Download JAR files tagged by like with all dependencies

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.

1) This module contains various Zookeeper recipe implementations. 2) The recipe directory name should specify the name of the recipe you are implementing - eg. zookeeper-recipes-lock. 3) It would be great if you can provide both the java and c recipes for the zookeeper recipes. C recipes go in to zookeeper-recipes/zookeeper-recipes-[recipe-name]/src/c Java implementation goes into zookeeper-recipes/zookeeper-recipes-[recipe-name]/src/java. 4) The recipes hold high standards like our zookeeper c/java libraries, so make sure that you include some unit testing with both the c and java recipe code. 5) Also, please name your c client public methods as zkr_recipe-name_methodname (eg. zkr_lock_lock in zookeeper-recipes-lock/src/c) 6) To run the c tests in all the recipes, - make sure the main zookeeper c libraries in zookeeper-client-c are compiled. Run autoreconf -if;./configure; make. The libraries will be installed in {top}/src/c/.libs. - run autoreconf if;./configure;make run-check in zookeeper-recipes/$recipename/src/c

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.

@Sumatera Model converter from ORM class to regular pojo class, the ORM term is a modelling class that doesn't like regular pojo, such like @Entity from JPA/Hibernate and so on. It can be used for mapping regular object, post data in request body where you cannot put ORM class directly in a parameter, or you cannot convert to json directly, or any other problem regarding it. You don't need anymore to create duplicate Dto / Pojo class which represent ORM class, just write your ORM class and annotate it with @Sumatera, then the regular pojo class would be generated for you.