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We consider the problem of causal structure learning from data with missing values, assumed to be drawn from a Gaussian copula model. First, we extend the 'Rank PC' algorithm, designed for Gaussian copula models with purely continuous data (so-called nonparanormal models), to incomplete data by applying rank correlation to pairwise complete observations and replacing the sample size with an effective...
Most view-based modelling approaches are today based on a "synthetic" approach in which the views hold all the information modelled about a system and are kept consistent using explicit, inter-view correspondence rules. The alternative "projective" approach, in which the contents of views are "projected" from a single underlying model on demand, is far less widely used...
The complexity of the mechanism brings great difficulty to the calculation of the mechanism motion reliability, and the computer simulation algorithm based on Monte Carlo method needs a great number of simulation. The further-development of LMS Virtual. Lab was carried out, and the least square support vector machine algorithm was used to construct the response surface proxy model. The PSO-GA algorithm...
Gaussian Processes (GPs) are state-of-the-art tools for regression. Inference of GP hyperparameters is typically done by maximizing the marginal log-likelihood (ML). If the data truly follows the GP model, using the ML approach is optimal and computationally efficient. Unfortunately very often this is not case and suboptimal results are obtained in terms of prediction error. Alternative procedures...
Following the Service-Oriented Architecture, a large number of diversified Cloud services are exposed as Web APIs (Application Program Interface), which serve as the contracts between the service providers and service consumers. Due to their massive and broad applications, any flaw in the cloud APIs may lead to serious consequences. API testing is thus necessary to ensure the availability, reliability,...
Humans possess an extraordinary ability to learn new skills and new knowledge for problem solving. Such learning ability is also required by an automatic model to deal with arbitrary, open-ended questions in the visual world. We propose a neural-based approach to acquiring task-driven information for visual question answering (VQA). Our model proposes queries to actively acquire relevant information...
Integrated environmental modelling (IEM) couples interdependent environmental models and data together to solve complex environmental problems. There are two major modelling frameworks for IEM: component based framework, and service oriented framework. This paper suggests to take the best of both to couple modelling components and services together. The result is a hybrid method to leverage Open Modelling...
Cloud computing has gained popularity in recent years due to its pay-as-you-go business model, high availability of services, and scalability. Service unavailability does not affect just user experience but is also translated into direct costs for cloud providers and companies. Part of this costs is due to SLA breaches, once interruption time greater than those signed in the contract generate financial...
Traffic anomalies can create network congestion, so its prompt and accurate detection would allow network operators to make decisions to guarantee the network performance avoiding services to experience any perturbation. In this paper, we focus on origin-destination (OD) traffic anomalies; to efficiently detect those, we study two different anomaly detection methods based on data analytics and combine...
Positioning Piezoelectric Actuators (PAs) are extremely versatile high precision investigative tools exploited in the field of micro-nanotechnology and they are also widely used in highly accurate industry production. PAs systems require appropriate controllers allowing to obtain fast and high-precision positioning performances but usually commercial systems provide integrated Proportional-Integral...
Kronecker-structured (K-S) models have been proposed for the efficient representation, processing, and classification of multidimensional signals such as images and video. In this paper, we study the classification performance of Kronecker-structured models in two asymptotic regimes. First, we study the diversity order, the slope of the error probability as the signal noise power goes to zero. We...
The authors propose a variational level set image segmentation method for intensity inhomogeneous texture image. The method first extracts the main image structure by a relative total variation image decomposition method, which can better decompose the image into structural and textural parts. Then only uses the structural part as the input image for the variational level set segmentation. The intensity...
Pedestrian tracking plays an essential role in the domain of visual tracking. Much research in recent years has focused on how to obtain the accurate tracking results. However, few researchers have addressed the problem of the smoothness for the tracking trajectory. Most trajectory results are skipped and lack of smoothness, which does not comply with human vision habits. Also, some incorrect data...
This paper considers the modelling of scalar fields exhibiting non-stationary noise in the context of Gaussian Process (GP) regression. We show how a Heteroscedastic GP produces more accurate predictions of the variance of a process of this type compared to the standard Homoscedastic model. We present a parametric model for the noise process and derive analytical solutions to the Log Marginal Likelihood...
In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of “interesting” outputs. Furthermore, and most importantly,...
The User Requirements Notation is a standard published by the International Telecommunication Union that contains two complementary notations for goal and scenario/workflow modeling. Use Case Maps (UCM) – the workflow notation – focuses on the causal relationships of the steps in a workflow without requiring the specification of detailed message exchanges and data. A UCM model captures the interactions...
Heterogeneous computing systems, e.g., those with accelerators than the host CPUs, offer the accelerated performance for a variety of workloads. However, most parallel programming models require platform dependent, time-consuming hand-tuning efforts for collectively using all the resources in a system to achieve efficient results. In this work, we explore the use of OpenMP parallel language extensions...
Despite the wide array of powerful classification techniques available, simple linear and quadratic models remain important tools in a researcher's toolkit, particularly for problems with relatively little training data. This paper introduces a classification model that allows careful control of model complexity, allowing intermediate levels of expressiveness between linear and quadratic models. Our...
Nearly all existing estimations of the central subspace in regression take the frequentist approach. However, when the predictors fall naturally into a number of groups, these frequentist methods treat all predictors indiscriminately and can result in loss of the group-specific relation between the response and the predictors. In this article, we propose a Bayesian solution for dimension reduction...
Cloud Computing allows users to control substantial computing power for complex data processing, generating huge and complex data. However, the virtual resources requested by users are rarely utilized to their full capacities. To mitigate this, providers often perform over-commitment to maximize profit, which can result in node overloading and consequent task eviction. This paper presents a novel...
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