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RELISON is a framework for experimentation on the link recommendation task in social networks, which aims to identify those people in the network with whom a user might be interested to connect, interact or befriend. RELISON provides tools for executing and evaluating contact recommendation approaches, considering not only their accuracy, but also aspects like their novelty, diversity and the effects that such recommendations have on global properties of the networks (as changes in the structural properties or in the characteristics of the information arriving to the users). In order to measure these effects, RELISON also provides functionality for a) analyzing the structural properties of social networks, b) detecting clusters of users (communities) and c) simulating the diffusion of information in a social network. These functionalities can be used regardless of whether recommendations have been provided to the users.

The Zello Channels SDK allows you to integrate Zello push-to-talk into your own application. The SDK communicates with a Zello server over a web socket connection using a JSON-based protocol, and offers a simple API to send and receive audio, images, and text over Zello channels. Supported features include: Send voice messages from the device microphone Play incoming voice messages through the device speaker Send voice messages from your own audio code, e.g. from a file Receive voice message data with your own audio code with optional pass-through to the device speaker Send and recieve text messages Send and receive images Send the device's current location, and receive location messages from other users The protocol specification is also available if you prefer to develop your own client in-house.

"CLIF is Load Injection Framework": open performance testing. Submit light or massive traffic to your application/service under test, and get response time measurements as well as computing and networking resources usage measurements. Support for large scale distributed load injection (up to +1000 load injectors) with unlimited number of virtual users. Multiple protocols and probes available, extendible through Java programming to generate traffic with arbitrary protocols and to measure arbitrary resources usage. Integration to Eclipse and Jenkins.

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

An empty artifact that Guava depends on to signal that it is providing ListenableFuture -- but is also available in a second "version" that contains com.google.common.util.concurrent.ListenableFuture class, without any other Guava classes. The idea is: - If users want only ListenableFuture, they depend on listenablefuture-1.0. - If users want all of Guava, they depend on guava, which, as of Guava 27.0, depends on listenablefuture-9999.0-empty-to-avoid-conflict-with-guava. The 9999.0-... version number is enough for some build systems (notably, Gradle) to select that empty artifact over the "real" listenablefuture-1.0 -- avoiding a conflict with the copy of ListenableFuture in guava itself. If users are using an older version of Guava or a build system other than Gradle, they may see class conflicts. If so, they can solve them by manually excluding the listenablefuture artifact or manually forcing their build systems to use 9999.0-....