Advanced Topics in Computer Networks (ATCN) 2019: Difference between revisions
Jump to navigation
Jump to search
| Line 61: | Line 61: | ||
* 6 June 2019 | * 6 June 2019 (Final topics release) | ||
***No Lecture | ***No Lecture | ||
| Line 68: | Line 68: | ||
**'''Deep Learning For Anomaly Detection''' | **'''Deep Learning For Anomaly Detection''' | ||
***Georgios Kaiafas, Georgios Varisteas, Sofiane Lagraa, Radu State, Cu D Nguyen, Thorsten Ries, Mohamed Ourdane, [https://ieeexplore.ieee.org/abstract/document/8406295 Detecting malicious authentication events trustfully], IEEE IFIP 2018. ('''Presented by Rezai, Masoud''') | ***Georgios Kaiafas, Georgios Varisteas, Sofiane Lagraa, Radu State, Cu D Nguyen, Thorsten Ries, Mohamed Ourdane, [https://ieeexplore.ieee.org/abstract/document/8406295 Detecting malicious authentication events trustfully], IEEE IFIP 2018. ('''Presented by Rezai, Masoud''') | ||
Yuhan Wang | ***('''Presented by Yuhan Wang''') | ||
| Line 90: | Line 90: | ||
* 11 July 2019 | * 11 July 2019 (Final slides submission) | ||
**'''Deep Learning For Anomaly Detection''' | **'''Deep Learning For Anomaly Detection (Each student selects one topic for review and submit the review report to StudIP)''' | ||
***Kun xie, Xiaocan Li, Xin Wang, Gaogang Xie, Jigang Wen, Jiannong Cao, Dafang Zhang, [https://dl.acm.org/citation.cfm?id=3180715 Fast Tensor Factorization for Accurate Internet Anomaly Detection], IEEE TON 2017. ('''Presented by Fangxi Deng''') | ***Kun xie, Xiaocan Li, Xin Wang, Gaogang Xie, Jigang Wen, Jiannong Cao, Dafang Zhang, [https://dl.acm.org/citation.cfm?id=3180715 Fast Tensor Factorization for Accurate Internet Anomaly Detection], IEEE TON 2017. ('''Presented by Fangxi Deng''') | ||
***Yi Zhao, Meina Qiao, Haiyang Wang, Rui Zhang, Dan Wang, Ke Xu, Qi Tan [ TDFI: Two-stage Deep Learning Framework for Friendship Inference via Multi-source Information], IEEE INFOCOM 2019. ('''Presented by Fangxi Deng''') | ***Yi Zhao, Meina Qiao, Haiyang Wang, Rui Zhang, Dan Wang, Ke Xu, Qi Tan [ TDFI: Two-stage Deep Learning Framework for Friendship Inference via Multi-source Information], IEEE INFOCOM 2019. ('''Presented by Fangxi Deng''') | ||
Revision as of 20:01, 23 May 2019
Details
| Workload/ECTS Credits: | 5 ECTS |
| Module: | M.Inf.1222: Seminar Telematik III -or- M.Inf.224: Seminar Computernetzewerke II (old Regulations) -or- 3.10: Advanced Topics in Internet Research (II)(ITIS); M.Inf.1223 (new Regulations) |
| Lecturer: | Prof. Xiaoming Fu; Dr. Yali Yuan |
| Teaching assistant: | [] |
| Time: | Thu. 14:00-16:00 |
| Place: | IfI 0.101 |
| UniVZ | link |
Course Overview
The purpose of this seminar is to discuss some advanced topics in computer networks. This course is a theory-oriented research seminar (5 ECTS, 2 SWS), held on a weekly base and comprising the following components:
- Weekly Presentation + Weekly Paper Reading and Discussion
- Final Presentation
- Final Report
The material in the seminar is mainly drawn from the research literature in top journals/conferences, like ToN,TMC, TPDS, SIGCOMM, SIGMETRICS, INFOCOM, MOBICOM, MOBIHOC, WWW, CoNEXT.
Requirements
- Each participant is required to read the assigned paper before the seminar and prepare the review of the paper, which should include the following parts:
- Summary of the paper
- Pros AND cons of the paper (your conclusion)
- NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 Wednesday). => the review form is available at [Paper_Review_Form_ATCN_WS201112.doc]
- During each weekly seminar, one participant is assigned for presenting the paper (each presentation for ~30 minutes). And the list of pros and cons is discussed by all the participants.
- In the middle of the semester, everyone is requested to pick a topic and prepare:
- Final report: Essay (5~6 pages, double columns, IEEE format) for your chosen research topic, which contains a comprehensive literature survey + a detailed discussion of some key enabling technologies
- Final presentation: each presentation for ~20 minutes, plus ~5 minutes Q&A
Schedule
- 18 April 2019
- Informational Meeting
- 2 May 2019
- Improving our mental health
- Rui Wang, Weichen Wang, Alex daSilva, Jeremy F. Huckins, William M. Kelley, Todd F. Heatherton, Andrew T. Campbell, Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing, ACM UbiComp 2018. (Presented by Yuhan Wang)
- Improving our mental health
- 9 May 2019
- Improving our mental health
- Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jayesh Kamath, Athanasios Bamis, Jinbo Bi, Alexander Russell, Bing Wang, Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure, ACM UbiComp 2018. (Presented by Cong Li)
- Improving our mental health
- 16 May 2019
- Improving our mental health
- Landu Jiang, Xinye Lin, Xue Liu, Chongguang Bi, Guoliang Xing, SafeDrive: Detecting Distracted Driving Behaviors Using Wrist-Worn Devices, ACM UbiComp 2018. (Presented by Rezai, Masoud)
- Improving our mental health
- 23 May 2019
- Dynamic Adaptive Streaming over QUIC
- Perkins, Colin, and Jörg Ott, Real-time Audio-Visual Media Transport over QUIC, EPIQ’18. (Presented by Ding-Ze Hu)
- Dynamic Adaptive Streaming over QUIC
- 30 May 2019
- No Lecture
- 6 June 2019 (Final topics release)
- No Lecture
- 13 June 2019
- Deep Learning For Anomaly Detection
- Georgios Kaiafas, Georgios Varisteas, Sofiane Lagraa, Radu State, Cu D Nguyen, Thorsten Ries, Mohamed Ourdane, Detecting malicious authentication events trustfully, IEEE IFIP 2018. (Presented by Rezai, Masoud)
- (Presented by Yuhan Wang)
- Deep Learning For Anomaly Detection
- 20 June 2019
- Cameras Everywhere
- Anhong Guo, Anuraag Jain, Shomiron Ghose, Gierad Laput, Chris Harrison, Jeffrey P. Bigham, Crowd-AI Camera Sensing in the Real World, IEEE UbiComp 2018. (Presented by Ding-Ze Hu)
- Cameras Everywhere
- 27 June 2019
- Cameras Everywhere
- Erin Griffiths, Salah Assana, Kamin Whitehouse, Privacy-preserving Image Processing with Binocular Thermal Cameras, IEEE UbiComp 2018. (Presented by Cong Li)
- Cameras Everywhere
- 4 July 2019
- Dynamic Adaptive Streaming over QUIC (Each student selects one topic for review and submit the review report to StudIP)
- Bastian Alt, Trevor Ballard, Ralf Steinmetz, Heinz Koeppl, Amr Rizk, CBA: Contextual Quality Adaptation for Adaptive Bitrate Video Streaming, IEEE INFOCOM 2019. (Presented by Adhatarao, Sripriya Srikant)
- Gadaleta, Matteo, et al., D-DASH: A Deep Q-Learning Framework for DASH Video Streaming, IEEE Transactions on Cognitive Communications and Networking 2017. (Presented by Adhatarao, Sripriya Srikant)
- Dynamic Adaptive Streaming over QUIC (Each student selects one topic for review and submit the review report to StudIP)
- 11 July 2019 (Final slides submission)
- Deep Learning For Anomaly Detection (Each student selects one topic for review and submit the review report to StudIP)
- Kun xie, Xiaocan Li, Xin Wang, Gaogang Xie, Jigang Wen, Jiannong Cao, Dafang Zhang, Fast Tensor Factorization for Accurate Internet Anomaly Detection, IEEE TON 2017. (Presented by Fangxi Deng)
- Yi Zhao, Meina Qiao, Haiyang Wang, Rui Zhang, Dan Wang, Ke Xu, Qi Tan [ TDFI: Two-stage Deep Learning Framework for Friendship Inference via Multi-source Information], IEEE INFOCOM 2019. (Presented by Fangxi Deng)
- Deep Learning For Anomaly Detection (Each student selects one topic for review and submit the review report to StudIP)
Final Presentations & Report
- Final Presentation:
- Date: 18 July 2019
- Each for ~20 minutes, plus ~5 minutes Q&A (Please make sure that your presentation time is within 20 minutes)
- Final Presentation Slots:
- Final Report:
- Essay (5~6 pages, double columns, IEEE format)
- Due by 23:59pm 30 September 2019