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As system of systems (SoS) models become increasingly complex and interconnected a new approach is needed to capture the effects of humans within the SoS. Many real-life events have shown the detrimental outcomes of failing to account for humans in the loop. This research introduces a novel and cross-disciplinary methodology for modeling humans interacting with technologies to perform tasks within...
Logs, which record valuable system runtime information, have been widely employed in Web service management by service providers and users. A typical log analysis based Web service management procedure is to first parse raw log messages because of their unstructured format, and then apply data mining models to extract critical system behavior information, which can assist Web service management. Most...
This paper presents the current state of a study that appealed to digital technologies as a privileged means of training, in the language of schooling of African students' parents. The main objective of this study was to involve these parents in the school life of the students. The research methodology was an action-research model, using a variety of qualitative data collection and analysis techniques:...
In this article the problem of improving the accuracy of virtual analyzers for petroleum products quality determination was researched. Virtual analyzers increase the quality of products data generation on efficiency to management problem solving and optimization at the pace of the process. The goal was to develop the virtual analyzers of sulfuric acid alkylation unit main product based on neural...
with the development of Internet, many companies announced the stock information by various medium, and stock buyers comment that information as well as make rational investment strategies to maximize their profit. At present, many retail investors, who lack of channels to obtain real information, scarce professional knowledge of investment theory and easy to be affected by public opinion of Internet,...
Computational engagement with the HathiTrust Digital Library (HTDL) is confounded by the in- copyright status and licensing restrictions on the majority of the content. Because of these limitations, computational analysis on the HTDL must either be carried out in a secure environment or on derivative datasets. The HathiTrust Research Center (HTRC) Data Capsule service provides researchers with a secure...
The current Internet should not only support simple packet forwarding, but also be able to provide diverse network functions to compose different routing services on the communication paths for network applications. Meanwhile, the user requirements for different applications are becoming more and more diversified, it is a challenge to distinguish the differences among these requirements in order to...
Linear discriminant analysis (LDA) is the most commonly used classification method for movement intention decoding from myoelectric signals. In this work, we review the performance of various discriminant analysis variants on the task of hand motion classification. We demonstrate that optimal classification performance is achieved with regularized discriminant analysis (RDA), a method which generalizes...
This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes (VHGP) [1] that capture the mean and variance of a stochastic function. We show that we can learn accurate...
The principal objective when monitoring compute and communications infrastructure is to minimize the Mean Time To Resolution of service-impacting incidents. Key to achieving that goal is determining which of the many alerts that are presented to an operator are likely to be the root cause of an incident. In turn this is critical in identifying which alerts should be investigated with the highest priority.
Aspect-based sentiment analysis has always been a difficult task since it consists of several core sub-tasks: feature detection, opinion extraction and polarity classification. Consequently, by now there is little work to summarize all of these works together. In this paper, we propose a brand new holistic system, which can deal with all the problems above simultaneously using aspect-based positive...
Data-driven predictive analytics are in use today across a number of industrial applications, but further integration is hindered by the requirement of similarity among model training and test data distributions. This paper addresses the need of learning from possibly nonstationary data streams, or under concept drift, a commonly seen phenomenon in practical applications. A simple dual-learner ensemble...
Gray mold causes a sharp drop in blueberries production, the modeling and analysis of blueberry gray mold are important for the future study of blueberry related diseases and insect pests. In the introduction, the related diseases and insect pests of blueberry were summarized. The modeling method of blueberry pests and diseases was introduced. On this basis, the blueberry gray mold was modeled. And...
The Oracle model has been used not only for comparison between techniques but also in the design of different methods in Multiple Classifier Systems (MCS). Even though the model represents the ideal classifier selection scheme, Dynamic Classifier Selection (DCS) techniques present a large performance gap from the Oracle. This means that, for a significant number of instances, the DCS techniques are...
Most work on tweet sentiment analysis is mono-lingual and the models that are generated by machine learning strategies do not generalize across multiple languages. Cross-language sentiment analysis is usually performed through machine translation approaches that translate a given source language into the target language of choice. Machine translation is expensive and the results that are provided...
In order to analyze the economic performance of thermal power plant, a partial least squares support vector machine coupling model was constructed. First of all, the coal consumption rate was selected as the evaluating indicator, which is an important indicator to evaluate the economy of power plant. At the same time, the physical quantities were established, which is closely related to coal consumption...
In this paper, we propose a hybrid method for background modeling, subtracting, and extracting moving objects, which is based on the use of W4 and Extended Centre Symmetric Local Binary Pattern (XCS-LBP) approaches. Initially, we apply W4 to get the foreground mask of the video scene, after that we use XCS-LBP to clarify the results obtained. The main focus of this paper is to illustrate moving objects...
Predicting the temporal evolution of images is an interesting problem that has applications in surveillance, content recommendation and behavioral analysis. Given a single image or a stream of images with timestamps, the goal of this work is to predict possible images that could appear at different time instances in the future. We propose a data-driven Riemannian shape theoretic approach for this...
Aiming at the problems of redundant construction, decentralized investment and extravagance in the current military resource allocation, this paper proposes a new combination evaluation model based on Analytic Hierarchy Process (AHP) and matter-element analysis. The model integrates the merits of AHP and matter-element analysis, and solves the common incompatibility problem of multi-index evaluation...
This research presents electrical signal waveforms analysis and classification by applying the principle and theory of ANFIS. The input data for training and testing the network were processed by using Fast Fourier Transform. There are three input variables and one output for the network. From the experiment to determine the number of nodes in the 1st layer in order to obtain the optimal Mean Square...
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