NET Wiki - User contributions [en]

Seminar on Internet Technologies (Summer 2025)

2025-05-06T13:29:30Z

Stong: /* 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
* '''10.08.2025''' : 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 (Summer 2024)

2025-02-12T15:54:57Z

Stong: /* 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]
|ta = Tong Shen[shen.tong@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/?cid=9d41fd6cc504b43ebbe4b1c33eef46bb]
}}

==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 (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==
* '''03.07.2024''': Deadline for registration to attend the final presentation
* '''20.07.2024''' : Final Presentations (Online, wait to decide)
* '''30.08.2024 (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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@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]
|
| No
|-
|-
| Biomass estimation from Satellite Images
| In this topic, you will study methods to estimate the biomass of trees from satellite images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| 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
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

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

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes 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 2024)

2025-02-12T15:04:35Z

Stong: /* 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]
|ta = Tong Shen[shen.tong@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 (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==
* '''03.07.2024''': Deadline for registration to attend the final presentation
* '''20.07.2024''' : Final Presentations (Online, wait to decide)
* '''30.08.2024 (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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@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]
|
| No
|-
|-
| Biomass estimation from Satellite Images
| In this topic, you will study methods to estimate the biomass of trees from satellite images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| 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
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

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

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes 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 2024/2025)

2025-01-20T14:09:26Z

Stong: /* 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]
|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 (Winter 2024/2025)

2025-01-14T15:23:09Z

Stong: /* 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]
|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
* '''TBD.02.2025''' : Final Presentations (Offline)
* '''TBD.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 (Winter 2024/2025)

2025-01-14T15:18:40Z

Stong: /* 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]
|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.'''
}}

==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
* '''TBD.02.2025''' : Final Presentations (Offline)
* '''TBD.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 (Winter 2024/2025)

2025-01-14T15:17:14Z

Stong: /* 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/dr-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]
|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.'''
}}

==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
* '''TBD.02.2025''' : Final Presentations (Offline)
* '''TBD.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 (Winter 2024/2025)

2025-01-14T15:14:29Z

Stong: /* 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]
|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]
|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.'''
}}

==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
* '''TBD.02.2025''' : Final Presentations (Offline)
* '''TBD.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 (Winter 2024/2025)

2025-01-14T14:19:10Z

Stong: /* Schedule */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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
* '''TBD.02.2025''' : Final Presentations (Offline)
* '''TBD.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 (Winter 2024/2025)

2024-10-22T14:53:54Z

Stong: /* Schedule */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Offline)
* '''TBD.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
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| 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 (Winter 2024/2025)

2024-10-22T13:15:53Z

Stong:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Online, wait to decide)
* '''TBD.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
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| 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 (Winter 2024/2025)

2024-08-23T16:06:56Z

Stong: /* Passing requirements */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Online, wait to decide)
* '''TBD.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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@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]
|
| No
|-
|-
| Biomass estimation from Satellite Images
| In this topic, you will study methods to estimate the biomass of trees from satellite images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| 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
|-
|-
|}

==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 2024/2025)

2024-08-23T16:05:08Z

Stong: /* 4. Prepare presentation */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Online, wait to decide)
* '''TBD.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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@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]
|
| No
|-
|-
| Biomass estimation from Satellite Images
| In this topic, you will study methods to estimate the biomass of trees from satellite images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| 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
|-
|-
|}

==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 2024/2025)

2024-08-23T16:04:37Z

Stong: /* 4. Prepare presentation */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Online, wait to decide)
* '''TBD.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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@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]
|
| No
|-
|-
| Biomass estimation from Satellite Images
| In this topic, you will study methods to estimate the biomass of trees from satellite images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| 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
|-
|-
|}

==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 2024/2025)

2024-08-23T16:03:13Z

Stong: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Online, wait to decide)
* '''TBD.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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@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]
|
| No
|-
|-
| Biomass estimation from Satellite Images
| In this topic, you will study methods to estimate the biomass of trees from satellite images.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| 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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| 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
|-
|-
|}

==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 2024/2025)

2024-08-23T15:59:41Z

Stong: /* Passing requirements */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Online, wait to decide)
* '''TBD.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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| 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
|-
|-
| The relationship between birds’ distribution and the health of the environment (Project possible)
| Birds are sensitive to environmental pressures and their populations can reflect changes in the health of the environment. By analyzing the change of the distribution of birds, perhaps we may evaluate the health of the environment.
| Basic Python knowledge, correlation analysis
| [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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV 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
|-
|-
| Performance of real 5G communication
| What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.
| Network protocol stack & 5G architecture
| [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Low latency transmission mechanism for real-time interactive application
| There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to "negative optimization". How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.
| Network Transmission Improvement & 5G architecture
| [Wanghong Yang, wanghong.yang@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 2024/2025)

2024-08-23T15:20:44Z

Stong:

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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 (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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Online, wait to decide)
* '''TBD.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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| 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
|-
|-
| The relationship between birds’ distribution and the health of the environment (Project possible)
| Birds are sensitive to environmental pressures and their populations can reflect changes in the health of the environment. By analyzing the change of the distribution of birds, perhaps we may evaluate the health of the environment.
| Basic Python knowledge, correlation analysis
| [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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV 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
|-
|-
| Performance of real 5G communication
| What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.
| Network protocol stack & 5G architecture
| [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Low latency transmission mechanism for real-time interactive application
| There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to "negative optimization". How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.
| Network Transmission Improvement & 5G architecture
| [Wanghong Yang, wanghong.yang@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 2024/2025)

2024-08-23T14:34:36Z

Stong: Created page with "== Details == {{CourseDetails |credits=5 ECTS (BSc/MSc AI); 5 (ITIS) |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu] |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 = Wanghong Yang [wang..."

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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 = Wanghong Yang [wanghong.yang@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.'''
}}

==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 (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==
* '''TBD.01.2025''': Deadline for registration to attend the final presentation
* '''TBD.01.2025''' : Final Presentations (Online, wait to decide)
* '''TBD.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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| 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
|-
|-
| The relationship between birds’ distribution and the health of the environment (Project possible)
| Birds are sensitive to environmental pressures and their populations can reflect changes in the health of the environment. By analyzing the change of the distribution of birds, perhaps we may evaluate the health of the environment.
| Basic Python knowledge, correlation analysis
| [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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV 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
|-
|-
| Performance of real 5G communication
| What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.
| Network protocol stack & 5G architecture
| [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Low latency transmission mechanism for real-time interactive application
| There are so many mechanisms designed for providing low latency transmission from application layer adaptive algorithms to transport layer protocols, even from network assistance. However, the incoordination between the upper and lower layers may lead to "negative optimization". How is the transmission performance of the current protocol stack? Which combination performs best? Does the new technology really improve performance? In this topic, you will build a 5G communication simulation network from the core to the edge, test and analyze the latency performance of current mechanisms.
| Network Transmission Improvement & 5G architecture
| [Wanghong Yang, wanghong.yang@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]]

AI-Empowered Networking and Mobile Communications(Summer 2024)

2024-05-21T21:44:51Z

Stong: /* Schedule (Tentative) */

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp: Advanced Topics in Computer Networks
B.Inf.1702.Mp: Vertiefung Computersysteme

M.Inf.1120.Mp: Mobilkommunikation

M.Inf.121.1: Mobilkommunikation I

M.Inf.225.Mp: Ausgewählte Themen der Mobilkommunikation

Note: You can choose any of them to attend this course, but only one! Please note that enrolling in the same course more than once will not grant additional credits.
|lecturer=Dr. Tingting Yuan, [http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|ta= Fabian Wölk
|time=Thursdays, 10-12am.
|place=IFI 2.101
|univz=[]
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include:
*AI meets Networking
*Segment Routing
*Intelligent Transportation Application based on V2I Networking
*Social Network Analysis
*Multimodal Sentiment Analysis
*Blockchain Technology and its Underlying P2P Networks
*Bio-inspired Networking
*Knowledge Graph and Knowledge Graph Completion
*Deep Learning with Differential Privacy for Image Classification

For each topic, basic structures, features and applied techniques will be taught.

If you have any questions, please contact Fabian Wölk (fabian.woelk@cs.uni-goettingen.de)

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 11.04.2024 (10:00-12:00am)
| Introduction
| Dr. Tingting Yuan
|
|-
| align="right" | 18.04.2024 (10:00-12:00am)
| AI meets Networking I
| Dr. Tingting Yuan
|
|-
| align="right" | 25.04.2024
| NO LECTURE (GIRL'S DAY)
|
|-
| align="right" | 02.05.2024 (10:00-12:00am)
| AI meets Networking II
| Dr. Tingting Yuan
|
|-
| align="right" | 09.05.2024
| NO LECTURE (PUBLIC HOLIDAY)
|
|
|-
| align="right" | 16.05.2024 (10:00-12:00am)
| Segment Routing I
| Fabian Wölk
|
|-
| align="right" | 23.05.2024 (10:00-12:00am)
| Segment Routing II
| Fabian Wölk
|
|-
| align="right" | 30.05.2024 (10:00-12:00am)
| Intelligent Transportation Application based on V2I Networking
| Yanlong Huang
|
|-
| align="right" | 06.06.2024 (10:00-12:00am)
| Social Network Analysis
| Zhengze Li
|
|-
| align="right" | 13.06.2024 (10:00-12:00am)
| From Words to Vision: A Journey Through Multimodal Sentiment Analysis
| Wenfang Wu
|
|-
| align="right" | 20.06.2024 (10:00-12:00am)
| Blockchain Technology and its Underlying P2P Networks
| Jin Xie
|
|-
| align="right" | 27.06.2024 (10:00-12:00am)
| Bio-inspired Networking
| Parisa
|
|-
| align="right" | 04.07.2024 (10:00-12:00am)
| Knowledge Graph and Knowledge Graph Completion
| Tong Shen
|
|-
| align="right" | 11.07.2024 (10:00-12:00am)
| Deep Learning with Differential Privacy for Image Classification
| Yanru Song
|
|-
| align="right" | TBD (??:00-??:00am)
| Written Examination (Room TBD)
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

==References===
* Yang, S., N. He, F. Li, and X. Fu, Resource Allocation in Network Function Virtualization: Problems, Models and Algorithms, Singapore: Springer, August 2022.

* James Kurose, Keith Ross, Computer Networking: A Top-Down Approach. 8th Edition, Pearson, June 2021

[[Category:Courses]]

Theses and Projects

2024-04-09T14:13:47Z

Stong: /* Open Theses and Student Project Topics */

== 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!''' 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. We expect you to have a background in point cloud processing/cloud computing, K8s, and programming skills.

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

=== * '''New!''' Blockchain-based Spectrum and Computation Resources Sharing in Mobile Networks===

The sixth-generation (6G) system is widely envisioned as a global network consisting of pervasive devices that interact with each other. Besides exchanging information, these peer entities also share heterogeneous and distributed network resources. Blockchain is a promising technology to secure resource sharing in a peer-to-peer way. We need students for this topic. We expect you have a background in computer network and programming skills in Python.

Please contact Jin Xie [jin.xie@stud.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!''' 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!''' 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)

=== * '''New!''' 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

2024-04-09T14:13:16Z

Stong: /* Open Theses and Student Project Topics */

Seminar on Internet Technologies (Summer 2024)

2024-03-26T14:31:15Z

Stong: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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 (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==
* '''03.07.2024''': Deadline for registration to attend the final presentation
* '''20.07.2024''' : Final Presentations (Online, wait to decide)
* '''30.08.2024 (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
|-
|-
| How to do efficient offline training
| In this topic, you will study how to do efficient offline training for reinforcement learning
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingting.yuan@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| 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
|-
|-
| The relationship between birds’ distribution and the health of the environment (Project possible)
| Birds are sensitive to environmental pressures and their populations can reflect changes in the health of the environment. By analyzing the change of the distribution of birds, perhaps we may evaluate the health of the environment.
| Basic Python knowledge, correlation analysis
| [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]
|
| Yes
|-
|-
| 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, student will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV 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
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

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

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes 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 2024)

2024-03-19T16:02:50Z

Stong: /* Details */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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]
|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.'''
}}

==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 (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==
* '''03.07.2024''': Deadline for registration to attend the final presentation
* '''20.07.2024''' : Final Presentations (Online, wait to decide)
* '''30.08.2024 (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
|-
|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
| Basic machine learning knowledge
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143] [https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs] [https://developer.adobe.com/document-services/apis/pdf-services/]
| Yes
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, students study how AI models / GNNs are explained with SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science 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
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, students 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Open topics
| Open topics in Data Science & Applied Statistics, especially XAI
| Depends
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based pedestrian distribution monitoring
| In this topic, you will study methods to do macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian.
| Basic CV & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based traffic usage analysis
| In this topic, you will study methods to analyze traffic usage on roads and highways, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic CV & 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
|-
|-
| 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
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

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

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes 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 2024)

2024-03-19T16:02:16Z

Stong: /* Details */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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 = Dr. Tong Shen[shen.tong@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.'''
}}

==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 (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==
* '''03.07.2024''': Deadline for registration to attend the final presentation
* '''20.07.2024''' : Final Presentations (Online, wait to decide)
* '''30.08.2024 (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
|-
|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
| Basic machine learning knowledge
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143] [https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs] [https://developer.adobe.com/document-services/apis/pdf-services/]
| Yes
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, students study how AI models / GNNs are explained with SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science 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
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, students 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Open topics
| Open topics in Data Science & Applied Statistics, especially XAI
| Depends
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based pedestrian distribution monitoring
| In this topic, you will study methods to do macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian.
| Basic CV & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based traffic usage analysis
| In this topic, you will study methods to analyze traffic usage on roads and highways, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic CV & 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
|-
|-
| 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
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

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

=== 4. Prepare presentation ===

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

AI-Empowered Networking and Mobile Communications(Summer 2024)

2024-03-05T15:16:25Z

Stong: /* Schedule (Tentative) */

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp: Advanced Topics in Computer Networks
B.Inf.1702.Mp: Vertiefung Computersysteme

M.Inf.1120.Mp: Mobilkommunikation

M.Inf.121.1: Mobilkommunikation I

M.Inf.225.Mp: Ausgewählte Themen der Mobilkommunikation

Note: You can choose any of them to attend this course, but only one! Please note that enrolling in the same course more than once will not grant additional credits.
|lecturer=Dr. Tingting Yuan, [http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|ta= Fabian Wölk
|time=Thursdays, 10-12am.
|place=IFI 2.101
|univz=[]
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include, but are not limited to:
*Software-defined Networking (?)
*Segment Routing
*Video Analysis in Edge Networks (?)
*Information Centric Network
*Big Data and Social Network

For each topic, basic structures, features and applied techniques will be taught.

If you have any questions, please contact Fabian Wölk (fabian.woelk@cs.uni-goettingen.de)

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 11.04.2024 (10:00-12:00am)
| Introduction
| Dr. Tingting Yuan
|
|-
| align="right" | 18.04.2024 (10:00-12:00am)
| AI meets Networking I
| Dr. Tingting Yuan
|
|-
| align="right" | 25.04.2024
| NO LECTURE (GIRL'S DAY)
|
|-
| align="right" | 02.05.2024 (10:00-12:00am)
| AI meets Networking II
| Dr. Tingting Yuan
|
|-
| align="right" | 09.05.2024
| NO LECTURE (PUBLIC HOLIDAY)
|
|
|-
| align="right" | 16.05.2024 (10:00-12:00am)
| Segment Routing I
| Fabian Wölk
|
|-
| align="right" | 23.05.2024 (10:00-12:00am)
| Segment Routing II
| Fabian Wölk
|
|-
| align="right" | 30.05.2024 (10:00-12:00am)
| Intelligent Transportation Application based on V2I Networking
| Yanlong Huang
|
|-
| align="right" | 06.06.2024 (10:00-12:00am)
| Social Network Analysis
| Zhengze Li
|
|-
| align="right" | 13.06.2024 (10:00-12:00am)
| From Words to Vision: A Journey Through Multimodal Sentiment Analysis ?
| Wenfang Wu
|
|-
| align="right" | 20.06.2024 (10:00-12:00am)
| ?
| Jin
|
|-
| align="right" | 27.06.2024 (10:00-12:00am)
| ?
| Parisa
|
|-
| align="right" | 04.07.2024 (10:00-12:00am)
| Knowledge Graph and Knowledge Graph Completion
| Tong
|
|-
| align="right" | 11.07.2024 (10:00-12:00am)
| ?
| Yanru
|
|-
| align="right" | TBD (12:00-14:00am)
| Written Examination (Room TBD)
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

==References===
* Yang, S., N. He, F. Li, and X. Fu, Resource Allocation in Network Function Virtualization: Problems, Models and Algorithms, Singapore: Springer, August 2022.

* James Kurose, Keith Ross, Computer Networking: A Top-Down Approach. 8th Edition, Pearson, June 2021

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2024)

2024-02-28T23:45:14Z

Stong: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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 = Dr. Tingting Yuan [tingting.yuan@informatik.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.'''
}}

==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 (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==
* '''03.07.2024''': Deadline for registration to attend the final presentation
* '''20.07.2024''' : Final Presentations (Online, wait to decide)
* '''30.08.2024 (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
|-
|-
|AI painter
| In this topic, you will study how AI has been used for painting. e.g. GAN.
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://topten.ai/ai-painting-generators/]
| Yes
|-

|-
|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
| Basic machine learning knowledge
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143] [https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs] [https://developer.adobe.com/document-services/apis/pdf-services/]
| Yes
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, students study how AI models / GNNs are explained with SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science 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
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Traffic prediction with GNN (Intern/Project/Thesis possible)
| In this topic, students 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Open topics
| Open topics in Data Science & Applied Statistics, especially XAI
| Depends
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based pedestrian distribution monitoring
| In this topic, you will study methods to do macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian.
| Basic CV & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based traffic usage analysis
| In this topic, you will study methods to analyze traffic usage on roads and highways, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic CV & 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
|-
|-
| 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
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

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

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes 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 2024)

2024-02-19T22:45:28Z

Stong: /* Schedule */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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 = Dr. Tingting Yuan [tingting.yuan@informatik.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.'''
}}

==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 (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==
* '''03.07.2024''': Deadline for registration to attend the final presentation
* '''20.07.2024''' : Final Presentations (Online, wait to decide)
* '''30.08.2024 (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
|-
|-
|AI painter
| In this topic, you will study how AI has been used for painting. e.g. GAN.
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://topten.ai/ai-painting-generators/]
| Yes
|-

|-
|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
| Basic machine learning knowledge
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143] [https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs] [https://developer.adobe.com/document-services/apis/pdf-services/]
| Yes
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, students study how AI models / GNNs are explained with SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science 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
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| The life-circle of vanished scientific journals (Intern/Project/Thesis possible)
| In this topic, students 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, students 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Open topics
| Open topics in Data Science & Applied Statistics, especially XAI
| Depends
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based pedestrian distribution monitoring
| In this topic, you will study methods to do macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian.
| Basic CV & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based traffic usage analysis
| In this topic, you will study methods to analyze traffic usage on roads and highways, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic CV & 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
|-
|-
| Performance of real 5G communication
| What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.
| Network protocol stack & 5G architecture
| [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

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

=== 4. Prepare presentation ===

* Present on your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes 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 2024)

2024-02-19T21:17:07Z

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|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 = Dr. Tingting Yuan [tingting.yuan@informatik.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.'''
}}

==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 (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==
* '''03.07.2023''': Deadline for registration to attend the final presentation
* '''20.07.2023''' : Final Presentations (Online, wait to decide)
* '''30.08.2023 (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
|-
|-
|AI painter
| In this topic, you will study how AI has been used for painting. e.g. GAN.
| Basic programming knowledge, Basic machine learning knowledge, need coding work
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://topten.ai/ai-painting-generators/]
| Yes
|-

|-
|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
| Basic machine learning knowledge
| [Tingting Yuan, tingt.yuan@hotmail.com]
|[https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143] [https://www.cameralyze.co/blog/how-can-i-detect-lines-in-images-or-pdfs] [https://developer.adobe.com/document-services/apis/pdf-services/]
| Yes
|-
| Change Detection in Satellite Image Time Series
| In this topic, you will study methods to detect changes in land-use, vegetation, etc. in Satellite Image Time Series.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| No
|-
|-
| Satellite Image Pixel Clustering for Change Estimation
| In this topic, you will study pixel clustering methods for satellite images and apply their outputs for regression-based estimation of changes between different points in time.
| Basic machine learning knowledge
| [Fabian Wölk, fabian.woelk@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Explainable AI(XAI) / graph neural network (XGNN)
| In this topic, students study how AI models / GNNs are explained with SOTA papers.
| Basic AI / GNN knowledge
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Social Media Comments Network (Intern/Project/Thesis possible)
| In this topic, you will study methods to crawl the dataset from social networks(e.g. YouTube) and utilize social science 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
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| No
|-
|-
| The life-circle of vanished scientific journals (Intern/Project/Thesis possible)
| In this topic, students 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, students 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
|-
|-
| ML/DL based industrial equipment predictive maintenance (Intern/Project/Thesis possible)
| In this topic, students will study how to use cutting-edge machine learning models to predict when industrial equipment need to be maintained before crashing.
| Python(Cleaning, EDA, Modeling and Visualization). XAI knowledge is a plus.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| AI for High-quality Image Restoration and Manipulation (Intern/Project/Thesis possible)
| Image restoration and manipulation are low-level vison problems aiming to either restore the degraded images for higher perceptual quality (such as better color, contrast brightness, etc.) or manipulate image styles content for better understanding or visual-appealing effects. Moreover, such problems also plays key role for many high-level computer vision tasks, including image detection, recognition and (semantic) segmentation... In this topic, students need to follow the new trends and advances in the area of many sup-problem and explore new methods for completive or superior opportunity for academic and industrial applications.
| Python & CV knowledge.
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Open topics
| Open topics in Data Science & Applied Statistics, especially XAI
| Depends
| [Zhengze Li, zhengze.li@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based pedestrian distribution monitoring
| In this topic, you will study methods to do macroscopic pedestrian detection aims to estimate crowd density without distinguishing each pedestrian.
| Basic CV & ML knowledge
| [Yanlong Huang, yanlong.huang@cs.uni-goettingen.de]
|
| Yes
|-
|-
| Vision-based traffic usage analysis
| In this topic, you will study methods to analyze traffic usage on roads and highways, e.g., in terms of traffic flow, speed, and density to identify patterns and trends.
| Basic CV & 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
|-
|-
| Performance of real 5G communication
| What are the key QoS requirements for future applications and scenarios? What are the shortcomings of today's 5G network? Where are the bottlenecks? How can performance be improved? In this topic, you will build an open-source 5G communication network from the core to the edge, test and analyze the real performance of 5G.
| Network protocol stack & 5G architecture
| [Wanghong Yang, wanghong.yang@cs.uni-goettingen.de]
|
| Yes
|-
|-
|}

==Workﬂow==

=== 1. Select a topic ===
Each student needs to choose a topic from the list. You can start to work on your selected topic '''at any time'''. However, please make sure to '''notify the advisor''' of your selected topic in advance, because you might be refused by the advisor if someone has registered on the same topic.

=== 2. Get your work advised ===
Each topic has an advisor, who will help you to solve problems regarding the topic. Please do not hesitate to contact your advisor.
It is recommended (and not mandatory) that you can schedule a skype or zoom meeting with your advisor right after you select your topic. Your advisor will give you some useful guidance and suggestions, which will help you to gain more from this course.

=== 3. Approach your topic ===

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

=== 4. Prepare presentation ===

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