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Seminar on Internet Technologies (Summer 2019): Difference between revisions

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|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[sding@gwdg.de Shichang Ding]  
|ta=[sding@gwdg.de Shichang Ding]  
|time=April 15'''''', 16:00ct: Introduction Meeting
|time=April 17'''''', 17:00ct: Introduction Meeting
|place=IFI Building, Room 2.101
|place=IFI Building, Room 2.101
|univz=[]
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=234426&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}
}}
{{Announcement|Note:  
{{Announcement|Note:  
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==Schedule==
==Schedule==
* '''April. 18, 16:00ct''': Introduction meeting  
* '''April. 17, 17:00ct''': Introduction meeting  
* '''June. 17''' : Deadline for registration
* '''July. 07''' : Deadline for registration
* '''June. 27 and June. 28''' : Presentations
* '''July. 18 and July. 19''' : Presentations
* '''Sep. 31, 2019, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)
* '''Sep. 31, 2019, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)


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|{{Hl2}} |'''Initial Readings'''
|{{Hl2}} |'''Initial Readings'''
|-
|-
| '''A survey of point of interests recommendation (assigned to )'''
| '''A survey of 5G (assigned to Hu)'''
| Point of interest recommendation with social and geographical influence. Abstract: Point of interest (POI) recommendation, a service which can help people discover useful and interesting locations has emerged rapidly with the development of location-based social networks (LBSNs), like Foursquare, Gowalla and Wechat.
| Reading papers about 5G, especially its influence on big data and give a survey
| Basic machine learning knowledge
|  
| [Shichang Ding--sding@gwdg.de]
| [Shichang Ding--sding@gwdg.de]
| [https://dl.acm.org/citation.cfm?id=3210042]
|  
|-
|-
| '''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
| [http://www.net.informatik.uni-goettingen.de/?q=people/osamah-barakat Osamah Barakat]
| [https://link.springer.com/article/10.1186/s13174-018-0087-2]
|-
| '''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
| [http://www.net.informatik.uni-goettingen.de/?q=people/sripriya-srikant-adhatarao Sripriya Adhatarao]
|
|-
| '''Smart health for citizens and the role of big data (Assigned to Tapashi Gosswami)'''
| 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. 
|[http://www.net.informatik.uni-goettingen.de/?q=people/yachao-shao 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). 
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-yali-yuan Yali Yuan]
|[https://ieeexplore.ieee.org/abstract/document/8513863]
|
|-
| '''User Location Prediction based on Geo-social Networking Data (already assigned to Hussain Nauman)'''
| 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 ]
| [http://staff.ustc.edu.cn/~cheneh/paper_pdf/2015/Yingzi-Wang-KDD.pdf][https://www.sciencedirect.com/science/article/pii/S1084804518300444][https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/11900][https://ieeexplore.ieee.org/abstract/document/7498303]
|
|-
|'''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. 
|[http://www.net.informatik.uni-goettingen.de/?q=people/dr-yali-yuan Yali Yuan]
|[https://ieeexplore.ieee.org/abstract/document/7297863]
|}
|}


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