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Seminar on Internet Technologies (Winter 2018/2019): Difference between revisions
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Seminar on Internet Technologies (Winter 2018/2019) (view source)
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{{CourseDetails | {{CourseDetails | ||
|credits= | |credits=5 ECTS (BSc/MSc AI); 5 (ITIS) | ||
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu] | |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu] | ||
|ta=[http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding] | |ta=[http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding] | ||
|time=Oct | |time=Oct 25'''''', 16:00ct: Introduction Meeting | ||
|place=IFI Building, Room 2.101 | |place=IFI Building, Room 2.101 | ||
|univz=[https://univz.uni-goettingen.de/qisserver | |univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=223974&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung] | ||
}} | }} | ||
{{Announcement|Note: | |||
we will have Introduction Meeting on the second week of new Winter Semester}} | |||
==Course description== | ==Course description== | ||
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==Schedule== | ==Schedule== | ||
* '''Oct. | * '''Oct. 25, 16:00ct''': Introduction meeting | ||
* '''Jan. | * '''Jan. 10''' : Deadline for registration | ||
* '''Jan. | * '''Jan. 16 and Jan. 17''' : Presentations | ||
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!) | * '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!) | ||
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|{{Hl2}} |'''Initial Readings''' | |{{Hl2}} |'''Initial Readings''' | ||
|- | |- | ||
| '''The application of Recurrent neural network in Human mobility modeling (assigned to | | '''The application of Recurrent neural network in Human mobility modeling (assigned to Md Saiful Islam)''' | ||
| RNN/LSTM is one of the most widely used deep learning method, especially good at sequence modeling. Human mobility is a typical class of sequence data. So until now, what and how is RNN working in human mobility area, especially in online check-ins? It requires you reading 5-6 best papers in this fields. Understanding this question will help you know the basic concept, general problems and important approahces in this field. | | RNN/LSTM is one of the most widely used deep learning method, especially good at sequence modeling. Human mobility is a typical class of sequence data. So until now, what and how is RNN working in human mobility area, especially in online check-ins? It requires you reading 5-6 best papers in this fields. Understanding this question will help you know the basic concept, general problems and important approahces in this field. | ||
| Basic machine learning knowledge | | Basic machine learning knowledge | ||
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| [https://www.ijcai.org/proceedings/2018/0477.pdf] | | [https://www.ijcai.org/proceedings/2018/0477.pdf] | ||
|- | |- | ||
| '''Getting a Practical Understanding of Segment Routing''' | | '''Repeat Buyer Prediction for E-Commerce(Assigned to Supreetha Sudeendra)''' | ||
| A large number of new buyers are often acquired by merchants during promotions. However, many of the attracted buyers are one-time deal hunters, and the promotions may have little long-lasting impact on sales. It is important for merchants to identify who can be converted to regular loyal buyers and then target them to reduce promotion cost and increase the return on investment (ROI). Our goal in this topic is to do a survey about the key factors leading to successful purchasing actions. On the basis, further work such as purchasing prediction and personalized recommendation can be carried out. | |||
| Basic machine learning knowledge | |||
|[Bo Zhao--<bo.zhao@gwdg.de>] | |||
| [https://dl.acm.org/citation.cfm?id=2939674][https://dl.acm.org/citation.cfm?id=3219826] | |||
|- | |||
| '''Recommender systems for E-commerce(Assigned to Kangcheng Wu)''' | |||
|The proliferation of mobile devices especially smart phones brings remarkable opportunities for the e-commerce development. Recommendation systems have been being widely used by almost all the e-commerce platforms to help consumers find their ideal products to purchase more quickly. Modern recommendation systems have become increasingly more complex compared to their early content-based and collaborative filtering versions. In this survey, we will cover recent advances in recommendation methods, focusing on matrix factorization, multi-armed bandits, and methods for blending recommendations. We will also describe evaluation techniques, and outline open issues and challenges. The ultimate goal of this tutorial is to present a toolkit of new recommendation methods in perspective to data-related problems, and highlight opportunities and new research paths for researchers and practitioners that work on problems in the intersection of recommendation systems and databases. | |||
|Basic knowledge about recommender system and machine learning | |||
|[Bo Zhao--<bo.zhao@gwdg.de>] | |||
| [https://dl.acm.org/citation.cfm?id=2789995][https://arxiv.org/abs/1801.02294] | |||
|- | |||
| '''Getting a Practical Understanding of Segment Routing Assigned to giulio sidoretti ''' | |||
| Segment Routing (SR) is a new data-plane paradigm that employs source routing and software defined networking (SDN) to present an ease to manage network. The main key advantage compared to openflow based SDN is that SR embeds the path in the packet header and hence no states are kept in the core network. Your task is to understand in practical and theory how this main key affects the way SR is doing relative network tasks such as traffic engineering and service function chaining. If you are not sure about your time schedule during this semester, please choose another topic. | | Segment Routing (SR) is a new data-plane paradigm that employs source routing and software defined networking (SDN) to present an ease to manage network. The main key advantage compared to openflow based SDN is that SR embeds the path in the packet header and hence no states are kept in the core network. Your task is to understand in practical and theory how this main key affects the way SR is doing relative network tasks such as traffic engineering and service function chaining. If you are not sure about your time schedule during this semester, please choose another topic. | ||
| The student should be at least familiar with one programming language (eg. Java or Python), and basic Linux skills. | | The student should be at least familiar with one programming language (eg. Java or Python), and basic Linux skills. | ||
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] | | [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] | ||
| [http://www.segment-routing.net/][http://www.segment-routing.org/] | | [http://www.segment-routing.net/][http://www.segment-routing.org/] | ||
|- | |||
| ''' Research and implementation of an OPC UA application in ICN''' (Assigned to: Cristian Di Paolo) | |||
| The OPC foundation is a consortium of industry partners that is responsible for creating and maintaining industry standards. There most recent standard is called Open Platform Communications Unified Architecture (OPC UA). OPC UA brings a significant enhancement to the existing OPC framework, especially making is platform independent, and turning it into a service oriented architecture. It is an open source architecture and in this topic you will be required to perform a research on the OPC UA standard and the communication protocols offered in this architecture and implement an application based on this architecture in Information Centric Networking (ICN). | |||
| Basic networking knowledge, C/C++ programming, Unix/Linux, Java and Information Centric Networking (ICN). | |||
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de) | |||
| [https://opcfoundation.org/] | |||
|- | |||
|- | |||
| ''' Mobile spatial and temporal data based user profiles identification(Assigned to Knopp, Cornelius)''' | |||
| When people using their APPs in smartphones, the communication base station will catch the URL requests from each customer. Each record is assigned to a specific user ID, and includes a time stamp and an URL request. Moreover, most frequently used APPs need location service provided by assembled GPS sensor in the smartphone, and some coordinates are contained in the URLs. With the information above, each user could be modeled by a set of spatial-temporal data. Different users could show different patterns, which includes the features like POI preference, trajectory type, usage frequency at different time and etc. We aim to cluster the users in different categories according to their spatial-temporal data. | |||
| Basic data science knowledge, Python programming and data science related libraries. | |||
| Jiaquan Zhang (jzhang@cs.uni-goettingen.de) | |||
| [https://arxiv.org/pdf/1711.04710.pdf] | |||
|- | |||
| ''' HTTP/2 Push-Based Streaming Over Mobile Networks With Stream Termination''' | |||
| HTTP adaptive streaming (HAS) has become popular for delivering videos over the Internet. However, most HAS services are currently based on the traditional HTTP/1.1, which has limitations for live streaming. Meanwhile, the new HTTP/2 was standardized in 2015 with many improvements. Therefore, a novel HTTP/2-based adaptation scheme for low-delay live streaming over mobile networks is required in nowadays. | |||
| Basic networking knowledge, at least familiar with one programming language (eg. C or Python). | |||
| Yali Yuan (yali.yuan@informatik.uni-goettingen.de) | |||
| [https://ieeexplore.ieee.org/abstract/document/8396293] | |||
|- | |||
| ''' A Survey for HTTP/2 Based Network Research''' | |||
| Superseding HTTP/1.1, the dominating web protocol, HTTP/2 promises to make web applications faster and safer by introducing many new features. Some important new features of the HTTP/2 are multiplexing, prioritization, server push and so on. Recently, some research work already used HTTP/2 new features to improve the performance of networks. This work requires the students to find the recent research work about the HTTP/2 based network and make a survey related this topic. | |||
| Basic networking knowledge. | |||
| Yali Yuan (yali.yuan@informatik.uni-goettingen.de) | |||
| [https://www.imperva.com/docs/Imperva_HII_HTTP2.pdf] | |||
| | |||
|- | |||
| '''Cache Replacement in Mobile Edge Computing''' | |||
| 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 (yali.yuan@informatik.uni-goettingen.de) | |||
| | |||
|- | |||
|''' Big data for intelligent transportation system ''' | |||
|Big data for social transportation brings us unprecedented oppotunities for resolving transportation problems for which traditional approaches are not competent and for building nest-generation intelligent transportation systems. There are still many challenges to develop such a system. We need to fuse data from different sources, such as drivers' GPS coordinates, mobile phones' billing records and so on. Then we select the useful information from abundant information, and can process the data with using machine learning technichs. | |||
|Basic data science knowledge, Python programming and related libraries. | |||
|Yachao Shao (yshao@gwdg.de) | |||
|[https://ieeexplore.ieee.org/document/7359138] | |||
|- | |||
| ''' A survey of Computer Vision Algorithms''' (Assigned to: Ding-Ze Hu) | |||
| In this topic you will perform a survey of existing computer vision algorithms and and the different tools available and compare them and show cost benefit analysis. | |||
| Basic machine learning | |||
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de) | |||
| [https://opcfoundation.org/] | |||
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|} | |} | ||