Seminar on Internet Technologies (Summer 2020)

Details

Workload/ECTS Credits:	5 ECTS (BSc/MSc AI); 5 (ITIS)
Lecturer:	Prof. Xiaoming Fu
Teaching assistant:	Shichang Ding and Sripriya Srikant Adhatarao
Time:	Please read this introduction slide (https://drive.google.com/open?id=1jNZ-k8WSu4tP6bMHvY73FBoUXdrXNLql). If there is any question, please contact teaching assistants.
UniVZ	[1]

{{Note: Please register the examination on Flexnow [2] before June 25th 2020 }}

Course description

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes 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 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 topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

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

Passing requirements

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

• Actively and frequently participate in the project communication with your topic advisor
  • This accounts for 20% of your grade.
• Present the selected topic (20 min. presentation + 10 min. Q&A).
  • This accounts for 40% of your grade.
• Write a report on the selected topic (12-15 pages) (LaTeX Template:[3]).
  • This accounts for 40% of your grade.
• Please check the #Schedule and adhere to it.

Schedule

• There will be no introduction meeting. Please read the introduction slides and contact the teaching assistants if you have any further questions.
• June. 20 : Deadline for registration (Tentative)
• July. 2 and July. 3, 10:00-12:00: Presentations, through Zoom
• Sep. 30, 2019, 23:59: Deadline for submission of the report (should be sent to the topic adviser through E-mail!)

Topics

Topic	Description	Prerequisites	Topic Advisor	Initial Readings
Knowledge Graph for Recommendation System(Akshay Katyal)	The success of the recommendation system makes it prevalent in Web applications, ranging from search engines, E-commerce, to social media sites and news portals. To predict user preference from the key (and widely available) source of user behavior data, much research effort has been devoted to collaborative filtering (CF). Despite its effectiveness and universality, CF methods suffer from the inability of modeling side information, such as item attributes, user profiles, and contexts, thus perform poorly in sparse situations where users and items have few interactions. To address the limitation of CF models, a solution is to take the graph of item side information, aka. knowledge graph into account to construct the predictive model.	Have basic knowledge for deep learning. Interested in this topic, patience and time for reading and concluding multiple papers.	[Shichang Ding,sding@gwdg.de]	[4]
Empirical study for QUIC Protocol (Emmanuel Charleson Dapaah)	Quick UDP Internet Connections (QUIC) is a new transport protocol developed by Google in 2012. QUIC is considered as a combination of TCP, TLS and HTTP on the top of UDP with some advantages such as reducing connection establishment time, improving congestion control, multiplexing without heads of line blocking and connection migration.	Programming skills. Interested in this topic, willing to follow the advisor's guidance, patience and time for reading multiple papers	[Yali Yuan, yali.yuan@cs.informatik.uni-goettingen.de]	[5]
Anomaly Detection for Road Traffic (Samiur Rahman)	Anomaly detection on road traffic has vast application prospects in urban traffic management and road safety. Due to the impact of many factors such as weather, viewpoints and road conditions in the real-world traffic scene, anomaly detection still faces many challenges. There are many causes for vehicle anomalies, such as crashes, vehicle on fires and vehicle faults, which exhibits different unknown behaviors. In this course, we need to learn efficient anomaly detection systems of the state-of-the-art and understand their design and implementation.	Programming skills. Interested in this topic, willing to follow the advisor's guidance, patience and time for reading multiple papers	[Yali Yuan, yali.yuan@cs.informatik.uni-goettingen.de]	[6]
Multimodal Deep Learning for Image and Text Datasets (Grigor Davtyan)	Learning semantic correspondence between image and text is significant as it bridges the semantic gap between vision and language. This course requires students to learn efficient multimodal deep learning for image and text datasets.	Programming skills. Interested in this topic, willing to follow the advisor's guidance, patience and time for reading multiple papers	[Yali Yuan, yali.yuan@cs.informatik.uni-goettingen.de]	[7]
Failure recovery from the breakpoint in service function chain	As we all know, if the packets are dropped in network, we need to retransmit them from the sender. However, in service function chain, failure links or nodes may drop packets that have already been processed by upstream NFs, retransmission from the sender may result in wasted work in the service chain. If we use SRv6 to steer traffic along with SFC, we could easily know the IP address of upstream NF, then we can leverages this information to realize in-network recovery. This project focuses on realizing in-network recovery with SRv6.	The student should know the basic knowledge about TCP/IP, network simulation	[Bangbang Ren, bangbang.ren@cs.uni-goettingen.de]
Learning Combinatorial Optimization Algorithms over Graphs	There are many NP-hard problems about graph. However, these NP-hard problems cannot be soloved fast by optimization solver. Approximation algorithms could solve them fast in the cost of sacrificing the accuracy. Recently, some algorithms based on machine learning have been proposed to solve these NP-hard problems in the manner of end-to-end. After reproducing one classical paper, the student is required to find solution for a new assignment problem	The student should be familiar with machine learning and Integer linear programming	[Bangbang Ren, bangbang.ren@cs.uni-goettingen.de]
Wireless Moving Video Surveillance System	Artificial Intelligence has been and is going to be popular for many years. Static object detection, recognition technique has been studied for many years. However, how these techniques work in a dynamic environment (eg. Self-driving ) is not clear. In this topic, we want to reveal which kind of technique performs better in a video surveillance system with limited computing and network resources. Based on this, our goal is to develop a real Wireless Moving Video Surveillance System which including video analysis, wireless data delivery, and data compression and fusion. Fortunately, we already have some preliminary work.	Interested in this topic, willing to follow the advisor's guidance, patience and time for reading multiple papers. Interested in embedded development, we will use Raspberry Pi and NVIDIA Jetson Nano Developer Kit. Have Fun With This Project!	[Weijun Wang, weijun.wang@cs.uni-goettingen.de]	[8]
Learning Combinatorial Optimization Algorithms on VRP problem (Ismot Jerin)	There are many NP-hard problems about graph. However, these NP-hard problems cannot be solved fast by optimization solver. Approximation algorithms could solve them fast in the cost of sacrificing the accuracy. Recently, some algorithms based on machine learning have been proposed to solve these NP-hard problems in the manner of end-to-end. After reproducing one classical paper, the student is required to find solution for a new Vihecl Routing Problem.	The student should be familiar with TSP, Matching, and Integer linear programming	[Weijun Wangn, weijun.wang@cs.uni-goettingen.de]
Advancements in the field of Video streaming technologies (Assigned to: Maxim Gkanoliak)	In this topic the student will perform a survey of the exsting and upcoming video streaming technologies and provide a detailed report containing a comparison among these technologies. The student will also have to implement these technologies and perform experiments and report the findings.	The student should be familiar with video streaming technologies like ABR, DASH, VBR and programming languages like Java and Python.	Sripriya Adhatarao
Imbalance learning in Healthcare area	Imbalance learning is an imbalanced classification problem where the learning phase and the prediction perfromance can be affected by the imbalance dataset. While the imbalance distribution of dataset is very common, student could implement imbalance learning methods in different healthcare area and finish a report on imbalance learning methods in healthcare area.	The student should be familiar with Machine learning and programming languages Python.	Yachao Shao
Advancements and challenges in Visible Light Communication (VLC)	Traditional wireless communications are based on microwave transmission, which is facing the challenge of rapidly increasing number of devices, as well as the amount of data transmission. White LED offers advantageous properties such as high brightness, reliability, lower power consumption and long lifetime. White LEDs are already widely applied in the current generation of lamps. If it could be used as wireless communication, many exsiting problems of wireless communication like high power comsumption, low security and unreliability.	The student should be familiar with basic wireless communication knowledge.	[Jiaquan Zhang (jzhang@cs.uni-goettingen.de)]

Workflow

1. Select a topic

A student picks a topic to work on. You can pick up a topic and start working at any time. However, make sure to notify the advisor of the topic before starting to work.

2. Get your work advised

For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

3. Approach your topic

• By choosing a topic, you choose the 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.
• You should include your own thoughts on your topic.

4. Prepare your presentation

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

You present your topic to an audience of students and other interested people (usually the NET group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation: 20 minutes are too short to present a topic fully. It is alright to focus just on one certain important aspect. Limit the introduction of basics. Make sure to finish 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. Summary of the topic: thinking in your own words.

5. Write your report

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

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

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.