Download JAR files tagged by where with all dependencies

JXTA technology is a set of open protocols that allow any connected device on the network ranging from cell phones and wireless PDAs to PCs and servers to communicate and collaborate in a P2P manner. JXTA peers create a virtual network where any peer can interact with other peers and resources directly even when some of the peers and resources are behind firewalls and NATs or are on different network transports.

AuelProject DataSift is a data validation and transformation framework built on top of the J2SE platform which mainly targets enterprise applications, creating an environment where data validation and transformation can be done in a simplistic yet powerful way. It allows the developer to create plugins for both looking for the data to be validated (resolving) and validating or transforming it.

IzPack is an installers generator for the Java platform. It produces lightweight installers that can be run on any operating system where a Java virtual machine is available. Depending on the operating system, it can be launched by a double-click or a simple 'java -jar installer.jar' on a shell. The most common use is to distribute applications for the Java platform, but you can also use it for other kinds of projects. The main benefit of IzPack is that it provides a clean and unique way of distributing a project to users using different operating systems.

This module was created to fix issue DIRSHARED-91 where the embedded Felix instance inside the default LdapCodecService implementation was making it very problematic to deploy the ldap-codec inside an RCP (OSGi) environment and hence Apache Directory Studio could not use it. This module is most likely temporary and will disappear once we are fully OSGi enabled. This module is a plain old jar, not a bundle to avoid accidental reuse as an OSGi module because it contains the version of the LdapCodecService that embeds Felix to make controls and extended ops pluggable in the codec.

This nifi-utils module should be a general purpose place to store widely and generally useful functions that any component might want to leverage. NO DEPENDENCIES should be added. This module is likely to be leveraged by every extension and should not bring along any other dependencies. The only dependency intended is the nifi-api and even this is expected to be already provided in any case where it would be used. The typical place this util would be found is within a nar and all nars already have nifi-api as a parent dependency. The nifi-api can be thought of as a NiFi Application Container level dependency.

Set of various Utils: stacktrace noise filter, String to/from unicode sequence converter, simple Http client JSON parser/serializer, Silent String parsing to Integer and other numeric types, Parsing String to time intervals with support for time unit suffixes, JSON parser that provides serialization/deserialization of classes to JSON, Version comparator and Version ranges operations, Self-throttling binary reader from Http request, File reader, A utility that automatically initiates a Factory with instances of all classes that implement user provided interface. An infrastructure for writing Scheduled Task classes where time interval for task execution is provided in humanly readable format (such as "9h" for 9 hours)

In C++, writing a loop that iterates over a sequence is tedious. We can either use iterators, which requires a considerable amount of boiler-plate, or we can use the std::for_each() algorithm and move our loop body into a predicate, which requires no less boiler-plate and forces us to move our logic far from where it will be used. In contrast, some other languages, like Perl, provide a dedicated "foreach" construct that automates this process. BOOST_FOREACH is just such a construct for C++. It iterates over sequences for us, freeing us from having to deal directly with iterators or write predicates.

COBYLA2 is an implementation of Powell's nonlinear derivative free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust region algorithm that employs linear approximations to the objective and constraint functions, where the approximations are formed by linear interpolation at n + 1 points in the space of the variables and tries to maintain a regular shaped simplex over iterations. It solves nonsmooth NLP with a moderate number of variables (about 100). Inequality constraints only. The initial point X is taken as one vertex of the initial simplex with zero being another, so, X should not be entered as the zero vector.

HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.