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Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) <xref ref-type="bibr" rid="ref1">[1]</xref>. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting...
Traffic classification plays an important and basic role in network management and cyberspace security. With the widespread use of encryption techniques in network applications, encrypted traffic has recently become a great challenge for the traditional traffic classification methods. In this paper we proposed an end-to-end encrypted traffic classification method with one-dimensional convolution neural...
Mining advisor-advisee relationships can benefit many interesting applications such as advisor recommendation and protege performance analysis. Based on the hypothesis that, advisor-advisee relationships among researchers are hidden in scholarly big data, we propose in this work a deep learning based advisor-advisee relationship identification method which considers the personal properties and network...
Due to the data diversity and complexity in industrial system, the accuracy of data-based modeling might be largely affected by such a series of issues. Aiming at the energy system in steel industry, this study proposes a fuzzy modeling based on Gaussian membership expression. First, in the stage of sample selection, the industrial data set is divided into a number of clusters, from which the representative...
Making the science assessment and prediction of the credit risk of the small and medium-sized enterprises (SME) is a significant part of risk management of commercial bank. This paper firstly integrates Genetic Algorithm (GA) with v-SVR model, creates the credit prediction model, GA-v-SVR, and then builds the SME credit risk indicator system. Using principal component analysis method screens out the...
Anomaly intrusion detection is an important issue in computer network security. As a step of data preprocessing, attribute normalization is essential to detection performance. However, many anomaly detection methods do not normalize attributes before training and detection. Few methods consider to normalize the attributes but the question of which normalization method is more effective still remains...
A robust inferential estimator model based on improved dynamic principal component analysis (DPCA) and multiple neural networks (MNN) was proposed. Data for building non-linear models was re-sampled using DPCA algorithm to form a number of sets of training and test data. For each data set, a neural network model was developed. To improve the robustness and accuracy of the neural networks, the MNN...
Since malicious shill bidding behaviors changes frequently, we need a profiling mechanism to profile malicious bidders' behaviors. In this study, we apply the self-organizing map (SOM) based on shilling features to propose a profiled malicious bidder behavior model. Via this model, the auctioneer can realize the changed malicious bidder behaviors and devise detection method for detecting malicious...
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