We present a brief introduction to a flexible, general network inference framework which models data as a network space, sampled to optimize network structure to a particular task. We introduce a formal problem statement related to influence maximization in networks, where the network structure is not given as input, but learned jointly with an influence maximization solution.

Mobile crowdsensing is considered as a promising technology to exploit the computing and sensing capabilities of decentralized wireless sensor nodes. Typically, quality information obtained from largely affected by various factors, such diverse requirements tasks, varying across different crowd workers, dynamic changes channels conditions environment. In this paper, considering dynamics’ we foc...

The existing Web service management solutions are almost exclusively focused on optimization of technical QoS metrics (e.g., availability). However, financial (e.g., profit) and other business value metrics (e.g., customer satisfaction) are more important than technical QoS metrics. We discuss the challenges of business value modeling and maximization in autonomic (e.g., self-healing) Web servi...

The parameters of reconstruction iteration and subset of the ordered subset expectation maximization (OS-EM) method have a great impact on image quality. In a cerebral blood flow (CBF) examination, it is critical to produce consistent results when analyzing clinical data. We evaluated the number of iterations and subset of the OS-EM method to stabilize image quality using a normal case and an i...

Abstract Maximum likelihood estimation of discrete latent variable (DLV) models is usually performed by the expectation-maximization (EM) algorithm. A well-known drawback related to multimodality log-likelihood function so that algorithm can converge a local maximum, not corresponding global one. We propose tempered EM explore parameter space adequately for two main classes DLV models, namely c...

Any clustering algorithm must synchronously learn to model the clusters and allocate data those in absence of labels. Mixture model-based methods with pre-defined statistical distributions based on cluster likelihoods. They iteratively refine distribution parameters member assignments following Expectation-Maximization (EM) algorithm. However, representability such hand-designed that employ a l...

In this paper, we study the autoregressive (AR) model with normal inverse Gaussian (NIG) innovations. The NIG distribution is semi heavy-tailed and helpful in capturing extreme observations present data. expectation-maximization (EM) algorithm used to estimate parameters of considered AR(p) model. efficacy estimation procedure shown on simulated data for AR(2) AR(1) models. A comparative presen...