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Distributed Systems Seminar -- Spring 2017

last modified Jun 02, 2017 05:49 PM

Wednseday 14:15-16:00 Ülikooli 17-220

Organization and requirements

 

15.02.2017: First meeting, topics introduction

22.02.2017: No meeting 

1.03.2017: Topic choice, consultation on how to proceed

8.03.2017: Meeting for Research Plan and initial Literature Review presentations 

15.03.2017: (No meeting, Individual work, Meetings with supervisors)

22.03.2017: (No meeting, Individual work, Meetings with supervisors) 

29.03.2017: No meeting, Do the tests and experiments, Start writing the final report, provide the supervisors with an access to the code as well as to the report draft

05.04.2017: Meeting: Intermediate presentations / Technical Demo, First draft of the final report ready for Peer-review

12.04.2017 (No meeting, writing the report) 

19.04.2017 (No meeting, writing the report)

26.04.2017: Last day to share the reports between the reviewers

03.05.2017: Last day to the reviewers to give their feedback

10.05.2017: The day of the final presentation and handing out the final report.

 

 

  

  • Deliverables:
    • Written results report (in LaTex IEEE technical report format)
    • Research plan (has to be provided in one week after topic selection, how to submit the report is declared by supervisor)
    • Weekly report to supervisor (how to submit the report is declared by supervisor)
    • Research results: The code in our Git repository
    • Research results: The collected related work publication list on our web page
    • Research results: The technical report LaTeX sources in our Git
    • Intermediate presentations / Technical Demo (in the middle of the term)
    • Peer-review of the report drafts between the seminar members
    • Final peer-review
    • Final presentation / technical demo

 

You can see the final reports online at: Previous: Distributed Systems Seminar (Fall 2016)

 

Seminar supervisors: Prof. Eero Vainikko, Dr Amnir Hadachi, Dr Benson Muite, Artjom Lind, Oleg Batrašev

Topic Areas (Concerned persons):

  1. Parallel Scientific Applications and Concurrent Computing (Eero Vainikko, Oleg Batrashev, Benson Muite)
  2. Network Applications and Protocols (Artjom Lind)
  3. Applied Computer Vision (Artjom Lind)
  4. Parallel Machine learning algorithms (Artjom Lind, Oleg Batrashev, Amnir Hadachi, Benson Muite)
  5. Geographic information systems (GIS) and Intelligent Transportation Systems (ITS) (Amnir Hadachi)
  6. Topics of Dmitri Danilov and Toivo Vajakas are to be confirmed first


LaTeX Resources

 

LaTeX Site

LaTeX Wikibook

 

Literature Search Resources

 


List of possible topics

Possible themes with some suggested materials to start with:

 

1. Parallel Scientific Applications and Concurrent Computing (eero at ut.ee)

 

  • firedrake -- library for numerical solution of PDEs, an overview and testing
  • deal.II — an open source finite element library

  • Best practices for parallel python programming using numpy and mpi4py
  • Computing for multicore systems (olegus at ut.ee)
    • Charm++

 

2. Parallel Scientific Applications and Concurrent Computing (benson punkt muite at ut punkt ee)

 

  • Scripting and high level language interfaces (eg Java and Python) to the Message Passing Interface and/or to OpenCL
  • Evaluation of Simscale  for simulation in a web browser
  • Information security on distributed systems
  • Build a data visualization solution using pbdR
  • Machine learning for in-situ visualization of solutions to differential equations
  • Evaluate performance of parallel file systems
  • Read the paper on distmesh, and re-implement the algorithms in another language such as Fortran or Python.
  • Experiment with RSVDPACK and compare performance for image compression or another application of your choice. A useful starting point is here
  • Efficient time integration schemes, geometric integration schemes
  • Examine open hardware for parallel computing, such as Nyuzi, Risc-V or OpenSPARC
  • Good parallel software development practices: Examine development of the visualization software ParaView and VisIt
  • Testing and improvement of FortranCL 
  • Financially sustainable open source parallel software development models and practices
  • Testing and improvement of Seedme for distributed computing
  • Testing MPPA accelerators such as Kalray and Parallela
  • Testing FPGAs from Altera and Xilinix
  • Parallel Graph Algorithms, for example GraphBLAS


3. Network Applications and Protocols (Artjom Lind)

Covering the topics related to distributed computing in peer-to-peer networks. Here I will focus you on own lab framework, however you can propose different topic (some existing framework with similar features or your own design).

  • Individual topic -> Contact me!

 

4. Applied Computer Vision (CV) (Artjom Lind)

Mostly the topics related to the application of latest results in CV. In this area we mostly use OpenCV library, which is recommended but not obligatory. The several topics we can focus on:

  • Structure from motion
  • Object detection/classification
  • Object tracking
  • Optical Character Recognition (OCR)
  • Augmented Reality

 More information ...


5. Parallel Machine learning algorithms (Artjom Lind, Oleg Batrashev, Amnir Hadachi, Benson Muite)

 

  • Machine digitization and translation for Estonian and non-latin scripts such as Arabic / Cyrillic / Chinese / Farsi / Hebrew / Hindi / Japanese / Korean
  • Character recognition algorithms
  • Evaluating Petuum for parallel machine learning

 

6. Exploratory search (Dimitri Danilov)

  • Collaborative search
  • Search Patterns
  • New Development in Search Engines
  • The Vision of Ted Nelson (the inventor of the internet?)
  • Xanadu (and undanax)
  • Graph Based Information Storage
  • New Search Interfaces in Mozilla (practical and theoretical topics available)
  • Machine learning based topic modeling in text documents (using the program Mallet).
  • More topics on demand

 

7. Modeling and analyzing semantic trajectories (Amnir Hadachi)

8. Mobility data modelling (Toivo Vajakas)

Fast data structure for trajectory data (further development of existing code)
o    Currently existing code for direct read of 1 individual trajectory
o    Add support for batch full-scan and batch subset.
o    Add support for indexing by time and space – to avoid (with high probability) the analysis of trajectories that do not intersect with time-space volume of given query.
Exploratory data analysis on mobile positioning data, using results of Jilles Vreeken group (JV was a speaker on ESSCASS summer school)
o    Separation of time of each person as „currently in routine“ and „non-typical behavior (tourist mode)“.
o    Describe the data, ie the behavior of radio network combined with behavior of people -- pattern mining based on information theory.
Clustering
o    Clustering of (relatively small) directed graphs, each edge and vertex has also attributes. (graphs are HMM of humans in timespace „states“ like work and home, after removing absolute location and travel direction info (but keeping relative travel distance info)
o    Clurstering of vertices in directed graphs (states of HMM in previous entry). Graphs are HMM of humans in timespace „states“ like work and home, after removing absolute location and travel direction info (but keeping relative travel distance info). Clustering of states gives something like „here many people spend there night“, „many people come here for work“, ...
Combination of traffic simuluation package and data from mobile positioning data, to get local traffic density estimates.

8A. GPS and INS, Lidar and panorama data coregistration (Toivo Vajakas)

Coregistration refers to any method for realigning images. This particular set of problems stems from Streetview-like application where GPS receiver, inertial sensors, 360degree camera and LIDAR sensor are fixed to top of vehicle. Vehicle drives along streets to collect images, lidar point cloud, location and orientation data. Different data sources must be combined to get 3D landscape model.

* combine GPS and inertial sensor readings to get maximum accuracy estimate of location and orientation at any time moment

* align images for stitching and/or to estimate orientation errors

* align images with lidar data

The student has good chances to become a co-author of a publication later.

 

9. Mobility data modeling and representation (Amnir Hadachi)

 

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