Seminar on Internet Technologies (Summer 2017)

Details

Workload/ECTS Credits:	5 ECTS (BSc/MSc AI); 5 (ITIS)
Module:	M.Inf.1124 -or- B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
Lecturer:	Dr. Hong Huang
Teaching assistant:	Tao Zhao
Time:	Apr 20, 16:00ct: Introduction Meeting
Place:	IFI Building, Room 3.101
UniVZ	[1]

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.

Passing requirements

• Actively and frequently participate in the project communication with your topic advisor. The topic advisor has the right to decide whether a student is eligible for the final presentation or not according to their communication.
  • 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:[2]).
  • This accounts for 40% of your grade.
• Please check the #Schedule and adhere to it.

Schedule

• Apr. 20, 16:00ct: Introduction meeting
• TBA : Deadline for registration
• TBA : Presentations
• September. 30, 2017, 23:59: Deadline for submission of report (should be sent to the topic advisor!)

Topics

Topic	Topic Advisor	Initial Readings
Deep into Google Translate This study is to provide a comprehensive study of one of the Google products - Google translate and aim to understand the technologies behind it.	Hong Huang	[3]
Inferring social capital from big data This study is to discover the state of art of social capital measuring, particularly, from big data perspective.	Hong Huang	[4][5]
An overview on deep learning framework In this work, you will be asked to do a survey on all popular deep learning framework either in academe or industry, like tensorflow, caffe and so on. You shall elaborate their shortcomings and advantages.	Hong Huang	[6]
Industrie 4.0: Networking prospective and challenges Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges. NOTE:This topic could be a good entry for master project and thesis later.	Osamah Barakat	[7][8][9]
Bitcoin: state of the art and position paper This study is to provide a comprehensive study of the current situation on Bitcoin. Latest advances in its structure, security and furture.	Osamah Barakat	[10][11]
Legacy devices support in SDN controllers NOTE: This topic could be a good entry for master project and thesis later. Supporting legacy network is an active research area in SDN. You should survey all techniques used up to date to solve this problem. Details may be provided later.	Osamah Barakat	a good start from [12][13]
Google QUIC QUIC is an experimental transport layer network protocol designed by Jim Roskind at Google, initially implemented in 2012. Investigate QUIC in detail and conduct some simple experiments to compare QUIC with TCP. The experiments should be designed by the student himself/herself.	Enhuan Dong	[14][15][16][17][18]
Google TCP BBR TCP BBR is developed by Google. Investigate BBR in detail and conduct some simple experiments to compare BBR with TCP Cubic.The experiments should be designed by the student himself/herself.	Enhuan Dong	[19][20][21]
Commercial usage of Multipath TCP MultiPath TCP (MPTCP) is an emerging extension for TCP and it is under discussion in IETF now. Study MPTCP protocol including architecture, data transmission, default congestion control, etc. Investigate how MPTCP is used in companies.	Enhuan Dong	[22][23][24][25][26][27][28]
Traffic Data Analysis Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also a way to learn some basic knowledge about data mining.	Shichang Ding	[29][30]
Machine Learning Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension -- as is the case in data mining applications -- machine learning uses that data to detect patterns in data and adjust program actions accordingly.	Shichang Ding	[31]
Deep Learning Deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. It is now broadly studied and used in following areas: Automatic speech recognition, Image recognition, Natural language processing, Drug discovery and toxicology, Customer relationship management, Recommendation systems, Biomedical informatics.	Shichang Ding	[32][33]