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#FridayFunFact: VR & AR are fast becoming the latest digital trend (and next marketing platform target). This is an interesting projection of what the market could be like for VR/AR apllications.

THIS IS THE VIRTUAL REALITY I WAS PROMISED

Presenter Erika Ishii presents a wireless solution for Virtual Reality experiences, with a high powered laptop strapped to the back with an Htc Vive pro (though it isn’t clear how long the batteries will last):

THIS IS THE VIRTUAL REALITY I WAS PROMISED. @TeaganMorrison built us a wireless VR rig! @Alienware 15 laptop, @htcvive pro, army frame backpack. 

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Death Mask

Programming project from Or Fleisher and Anastasis Germanidis combines Augmented Reality and Machine Learning, using a Neural Net trained for age prediction through mobile camera device:

‘Death-Mask’ predicts how long people have to live and overlays that in the form of a “clock” above they’re heads in augmented reality.  The project uses a machine learning model titled AgeNet for the prediction process. Once predicted it uses the average life expectancy in that location to try and estimate how long one has left.

The aesthetic inspiration derives from the concept of death masks. These are sculptures meant to symbolize the death of a person by casting his face into a sculpture (i.e mask).

The experiment uses ARKit to render the visual content in augmented reality on an iPad and CoreML to run the machine learning model in real-time. The project is by no means an accurate representation of one’s life expectancy and is more oriented towards the examination of public information in augmented reality in the age of deep learning.

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Piano player wears an eye tracker so you can see exactly where their eyes move to as they play. Amazing video.

AR Spatial Audio Recorder

Another smart AR experiment from Zach Lieberman proving Augmented Reality is an interesting creative platform: this one visualizes audio as it is recording and plays back as you follow the path both forwards and backwards:

A post shared by zach lieberman (@zach.lieberman) on Sep 6, 2017 at 5:55am PDT

Quick test recording audio in space and playing back – (video has audio !) #openframeworks 

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Introducing SAMOA, an open source platform for mining big data streams.

https://github.com/yahoo/samoa

Machine learning and data mining are well established techniques in the world of IT and especially among web companies and startups. Spam detection, personalization and recommendations are just a few of the applications made possible by mining the huge quantity of data available nowadays. However, “big data” is not only about Volume, but also about Velocity (and Variety, 3V of big data).

The usual pipeline for modeling data (what “data scientists” do) involves taking a sample from production data, cleaning and preprocessing it to make it usable, training a model for the task at hand and finally deploying it to production. The final output of this process is a pipeline that needs to run periodically (and be maintained) in order to keep the model up to date. Hadoop and its ecosystem (e.g., Mahout) have proven to be an extremely successful platform to support this process at web scale.

However, no solution is perfect and big data is “data whose characteristics forces us to look beyond the traditional methods that are prevalent at the time”. The current challenge is to move towards analyzing data as soon as it arrives into the system, nearly in real-time.

For example, models for mail spam detection get outdated with time and need to be retrained with new data. New data (i.e., spam reports) comes in continuously and the model starts being outdated the moment it is deployed: all the new data is sitting without creating any value until the next model update. On the contrary, incorporating new data as soon as it arrives is what the “Velocity” in big data is about. In this case, Hadoop is not the ideal tool to cope with streams of fast changing data.

Distributed stream processing engines are emerging as the platform of choice to handle this use case. Examples of these platforms are Storm, S4, and recently Samza. These platforms join the scalability of distributed processing with the fast response of stream processing. Yahoo has already adopted Storm as a key technology for low-latency big data processing.

Alas, currently there is no common solution for mining big data streams, that is, for doing machine learning on streams on a distributed environment.

Enter SAMOA

SAMOA (Scalable Advanced Massive Online Analysis) is a framework for mining big data streams. As most of the big data ecosystem, it is written in Java. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm and S4. SAMOA includes distributed algorithms for the most common machine learning tasks such as classification and clustering. For a simple analogy, you can think of SAMOA as Mahout for streaming.

SAMOA is both a platform and a library. As a platform, it allows the algorithm developer to abstract from the underlying execution engine, and therefore reuse their code to run on different engines. It also allows to easily write plug-in modules to port SAMOA to different execution engines.

As a library, SAMOA contains state-of-the-art implementations of algorithms for distributed machine learning on streams. The first alpha release allows classification and clustering.

For classification, we implemented a Vertical Hoeffding Tree (VHT), a distributed streaming version of decision trees tailored for sparse data (e.g., text). For clustering, we included a distributed algorithm based on CluStream. The library also includes meta-algorithms such as bagging.

HOW DOES IT WORK?

An algorithm in SAMOA is represented by a series of nodes communicating via messages along streams that connect pairs of nodes (a graph). Borrowing the terminology from Storm, this is called a Topology. Each node in the Topology is a Processor that sends messages to a Stream. The user code that implements the algorithm resides inside a Processor. Figure 3 shows an example of a Processor joining two stream from two source Processors. Here is a code snippet to build such a topology in SAMOA.

TopologyBuilder builder; Processor sourceOne = new SourceProcessor(); builder.addProcessor(sourceOne); Stream streamOne = builder.createStream(sourceOne); Processor sourceTwo = new SourceProcessor(); builder.addProcessor(sourceTwo); Stream streamTwo = builder.createStream(sourceTwo); Processor join = new JoinProcessor(); builder.addProcessor(join).connectInputShuffle(streamOne).connectInputKey(streamTwo);

SWEET! HOW DO I GET STARTED?

1. Download SAMOA

git clone git@github.com:yahoo/samoa.git cd samoa mvn -Pstorm package

2. Download the Forest CoverType dataset.

wget "http://downloads.sourceforge.net/project/moa-datastream/Datasets/Classification/covtypeNorm.arff.zip" unzip covtypeNorm.arff.zip

Forest CoverType contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. It contains 581,012 instances and 54 attributes, and it has been used in several papers on data stream classification.

3. Download a simple logging library.

wget "http://repo1.maven.org/maven2/org/slf4j/slf4j-simple/1.7.2/slf4j-simple-1.7.2.jar"

4. Run an Example. Classifying the CoverType dataset with the VerticalHoeffdingTree in local mode.

java -cp slf4j-simple-1.7.2.jar:target/SAMOA-Storm-0.0.1.jar com.yahoo.labs.samoa.DoTask "PrequentialEvaluation -l classifiers.trees.VerticalHoeffdingTree -s (ArffFileStream -f covtypeNorm.arff) -f 100000"

The output will be a sequence of the evaluation metrics for accuracy, taken every 100,000 instances.

To run the example on Storm, please refer to the instructions on the wiki.

I WANT TO KNOW MORE!

For more information about SAMOA, see the README and the wiki on github, or post a question on the mailing list.

SAMOA is licensed under an Apache Software License v2.0. You are welcome to contribute to the project! SAMOA accepts contributions under an Apache style contributor license agreement.

Good luck! We hope you find SAMOA useful. We will continue developing the framework by adding new algorithms and platforms.

Gianmarco De Francisci Morales (gdfm@yahoo-inc.com) and Albert Bifet (abifet@yahoo.com) @ Yahoo Labs Barcelona

Plastic Girls

Short film by Nils Clauss explores the spaces that use female-form robotic mannequins around South Korea to attract business:

With slow graceful movements and digitally generated messages, Plastic Girls make a conspicuous contribution to the sexualisation of public space in front of their owners premises.

PLASTIC GIRLS is the last film of a Korea related trilogy following BIKINI WORDS and LAST LETTERS:

vimeo.com/151000182 vimeo.com/195284427

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Hey, he is running away….????? #love #instagood #photooftheday #beautiful #fashion #happy #tbt #cute #followme #like4like #selfie #summer #fun #smile #style #amazing #sun #bestoftheday #pretty #cool #funny #ootd #potd #holiday #lifestyle #일상 #sweet #happiness #awesome #travel

Just a reminder for the community!

Run a Bitcoin Core 0.14.1 FullNode and support SegWit!

Support my electricity bill if you like: Bitcoin: 1FSZytTNZNqs69mSh5grU73DmrPVtBkz7m