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]

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.