* [[Seminar on Internet Technologies (Summer 2024) | Seminar on Internet Technologies(Summer 2024)]](MSc, BSc) (Tong)

* [[Data Science in Smart City (Summer 2024) ]](MSc) (Zhengze, Yanlong)

* [[Advanced Topics in AI for Computing and Networking (Summer 2024) | Advanced Topics in AI for Computing and Networking (Summer 2024)]] (MSc, BSc) (Wenfang)

* [[Practical Course Networking Lab (Summer 2024) | Practical Course Networking Lab (Summer 2024) ]] (BSc)(Jin)

== Winter Semester 2023/2024 ==
* [[Computer Networks (Winter 2023/2024)| Computer Networks (Winter 2023/2024)]] (BSc) (Fabian)

* [[Seminar on Internet Technologies (Winter 2023/2024) | Seminar on Internet Technologies (Winter 2023/2024)]](MSc, BSc) (Wanghong)

* [[Data Science in Smart City (Winter 2023/2024) ]](MSc) (Zhengze, Yanlong)

* [[Advanced Topics in AI for Networking (Winter 2023/2024) | Advanced Topics in AI for Networking (Winter 2023/2024)]] (MSc, BSc) (Wenfang, Tingting)

* [[Practical Course Networking Lab (Winter 2023/2024) | Practical Course Networking Lab (Winter 2023/2024) ]] (BSc) (Wanghong)

== Summer Semester 2023 ==
* [[Advanced Computer Networks (Summer 2023) | Advanced Computer Networks(Summer 2023)]] (MSc) (Fabian, ALL)

* [[Computer Networks (Summer 2023) | Computer Networks (Exam only!) (Summer 2023)]] (BSc) (Fabian)

* [[Seminar on Internet Technologies (Summer 2023) | Seminar on Internet Technologies(Summer 2023)]](MSc, BSc) (Tingting)

* [[Data Science in Smart City (Summer 2023) ]](MSc) (Zhengze, Yanlong)

* [[Advanced Topics in AI for Computing and Networking (Summer 2023) | Advanced Topics in AI for Computing and Networking (Summer 2023)]] (MSc, BSc) (Wenfang, Tingting)

* [[Practical Course Networking Lab (Summer 2023) | Practical Course Networking Lab (Summer 2023) ]] (BSc)(Wanghong)

== Winter Semester 2022/23 ==
* [[Computer Networks (Winter 2022/2023)|Computer Networks (Winter 2022/2023)]] (BSc) (Fabian, Yanlong)

* [[Seminar on Internet Technologies (Winter 2022/2023) | Seminar on Internet Technologies (Winter 2022/2023)]](MSc, BSc) (Wanghong)

* [[Data Science in Smart City (Winter 2022/2023) ]](MSc) (Zhengze, Bowen)

* [[Advanced Topics in AI for Networking (Winter 2022/2023) | Advanced Topics in AI for Networking (Winter 2022/2023)]] (MSc, BSc) (Tingting)

* [[Practical Course Networking Lab (Winter 2022/2023) | Practical Course Networking Lab (Winter 2022/2023) ]] (BSc) (Wanghong)

== Summer Semester 2022 ==
* [[Advanced Computer Networks (Summer 2022) | Advanced Computer Networks(Summer 2022)]] (MSc) (Fabian)

* [[Computer Networks (Summer 2022) | Computer Networks (Exam only!) (Summer 2022)]] (BSc) (Fabian)

* [[Seminar on Internet Technologies (Summer 2022) | Seminar on Internet Technologies(Summer 2022)]](MSc, BSc) (Tingting)

* [[Data Science in Smart City (Summer 2022) ]](MSc) (Zhengze, Weijun)

* [[Advanced Topics in AI for Networking (Summer 2022) | Advanced Topics in AI for Networking (Summer 2022)]] (MSc, BSc) (Tingting)

* [[Practical Course Networking Lab (Summer 2022) | Practical Course Networking Lab(Summer 2022) ]] (BSc) (Yunxiao)

== Winter Semester 2021/2022 ==
* [[Computer Networks (Winter 2021/2022)|Computer Networks]] (BSc) (Fabian)

* [[Seminar on Internet Technologies(Winter 2021/2022) |Seminar on Internet Technologies(Winter 2021/2022)]] (MSc, BSc) (Zhengze)

* [[Advanced Practical Course Data Science (Winter 2021/2022)|Data Science in Smart City(Winter 2021/2022) ]](MSc) (Weijun)

* [[Advanced topics in mobile and social computing (AToMSC) (Winter 2021/2022)|Advanced topics in mobile and social computing (AToMSC) (Winter 2021/2022)]] (Tingting)

* [[Practical Course Networking Lab (Winter 2021/2022) | Practical Course Networking Lab ]] (BSc) (Yunxiao)

== Summer Semester 2021 ==
* [[Advanced Computer Networks (Summer 2021) | Advanced Computer Networks]] (MSc) (Bangbang)

* [[Computer Networks (Summer 2021) | Computer Networks (Exam only!) (Summer 2021)]] (BSc) (Fabian)

* [[Seminar on Internet Technologies (Summer 2021) | Seminar on Internet Technologies]](MSc, BSc) (Jiaquan)
* [[Advanced Practical Course Data Science (Summer 2021) ]](MSc) (Fabian, Jiaquan)
* [[Advanced topics in mobile and social computing (AToMSC) (Summer 2021) | Advanced topics in mobile and social computing (AToMSC) (Summer 2021)]] (MSc, BSc) (Tingting)
* [[Practical Course Networking Lab (Summer 2021) | Practical Course Networking Lab ]] (BSc) (Cong, Bangbang)
* [[Smart city (Summer 2021)]] (MSc, BSc) (Weijun, Fabian)

== Winter Semester 2020/2021 ==
* [[Computer Networks (Winter 2020/2021) | Computer Networks]] (BSc) (Fabian,Yachao)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Seminar_on_Internet_Technologies_(Winter_2020_2021) Seminar on Internet Technologies(Winter 2020/2021)] (MSc, BSc) (Tingting, Shichang, Sripriya)
* [[Advanced Practical Course Data Science (Winter 2020/2021) ]](MSc) (Jiaquan)
* [[Advanced topics in mobile and social computing (AToMSC) (Winter 2020/2021) | Advanced topics in mobile and social computing (AToMSC) (Winter 2020/2021)]] (MSc, BSc) (Sripriya)
* [[Practical Course Networking Lab (Winter 2020/2021) | Practical Course Networking Lab ]] (BSc) (Cong, Bangbang)
* [[Smart city]] (MSc, BSc) (Fabian, Weijun,Tingting)

== Summer Semester 2020 ==
* [[Advanced Computer Networks (Summer 2020) | Advanced Computer Networks]] (MSc)
* [[Advanced topics in mobile and social computing (AToMSC) (Summer 2020) | Advanced topics in mobile and social computing (AToMSC) (Summer 2020)]] (MSc, BSc)
* [[Advanced Practical Course Data Science (Summer 2020) ]](MSc)
* [[Seminar on Internet Technologies (Summer 2020) ]](MSc, BSc)
* [[Computer Networks (Summer 2020) | Computer Networks (Exam only!) (Summer 2020)]] (BSc)
* [[Practical Course Networking Lab (Summer 2020) | Practical Course Networking Lab ]] (BSc)

== Winter Semester 2019/2020 ==
* [[Computer Networks (Winter 2019/2020) | Computer Networks]] (BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Seminar_on_Internet_Technologies_(Winter_2019_2020) Seminar on Internet Technologies(Winter 2019/2020)] (MSc, BSc)
* [[Advanced Practical Course Data Science (Winter 2019/2020) ]](MSc)
* [[Advanced topics in mobile and social computing (AToMSC) (Winter 2019/2020) | Advanced topics in mobile and social computing (AToMSC) (Winter 2019/2020)]] (MSc, BSc)
* [[Practical Course Networking Lab (Winter 2019/2020) | Practical Course Networking Lab ]] (BSc)
* [https://www.stud.informatik.uni-goettingen.de/bcs/ Advanced Blockchain] (MSc, BSc)

== Summer Semester 2019 ==
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Advanced_Topics_in_Computer_Networks_(ATCN)_2019 Seminar ATCN] (MSc, BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Seminar_on_Internet_Technologies_(Summer_2019) Seminar on Internet Technologies(Summer 2019)] (MSc, BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Advanced_Computer_Networks_(Summer_2019)#Schedule_.28Tentative.29 Advanced Computer Networks (Summer 2019)] (MSc)
* [[Computer Networks (Summer 2019) | Computer Networks (Exam only!)]] (BSc)
* [[Practical Course Networking Lab (Summer 2019) | Practical Course Networking Lab ]] (BSc)
* [https://www.stud.informatik.uni-goettingen.de/bcs/ss/ Introduction to Blockchain Technology] (MSc, BSc)
* [[Advanced Practical Course Data Science (Summer 2019) ]](MSc)

== winter Semester 2018/2019 ==
* [https://www.stud.informatik.uni-goettingen.de/bcs/ws2018/ Introduction to Blockchain Technology (Examination Only)] (MSc, BSc)
* [https://www.stud.informatik.uni-goettingen.de/bcs/ws2018-advanced/ Advanced Blockchain] (MSc, BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Advanced_Topics_in_Mobile_Communications_(AToMIC)_2018 Seminar ATCN/AToMIC] (MSc, BSc)
* [https://wiki.net.informatik.uni-goettingen.de/wiki/Seminar_on_Internet_Technologies_(Winter_2018) Seminar on Internet Technologies(Winter 2018/2019)] (MSc, BSc)
* [[Computer Networks (Winter 2018/2019) | Computer Networks]] (BSc)

== Summer Semester 2018 ==
* [https://www.stud.informatik.uni-goettingen.de/bcs/ss/ Introduction to Blockchain Technology] (MSc, BSc)
* [[Practical Course Data Science (Summer 2018) ]] (MSc)
* [[Practical Course Networking Lab (Summer 2018) | Practical Course Networking Lab ]] (BSc)
* [[Advanced Computer Networks (Summer 2018) | Advanced Computer Networks ]] (MSc)
* [[Computer Networks (Summer 2018) | Computer Networks (Exam only!)]] (BSc)
* [[Seminar on Internet Technologies (Summer 2018) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Winter Semester 2017/2018 ==
Note: We will update the respective pages soon.
* [[Computer Networks (Winter 2017/2018) | Computer Networks]] (BSc)
* [[Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018) | Practical Course: Data Science]] (MSc) (PhD/BSc welcome)
* [[Practical Course Networking Lab (Winter 2017/2018) | Practical Course Networking Lab ]] (BSc)
* [[Software-defined Networking (Winter 2017/2018) | Block Course: Software-defined Networking]] (MSc) (''Course period: 9 October 2017 (Mon) - 13 Oct 2017 (Fri)'') (NOTE: The course structure will be different to past years)
* [[Seminar on Internet Technologies (Winter 2017/2018) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2017 ==
* [[Advanced Practical Course Data Science for Computer Networks (Summer 2017) | Advanced Practical Course: Data Science for Computer Networks ]] (MSc) (BSc welcome)
* [[Seminar on Internet Technologies (Summer 2017) | Seminar on Internet Technologies (Summer 2017) ]] (MSc)
* [[Advanced Computer Networks (Summer 2017) | Advanced Computer Networks ]] (MSc)
* [[Practical Course Networking Lab (Summer 2017) | Practical Course Networking Lab ]] (BSc)
* [[Computer Networks (Summer 2017) | Computer Networks (Exam only!)]] (BSc)

== Courses Winter Semester 2016/2017 ==
Note: We will update the respective pages soon.
* [[Practical Course Networking Lab (Winter 2016/2017) | Practical Course Networking Lab]] (BSc)
* [[Computer Networks (Winter 2016/2017) | Computer Networks]] (BSc)
* [[Practical Course on Data Science for Computer Networks (Winter 2016/2017) | Practical Course on Data Science for Computer Networks]] (MSc)
* [[Software-defined Networking (Winder 2016/2017) | Block Course: Software-defined Networking]] (MSc) (''Course period: 22 Feb 2017 (wed) - 2 Mar 2017 (Thu)'')
* [[Seminar on Internet Technologies (Winter 2016/2017) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2016 ==
* [[Practical Course Networking Lab (Summer 2016) | Practical Course Networking Lab ]] (BSc)
* [[Practical Course Advanced Networking (Summer 2016) | Practical Course Advanced Networking: Data Science Edition]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2016) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2016) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2015/2016 ==

* [[Advanced Topics in Computer Networking (Winter 2015/2016) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2015/2016) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2015/2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2015/2016) | Computer Networks]] (BSc)
Block courses:
* [[Introduction to Software-defined Networking (Winter 2015/2016) | Introduction to Software-defined Networking]] (MSc) (14-18 March 2016)
* [[Specialization Software-defined Networking (Winter 2015/2016) | Specialization Software-defined Networking]] (MSc) (21-25 March 2016)

== Courses Summer Semester 2015 ==
* [[Practical Course Networking Lab (Summer 2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2015) | Practical Course Advanced Networking]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2015) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2015) | Advanced Computer Networks]] (MSc)
* [[Machine Learning and Pervasive Computing (Summer 2015) | Machine Learning and Pervasive Computing]] (MSc)

== Courses Winter Semester 2014/2015 ==
* [[Advanced Topics in Computer Networking (Winter 2014/2015) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2014/2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2014/2015) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2014/2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2014/2015) | Computer Networks]] (BSc)
* [[Machine Learning and Pervasive Computing (Winter 2014/2015) | Machine Learning and Pervasive Computing]] (MSc)
* [[Introduction to Software-defined Networking (Winter 2014/2015) | Introduction to Software-defined Networking]] (MSc)
* [[Specialization Software-defined Networking (Winter 2014/2015) | Specialization Software-defined Networking]] (MSc)

== Courses Summer Semester 2014 ==
* [[Advanced Topics in Social Network and Big Data Methods(Summer 2014) | Advanced Topics in Social Network and Big Data Methods ]] (MSc)
* [[Advances in Mobile Applications and Mobile Cloud Computing(Summer 2014) | Advances in Mobile Applications and Mobile Cloud Computing ]] (MSc)
* [[Practical Course Networking Lab (Summer 2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2014) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2014) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2013/14 ==
* [[Advanced Topics in Computer Networking (Winter 2013/2014) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2013/2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2013/2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2013/2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2013/2014) | Computer Networks]] (BSc)
* [[Selected topics in Pervasive Computing (Winter 2013/2014) | Selected Topics in Pervasive Computing]] (MSc)

== Courses Summer Semester 2013 ==
* [[Advanced Topics in Mobile Communications (Summer 2013) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2013) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2013) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2012/13 ==
* [[Advanced Topics in Computer Networking (Winter 2012/2013) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2012/2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2012/2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2012/2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2012/2013) | Computer Networks]] (BSc)
* [http://www.swe.informatik.uni-goettingen.de/lectures/social-networks-seminar-ws2012 Social Networks Seminar] (MSc)

== Courses Summer Semester 2012 ==
* [[Advanced Topics in Mobile Communications (Summer 2012) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2012) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2012) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2011/2012 ==
* [[Advanced Topics in Computer Networking (Winter 2011/2012) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2011/2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2011/2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2011/2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2011/2012) | Computer Networks]] (BSc)
* [[Social Networks Colloquium (Winter 2011/2012) | Social Networks Colloquium]] (BSc/MSc)

== Courses Summer Semester 2011 ==
* [[Advanced Topics in Mobile Communications (Summer 2011) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2011) | Computer Networks]] (BSc)

== Courses Winter Semester 2010/2011 ==
* [[Advanced Topics in Computer Networking (Winter 2010/2011) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2010/2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2010/2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2010/2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2010/2011) | Computer Networks (previously Telematik)]] (BSc)
* [[Seminar on Mathematical Models in Computer Networks (Winter 2010/2011) | Seminar on Mathematical Models]] (MSc/PhD)

== Courses Summer Semester 2010 ==
* [[Advanced Topics in Mobile Communications (Summer 2010) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Summer 2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematics (Summer 2010) | Telematik/Telematics (Exam only)]] (BSc)

<noinclude>

== Courses Winter Semester 2009/2010 ==
* [[Advanced Topics in Computer Networking (Winter 2009/2010) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2009/2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2009/2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematik (Winter 2009/2010) | Telematik]] (BSc)

<noinclude>

== Courses Summer Semester 2009 ==
* [http://www.net.informatik.uni-goettingen.de/teaching/1595 Advanced Topics in Mobile Communications (AToMIC)]
* [[Practical Course Networking Lab (Summer 2009) | Practical Course Networking Lab]]
* [[Seminar on Internet Technologies (Summer 2009) | Seminar on Internet Technologies]]
* [http://www.net.informatik.uni-goettingen.de/teaching/1599 Telematik Exam]

== Courses before Summer 2009==
* For a list of older courses please go [http://www.net.informatik.uni-goettingen.de/teaching here].
</noinclude>

Seminar on Internet Technologies (Summer 2025)

2025-03-03T12:51:58Z

Dwu1: /* Schedule */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''03.07.2025''': Deadline for registration to attend the final presentation
* '''20.07.2025''' : Final Presentations (Offline)
* '''30.08.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Remote Sensing Image Registration
| In this topic, you will study and apply methods for the registration of multimodal remote sensing images with different resolution.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, student will study how AI models / GNNs are explained by SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Anomaly Detection in Graphs
| In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| Influence of LLM robots in social networks (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| The life-circle of vanished scientific journals (Intern/Project/Thesis possible)
| In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.
| Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2025)

2025-03-03T12:45:56Z

Dwu1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de],Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''31.01.2025''': Deadline for registration to attend the final presentation
* '''11.02.2025''' : Final Presentations (Offline)
* '''27.02.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Remote Sensing Image Registration
| In this topic, you will study and apply methods for the registration of multimodal remote sensing images with different resolution.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, student will study how AI models / GNNs are explained by SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Anomaly Detection in Graphs
| In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| Influence of LLM robots in social networks (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| The life-circle of vanished scientific journals (Intern/Project/Thesis possible)
| In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.
| Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2025)

2025-03-03T12:45:35Z

Dwu1:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de],ta = Dongkuo Wu[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''31.01.2025''': Deadline for registration to attend the final presentation
* '''11.02.2025''' : Final Presentations (Offline)
* '''27.02.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Remote Sensing Image Registration
| In this topic, you will study and apply methods for the registration of multimodal remote sensing images with different resolution.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, student will study how AI models / GNNs are explained by SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Anomaly Detection in Graphs
| In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| Influence of LLM robots in social networks (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| The life-circle of vanished scientific journals (Intern/Project/Thesis possible)
| In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.
| Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2025)

2025-03-03T12:44:36Z

Dwu1: Created page with "== Details == {{CourseDetails |credits=5 ECTS (BSc/MSc AI); 5 (ITIS) |module=M.Inf.1124 |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan] |ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang]; |'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?u..."

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module=M.Inf.1124
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu];[http://www.net.informatik.uni-goettingen.de/?q=people/tingting-yuan Tingting Yuan]
|ta =[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang];
|'''Please read this introduction slide [https://docs.google.com/presentation/d/13hmKYBmB4tbTFNeK1GvBAs1qZntMYo75o8ycb1NgYXI/edit?usp=sharing]. If there is any question, please contact teaching assistants.'''
|ta = Tong Shen[shen.tong@cs.uni-goettingen.de];|ta = Tong Shen[dongkuo.wu@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.'''
|univz=[https://studip-ecampus.uni-goettingen.de/dispatch.php/course/details/index/4f4ce922cd439f8a00f299fec776c727]
}}

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

==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 for 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 '''offline''' (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:[https://www.overleaf.com/latex/templates/template-sobraep-english/vnqtqpynnymb]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''31.01.2025''': Deadline for registration to attend the final presentation
* '''11.02.2025''' : Final Presentations (Offline)
* '''27.02.2025(23:59) ''': Deadline for submission of the report (should be sent to the topic adviser!).

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Readings'''
|{{Hl2}} |'''Available'''
|-
| Privacy protection in video analytics
| In this topic, you will study how to do privacy protection in video analytics, e.g., video blur
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Remote Sensing Image Registration
| In this topic, you will study and apply methods for the registration of multimodal remote sensing images with different resolution.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Disaster Monitoring
| In this topic, you will study methods to monitor disaster scenarios (e.g. floodings) with aerial images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, student will study how AI models / GNNs are explained by SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Anomaly Detection in Graphs
| In this topic, student will read papers to learn how to detect anomaly edge/graph/subgraph… with the help of GNN.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the dataset from social networks(e.g. X, YouTube) and utilize social network analysis in any topic you are interested in (science/education/language…) to find out the network structure and compare the difference among different topics.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| Influence of LLM robots in social networks (Intern/Project/Thesis possible)
| In this topic, student will study methods to crawl the data of LLM robots from social networks(e.g. X, Facebook) and utilize NLP and SNA to evaluate the influence of LLM robots in a specific topic.
| Python(Data crawling, cleaning, statistical data analysis, modeling and visualization), basic graph knowledge would be a plus
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| The life-circle of vanished scientific journals (Intern/Project/Thesis possible)
| In this topic, student will mine the information of vanished/(ongoing)/top journals, try to find out the difference features(manually/ML-based method) between journals facing different destinies.
| Python(Data Crawling, Cleaning, EDA, Modeling). Basic graph, XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, student will study how to use XGNN to predict traffic volumn.
| Python(Modeling and Visualization). Graph and XAI knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Lidar-based traffic flow analysis
| In this topic, you will study methods to analyze traffic usage on roads, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic point cloud processing & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Personalized chatbot based on ChatGPT
| In this topic, you will learn about ChatGPT and learn to use OpenAI ChatGPT API to create a personalized chatbot.
| NLP & ChatGPT
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Multimodal Large Language Model Evaluation for Multimodal Tasks
| This topic focuses on assessing the performance and effectiveness of large language models in handling tasks that involve multiple modalities, such as text, images, and audio. It involves the evaluation of these large models using specialized multimodal datasets, considering both quantitative metrics and qualitative analysis. In this topic, you will gain insights into various large models, including GPT-4, and acquire knowledge about a wide range of multimodal tasks. Additionally, you will become proficient in implementing evaluations related to these tasks.
| Large Language Model & multimodal setting
| [Wenfang Wu, wenfang.wu@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Knowledge Graph Completion
| What are the Knowledge Graph (KG) requirements for future applications and scenarios? What is the task of Knowledge Graph Completion? What is the correlation between KGs and NLP? How to use popular large language models (LLMs) to assist in the implementation of knowledge graph completion? In this topic, you will learn about KGs and learn to use LLMs to perform a KGC task.
| Knowledge Graph & NLP
| [Tong Shen, shen.tong@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Emotional Support Conversation Generation
| Does the large language model have emotions? Can it provide emotional support to users? In this topic, you will learn about techniques of large language models, such as prompt engineering and instruction fine-tuning, and use the above approaches to implement the emotional support conversation.
| Large Language Model & Emotional Support
| [Jing Li, jing.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Intelligent Routing
| In this topic, you will learn how to configure an environment based on Software-Defined Networking, and then deploy reinforcement learning algorithms on it to achieve automated routing decision.
| Basic knowledge of reinforcement learning, fundamental computer network concepts, and coding work are required.
| [peichen.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Rumor control and detection
| This topic focuses on how to analyze social networks, study information propagation models and design rumor control strategies. At the same time, you will consider automatically identifying and preventing the spread of false or misleading information in social networks to help reduce the spread of rumor information.
| Information Propagation & GCN.
| [Fei Gao, fei.gao@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Resource Optimization in Edge Computing
| This topic focuses on designing algorithms to better optimize various resources in edge computing, such as computing resources, storage resources, or network resources to realize a more efficient edge computing system.
| Task Scheduling & Caching & Flow Scheduling.
| [Chi Zhang, chi.zhang@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 your topic to the audience (in English).
* The final presentation will be conducted '''offline'''.
* 20 minutes of presentation followed by 10 minutes of discussion.

You need to present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ 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 with 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, and 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.

[[Category:Courses]]