Advanced Topics in AI for Computing and Networking (Summer 2024): Difference between revisions
(3 intermediate revisions by the same user not shown) | |||
Line 32: | Line 32: | ||
==List of Papers == | ==List of Papers == | ||
1. | 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] | ||
Line 44: | Line 44: | ||
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. | 7. AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization. [https://ieeexplore.ieee.org/document/10228858] | ||
8. | 8. Energy-Efficient 360-Degree Video Streaming on Multicore-Based Mobile Devices [https://ieeexplore.ieee.org/document/10228863] | ||
9. | 9. Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning [https://ieeexplore.ieee.org/document/10228869] | ||
10. | 10. WiseCam: Wisely Tuning Wireless Pan-Tilt Cameras for Cost-Effective Moving Object Tracking [https://ieeexplore.ieee.org/document/10228926] | ||
11. | 11. Two-level Graph Caching for Expediting Distributed GNN Training [https://ieeexplore.ieee.org/document/10228911] | ||
12. | 12. From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning [https://ieeexplore.ieee.org/document/10228909] | ||
13. | 13. HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN [https://ieeexplore.ieee.org/document/10229047] | ||
14. | 14. AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics [https://ieeexplore.ieee.org/document/10228933] | ||
15. | 15. DTrust: Toward Dynamic Trust Levels Assessment in Time-Varying Online Social Networks [https://ieeexplore.ieee.org/document/10228962] | ||
16. | 16. Federated Few-Shot Learning for Mobile NLP [https://dl.acm.org/doi/10.1145/3570361.3613277] | ||
17. | 17. A Joint Analysis of Input Resolution and Quantization Precision in Deep Learning [https://dl.acm.org/doi/10.1145/3570361.3615753] | ||
18. | 18. Automated Spray Control using Deep Learning and Image Processing [https://dl.acm.org/doi/10.1145/3570361.3615757] | ||
19. | 19. Cross-Modal Perception for Customer Service [https://dl.acm.org/doi/10.1145/3570361.3615751] | ||
20. | 20. Cross-modal meta-learning for WiFi-based human activity recognition [https://dl.acm.org/doi/10.1145/3570361.3615754] | ||
==Schedule== | ==Schedule== |
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)
- 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.