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We propose two efficient algorithms for exploring topic diversity in large document corpora such as user generated content on the social web, bibliographic data, or other web repositories. Analyzing diversity is useful for obtaining insights into knowledge evolution, trends, periodicities, and topic heterogeneity of such collections. Calculating diversity statistics requires averaging over the similarity of all object pairs, which, for large corpora, is prohibitive from a computational point of view. Our proposed algorithms overcome the quadratic complexity of the average pair-wise similarity computation, and allow for constant time (depending on dataset properties) or linear time approximation with probabilistic guarantees.

Package for loading a directory containing MRI data in NIfTI format. The directory to be loaded must contain as many subdirectories as there are classes of MRI data. Each subdirectory name will be used as the class label for the corresponding .nii files in that subdirectory. (This is the same strategy as the one used by WEKA's TextDirectoryLoader.) Currently, the package only reads volume information for the first time slot from each .nii file. The readDoubleVol(short ttt) method from the Nifti1Dataset class (http://niftilib.sourceforge.net/java_api_html/Nifti1Dataset.html) is used to read the data for each volume into a sparse WEKA instance (with ttt=0). For an LxMxN volume (the dimensions must be the same for each .nii file in the directory!), the order of values in the generated instance is [(z_1, y_1, x_1), ..., (z_1, y_1, x_L), (z_1, y_2, x_1), ..., (z_1, y_M, x_L), (z_2, y_1, x_1), ..., (z_N, y_M, x_L)]. If the volume is an image, then only x and y coordinates are used.

<p>Generates a unique Exception ID, that can be used in Java source code.</p> <p>In order to use this plugin type <code>Ctrl+Space</code> inside empty String literal. This will generate new unique identifier as a <i>Code Completion</i>. That generated EID can be used, for example, to identify your exceptions.</p> <p> Example usage (This is technical runtime exception, a posible bug. This plugin can be used to quickly fill unique bug id.):<br /> <code>try {<br /> &nbsp;&nbsp;&nbsp;&nbsp;shuldWorkIfNoBugsExists();<br /> } catch (IOException ex) {<br /> &nbsp;&nbsp;&nbsp;&nbsp;throw new EidRuntimeException("20140218:161429", "Something wrong with HDD, permissions?", ex);<br /> }</code> </p>

The HTTP server module (ks-server-http) implements the Web API of the KnowledgeStore, which includes the two CRUD and SPARQL endpoints. The CRUD Endpoint supports the retrieval and manipulation of semi-structured data about resource, mention, entity and axiom records (encoded in RDF, possibly using JSONLD), and the upload / download of resource representation. The SPARQL Endpoint supports SPARQL SELECT, CONSTRUCT, DESCRIBE and ASK queries according to the W3C SPARQL protocol. The two endpoints are implemented on top of a component implementing the KnowledgeStore Java API (the Store interface), which can be either the the KnowledgeStore frontend (ks-frontend) or the Java Client. The implementation of the module is based on the Jetty Web sever (run in embedded mode) and the Jersey JAX-RS implementation. Reference documentation of the Web API is automatically generated using the Enunciate tool.

This small subproject regroup most of the stuff needed to build pmd. As some java code has been produced to reduce the use of scripts when generating the website, the build also requires a small jar, which is generated by this pom. IMPORTANT NOTE: the pmd-build.jar does not need to be recompile if you modify any ant or xslt files. You can alter the files that are in the xslt directory and directly uses the appropriate ant or java task. pmd-build should be recompiled and package (and its version incrememented) only if you modify the java code.

The biblio transformation engine is a java framework developed by the Hellenic National Documentation Centre (EKT, www.ekt.gr) and consists of programmatic APIs for filtering and modifying records that are retrieved from various types of data sources (eg. databases, files, legacy data sources) as well as for outputing them in appropriate standards formats (eg. database files, txt, xml, Excel). The framework includes independent abstract modules that are executed seperately, offering in many cases alternative choices to the user depending of the input data set, the transformation workflow that needs to be executed and the output format that needs to be generated.

'Benerator' is a framework for creating realistic and valid high-volume test data, used for testing (unit/integration/load) and showcase setup. Metadata constraints are imported from systems and/or configuration files. Data can imported from and exported to files and systems, anonymized or generated from scratch. Domain packages provide reusable generators for creating domain-specific data as names and addresses internationalizable in language and region. It is strongly customizable with plugins and configuration options.

Customizable cruft-free Scala archetype. Options: -sourceFolders: [all-in-src-java, scala-only, both-split-src]. DEFAULT: all-in-src-java. --all-in-src-java: Scala and Java code are both in src/main/java (same for test). Plays nicely with IDEs. --scala-only: only src/main/scala set up, and set as source folder. --both-split-src: both src/main/java and src/main/scala. Can play merry havock with IDEs, so not the default. -testLibrary: [scalatest, specs2, scalacheck-only, junit-only]. DEFAULT: scalatest. --scalatest: adds Scalatest AND Scalacheck AND JUnit for most of your testing needs. --specs2: adds specs2 AND Scalacheck AND JUnit. --scalacheck-junit: adds Scalacheck AND JUnit. --junit-only: adds JUnit ONLY. -scalaVersion: 2.10.x+. DEFAULT: 2.11.2. *The Scala tool version is generated automatically. *Some Scala versions may not be compatible with selected test library versions. Adjust as necessary.

This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier. Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. For more information, see: Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.