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Advanced Topics in AI for Computing and Networking (Summer 2024): Difference between revisions

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==List of Papers ==
==List of Papers ==
1. FedAdapter: Efficient Federated Learning for Modern NLP [https://arxiv.org/pdf/2205.10162.pdf]
1. ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction [https://dl.acm.org/doi/10.1145/3603269.3604819]


2. AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving [https://dl.acm.org/doi/10.1145/3570361.3592517]
2. AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving [https://dl.acm.org/doi/10.1145/3570361.3592517]
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6. Practically Adopting Human Activity Recognition [https://dl.acm.org/doi/10.1145/3570361.3613299]
6. Practically Adopting Human Activity Recognition [https://dl.acm.org/doi/10.1145/3570361.3613299]


7. Cosmo: Contrastive Fusion Learning with Small Data for Multimodal Human Activity Recognition [https://dl.acm.org/doi/pdf/10.1145/3495243.3560519]
7. AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization. [https://ieeexplore.ieee.org/document/10228858]


8. Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee [https://ieeexplore.ieee.org/document/9796960]  
8. Energy-Efficient 360-Degree Video Streaming on Multicore-Based Mobile Devices [https://ieeexplore.ieee.org/document/10228863]  


9. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796875]
9. Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning [https://ieeexplore.ieee.org/document/10228869]


10. Batch Adaptative Streaming for Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796853]
10. WiseCam: Wisely Tuning Wireless Pan-Tilt Cameras for Cost-Effective Moving Object Tracking [https://ieeexplore.ieee.org/document/10228926]


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]
11. Two-level Graph Caching for Expediting Distributed GNN Training [https://ieeexplore.ieee.org/document/10228911]


12. AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796732]
12. From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning [https://ieeexplore.ieee.org/document/10228909]


13. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796944]
13. HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN [https://ieeexplore.ieee.org/document/10229047]


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]
14. AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics [https://ieeexplore.ieee.org/document/10228933]


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]
15. DTrust: Toward Dynamic Trust Levels Assessment in Time-Varying Online Social Networks [https://ieeexplore.ieee.org/document/10228962]


16. FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796926]
16. Federated Few-Shot Learning for Mobile NLP [https://dl.acm.org/doi/10.1145/3570361.3613277]


17. TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796878]
17. A Joint Analysis of Input Resolution and Quantization Precision in Deep Learning [https://dl.acm.org/doi/10.1145/3570361.3615753]


18. Mousika: Enable General In-Network Intelligence in Programmable Switches by Knowledge Distillation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796936]
18. Automated Spray Control using Deep Learning and Image Processing [https://dl.acm.org/doi/10.1145/3570361.3615757]


19. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [https://www.usenix.org/conference/nsdi22/presentation/bhardwaj]
19. Cross-Modal Perception for Customer Service [https://dl.acm.org/doi/10.1145/3570361.3615751]


20. Top-K Deep Video Analytics: A Probabilistic Approach [https://dl.acm.org/doi/pdf/10.1145/3448016.3452786]
20. Cross-modal meta-learning for WiFi-based human activity recognition [https://dl.acm.org/doi/10.1145/3570361.3615754]


==Schedule==
==Schedule==
W1: Open Talk (02.11)
W1: Open Talk (11.04)


W2: Select papers and create schedule
W2: Select papers and create schedule
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'''!!  xx.xx deadline for registration on Flexnow'''
'''!!  xx.xx deadline for registration on Flexnow'''


Final Presentation (xx.01)
Final Presentation (xx.07)
*'''Paper Title''':
*'''Paper Title''':
*'''Paper Title''':
*'''Paper Title''':
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*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 February.
**Due by 23:59pm 15th August.


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

Latest revision as of 15:30, 11 April 2024

Details

Workload/ECTS Credits: 5 ECTS
Module: M.Inf.1123
Lecturer: Prof. Xiaoming Fu; Dr. Tingting Yuan; Wenfang Wu;
Teaching assistant: [NA]
Time: Thursday 14:00-16:00


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 40%
  • 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.

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 [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. ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction [1]

2. AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving [2]

3. mmFER: Millimetre-wave Radar based Facial Expression Recognition for Multimedia IoT Applications [3]

4. NeRF2: Neural Radio-Frequency Radiance Fields [4]

5. Exploiting Contactless Side Channels in Wireless Charging Power Banks for User Privacy Inference via Few-shot Learning [5]

6. Practically Adopting Human Activity Recognition [6]

7. AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization. [7]

8. Energy-Efficient 360-Degree Video Streaming on Multicore-Based Mobile Devices [8]

9. Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning [9]

10. WiseCam: Wisely Tuning Wireless Pan-Tilt Cameras for Cost-Effective Moving Object Tracking [10]

11. Two-level Graph Caching for Expediting Distributed GNN Training [11]

12. From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning [12]

13. HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN [13]

14. AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics [14]

15. DTrust: Toward Dynamic Trust Levels Assessment in Time-Varying Online Social Networks [15]

16. Federated Few-Shot Learning for Mobile NLP [16]

17. A Joint Analysis of Input Resolution and Quantization Precision in Deep Learning [17]

18. Automated Spray Control using Deep Learning and Image Processing [18]

19. Cross-Modal Perception for Customer Service [19]

20. Cross-modal meta-learning for WiFi-based human activity recognition [20]

Schedule

W1: Open Talk (11.04)

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 ~20 minutes Q&A


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


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