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Dryuf BigIo is stateless, unlimited IO framework, solving few design problems of original JDK. It mainly provides three things - FlatBuffer with 64-bit address space and being stateless. Additionally, it provides FlatChannel interface and few implementations - providing the arbitrary reads and writes from chosen position. This is implemented only in FileChannel in NIO so this interface makes it easier to provide those stateless reads and writes on virtual channels too. BigIo also provides committable objects, allowing marking the object as completed or incomplete, so it correctly results into success or exception on receiving side.

Apache Rat is a release audit tool. It improves accuracy and efficiency when checking releases. It is heuristic in nature: making guesses about possible problems. It will produce false positives and cannot find every possible issue with a release. Its reports require interpretation. In response to demands from project quality tool developers, Rat is available as a library suitable for inclusion in tools. This POM describes that library. Note that binary compatibility is not guaranteed between 0.x releases. Apache Rat is developed by the Apache Creadur project, a language and build agnostic home for software distribution comprehension and audit tools.

Java Simplified. Extensions and Useful Constructs for the Java Platform. Codeprimate Elements (a.k.a. cp-elements) is a Java library and micro-framework used to simplify the development of software applications written in Java. Elements packages several APIs into one library in order to address various application concerns and aspects of software design and development collectively and conveniently. Elements is a highly simple, yet robust and proven library built on solid OO principles, software design patterns and best practices to effectively solve common and reoccurring problems in software development. Write better Java!

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

Java Simplified. Extensions and Useful Constructs for the Java Platform. Codeprimate Elements (a.k.a. cp-elements) is a Java library and micro-framework used to simplify the development of software applications written in Java. Elements packages several APIs into one library in order to address various application concerns and aspects of software design and development collectively and conveniently. Elements is a highly simple, yet robust and proven library built on solid OO principles, software design patterns and best practices to effectively solve common and reoccurring problems in software development.

Concourse is a distributed database warehouse for transactions, search, and analytics across time. Developers use Concourse to quickly build mission critical software while also benefiting from real time insight into their most important data. With Concourse, end-to-end data management requires no extra infrastructure, no prior configuration and no additional coding–all of which greatly reduce costs and allow developers to focus on core business problems.

<p>Integrate the Mantis bugtracker system into netbeans</p> <p>Note for installation - to make the "Add patch" and "Checking" comment functions work correctly the mantis system has to configured correctly please check these options, if you encounter problems:</p> <ul> <li>source_control_notes_view_status (default private, integer value 10 -> public)</li> <li>source_control_set_resolution_to (integer value 80->resolved, 90->closed)</li> <li>source_control_set_status_to (20->fixed)</li> </ul> <p>If a self-signed SSL certificate or an invalid certificate need to be accepted, please install the module "SSL Certificate Exception".</p>

ModelCC is a model-based parser generator (a.k.a. compiler compiler) that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual language and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a built-in reference resolution mechanism that results in abstract syntax graphs, rather than mere abstract syntax trees.

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001. Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.