Advanced Topics in AI for Networking (Summer 2023)
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 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 [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 [1]
2. Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee [2]
3. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [3]
4. Batch Adaptative Streaming for Video Analytics [4]
5. FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics [5]
6. AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning [6]
7. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [7]
8. Deep Reinforcement Learning-Based Control Framework for Radio Access Networks [8]
9. NeuLens: Spatial-based Dynamic Acceleration of Convolutional Neural Networks on Edge [9]
10. FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning [10]
11. TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers [11]
12. Mousika: Enable General In-Network Intelligence in Programmable Switches by Knowledge Distillation [12]
13. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [13]
14. Top-K Deep Video Analytics: A Probabilistic Approach [14]
15. Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach [15]
Schedule
W1: Open Talk (13.4)
W2: Assignment papers
W4: Paper ID:
W...
W10: Rehearsal:
!! 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.