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SynapseGrid is a framework for constructing reactive real-time immutable data flow systems. -core contains everything to run a single-threaded system, -akka contains everything to run systems over Akka actors, -slf4j - enables logging, -concurrent - running systems directly over ExecutorContext without the need for Akka, -examples - a few test systems. Also there are -frames - a framework for type construction with meta information (see relations.scala). -typed-expressions - a framework for defining parsers and generators for natural language. The current version is 1.4.0

Keystone project is a bootstrap that loads your application and its dependencies from a single archive. With his maven plugin and few line of configuration, your application will be available with all his dependencies in a single archive. The four main ideas which make the difference with other solution are: 1. all dependencies are not exploded in final archive, 'jar' file stay without any modification, so manifest also... 2. use our maven plugin to package all that you need (and we'll loading dependencies automatically for you) 3. you could load native library 4. you could restart your application Why named Keystone? 1. Stone wedge-shaped arch which maintains a vault [Architecture]. 2. A central element on which everything depends.

GroupDocs.Annotation lets you add notes to PDF and Word documents, as well as to image files – all directly from a web browser. It is a convenient web-based tool that doesn’t require any software installation and allows you and your colleagues to annotate documents online. Moreover, with GroupDocs.Annotation, you can add your notes to a document and then send it for approval or review, or share the document with others for online collaborative review in real-time. This way you get feedback faster and can keep everyone’s notes and comments in a single file.

An instance filter that collapses instances with a common grouping ID value into a single instance. Useful for converting transactional data into a format that Weka's association rule learners can handle. IMPORTANT: assumes that the incoming batch of instances has been sorted on the grouping attribute. The values of nominal attributes are converted to indicator attributes. These can be either binary (with f and t values) or unary with missing values used to indicate absence. The later is Weka's old market basket format, which is useful for Apriori. Numeric attributes can be aggregated within groups by computing the average, sum, minimum or maximum.

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.

An low latency, lock free, primitive bounded blocking queue backed by an int[]. This class mimics the interface of {@linkplain java.util.concurrent.BlockingQueue BlockingQueue}, however works with primitive ints rather than objects, so is unable to actually implement the BlockingQueue. This class takes advantage of the Unsafe.putOrderedObject, which allows us to create non-blocking code with guaranteed writes. These writes will not be re-orderd by instruction reordering. Under the covers it uses the faster store-store barrier, rather than the the slower store-load barrier, which is used when doing a volatile write. One of the trade off with this improved performance is we are limited to a single producer, single consumer.

JAXB 2.1.x XJC Boolean-Getter Plugin This plugin causes getter methods for Boolean Objects to be called "getXX" instead of "isXX". Downloaded from http://fisheye5.atlassian.com/browse/~raw,r=1.1/jaxb2-commons/www/boolean-getter/index.html on 18/03/10 Yiannis Paschalidis Sanity4J was created to simplify running multiple static code analysis tools on the Java projects. It provides a single entry point to run all the selected tools and produce a consolidated report, which presents all findings in an easily accessible manner.

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).