Download JAR files tagged by identity with all dependencies

Virgil Security is presenting an implementation of the [Double Ratchet](https://signal.org/docs/specifications/doubleratchet/) algorithm, which is used by parties to exchange encrypted messages based on a shared secret key. The implementation includes: - **Virgil Perfect Forward Secrecy (PFS) service** – a standalone web-service that is dedicated to managing one-time keys and long-time keys that are based on their Identity Public Keys (public keys that are contained in user cards published on Virgil Cards service); - **Ratchet SDK** – interacts with PFS service for publishing and managing one-time keys and long-time keys and interacts with Virgil Cards service for retrieving user's indentity cards which the OTK and LTK are based on. The parties derive new keys for every Double Ratchet message so that previous private keys cannot be calculated from new ones. The parties that participate in the communication also send Diffie-Hellman public values attached to their messages. The results of Diffie-Hellman calculations are mixed into the derived keys so that the new private keys cannot be calculated from the previous ones.

Virgil Security is presenting an implementation of the [Double Ratchet](https://signal.org/docs/specifications/doubleratchet/) algorithm, which is used by parties to exchange encrypted messages based on a shared secret key. The implementation includes: - **Virgil Perfect Forward Secrecy (PFS) service** – a standalone web-service that is dedicated to managing one-time keys and long-time keys that are based on their Identity Public Keys (public keys that are contained in user cards published on Virgil Cards service); - **Ratchet SDK** – interacts with PFS service for publishing and managing one-time keys and long-time keys and interacts with Virgil Cards service for retrieving user's indentity cards which the OTK and LTK are based on. The parties derive new keys for every Double Ratchet message so that previous private keys cannot be calculated from new ones. The parties that participate in the communication also send Diffie-Hellman public values attached to their messages. The results of Diffie-Hellman calculations are mixed into the derived keys so that the new private keys cannot be calculated from the previous ones.

The UnboundID LDAP SDK for Java is a fast, comprehensive, and easy-to-use Java API for communicating with LDAP directory servers and performing related tasks like reading and writing LDIF, encoding and decoding data using base64 and ASN.1 BER, and performing secure communication. This package contains the Commercial Edition of the LDAP SDK, which includes all of the general-purpose functionality contained in the Standard Edition, plus additional functionality specific to UnboundID/Ping Identity server products.

Allow users to login to your Play! application via Shibboleth. Shibboleth is a federated identity management protocol popular in higher education. The protocol provides a single sign on service across multiple organizational boundaries with secure attribute delivery. Shibboleth support requires that your application be proxied behind a Shibboleth capable web server such as Apache, Lighttpd, or IIS with the corresponding Shibboleth module installed. For more information on the protocol visit: http://shibboleth.internet2.edu/

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.