822 resultados para Distributed programming
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These are the resources for an introductory lecture in JavaScript programming, intended to support use of node.js and divorced from browser programming.
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An Arbor Networks paper describing DDoS attacks and related attacks. The first 9-10 pages or so are good background reading for INFO6003. Students may also find the rest of the paper interesting.
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Event driven programming is a way of writing a program that works by responding to things happening (rather than executing a preplanned series of tasks). It is most often used to manage more advanced user interactions, such as GUI programs. In this session we look at how event driven programming works in Java GUIs, as both an introduction to events (using MouseListeners), and also to the way that GUI programs are constructed.
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This is optional reading, it provides a very nice and clear reference to BASH with references to CShell
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Presentation at WAIS Away Day, April 2016
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Se presenta el análisis de sensibilidad de un modelo de percepción de marca y ajuste de la inversión en marketing desarrollado en el Laboratorio de Simulación de la Universidad del Rosario. Este trabajo de grado consta de una introducción al tema de análisis de sensibilidad y su complementario el análisis de incertidumbre. Se pasa a mostrar ambos análisis usando un ejemplo simple de aplicación del modelo mediante la aplicación exhaustiva y rigurosa de los pasos descritos en la primera parte. Luego se hace una discusión de la problemática de medición de magnitudes que prueba ser el factor más complejo de la aplicación del modelo en el contexto práctico y finalmente se dan conclusiones sobre los resultados de los análisis.
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When allocating a resource, geographical and infrastructural constraints have to be taken into account. We study the problem of distributing a resource through a network from sources endowed with the resource to citizens with claims. A link between a source and an agent depicts the possibility of a transfer from the source to the agent. Given the supplies at each source, the claims of citizens, and the network, the question is how to allocate the available resources among the citizens. We consider a simple allocation problem that is free of network constraints, where the total amount can be freely distributed. The simple allocation problem is a claims problem where the total amount of claims is greater than what is available. We focus on consistent and resource monotonic rules in claims problems that satisfy equal treatment of equals. We call these rules fairness principles and we extend fairness principles to allocation rules on networks. We require that for each pair of citizens in the network, the extension is robust with respect to the fairness principle. We call this condition pairwise robustness with respect to the fairness principle. We provide an algorithm and show that each fairness principle has a unique extension which is pairwise robust with respect to the fairness principle. We give applications of the algorithm for three fairness principles: egalitarianism, proportionality and equal sacrifice.