Seminar on Internet Technologies (Summer 2015): Difference between revisions
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| [http://dl.acm.org/citation.cfm?id=2342501], [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6957152&tag=1],[http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6883812], [http://dl.acm.org/citation.cfm?id=2643105] | | [http://dl.acm.org/citation.cfm?id=2342501], [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6957152&tag=1],[http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6883812], [http://dl.acm.org/citation.cfm?id=2643105] | ||
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| '''What is deep learning and its applications?''' ''( Assigned to Alexander Warnecke)'' | | '''What is deep learning and its applications?''' ''( Assigned to Alexander Warnecke)'' "ELIGIBLE" | ||
The study is to have a basic knowledge of deep learning and learn to use some tools to implement some deep learning algorithms. | The study is to have a basic knowledge of deep learning and learn to use some tools to implement some deep learning algorithms. | ||
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang] | | [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang] | ||
| TBA | | TBA | ||
|- | |- | ||
| '''Tensor methods for machine learning''' ''( Assigned to Burcu Coskun)'' | | '''Tensor methods for machine learning''' ''( Assigned to Burcu Coskun)'' "ELIGIBLE" | ||
Tensor methods for machine learning are fast, accurate, and scalable, but we'll need well-developed libraries. In this work, we will try to understand tensor based methods. | Tensor methods for machine learning are fast, accurate, and scalable, but we'll need well-developed libraries. In this work, we will try to understand tensor based methods. | ||
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang] | | [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang] | ||
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| [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6558130] [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6969026] | | [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6558130] [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6969026] | ||
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|'''Survey of Network and Application Load balancing techniques ''' ''(Assigned to Benjamin Baum)'' | |'''Survey of Network and Application Load balancing techniques ''' ''(Assigned to Benjamin Baum)'' "ELIGIBLE" | ||
The work here is to study and compare the state-of-the-art techniques employed in Network and Server Load balancing techniques. Identify different network layer (L2 to L7) load balancing techniques and mechanisms followed in the inter and intra data centres and Clouds. | The work here is to study and compare the state-of-the-art techniques employed in Network and Server Load balancing techniques. Identify different network layer (L2 to L7) load balancing techniques and mechanisms followed in the inter and intra data centres and Clouds. |
Revision as of 13:31, 8 July 2015
IMPORTANT: Please send a mail to Mayutan with the topic supervisor on CC and the topic that you would like to take |
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. Mayutan Arumaithurai |
Teaching assistant: | None |
Time: | April 16th (Thursday), 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 on April 16th, there are no regular meetings, lectures or classes for this course. The students have to keep in constant touch with their topic advisor in order to prepare for the final presentation. 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.
Passing requirements
- Actively participate in the project communication with your topic advisor
- This accounts for 30% of your grade.
- Present the selected topic (15 min. presentation + 5 min. Q&A).
- In order to receive a presentation slot, the topic advisor needs to agree atleast two weeks before the presentation that the work done is sufficient, the student has submitted a first draft of the slides and has agreed on a Table of Content for the final report.
- This accounts for 40% of your grade.
- The grade is based on your presentation as well as your interaction level during the presentation sessions (Hint: Be attentive and ask questions)
- Write a report on the selected topic (12-15 pages) (LaTeX Template:[2]).
- This accounts for 30% of your grade.
- Please check the #Schedule and adhere to it.
Schedule
- April 16th, 16:00ct: Informational meeting (+ intro to presenting and writing) Introductory Slides (pdf)Introductory Slides (ppt)
- July 8th, 23:59 CET: Deadline for getting confirmation from topic advisor that you are eligible for the final presentations.
- July 15th, 23:59 CET: Deadline for registration in FlexNow/ITIS System
- July 23rd, 30th (tentative) : Presentations
- September 30, 2015, 23:59: Deadline for submission of report
Presentation Schedule
Topics
IMPORTANT: Please send a mail to Mayutan with the topic supervisor on CC and the topic that you would like to take |
Topic | Topic Advisor | Initial Readings |
What is the current status and future of SDN research? What are the main research problems of SDN? (assigned to Mahmoud Alzaitoun)
There is a lot of hype about SDN with industries, operators and Academia showing interest. The aim of this work is to study what research problems exist and also identify promising solutions. |
Mayutan Arumaithurai | [3][4] |
A survey of SDN standardization
There is a lot of hype about SDN with industries, operators and Academia showing interest. The aim of this work is to study the standardization efforts ongoing in SDN. |
Mayutan Arumaithurai | [5] |
Can we use machine learning methods in SDN?
The SDN technology is revolutionizing both the network research and industry. The idea that using machine learning methods in SDN has been under the exploitation by some companies and researchers. In this project, a survey of the work related to this idea is required. |
Narisu Tao | [6] , [7], [8] |
What is Scala?
The functional programming paradigm is crucial for the coming Big Data challenge. Scala is a programming language which fits very well this paradigm. A brief introduction on the history, main ideas and industry adaptiveness of Scala are required. |
Narisu Tao | [9] |
A survey of publish/subscribe in ICN
The aim of this work is to study and compare the existing publish/subscribe communication support in different ICN solutions. |
Jiachen Chen | [10], [11] |
Information-centric networks
The study here will mainly focus on different Information-Centric Network (ICN) proposals and compare the difference among them. |
Jiachen Chen | [12] |
Routing in NDN
NDN is one of the most recent information-centric network designs. The focus of the network has shifted from location (IP addresses) to the content (names of the contents). In IP, address spaces are divided into domains by some network management entities (ISPs) and the routing take advantage of this division. But in NDN, content names no longer follow this rule. This complicates the routing in NDN. This study will focus on the different routing mechanisms and compare the pros and cons of each mechanism. Students who have knowledge about routing preferred. |
Jie Li | [13], [14], [15], [16], [17] |
Caching in ICN
As one of the most significant properties of ICN, in-network caching is excepted to improve network performance. In this topic, the student need to give a survey on the status quo of caching in ICN. |
Jie Li | [18], [19],[20], [21] |
What is deep learning and its applications? ( Assigned to Alexander Warnecke) "ELIGIBLE"
The study is to have a basic knowledge of deep learning and learn to use some tools to implement some deep learning algorithms. |
Hong Huang | TBA |
Tensor methods for machine learning ( Assigned to Burcu Coskun) "ELIGIBLE"
Tensor methods for machine learning are fast, accurate, and scalable, but we'll need well-developed libraries. In this work, we will try to understand tensor based methods. |
Hong Huang | [22][23][24][25] |
Applying Machine Learning to Computer Networks ( Assigned to Omar Shaya)
Machine Learning is one of the big topics in computer science today. However, it has rarely been applied to computer networking. In this topic, we will investigate one of the few applications thought of so far to answer one question: Can and should we apply machine learning to detect attacks in networks? |
David Koll | [26] |
Controllers in Software-defined Networks - A Survey (Assigned to Pouya Saeedfar)
SDN introduces the concept of a logically centralized controller in charge of operating the network, while routers and switches are reduced to simple forwarding elements. In this topic, the student will provide a survey over the state-of-the-art in SDN controllers. |
David Koll | [27][28] |
What Will 5G Be? - A Survey
This study is to provide a comprehensive survey on the key enabling communication technologies for 5G networks. |
Xu Chen | [29] |
What Will Smart Grid Be? – A Survey (assigned to Kirill Bulert)
The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. This study is to provide a comprehensive survey on the key enabling technologies and services for Smart Grid. |
Xu Chen | [30] |
On the recognition of emotion from Social media and sensor readings (assigned to Amine Lasfar)
Recently, there are multiple efforts to extend the capabilities of data analysis tools e.g. for social media or wearable systems towards the recognition of sentiment - human emotions and internal states. Examples are studies on emotion contagion on facebook, or the introduction of wearable technology capable to capture sentiment like prominently glasses or watches. The student shall give a structured overview on recent advances in this field. (Note: An independent literature survey is expected. It is NOT sufficient to just read and summarise the literature provided.) |
Stephan Sigg | [31] [32] [33] [34] |
Utilising the wireless channel as a mathematical calculator - A comprehensive technical dicussion
This topic focuses recent research efforts towards the calculation of mathematical functions on the wireless channel. The student shall give a comprehensive and technical introduction of different approaches and recent advances in this field. (Note: An independent literature survey is expected. It is NOT sufficient to just read and summarise the literature provided.) |
Stephan Sigg | [35] [36] |
Virtual Machine and Data Migration in Cloud computing
This topic requires to study and compare the state-of-the-art techniques employed in Virtual machine and data migration in Cloud Computing systems. Then try to propose new mechanisms and optimisations for carrying out the migration efficiently. |
Sameer Kulkarni | [37] [38] |
Survey of Network and Application Load balancing techniques (Assigned to Benjamin Baum) "ELIGIBLE"
The work here is to study and compare the state-of-the-art techniques employed in Network and Server Load balancing techniques. Identify different network layer (L2 to L7) load balancing techniques and mechanisms followed in the inter and intra data centres and Clouds. |
Sameer Kulkarni | [39] [40] [41] |
Compressive sensing -- An introduction and overview
Compressive sensing is a method to reconstruct a sparse signal, by solving underdetermined linear equations. In the scope of this topic, a comprehensive introduction and overview is given on compressive sensing, applications and recent developments. (Note: An independent literature survey is expected. It is NOT sufficient to just read and summarise the literature provided.) |
Stephan Sigg | |
Random Matrix Theory for signal processing Random matrix theory has numerous applications in physics, statistics and engineering. Although initially motivated by practical experimental problems, random matrices are now used in very diverse fields including physics, mathematics, but also neural networks, information theory and signal processing. The student will recapitulate the basic concepts of random matrix theory and summarise recent advances in this field with relation to signal processing, in particular in communications. |
Stephan Sigg | For general introduction: |
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 Mayutan and 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, 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).
- 15 minutes of presentation followed by 5-10 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 15 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:
- 15 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 (5 min.).
- Make sure to finish in time.
Suggestions for preparing the slides:
- No more than 15 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.