H2 support 1.5 for Alfresco 4.1.1 Enterprise
After Alfresco version 4.1.1 Enterprise came out earlier this month, H2 support needed to stay up to date and release a new version which incorporates the few schema changes in the Alfresco database. Thanks to Jon Evans and Ixxus, H2 Support v1.5 it's already out there!
Version management
As usual, H2 support follows its own version numbering scheme, and you need to double check which one to use depending on the Alfresco version you're running: version 1.5 only supports Alfresco 4.1.1 Enterprise, so if you're targeting other versions go and update your POM.
Availability on Maven Central
By the time of this writing, H2 support was just pushed to Sonatype OSS, and while it should happen shortly, it still has to be synchronized with Maven Central. If the following Maven dependency fails to resolve, try to download and install it from here:
<dependency>
<groupId>tk.skuro.alfresco</groupId>
<artifactId>h2-support</artifactId>
<version>1.5</version>
</dependency>
On a side note, the stats feature of Sonatype Nexus are plain awesome. I can have direct insights of the amount of downloads of H2 support over time, split by version number. Check it out:
Your chance to be awesome
Jon was just great to help H2 support and provide the patch that made 1.5 happen. It's always inspiring to see how OpenSource drives people to build something together, spending time to shape technology to be the way we need, asking for nothing else in return than just be part of the community.
H2 support is a tiny project, and requires little development and maintenance. Still, you can always help by either testing it against undocumented Alfresco versions, or reporting bugs or your wish list as soon as you figure them out.
Thanks for reading this and happy Alfresco hacking!
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Gracia Soldier Corpse:
(You have found the dead body of a Gracia Soldier. Cold and stiff, it is impossible to determine how long it has been here.)
Quest


BRUNCH
BURGER
LA FRIEDA BEEF, CARAMELIZED ONIONS,BACON, FRIES
14
ADD-ONS $1 FRIED EGG OR CHEESE
PULLED CHICKEN SANDWICH
LEMON THYME, FRESH SPINACH
10
SHORT RIBS SANDWICH
BRAISED BEEF, CARAMELIZED ONION
12
PORTABELLA CAPRESE
MUSHROOM, HOMEMADE MOZZARELLA MELTEDON FRESH BREAD W/PESTO, TOMATO
10
STEAK AND EGG SANDWICH
OVER MEDIUM EGG,CHEESE MELTED ON FRESH BREAD
12
MARKET SALAT
WINTER GREENS, CARROT, FENNEL, GOAT CHEESE, PUMPKIN SEED, SHALLOT VINEGRETTE
10
STEAK SALAT
HANGARSTEAK, SPINACH, TOMATOES,CRISPY SHALLOTS, DRIED CHERRIES, WALNUTS, GORGONZOLA
14
COBBLE HILL CRISTO
POACHED EGG, HOLLANDAISE, SPECK,LINGONBERRIES, MELTED GRUYERE, BATTERED BRIOCHE
11
EGGS ANY STYLE W/BACON & HASH-BROWNS
10
BISCUITS, SAUSAGE & GRAVY
FRESH BISCUITS, PORK SAUSAGE & COUNTRY GRAVY
10
GERMAN APPLE PANCAKES
TRADITIONAL PANCAKE WITH FRESH APPLES,CINNAMON, ORANGE MARMALADE, LINGONBERRIES
11
SHORT-RIB HASH & EGGS
11


A few years ago, a former Tomorrow Lab intern led a really cool project that repurposed some LED matrix panels (the kind they use for Times Square news tickers or halal food carts) into an artistic, cloud connected data display. It was a truly end-to-end piece of digital artwork. A raspberry pi ran a small server that could accept inputs over the internet and configure three
Teensy 3.0 boards, each of which would drive the display on one of the panels. The panels could display localized data like weather, color gradients, or a few other options. Over time, the display had stopped working, and none of the software it was running could be recovered. So I set about rebuilding it piece by piece.
I started by replacing the broken Teensy boards and rebuilding a set of commands on top of the
PixelMatrix library that could draw or animate gradients or write text to the panels. With that done, I recreated from scratch a communication protocol between the Raspberry Pi and the Teensy boards by which the Pi could send serial messages, addressed to one of the three Teensies, that would be parsed on the Teensy side and interpreted as drawing commands. And lastly, I replaced the older Raspberry Pi and wifi attachment with the newer Model 3, which includes built-in wifi. I then put it back on line, recreated its small web page to accept basic text and configuration inputs, use Google and ForecastIO APIs to retrieve weather forecasts, and push this data to the panels.


Unterminated attribute, found '{0}' but no matching '{1}' found.
This error occurs when using multi-element directives and a directive-start
attribute fails to form a matching pair with a corresponding directive-end
attribute.
A directive-start
should have a matching directive-end
on a sibling node in the DOM. For instance,

I get repeated

I also get repeated

is a valid example.
This error can occur in several different ways. One is by leaving out the directive-end
attribute, like so:

Another is by nesting a directive-end
inside of directive-start
, or vice versa:

To avoid this error, make sure each directive-start
you use has a matching directive-end
on a sibling node in the DOM.


{% extends "admin/base_site.html" %} {% load i18n static admin_modify grp_tags %} {% block branding %}
{% endblock %} {% block title %}openNPL{% endblock %} {% block extrastyle %} {% endblock %} {% block breadcrumbs %}openNPL is an open source platform for the management of non-performing loans. It implements detailed European Banking Authority loan templates for NPL data. Click on Home to get started!
Non-Performing loans is a serious and ongoing issue that affects many economies. Developing tools and methodologies that will reduce the burden and improve the ability to manage problem loans is thus an important objective. For more background and previous work see the Open Risk Blog Post.
openNPL is a web application that works together with a tailored data schema to provide easy access to NPL data adhering to the regulatory recommendation. Running the application creates a server that can then be accessed via any regular browser. For easy installation and testing the current release uses an sqlite database instead of a production database.
Once the platform is up and running a wealth of functionality is available. Just as a sample:
| User Functionality |
|---|
| A user can log-in into the application with their credentials |
| Inspect the available data sets (tables or relations) that capture the EBA Template recommendations |
| Insert, Update or Delete records (e.g. new counterparty or loan data) |
| Consult the documentation as to the meaning and requirements of each data element |
| Administrator Functionality |
|---|
| An administrator can create users and user groups and assign permissions |
| Perform bulk insertion or deletion of data sets |
| Developer Functionality |
|---|
| openNPL is based on the well known and loved Python web framework Django |
| Developers can customize and adapt the platform in unlimited ways |
The project aims to be driven by community needs. We welcome code contributions and feature requests via github.
{% endblock %}


NeuroCircle: An Alternative EEG Signal Visualization
The field of neuroscience is becoming more important everyday as our interst in inner workings of our own brains and cognition grows exponentially each day. The words ‘neuro’ and ‘cognitive’ are also becoming buzz words too, being added to words back and forth.
One of the tools that plays a crucial role in this neuroscientific expansion is EEG(Electro encephelo gram), where brain signals are recorded transcranially with electrodes. These recordings are of course coarse averages of all signals in a specific brain region and dont give us the full picture, but combined with other powerful tools such as fMRI and deep brain electrodes, they play an important role in neuroscientific research.
A standard EEG signal series may look like this:
The figure above shows three different samples. It is obvious that they are different and blue and orange samples are possibly resultant of some underlying phenomena, but overall the series data is not very informative to an untrained eye such as myself. To derive information from these signals multiple methods are used to extract features[1], and classify signals[2]. These methods provide valuable information and help derive useful conclusions from the EEG measurements in compact numerical representations.
But since us humans are highly dependent on our visual perception, I wante to create a more visually stimulating representation of signal properties. After attending a talk by Refik Anadol about utilizing data and algorithms as a tool to create visual and auditory media and seeing his Melting Memories installation, I decided to implement an alternative visualization method for EEG signals. I used Python to extract various signal statistics and used these statistics in Processing as parameters determining the shape and motion of the figure.
The resulting figure looks something like this:
Each branch in the figure represents a seperate EEG measurement. The locations of branches are currently homogenously dispersed in a circle but can be changed to convey another information derived from the analysis. Since the figure has a circular form, the visualization method is aptly called NeuroCircle
For this current example I decided to use mean, standart deviation, kurtosis and the periodicity - which is simply the signal convolved with itself - of the signal to determine the properties of the figure, specifically:
- Mean -> The color change coefficient of branches
- Standart Deviation -> The movement speed coefficient of branch points
- Periodicity -> Length of branches
- Kurtosis -> Thickness coefficient of the branches
A Python script calculates the statistics for each EEG sample and Proceesing uses these statistics to initialize branches.
The EEG signals that were shown as time-series look like this:
The long branches being the blue and orange smaples, respectively.
The EEG signals used for this implementation was acquired from here, which has 100 normal and epileptic EEG recordings. At this point this was more of an aesthetic driven project and the stats to be used as parameters were chosen randomly, and more specifically ease of implementation, but can easily be tweaked to be to include different statistics based on area specific details of the measurements.
1 - A. Al-Fahoum and A. Al-Fraihat, “Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains”, ISRN Neuroscience, vol. 2014, pp. 1-7, 2014.
2 - T. Felzer and B. Freisleben, “Analyzing EEG signals using the probability estimating guarded neural classifier”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 4, pp. 361-371, 2003.