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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...
In order to see if process assessment drives processes learning, process assessments were performed in the capstone project of a Bachelor in Computer Science. Assessments use an ability model based on a small subset of ISO/IEC 15504 processes, its main Base Practices and Work Products. Students' point of view was also collected through an anonymous questionnaire. Self-assessment using a competency...
Analysis of spatio-temporal data is a common research topic that requires the interpolations of unknown locations and the predictions of feature observations by utilizing information about where and when the data were observed. One of the most difficult problems is to make predictions of unknown locations. Tensor factorization methods are popular in this field because of their capability of handling...
Co-evolution exists ubiquitously in biological systems. At the molecular level, interacting proteins, such as ligands and their receptors and components in protein complexes, co-evolve to maintain their structural and functional interactions. Many proteins contain multiple functional domains interacting with different partners, making co-evolution of interacting domains occur more prominently. Multiple...
We consider an infrastructure of networked systems with discrete components that can be reinforced at certain costs to guard against attacks. The communications network plays a critical, asymmetric role of providing the vital connectivity between the systems. We characterize the correlations within this infrastructure at two levels using (a) aggregate failure correlation function that specifies the...
Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable...
Very large-scale Deep Neural Networks (DNNs) have achieved remarkable successes in a large variety of computer vision tasks. However, the high computation intensity of DNNs makes it challenging to deploy these models on resource-limited systems. Some studies used low-rank approaches that approximate the filters by low-rank basis to accelerate the testing. Those works directly decomposed the pre-trained...
We study the problem of approximating the partition function of the ferromagnetic Ising model in graphs and hypergraphs. Our first result is a deterministic approximation scheme (an FPTAS) for the partition function in bounded degree graphs that is valid over the entire range of parameters β (the interaction) and λ (the external field), except for the case |λ|=1 (the...
Region of Interest (ROI) crowd counting can be formulated as a regression problem of learning a mapping from an image or a video frame to a crowd density map. Recently, convolutional neural network (CNN) models have achieved promising results for crowd counting. However, even when dealing with video data, CNN-based methods still consider each video frame independently, ignoring the strong temporal...
The aim of this paper is to perform a precise assessment of the misalignment of high-density surface electromyogram (HD-sEMG) grid according to its rotation with the muscle fibers. For this purpose, a generic and quantitative method was used in this study, namely the nonlinear correlation coefficient (h2) that is widely applied in connectivity and directionality of stochastic and complex signals....
Overhead distribution lines are usually subject to voltage transients during thunderstorms. Lightning flashes, whether direct or indirect, are one of the main causes of power quality disturbances on the electric grid. In this paper, a methodology to establish a correlation between the incidence point of a lightning flash, the stroke current amplitude and polarity and its effects on the electric distribution...
This paper investigates the performance of ejectors with a streamline structure. The parabola fitting is adopted to simulate the ejectors streamline structure and the effects of parabola fitting structure on ejectors are studied using the Computational Fluid Dynamics (CFD) technique. The contrast simulations of the parabola models and the original model are conducted in which the entrainment ratio...
Current industrial system develops more and more complex and intelligent, whose safety and reliability relies on fault diagnosis technology. In the age of big-data, data-driven fault diagnosis becomes the state of the art, and the demand for the diagnostic toolbox also increases. In this paper, theories of data-driven fault detection models, both static model and dynamic, are revealed. And then a...
In this work, we address multimodal learning problem with Gaussian process latent variable models (GPLVMs) and their application to cross-modal retrieval. Existing GPLVM based studies generally impose individual priors over the model parameters and ignore the intrinsic relations among these parameters. Considering the strong complementarity between modalities, we propose a novel joint prior over the...
In this paper we present a correlation between a variation of the list problem coloring in graphs, the (γ, μ)-coloring, and the property of choosability in graphs, resulting in the k-(γ, μ)-choosability. The list coloring problem is a variation of the classical vertex coloring problem, introduced by Erdos et al. in 1979, along with a property very studied in list coloring: the choosability in graphs...
Software Quality model is a well-accepted way for assessing high-level quality characteristics (e.g., maintainability) by aggregation from low-level metrics. Aggregation method in a software quality model denotes how to aggregate low-level metrics to high-level quality characteristics. Most of the existing quality models adopt the weighted linear aggregation method. The main drawback of weighted linear...
The existing methods for monitoring of technological processes are analyzed by means of software and hardware. Theoretical foundations of the proposed method of control over the parameters of quasi-stationary technological process are substantiated. Software interaction between the operator and intellectual sensor information system with a deep level of parallelization of computational processes is...
Prediction of fouling in power plant heat exchanger is greatly influenced by the periodic fouling process and change in dynamics of the operational parameters. In order to address this, a prediction model using Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network is proposed to monitor cleanliness factor. The experimental results demonstrate that the proposed model can predict the...
The use of virtual simulation in industrial design and validation processes has considerably increased over the last years, due to better algorithms, affordable computational power with good performance and gradual acceptance of its results by technical experts. Nevertheless, although electromagnetic simulation has also evolved, it still does not have the same penetration in validation processes as...
AbstractłIn order to enhance the robustness of kernel correlation filters(KCF) in complex background environment, this paper proposes a mean shift method with adaptive local object tracking algorithm. KCF algorithm has speed advantage by using the single template, we introduce the confidence map in the process of the tracking to determine the result of the current frame. If the result of confidence...
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