Seminar on Internet Technologies (Summer 2019): Difference between revisions
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|'''RoMS: An intelligent monitoring system for road surface distresses inspection using smart phones (still available for three students)''' | |'''RoMS: An intelligent monitoring system for road surface distresses inspection using smart phones (still available for three students)''' | ||
| Design and develop an intelligent road condition monitoring system | | Design and develop an intelligent road condition monitoring system. | ||
| Basic machine learning knowledge, familiar with Python. | | Basic machine learning knowledge, familiar with Python. | ||
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-yali-yuan Yali Yuan] | |[http://www.net.informatik.uni-goettingen.de/?q=people/dr-yali-yuan Yali Yuan] |
Revision as of 08:48, 18 April 2019
Details
Workload/ECTS Credits: | 5 ECTS (BSc/MSc AI); 5 (ITIS) |
Lecturer: | Prof. Xiaoming Fu |
Teaching assistant: | [sding@gwdg.de Shichang Ding] |
Time: | April 17', 17:00ct: Introduction Meeting |
Place: | IFI Building, Room 2.101 |
UniVZ | [1] |
Note: |
Course description
This course covers selected topics on 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 an 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 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 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:[2]).
- This accounts for 40% of your grade.
- Please check the #Schedule and adhere to it.
Schedule
- April. 17, 17:00ct: Introduction meeting
- July. 07 : Deadline for registration
- July. 18 and July. 19 : Presentations
- Sep. 31, 2019, 23:59: Deadline for submission of report (should be sent to the topic adviser!)
Topics
Topic | Description | Prerequisites | Topic Advisor | Initial Readings | |
A survey of 5G (assigned to Hu) | Reading papers about 5G, especially its influence on big data and give a survey | [Shichang Ding--sding@gwdg.de] | |||
A survey of human mobility and deep learning (assigned to Sun) | Reading papers about HM and DL and give a survey | [Shichang Ding--sding@gwdg.de] | |||
Network Meets AI & Machine Learning (assigned to hamed roknizadeh) | AI & ML have been successfully applied to various perceptual domains, including computer vision, natural language processing, and voice recognition. In addition, ML techniques are showing impressive results in new domains such as medicine, finance, and astronomy, to name a few. This success in non-perceptual domains suggests that ML techniques could be successfully applied to simplify network management. For at least a decade, networking researchers, equipment vendors, and Internet service providers alike have argued for “autonomous” or “self-driving” networks, where network management and control decisions are made in real time and in an automated fashion. Yet, building such “self-driving” networks that are practically deployable has largely remained unrealized. | The student should be at least familiar with machine learning and AI | Osamah Barakat | [3] | |
360 video streaming (Assigned to: Albert Demba) | Currently, video streaming occupies a major part of the internet traffic. Increasingly 360 video streaming has become very popular. The concept revolves around capturing video from multiple angles and streaming it for a flat display | The student should perform a detailed study of the current advancements in the 360 video streaming technology | Sripriya Adhatarao | ||
Smart health for citizens and the role of big data | Nowadays, the development of wearable devices enables people to monitor personal health related indexes like heart rate, blood pressure and sleeping time, as well as personal daily activities. The devices could record data throughout a whole day and generate large amount of data. By studying the above data of certain patients, it is possible to find out how the change of the health relevant indexes and personal activities could infect the physical condition and cause disease. | The student should perform a review of the medical big data for advanced diagnosis | [Jiaquan Zhang--jzhang@cs.uni-goettingen.de] | ||
Artificial intelligence in venture capital industry | Venture investment decision-making could be optimized by machine learning applied to previous deals, company data, founder data, and more. It is quite possible that a system could analyze founder personalities, company metrics, and team attributes and improve venture capitalist's decision-making. | The student shoud have basics of artificail intelligence and be able to program in python. | Yachao Shao | ||
Cache Replacement in Mobile Edge Computing (assigned to Marjan Olesch) | Implement the algorithm for cache replacement in mobile edge computing. | Basic networking knowledge, at least familiar with one programming language (eg. C or Python). | Yali Yuan | [4] | |
User Location Prediction based on Geo-social Networking Data | The increasing amount of user and location information in GSN makes the information overload phenomenon more and more serious. Although massive user generated data brings convenience to users' social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSN, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and have received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and their personal preferences, thus determining the visiting location of users in the future | Applicants should master basic knowledge on data mining | [Shuai Xu--shuai.xu@cs.uni-goettingen.de ] | [5][6][7][8] | |
RoMS: An intelligent monitoring system for road surface distresses inspection using smart phones (still available for three students) | Design and develop an intelligent road condition monitoring system. | Basic machine learning knowledge, familiar with Python. | Yali Yuan | [9] |
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.