86,727 research outputs found
On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
We report on a data-driven investigation aimed at understanding the dynamics
of message spreading in a real-world dynamical network of human proximity. We
use data collected by means of a proximity-sensing network of wearable sensors
that we deployed at three different social gatherings, simultaneously involving
several hundred individuals. We simulate a message spreading process over the
recorded proximity network, focusing on both the topological and the temporal
properties. We show that by using an appropriate technique to deal with the
temporal heterogeneity of proximity events, a universal statistical pattern
emerges for the delivery times of messages, robust across all the data sets.
Our results are useful to set constraints for generic processes of data
dissemination, as well as to validate established models of human mobility and
proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data
diffusion in ad-hoc networks, Ad Hoc Netw. (2011
An Emergentist Account of Collective Cognition in Collaborative Problem Solving
As a first step toward an emergentist theory of collective cognition in collaborative problem solving, we present a proto-theoretical account of how one might conceive and model the intersubjective processes that organize collective cognition into one or another--convergent, divergent, or tensive--cognitive regime. To explore the sufficiency of our emergentist proposal we instantiate a minimalist model of intersubjective convergence and simulate the tuning of collective cognition using data from an empirical study of small-group, collaborative problem solving. Using the results of this empirical simulation, we test a number of preliminary hypotheses with regard to patterns of interaction, how those patterns affect a cognitive regime, and how that cognitive regime affects the efficacy of a problem-solving group
Probabilistic Graphical Models on Multi-Core CPUs using Java 8
In this paper, we discuss software design issues related to the development
of parallel computational intelligence algorithms on multi-core CPUs, using the
new Java 8 functional programming features. In particular, we focus on
probabilistic graphical models (PGMs) and present the parallelisation of a
collection of algorithms that deal with inference and learning of PGMs from
data. Namely, maximum likelihood estimation, importance sampling, and greedy
search for solving combinatorial optimisation problems. Through these concrete
examples, we tackle the problem of defining efficient data structures for PGMs
and parallel processing of same-size batches of data sets using Java 8
features. We also provide straightforward techniques to code parallel
algorithms that seamlessly exploit multi-core processors. The experimental
analysis, carried out using our open source AMIDST (Analysis of MassIve Data
STreams) Java toolbox, shows the merits of the proposed solutions.Comment: Pre-print version of the paper presented in the special issue on
Computational Intelligence Software at IEEE Computational Intelligence
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