HP MediaSmart LX195 Windows Home Server

When you first announced the Microsoft will build a version of the operating system Windows Home Server (for individuals), many vendors and users are pessimistic about the step. Now the computer to Windows Home Server been a serious world computer manufacturer Hewlett Packard (HP). With the launch of series of Computer LX195 HP MediaSmart, HP will be a vendor authorized to make the first series of computer servers to consumer WHS.

The following is a specification of the computer HP MediaSmart LX195 plan that will be sold with the retail price range is $ 399.99:

    1. Processor: Intel Atom 230@1.6GHz
    2. Memory: 1GB DDR2 RAM
    3. Networking: Gigabit Ethernet
    4. Operating System: Windows Home Server (based on Windows Server 2003)
    5. Inputs: 4 USB2.0 ports
    6. Power supply: External
    7. Dimensions: 8 x 4 x 8 (LxWxH)
    8. Warranty: One-year limited warranty

From the physical design, the HP MediaSmart LX195 looks smaller than most home computer server. This is because Hewlett Packard put the power supply series from the HP MediaSmart LX195 this outside. But on the other hand, there are other things to note from this HP MediaSmart LX195, which only has a single drive, in other words we can only add additional media storage via the USB connector.

that many sought : Asus P6X58 Premium, Motherbard First USB 3.0 Support

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Tone An experimental Chrome extension for instant sharing over audio

Posted by Alex Kauffmann, Interaction Researcher, and Boris Smus, Software Engineer

Sometimes in the course of exploring new ideas, well stumble upon a technology application that gets us excited. Tone is a perfect example: its a Chrome extension that broadcasts the URL of the current tab to any machine within earshot that also has the extension installed. Tone is an experiment that we’ve enjoyed and found useful, and we think you may as well.

As digital devices have multiplied, so has the complexity of coordinating them and moving stuff between them. Tone grew out of the idea that while digital communication methods like email and chat have made it infinitely easier, cheaper, and faster to share things with people across the globe, theyve actually made it more complicated to share things with the people standing right next to you. Tone aims to make sharing digital things with nearby people as easy as talking to them.

The first version was built in an afternoon for fun (which resulted in numerous rickrolls), but we increasingly found ourselves using it to share documents with everyone in a meeting quickly, to exchange design files back and forth while collaborating on UI design, and to contribute relevant links without interrupting conversations.

Tone provides an easy-to-understand broadcast mechanism that behaves like the human voice—it doesnt pass through walls like radio or require pairing or addressing. The initial prototype used an efficient audio transmission scheme that sounded terrible, so we played it beyond the range of human hearing. However, because many laptop microphones and nearly all video conferencing systems are optimized for voice, it improved reliability considerably to also include a minimal DTMF-based audible codec. The combination is reliable for short distances in the majority of audio environments even at low volumes, and it even works over Hangouts.

Because its audio based, Tone behaves like speech in interesting ways. The orientation of laptops relative to each other, the acoustic characteristics of the space, the particular speaker volume and mic sensitivity, and even where youre standing will all affect Tones reliability. Not every nearby machine will always receive every broadcast, just like not everyone will always hear every word someone says. But resending is painless and debugging generally just requires raising the volume. Many groups at Google have found that the tradeoffs between ease and reliability worthwhile—it is our hope that small teams, students in classrooms, and families with multiple computers will too.

To get started, first install the Tone extension for Chrome. Then simply open a tab with the URL you want to share, make sure your volume is on, and press the Tone button. Your machine will then emit a short sequence of beeps. Nearby machines receive a clickable notification that will open the same tab. Getting everyone on the same page has never been so easy!

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Software Engineering vs Computer Science

At The University of Auckland, you can get a Bachelor of Science (BSc), with a major in Computer Science (COMPSCI), and a Bachelor of Engineering (BE), with a specialisation in Software Engineering (SOFTENG). Naturally a common question we get is, whats the difference?

There are many opinions and explanations as to what is the difference between Computer Science and Software Engineering, including "there is no difference" and "one is a subset of the other". My answer has been developed over years of being asked this question at University Open days, Career symposiums, Industry events, and such like.

My answer has two parts — one philosophical and the other practical. The philosophical part is that the difference is one of theory versus practice. The goal of Computer Science research is to to develop a theory of "computation", to understand what computation means, what its limits are, and how it might be applied. Software Engineering is about building effective software systems efficiently.

Clearly there are close connections between the two. Software is the embodiment of computation. It is how most people experience computation, even if they dont think of it that way (any more than they would think of turning on an electric light as physics). While some aspects of computation do not require any software, or even a physical computer (e.g. a Turing machine), much of our understanding of computation is demonstrated through developing software.

But there are aspects of Computer Science that are at best a curiosity to those building software systems (Turing machines again) and there are topics that are clearly important to software engineering that have little to do with any theory of computation (requirements elicitation, for example).

The practical part of my answer is that the B.Sc (COMPSCI) is a 3-year general degree whereas the BE (SOFTENG) is a 4-year professional degree. The B.Sc. has relatively few restrictions as to what courses students must take (mostly science, and mostly in the Major), whereas the BE is very prescribed, with only a little opportunity for choice. Both the BSc and the BE meet the requirements of a university degree, as dictated by the Tertiary Education Commission (TEC), but in addition the BE is an internationally accredited engineering qualification.

So, if you want a professional qualification, you probably should consider the BE. If you would prefer a reasonable amount of flexibility and choice, the B.Sc would probably be the better option. If you are interested in different aspects of computation, then major in Computer Science. If you really want to be able to build significant software systems, then specialise in Software Engineering.

from The Universal Machine http://universal-machine.blogspot.com/

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Controlling music with your mind

Last year I bought an EEG headset (the Mindwave Mobile) to play with my Raspberry Pi and then ended up putting it down for a while. Luckily, this semester I started doing some more machine learning and decided to try it back out. I thought it might be possible to have it recognize when you dislike music and then switch the song on Pandora for you. This would be great for when you are working on something or moving around away from your computer.

So using the EEG headset, a Raspberry Pi, and a bluetooth module, I set to work on recording some data. I listened to a couple songs I liked and then a couple songs I didnt like with labeled data. The Mindwave gives you the delta, theta, high alpha, low alpha, high beta, low beta, high gamma, and mid gamma brainwaves. It also approximates your attention level and meditation level using the FFT (Fast Fourier Transform) and gives you a skin contact signal level (with 0 being the best and 200 being the worst).

Since I know very little about brainwaves, I cant make an educated decision on what changes to look at to detect this; thats where machine learning comes in. I can use Bayesian Estimation to construct two multivariate Gaussian models, one that represents good music and one that represents bad music.



----TECHNICAL DETAILS BELOW----
We construct the model using the parameters below (where ? is the mean of the data and ? is the standard deviation of the data):

Now that we have the model above for both good music and bad music, we can use a decision boundary to detect what kind of music you are listening to at each data point.

where:

The boundary will be some sort of quadratic (hyper ellipsoid, hyper parabola, etc) and it might look something like below (though ours is a 10 dimensional function):

 

----END TECHNICAL DETAILS----

The result is an algorithm that is accurate about 70% of the time, which isnt reliable enough. However, since we have temporal data, we can utilize that information, and we wait until we get 4 bad music estimations in a row, then we skip the song.

Ive created a short video (dont worry, I skip around so you dont have to watch me listen to music forever) as a proof of concept. Then end result is a way to control what song is playing with only your brainwaves.

This is an extremely experimental system and only works because there are only two classes to choose and it is not even close to good accuracy. I just thought it was cool. Im curious to see if training using my brainwaves will work for other people as well but I havent tested it yet. There is a lot still to refine but its cool to have a proof of concept. You cant buy one of these off the shelf and expect it to change your life. Its uncomfortable and not as accurate as an expensive EEG but it is fun to play with. Now I need to attach one to Google Glass.

NOTE: This was done as a toybox example as fun. You probably arent going to see EEG controlled headphones in the next couple years. Eventually maybe, but not due to work like this.

How to get it working

HERE IS THE SOURCE CODE

I use pianobar to stream Pandora and have a modified version of the control-pianobar.sh control scripts I have put in the github repository below.
I have put the code on Github here but first you need to make sure you have python >= 3.0, bluez, pybluez, and pianobar installed to use it. You will also need to change the home directory information, copy the control-pianobar.sh script to /usr/bin, change the MAC address (mindwaveMobileAddress) in  mindwavemobile/MindwaveMobileRawReader.py to the MAC address of your mindwave mobile device (which I got the python code from here), and run sudo python setup.py install.

I start pianobar with control-pianobar.sh p then I start the EEG program with python control_music.py, it will tell you what it thinks the song is in real time and then will skip it if it detects 4 bad signals in a row. It will also tell you whether the headset is on well enough with a low signal warning.

Thanks to Dr. Aaron Bobick (whose pictures and equations I used), robintibor (whose python code I used), and Daniel Castro (who showed me his code for Bayesian Estimation in python since my implementation was in Matlab).

Consider donating to further my tinkering.

Places you can find me

• Facebook
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Cool HD Holiday and Blue Winter Nature Wallpaper Download

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Automatically making sense of data

Posted by Kevin Murphy, Research Scientist and David Harper, Head of University Relations, EMEA

While the availability and size of data sets across a wide range of sources, from medical to scientific to commercial, continues to grow, there are relatively few people trained in the statistical and machine learning methods required to test hypotheses, make predictions, and otherwise create interpretable knowledge from this data. But what if one could automatically discover human-interpretable trends in data in an unsupervised way, and then summarize these trends in textual and/or visual form?

To help make progress in this area, Professor Zoubin Ghahramani and his group at the University of Cambridge received a Google Focused Research Award in support of The Automatic Statistician project, which aims to build an "artificial intelligence for data science".

So far, the project has mostly been focussing on finding trends in time series data. For example, suppose we measure the levels of solar irradiance over time, as shown in this plot:This time series clearly exhibits several sources of variation: it is approximately periodic (with a period of about 11 years, known as the Schwabe cycle), but with notably low levels of activity in the late 1600s. It would be useful to automatically discover these kinds of regularities (as well as irregularities), to help further basic scientific understanding, as well as to help make more accurate forecasts in the future.

We can model such data using non-parametric statistical models based on Gaussian processes. Such methods require the specification of a kernel function which characterizes the nature of the underlying function that can accurately model the data (e.g., is it periodic? is it smooth? is it monotonic?). While the parameters of this kernel function are estimated from data, the form of the kernel itself is typically specified by hand, and relies on the knowledge and experience of a trained data scientist.

Prof Ghahramanis group has developed an algorithm that can automatically discover a good kernel, by searching through an open-ended space of sums and products of kernels as well as other compositional operations. After model selection and fitting, the Automatic Statistician translates each kernel into a text description describing the main trends in the data in an easy-to-understand form.

The compositional structure of the space of statistical models neatly maps onto compositionally constructed sentences allowing for the automatic description of the statistical models produced by any kernel. For example, in a product of kernels, one kernel can be mapped to a standard noun phrase (e.g. ‘a periodic function’) and the other kernels to appropriate modifiers of this noun phrase (e.g. ‘whose shape changes smoothly’, ‘with growing amplitude’). The end result is an automatically generated 5-15 page report describing the patterns in the data with figures and tables supporting the main claims. Here is an extract of the report produced by their system for the solar irradiance data:

Extract of the report for the solar irradiance data, automatically generated by the automatic statistician.

The Automatic Statistician is currently being generalized to find patterns in other kinds of data, such as multidimensional regression problems, and relational databases. A web-based demo of a simplified version of the system was launched in August 2014. It allowed a user to upload a dataset, and to receive an automatically produced analysis after a few minutes. An expanded version of the service will be launched in early 2015 (we will post details when available). We believe this will have many applications for anyone interested in Data Science.

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Alan Turings manuscript sells for 1 025 000

The auction house Bonhams in New York have just sold a handwritten scientific document written by Alan Turing, in which he worked on the foundations of mathematical notation and computer science, for $1,025,000. Before the auction Bonhams described the document as "Made up of 56 pages contained in a simple notebook bought from a stationers in Cambridge, UK, it is almost certainly the only extensive autograph manuscript by Turing in existence, and has never been seen in public. From internal evidence, it dates from 1942 when he was working at Bletchley Park to break the German Enigma Code, and provides remarkable insight into the thought process of a genius."

from The Universal Machine http://universal-machine.blogspot.com/

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