28,045 research outputs found

    Towards an HLA Run-time Infrastructure with Hard Real-time Capabilities

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    Our work takes place in the context of the HLA standard and its application in real-time systems context. The HLA standard is inadequate for taking into consideration the different constraints involved in real-time computer systems. Many works have been invested in order to providing real-time capabilities to Run Time Infrastructures (RTI) to run real time simulation. Most of these initiatives focus on major issues including QoS guarantee, Worst Case Transit Time (WCTT) knowledge and scheduling services provided by the underlying operating systems. Even if our ultimate objective is to achieve real-time capabilities for distributed HLA federations executions, this paper describes a preliminary work focusing on achieving hard real-time properties for HLA federations running on a single computer under Linux operating systems. Our paper proposes a novel global bottom up approach for designing real-time Run time Infrastructures and a formal model for validation of uni processor to (then) distributed real-time simulation with CERTI

    Tail probabilities for infinite series of regularly varying random vectors

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    A random vector XX with representation X=∑j≄0AjZjX=\sum_{j\geq0}A_jZ_j is considered. Here, (Zj)(Z_j) is a sequence of independent and identically distributed random vectors and (Aj)(A_j) is a sequence of random matrices, `predictable' with respect to the sequence (Zj)(Z_j). The distribution of Z1Z_1 is assumed to be multivariate regular varying. Moment conditions on the matrices (Aj)(A_j) are determined under which the distribution of XX is regularly varying and, in fact, `inherits' its regular variation from that of the (Zj)(Z_j)'s. We compute the associated limiting measure. Examples include linear processes, random coefficient linear processes such as stochastic recurrence equations, random sums and stochastic integrals.Comment: Published in at http://dx.doi.org/10.3150/08-BEJ125 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

    Interfacing to Time-Triggered Communication Systems

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    Time-triggered communication facilitates the construction of multi-component real-time systems whose components are in control of their temporal behavior. However, the interface of a time-triggered communication system has to be accessed with care, to avoid that the temporal independence of components gets lost. This paper shows two interfacing strategies, one for asynchronous interface access (in two variants, one being the new Rate-Bounded Non-Blocking Communication protocol) and one for time-aware, synchronized interface access, that allow components to maintain temporal independence. The paper describes and compares the interfacing strategies.Final Accepted Versio

    Early Warning Analysis for Social Diffusion Events

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    There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks
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