Advanced Topics in AI for Networking (Winter 2023/2024) - Revision history

Wwu1: /* Final Presentations & Report */

2023-11-02T09:37:24Z

Final Presentations & Report

← Older revision		Revision as of 11:37, 2 November 2023
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	*Final Presentation:		*Final Presentation:
	**Each for ~20 minutes, plus ~10 minutes Q&A		**Each for ~20 minutes, plus ~20 minutes Q&A

Wwu1: /* Course Overview */

2023-11-01T16:02:21Z

Course Overview

← Older revision		Revision as of 18:02, 1 November 2023
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	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 comprises the following components:		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 comprises the following components:
	* Weekly Presentation + Weekly Paper Reading and Discussion 40%		* Weekly Presentation + Weekly Paper Reading and Discussion 40%
	* Final Presentation 30%		* Final Presentation 40%
	* Final Report 30%		* Final Report 20%

	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.		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.

Wwu1: /* Schedule */

2023-10-23T08:15:23Z

Schedule

← Older revision		Revision as of 10:15, 23 October 2023
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	==Schedule==		==Schedule==
	W1: Open Talk (26.10)		W1: Open Talk (02.11)

	W2: Select papers and create schedule		W2: Select papers and create schedule

Wwu1: /* List of Papers */

2023-10-15T21:10:30Z

List of Papers

← Older revision		Revision as of 23:10, 15 October 2023
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	==List of Papers ==		==List of Papers ==
	1. ~~Cosmo~~: ~~Contrastive Fusion~~ Learning ~~with Small Data~~ for ~~Multimodal Human Activity Recognition~~ [https://~~dl.acm~~.org~~/doi~~/pdf/10.~~1145/3495243~~.~~3560519~~]		1. FedAdapter: Efficient Federated Learning for Modern NLP [https://arxiv.org/pdf/2205.10162.pdf]

	2. Learning for ~~Crowdsourcing: Online Dispatch for Video Analytics with Guarantee~~ [https://~~ieeexplore~~.~~ieee~~.org/~~document~~/~~9796960~~]		2. AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving [https://dl.acm.org/doi/10.1145/3570361.3592517]

	3. ~~CASVA~~: ~~Configuration~~-~~Adaptive Streaming~~ for ~~Live Video Analytics~~ [https://~~ieeexplore~~.~~ieee~~.org/~~stamp~~/~~stamp~~.~~jsp?tp=&arnumber=9796875~~]		3. mmFER: Millimetre-wave Radar based Facial Expression Recognition for Multimedia IoT Applications [https://dl.acm.org/doi/10.1145/3570361.3592515]

	4. ~~Batch Adaptative Streaming for Video Analytics~~ [https://~~ieeexplore~~.~~ieee~~.org/~~stamp~~/~~stamp~~.~~jsp?tp=&arnumber=9796853~~]		4. NeRF2: Neural Radio-Frequency Radiance Fields [https://dl.acm.org/doi/10.1145/3570361.3592527]

	5. ~~FlexPatch: Fast and Accurate Object Detection~~ for ~~On-device High~~-~~Resolution Live Video Analytics~~ [https://~~ieeexplore~~.~~ieee~~.org/~~stamp~~/~~stamp~~.~~jsp?tp=&arnumber=9796984~~]		5. Exploiting Contactless Side Channels in Wireless Charging Power Banks for User Privacy Inference via Few-shot Learning [https://dl.acm.org/doi/10.1145/3570361.3613288]

	6. ~~AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning~~ [https://~~ieeexplore~~.~~ieee~~.org/~~stamp~~/~~stamp~~.~~jsp?tp=&arnumber=9796732~~]		6. Practically Adopting Human Activity Recognition [https://dl.acm.org/doi/10.1145/3570361.3613299]

	7. ~~RouteNet-Erlang~~: ~~A Graph Neural Network~~ for ~~Network Performance Evaluation~~ [https://~~ieeexplore~~.~~ieee~~.org/~~stamp~~/~~stamp~~.~~jsp?tp=&arnumber=9796944~~]		7. Cosmo: Contrastive Fusion Learning with Small Data for Multimodal Human Activity Recognition [https://dl.acm.org/doi/pdf/10.1145/3495243.3560519]

	8. ~~Deep Reinforcement~~ Learning~~-Based Control Framework~~ for ~~Radio Access Networks~~ [https://dl.~~acm~~.org/~~doi/pdf~~/~~10.1145/3495243.3558276?casa_token=IlUioU8GgjwAAAAA:ygqCMlQv28WBdO-z65UXYjJIoeVoyEiwVI00D5nw_cNW0N6aHZCEdBzqt5r2A8jGT7pYuU8xJMJGIrs~~]		8. Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee [https://ieeexplore.ieee.org/document/9796960]

	9. ~~NeuLens~~: ~~Spatial~~-~~based Dynamic Acceleration of Convolutional Neural Networks on Edge~~ [https://dl.~~acm~~.org/~~doi~~/~~pdf/10.1145/3495243~~.~~3560528~~?~~casa_token~~=~~mrLc2kitFkcAAAAA:lKf6MnXcwXxhr0SrODcIX7qP7DrthKc_yp7jZ-2MYoxmnutM4lHPuYXD5DrLrBjS38S15TbVwPSD-NA~~]		9. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796875]

	10. ~~FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning~~ [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=~~9796926~~]		10. Batch Adaptative Streaming for Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796853]

	11. ~~TrojanFlow~~: ~~A Neural Backdoor Attack to Deep Learning~~-~~based Network Traffic Classifiers~~ [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=~~9796878~~]		11. FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796984]

	12. ~~Mousika: Enable General In~~-~~Network Intelligence in Programmable Switches~~ by ~~Knowledge Distillation~~ [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=~~9796936~~]		12. AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796732]

	13. ~~Ekya~~: ~~Continuous Learning of Video Analytics Models on Edge Compute Servers~~ [https://~~www~~.~~usenix~~.org/~~conference~~/~~nsdi22/presentation/bhardwaj~~]		13. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796944]

	14. ~~Top~~-~~K Deep Video Analytics: A Probabilistic Approach~~ [https://dl.acm.org/doi/pdf/10.1145/~~3448016~~.~~3452786~~]		14. Deep Reinforcement Learning-Based Control Framework for Radio Access Networks [https://dl.acm.org/doi/pdf/10.1145/3495243.3558276?casa_token=IlUioU8GgjwAAAAA:ygqCMlQv28WBdO-z65UXYjJIoeVoyEiwVI00D5nw_cNW0N6aHZCEdBzqt5r2A8jGT7pYuU8xJMJGIrs]

	15. ~~Delay~~-~~Aware Microservice Coordination in Mobile~~ Edge ~~Computing: A Reinforcement Learning Approach~~ [https://~~ieeexplore~~.~~ieee~~.org/~~document~~/~~8924682~~]		15. NeuLens: Spatial-based Dynamic Acceleration of Convolutional Neural Networks on Edge [https://dl.acm.org/doi/pdf/10.1145/3495243.3560528?casa_token=mrLc2kitFkcAAAAA:lKf6MnXcwXxhr0SrODcIX7qP7DrthKc_yp7jZ-2MYoxmnutM4lHPuYXD5DrLrBjS38S15TbVwPSD-NA]

	16. ~~Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments Using A3C~~ Learning ~~and Residual Recurrent Neural Networks~~ [https://ieeexplore.ieee.org/~~document~~/~~9169832~~]		16. FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796926]

	17. ~~Multi~~-~~Task Federated Learning for Personalised Deep Neural Networks in Edge Computing~~ [https://ieeexplore.ieee.org/~~document~~/~~9492755~~]		17. TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796878]

	18. ~~Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual~~ Network ~~Function Placement and Routing~~ [https://ieeexplore.ieee.org/~~document~~/~~10029903~~]		18. Mousika: Enable General In-Network Intelligence in Programmable Switches by Knowledge Distillation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796936]

	19. ~~Pyramid~~: ~~Enabling Hierarchical Neural Networks with~~ Edge ~~Computing~~ [https://dl.~~acm~~.org/~~doi~~/~~10.1145~~/~~3485447.3511990~~]		19. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [https://www.usenix.org/conference/nsdi22/presentation/bhardwaj]

	20. ~~Index~~-~~aware reinforcement learning for adaptive video streaming at the wireless edge~~ [https://dl.acm.org/doi/10.1145/~~3492866~~.~~3549726~~]		20. Top-K Deep Video Analytics: A Probabilistic Approach [https://dl.acm.org/doi/pdf/10.1145/3448016.3452786]

	==Schedule==		==Schedule==

Wwu1: /* Final Presentations & Report */

2023-10-09T21:56:27Z

Final Presentations & Report

← Older revision		Revision as of 23:56, 9 October 2023
Line 112:		Line 112:
	*Final Report:		*Final Report:
	**Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/)		**Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/)
	**Due by 23:59pm 15th ~~August~~.		**Due by 23:59pm 15th February.

	[[Category:Courses]]		[[Category:Courses]]

Wwu1: /* Schedule */

2023-10-09T21:55:19Z

Schedule

← Older revision		Revision as of 23:55, 9 October 2023
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	'''!! xx.xx deadline for registration on Flexnow'''		'''!! xx.xx deadline for registration on Flexnow'''

	Final Presentation (xx.07)		Final Presentation (xx.01)
	*'''Paper Title''':		*'''Paper Title''':
	*'''Paper Title''':		*'''Paper Title''':

Wwu1: /* Schedule */

2023-10-09T21:51:01Z

Schedule

← Older revision		Revision as of 23:51, 9 October 2023
Line 73:		Line 73:

	==Schedule==		==Schedule==
	W1: Open Talk (13.4)		W1: Open Talk (26.10)

	W2: Select papers and create schedule		W2: Select papers and create schedule

Wwu1: Created page with "== Details == {{CourseDetails |credits=5 ECTS |module=M.Inf.1123 |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik...."

2023-09-19T12:30:26Z

Created page with "== Details == {{CourseDetails |credits=5 ECTS |module=M.Inf.1123 |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik...."

New page

== Details ==

{{CourseDetails
|credits=5 ECTS
|module=M.Inf.1123
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan, Dr. Tingting Yuan]; Wenfang Wu;
|ta=[NA]
|time=Thursday 14:00-16:00
|place=
}}

==Announcements==
Please contact me by email: wenfang.wu@cs.uni-goettingen.de if you have any questions.

==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 comprises the following components:
* Weekly Presentation + Weekly Paper Reading and Discussion 40%
* Final Presentation 30%
* Final Report 30%

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 on Wedesday). => the review form is available at [[http://user.informatik.uni-goettingen.de/~fu/Paper_Review_Form_ATCN_WS201112.doc Paper_Review_Form_ATCN_WS201112.doc]]
* During each weekly seminar, one participant is assigned for presenting the paper (each presentation lasts for ~20 minutes) and the list of pros and cons are discussed by all the participants.
* In the middle of the semester, everyone is requested to 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 lasts for ~20 minutes, plus ~10 minutes Q&A

==List of Papers ==
1. Cosmo: Contrastive Fusion Learning with Small Data for Multimodal Human Activity Recognition [https://dl.acm.org/doi/pdf/10.1145/3495243.3560519]

2. Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee [https://ieeexplore.ieee.org/document/9796960]

3. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796875]

4. Batch Adaptative Streaming for Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796853]

5. FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796984]

6. AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796732]

7. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796944]

8. Deep Reinforcement Learning-Based Control Framework for Radio Access Networks [https://dl.acm.org/doi/pdf/10.1145/3495243.3558276?casa_token=IlUioU8GgjwAAAAA:ygqCMlQv28WBdO-z65UXYjJIoeVoyEiwVI00D5nw_cNW0N6aHZCEdBzqt5r2A8jGT7pYuU8xJMJGIrs]

9. NeuLens: Spatial-based Dynamic Acceleration of Convolutional Neural Networks on Edge [https://dl.acm.org/doi/pdf/10.1145/3495243.3560528?casa_token=mrLc2kitFkcAAAAA:lKf6MnXcwXxhr0SrODcIX7qP7DrthKc_yp7jZ-2MYoxmnutM4lHPuYXD5DrLrBjS38S15TbVwPSD-NA]

10. FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796926]

11. TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796878]

12. Mousika: Enable General In-Network Intelligence in Programmable Switches by Knowledge Distillation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796936]

13. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [https://www.usenix.org/conference/nsdi22/presentation/bhardwaj]

14. Top-K Deep Video Analytics: A Probabilistic Approach [https://dl.acm.org/doi/pdf/10.1145/3448016.3452786]

15. Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach [https://ieeexplore.ieee.org/document/8924682]

16. Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments Using A3C Learning and Residual Recurrent Neural Networks [https://ieeexplore.ieee.org/document/9169832]

17. Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing [https://ieeexplore.ieee.org/document/9492755]

18. Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing [https://ieeexplore.ieee.org/document/10029903]

19. Pyramid: Enabling Hierarchical Neural Networks with Edge Computing [https://dl.acm.org/doi/10.1145/3485447.3511990]

20. Index-aware reinforcement learning for adaptive video streaming at the wireless edge [https://dl.acm.org/doi/10.1145/3492866.3549726]

==Schedule==
W1: Open Talk (13.4)

W2: Select papers and create schedule

W4: Paper ID:

W6: ...

W8: ...

W10: ...

W12: ...

W14: ..

'''!! xx.xx deadline for registration on Flexnow'''

Final Presentation (xx.07)
*'''Paper Title''':
*'''Paper Title''':

Report deadline

==Final Presentations & Report==

*Final Registration in '''FlexNow''': To Be Announced (TBA).

*Final Presentation:
**Each for ~20 minutes, plus ~10 minutes Q&A

*Final Presentation Slots:
**To Be Announced (TBA)

*Final Report:
**Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/)
**Due by 23:59pm 15th August.

[[Category:Courses]]