Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enha...

A main challenge in target localization arises from the lack of reliable distance measures. This issue is especially pronounced indoor settings due to presence walls, floors, furniture, and other dynamically changing conditions, such as movement people goods, varying temperature air flows. Here, we develop a new computational framework estimate location without need for The method, which term O...

A kriging-based metamodelling approach for analysing the structural reliability of a sheetpile wall in dyke is formulated. This specific problem characterised by high target reliabilities (Pf∼10−7) combination with noisy and incomplete numerical model response. Starting from original formulation active learning Monte Carlo simulation (AK-MCS), robust two-stage metamodel framework formulated ada...

We present a novel approach to training data-driven dependency parsers on incomplete annotations. Our parsers are simple modifications of two well-known dependency parsers, the transition-based Malt parser and the graph-based MST parser. While previous work on parsing with incomplete data has typically couched the task in frameworks of unsupervised or semi-supervised machine learning, we essent...

RGB salient object detection (SOD) performs poorly in low-contrast and complex background scenes. Fortunately, the thermal infrared image can capture heat distribution of scenes as complementary information to image, so RGB-T SOD has recently attracted more attention. Many researchers have committed accelerating development SOD, but some problems still remain be solved. For example, defective s...

Introduction: The purpose of multimodal and co-registration In MR Neuroimaging is to fuse two or more sets images (T1, T2, fMRI, DTI, pMRI, …) for combining the different information into a composite correlated data set in order to visualization, re-alignment and generating transform to functional Matrix. Multimodal registration and motion correction in spinal cord MR Neuroimag...

We present a simple but very effective approach to identifying high-quality data in noisy data sets for structured problems like parsing, by greedily exploiting partial structures. We analyze our approach in an annotation projection framework for dependency trees, and show how dependency parsers from two different paradigms (graph-based and transition-based) can be trained on the resulting tree...

The field of Operational Modal Analysis (OMA) has recently become an emerging research interest. OMA, also known as Response-Only Modal Analysis, extracts the modal parameters by processing only the system response data. In many experimental measurement situations, the system’s output data series is short in length and buried under potentially correlated noise. In such cases, the separation of ...

introduction: the accuracy of analyzing functional mri (fmri) data is usually decreases in the presence of noise and artifact sources. a common solution in for analyzing fmri data having high noise is to use suitable preprocessing methods with the aim of data denoising. some effects of preprocessing methods on the parametric methods such as general linear model (glm) have previously been evalua...

The issues of Real World are Very large data sets, Mixed types of data (continuous valued, symbolic data), Uncertainty (noisy data), Incompleteness (missing, incomplete data), Data change, Use of background knowledge etc. Lot of knowledge related to the application can be generated through these large data sets. Rough set is the methodology which can be used to deduce rules from these data sets.