Just Got a Reviewer Certificate from Data Mining and Knowledge Discovery (WIREs)

Thanks to the Editors and Board of WIREs for supporting me. As an independent reviewer, I will be fair to everyone and never give in to the “scientific mafia” and “citation cartels”.

Data Mining and Knowledge Discovery (WIREs) (Impact Factor: 2.541)

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Roads from Above: Augmenting Civil Engineering & Geospatial Workflows with Machine Learning

Road from Above is partly based on my Australia Postgraduate Intern Projects (Computer Vision and Machine Learning for Feature Extraction) within Aureon Group in Melbourne.

Aurecon’s experts, across Cape Town, Melbourne and Auckland offices, have been teamed up to develop and test approaches that capture and validate new and existing measurements of the metropolitan road network. Due to the confidentiality, we reduced the resolutions of the aerial images and only opened limited results on the public domain at https://roadsfromabove.netlify.com/. Thanks to Greg More, the design of this website got the best feedback from the workshop (Visualization for AI Explainability) of the IEEE VIS 2018 conference in Berlin, Germany

Visualization for AI Explainability: Projections and Dimensionality Reduction. The goal of this workshop is to initiate a call for “explainable” that explain how AI techniques work using visualizations. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.

Road from Above

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MacOS X: Installing TensorFlow from Sources [TF Binary Attached]

When I am using TensorFlow on my MacBook Air, I always get annoyed by the warnings comes from nowhere, so I followed the documentation below to build TensorFlow sources into a TensorFlow binary and installed it successfully.  In theory, this will make the TF running faster on my machine.

Here is the document:

If you are a Mac user, you could download the TF binary from here:

Then, you could use conda to initialize an environment with Python=3.6 and install TF by typing:

sudo pip install tensorflow-1.8.0-py2-none-any.whl

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A Taste of TensorFlow on My Android Phone (III)

This is the 3rd post about my implementation of TensorFlow Apps on my Android Phone.

This time I fixed one small bug in the app of  “TF Detect” so the object tracking function could work. The project is compiled by cmake with NDK Archives in this version. You can download the new “apk files here:  Tensorflow_Demo_Debug.apk.

“Once the app is installed it can be started via the “TF Classify”, “TF Detect”, “TF Stylize”, and “TF Speech” icons, which have the orange TensorFlow logo as their icon. 

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QR Code Detector with Webcam (Python / OpenCV / Pyzbar)

This project is forked from zbar library, I added some modifications, so webcam can be used as an image reader to detect QR and Barcodes.

code128_1

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Sharing My Data Science Notebook (Python & TensorFlow)

Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (Numpy, SciPy, Matplotlib, TensorFlow) it becomes a powerful environment for scientific computing and data analysis.

I start sharing my notebooks on learning Python and TensorFlow, here is my GitHub repository:  Data_Science_Python.

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