Advanced topics in mobile and social computing (AToMSC) (Winter 2021/2022): Difference between revisions

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==List of Papers ==
==List of Papers ==
1. Lightweight and Robust Representation of Economic Scales from Satellite Imagery [https://ojs.aaai.org/index.php/AAAI/article/download/5379/5235]
1. Lightweight and Robust Representation of Economic Scales from Satellite Imagery [https://ojs.aaai.org/index.php/AAAI/article/download/5379/5235]
2. AutoML for Video Analytics with Edge Computing [https://ieeexplore.ieee.org/abstract/document/9488704]
2. AutoML for Video Analytics with Edge Computing [https://ieeexplore.ieee.org/abstract/document/9488704]
3. Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision [https://dl.acm.org/doi/10.1145/3300061.3345448]
3. Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision [https://dl.acm.org/doi/10.1145/3300061.3345448]
4. NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices [https://dl.acm.org/doi/10.1145/3458864.3467882]
4. NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices [https://dl.acm.org/doi/10.1145/3458864.3467882]
5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [https://dl.acm.org/doi/10.1145/3387514.3405874]
5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [https://dl.acm.org/doi/10.1145/3387514.3405874]
6. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf]
6. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf]
7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [https://dl.acm.org/doi/pdf/10.1145/3447548.3467070]
7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [https://dl.acm.org/doi/pdf/10.1145/3447548.3467070]
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Revision as of 15:02, 11 September 2021

Details

Workload/ECTS Credits: 5 ECTS
Module: M.Inf.1222.Mp: Specialization Computer Networks Module Description -or- 3.10: Advanced Topics in Internet Research (II)(ITIS); M.Inf.1223 (new Regulations)
Lecturer: Prof. Xiaoming Fu; Dr. Tingting Yuan
Teaching assistant: [NA]
Time: Thu. 14:00-16:00
Place: IfI 0.101
UniVZ [1]


Announcements

Please contact me by email: tingting.yuan@cs.uni-goettingen.de if you have any questions. Choose your topic and email Tingting.

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 50%
  • Final Presentation 25%
  • Final Report 25%

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 Wednesday). => the review form is available at [Paper_Review_Form_ATCN_WS201112.doc]
  • During each weekly seminar, two participants are 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 pick a topic and 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. Lightweight and Robust Representation of Economic Scales from Satellite Imagery [2]

2. AutoML for Video Analytics with Edge Computing [3]

3. Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision [4]

4. NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices [5]

5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [6]

6. Deep Interest Network for Click-Through Rate Prediction [7]

7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [8]

8.

Schedule

W1: 28 Oct. Open talk

W2:

W3:

W4:

W5:

W6:

W7:

W8:

W9:

W10:

Final Presentation

  • Paper Title:
  • Paper Title:
  • Paper Title:

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)


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