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A platform for discovery and communication between various parts of a distributed service or application. At its minimum, the platform is a library which can be used to serialize and deserialize application constructs into payloads which it can then send to and receive from remote endpoints. For now it supports TCP and HTTP communication protocols with WebSockets coming soon. It supports raw byte streams via TCP, as well as application/json via JAX-RS as serialization protocols. It offers plain or SSL/TLS communication for security of your data in transit. For now it is only available in Java, with bindings for other languages coming soon. The communication pattern is request-reply, with fail fast semantics, and with service consumers (clients) knowing beforehand the coordinates of the service providers (servers). The TCP communication protocol offers blocking/unblocking/asynchronous flavors whereas the rest are only blocking for now. Coming soon, the platform will provide a Discovery Service to facilitate the registration of the service providers (servers) and service consumers (clients). In this case, the callers don't need to resolve or even balance their calls towards various endpoints, since the Platform handles this tasks. In this case, the pub/sub communication pattern will also be available (coming soon) with at-most-once delivery guarantees. We intend to provide a Security Service for managing the keys/and certificates of various services as well as Metrics and Logging services (coming soon).

Rouplex-Niossl is a java SPI (service provider interface) for secure (SSL/TLS), selectable, socket channels. Some of the classes in the java.nio.channels package have been extended by secure counterparts that can be used side by side, or replace existing instances of the plain implementations. This package contains just the entry point calls for instantiating such instances, as well as a non-functional, default implementation. For a concrete implementation of these classes you can take a look at Rouplex-Niossl-Spi, which would be included as a separate dependency to your applications. More specifically this library defines SSLSocketChannel class to inherit from SocketChannel, SSLServerSocketChannel to inherit from ServerSocketChannel and SSLSelector to inherit from SSLSelector. One or more instances of SSLSocketChannel can be registered with an (or more) instance of SSLSelector to be selected upon, with the same exact semantics a SocketChannel would expect from registering with a Selector. Further, a mixture of SocketChannels and SSLSocketChannels can be registered simultaneously with an SSLSelector. The secure counterparts abide to the same API and semantics defined for plain channels at https://docs.oracle.com/javase/8/docs/api/java/nio/channels/package-summary.html. This way, the existing products can be easily updated to provide secure communication and new products can achieve security of data in transit by using the already proven and excellent patterns for communication such as nio.

A library extending the Jdk1.8 java.nio.channels package with SSL implementations for SocketChannel, ServerSocketChannel, Selector and SelectorProvider.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.