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Semantic Turkey parent project. It contains the three modules about: core business logic, core services and firefox extension (ui and client in general) of the system. Three additional modules provide an implementation for the ontology manager to be bundled with the tool, an applet for graph visualization of ontologies and concept schemes and a test suite to run tests on a java client framework. An ANT build file allows to package the xpi for Firefox installation (read README2BUILD.TXT file for info)

The standardization of KHQR code specifications will help promote wider use of mobile retail payments in Cambodia and provide consistent user experience for merchants and consumers. It can enable interoperability in the payment industry. A common QR code would facilitate payments among different schemes, e-wallets and banks and would encourage small merchants to adopt KHQR code as payment method. KHQR is created for retail or remittance in Cambodia and Cross-Border. It only requires a single QR for receiving transactions from any payment provider through Bakong including Bakong App.

The standardization of KHQR code specifications will help promote wider use of mobile retail payments in Cambodia and provide consistent user experience for merchants and consumers. It can enable interoperability in the payment industry. A common QR code would facilitate payments among different schemes, e-wallets and banks and would encourage small merchants to adopt KHQR code as payment method. KHQR is created for retail or remittance in Cambodia and Cross-Border. It only requires a single QR for receiving transactions from any payment provider through Bakong including Bakong App.

Provides a time series forecasting environment for Weka. Includes a wrapper for Weka regression schemes that automates the process of creating lagged variables and date-derived periodic variables and provides the ability to do closed-loop forecasting. New evaluation routines are provided by a special evaluation module and graphing of predictions/forecasts are provided via the JFreeChart library. Includes both command-line and GUI user interfaces. Sample time series data can be found in ${WEKA_HOME}/packages/timeseriesForecasting/sample-data.

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.