- We provide additional OperationTypes now, namely DELETIONREPLACEMENT and INSERTIONREPLACEMENT to model the behaviour of Dynamic Time Warping, which reads from one of the input sequences, but does not consume the respective node.
- We support Dynamic Time Warping now as a default grammar in the adp module.
- We have added a Metric Learning Module (learning), that provides implementation distance based classifiers (k-Nearest Neighbor and Large Margin Nearest Neighbor) as well as a gradient-based optimization scheme of metric parameters with respect to the LMNN cost function. More details regarding this approach can be found in the master's thesis Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming.
- We have added a export and import module (csv) that enables you to store NodeSpecification objects as JSON data (and reimport them from JSON data) as well as store Sequence objects as CSV data (and reimport them from CSV data). The generated data is aimed to be human-readable and, thus, hopefully compatible with other applications.
At June 27th, we presented a Demonstrator for the TCS Alignment Toolbox and its applicability to Intelligent Tutoring Systems at the 8th International Conference on Educational Datamining in Madrid. This demonstrator also provides some general insight with respect to the capabilities of this toolbox, so we thought it best to provide it as free software here as well. You can find it at this link.
- The architecture of the toolbox has been revised. The toolbox is now structured into 9 different modules. Also the package names have been revised, such that backwards compatibility is not garantueed.
- All of those modules are available on maven central (For matlab users a binary distribution as a single, monolithic .jar file is still available)
- The toolbox now supports Algebraic Dynamic Programming, which enables users to design their own alignment schemes in an abstract fashion, without having to worry about the (efficient) implementation. An example of this technique can be seen in a new example.
- The Parallel processing capabilities of the toolbox have been improved, such that they now use the java standard library FixedSizeThreadPool.
- We further improved the documentation by reworking the available uml diagrams, updating the wiki page, updating all examples and extending the javadoc.
Also we will present the Toolbox at the 8th International Conference on Educational Datamining 2015 in Madrid in the Demo track.
FSMT 0.18 has been released and now features remote (SSH) process execution: please visit the documentation for detailed information:
Websocket Connection feature has been removed (for now). Please see issue #382 for details
- Performance Improvements (stoutobserver)
- Faster log reader (decreased sleep values, some more CPU usage tough)
- Fixed Exception in STDOUT observer (if component name not defined)
- Improved Log Messages (more meaningful)
- Added Softlink feature to latest run of a test (*.zip and *.xml files)
- Switched from mixed indent to spaces only
- Dropped 79 chars line width, as defined by pep-8 coding standards, to improve readability (pep tests will fail)
- Removed fsmt-*-tools from installation since they are only used during unit testing
The SCXML Transition Event Name Has Changed! What does that mean?
In earlier versions the transition event was names sth. like:
NOW, the event is named, i.e,
This happened in order to provide a more meaningful console output and execution logic. On the downside you will need to regenerate your existing scxml files using the fsmt_iniparser.
We released a new version of the TCSAlignmentToolbox with the main changes:
- Version 1.4.0 now suports local (and affine) alignment (strict as well as soft)
- The bellmans gap sources have been improved and support parameter setting and alignment derivatives now
- The examples have been extended to incorporate new possibilities
Also check out the Wiki page https://opensource.cit-ec.de/projects/tcs/wiki where we now present runtime considerations.
- Implementations of Dynamic Time Warping and Global Sequence Alignment (Needleman-Wunsch)
- Support for multi-dimensional sequential data
- Support for non-vectorial and multi-modal sequence data, e.g. strings, object sequences, structured data
- Support for metric learning by gradient descent: For our algorithms we provide several derivative schemes
- Examples for usage in Java and Matlab
- Parallel Processing support for alignment calculation and derivative calculation
More information can be found at: https://opensource.cit-ec.de/projects/tcs/wiki
Surface_mesh 1.0 is now available for download. Go to Surface mesh to grab a copy.
Also available in: Atom