Download JAR files tagged by itself with all dependencies

This project contains a JNA (Java Native Access) implementation built on top of the native Hnswlib (Hierarchical Navigable Small World Graph) which offers a fast approximate nearest neighbor search. It includes some modifications and simplifications in order to provide Hnswlib features with native like performance to applications written in Java. Differently from the original Python implementation, the multi-thread support is not included in the bindings itself but it can be easily implemented on the Java side.

This module provides algorithms to compare sets, that is, order-invariant lists. These algorithms are implementations of the AlignmentAlgorithm interface defined in the algorithms module. In particular, this module contains the StrictSetAlignmentScoreAlgorithm for computing the cost of the optimal unordered alignment of two sets, the StrictSetAlignmentFullAlgorithm which provides the Alignment itself as well, and the GreedySetAlignmentScoreAlgorithm as well as the GreedySetAlignmentFullAlgorithm for computing a potentially sub-optimal but faster alignment of two sets. The optimal alignments rely on the HungarianAlgorithm for solving the assignment problem in bipartite graphs. Here, we rely on the implementation provided by Kevin L. Stern which is provided within this distribution.

An empty artifact that Guava depends on to signal that it is providing ListenableFuture -- but is also available in a second "version" that contains com.google.common.util.concurrent.ListenableFuture class, without any other Guava classes. The idea is: - If users want only ListenableFuture, they depend on listenablefuture-1.0. - If users want all of Guava, they depend on guava, which, as of Guava 27.0, depends on listenablefuture-9999.0-empty-to-avoid-conflict-with-guava. The 9999.0-... version number is enough for some build systems (notably, Gradle) to select that empty artifact over the "real" listenablefuture-1.0 -- avoiding a conflict with the copy of ListenableFuture in guava itself. If users are using an older version of Guava or a build system other than Gradle, they may see class conflicts. If so, they can solve them by manually excluding the listenablefuture artifact or manually forcing their build systems to use 9999.0-....

--------------------------------------------------------------------------- Version for VIVO 1.9 This uses the source and test code from the Version for VIVO 1.10, edited to make it compatible with VIVO 1.9. It also borrows some code from VIVO 1.10 itself, changes the package name to avoid conflicts, and adds it here. * Vitro 1.9 uses Jena 2, not Jena 3, and Commons Lang 2, not 3, so modify the "import" statements in the Data Distribution code accordingly. * Vitro 1.9 has an earlier version of "...webapp.utils.sparqlrunner" package, so borrow the source from Vitro 1.10, move it to a different package so it won't conflict with the 1.9 version, modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this sparqlrunner package. * Vitro 1.9 has an earlier version of "...webapp.utils.configuration" package, so borrow the source from Vitro 1.10, move it to a different package so it won't conflict with the 1.9 version, modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this configuration package. Make these modifications, compile, JAR it up, and deploy. ---------------------------------------------------------------------------

zlib is designed to be a free, general-purpose, legally unencumbered -- that is, not covered by any patents -- lossless data-compression library for use on virtually any computer hardware and operating system. The zlib data format is itself portable across platforms. Unlike the LZW compression method used in Unix compress(1) and in the GIF image format, the compression method currently used in zlib essentially never expands the data. (LZW can double or triple the file size in extreme cases.) zlib's memory footprint is also independent of the input data and can be reduced, if necessary, at some cost in compression.

This module creates two things: (1) a text file that contains a list of GAV coordinates needed for the 2nd stage boot loader (2) jar files mentioned above, in the Maven repository directory layout. Using Maven repository directory layout, as opposed to more simpler "flat directory with full of jars", allow us to share the jar files between fallback bees-driver dependencies and boot2, which saves disk space a lot. We do this in a separate module outside boot2, so that the list includes the bees-boot2 jar itself.

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance).