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IS Offshoring call center employees engaging with both the vendor and the client when performing outsourced IS services may develop dual identification with the vendor and the client. However, the antecedents and consequences of such dual identification, and the relationships between them are poorly understood. We draw on social and organizational identity theory to seek answers to the preceding concerns...
SMS has become an indispensable tool in people's life. How to help people effectively anti spam SMS and create a healthy, harmonious and ordered environment has become a new research hotspot. On the basis of learning Bayesian learning theory, this thesis mainly researches Bayesian classification model and Bayesian decision based on the minimum risk.
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...
As viruses become more complex, existing antivirus methods are inefficient to detect various forms of viruses, especially new variants and unknown viruses. Inspired by immune system, a hierarchical artificial immune system (AIS) model, which is based on matching in three layers, is proposed to detect a variety of forms of viruses. In the bottom layer, a non-stochastic but guided candidate virus gene...
This paper proposed a LDA-based cross-language retrieval model that did not rely on word-by-word translation of query or document. Instead, a parallel corpus was used to estimate a cross-language LDA (Latent Dirichlet Allocation) model. We assumed that a topic variable Z in LDA could generate both an English token and a Chinese token, given that the parallel corpus contained two languages: English...
In this paper, we propose a novel approach to automatically generating, instead of manually designing, discriminative visual features for face detection. The features are composed by multiple local features (e.g., Haar features), and such features can capture not only the local texture information but also their spatial configurations. Therefore, the proposed feature contains rich semantic information...
As the first step in the basic cognitive cycle, spectrum sensing is a vital part of cognitive radio to become reliant and swift. And due to excellent detection performance, cooperative spectrum sensing has been applied more and more frequently. However, in cooperative spectrum sensing, the detection results of the secondary users (SUs) have a great degree of uncertainty. In this paper, in order to...
In this paper, we introduce a new classifier ensemble approach, applied to tissue segmentation in optical images of the uterine cervix. Ensemble methods combine the predictions of a set of diverse classifiers. The main contribution of our approach is an effective way of combination based on each classifier's performance level-namely, the sensitivity p and specificity q, which also produces an optimal...
We empirically evaluate a distance-guided learning method embedded in a multiple classifier system (MCS) for tissue segmentation in optical images of the uterine cervix. Instead of combining multiple base classifiers as in traditional ensemble methods, we propose a Bhattacharyya distance based metric for measuring the similarity in decision boundary shapes between a pair of statistical classifiers...
The ultimate bearing capacity of single driven pile increases with time after pile installation, and it is of great importance to foundation design. Although various methods have been established to study this time-dependent ultimate bearing capacity of driven pile, they have different occasions for application, and the deduced results of them may differ greatly. In this paper, four typical types...
The problem of ranking has recently gained attention in data learning. The goal ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. In this paper, we apply popular Bayesian techniques on ranking support vector machine. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural...
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...
DDoS attacks are major threats in current computer networks. However, DDoS attacks are difficult to be quickly detected. In this paper, we introduce a system that only extracts several important attributes from network traffic for DDoS attack detection in real computer networks. We collect a large set of DDoS attack traffic by implementing various DDoS attacks as well as normal data during normal...
The current study presented a generalized regression neural network (GRNN) based approach to predict nitrogen oxides (NOx) emitted from coal-fired boiler. A novel 'multiple' smoothing parameters, which is different from the standard algorithm in which only single smoothing parameter was adopted (Matlab neural network toolbox, for example), were assigned to GRNN model. K-means clustering algorithm...
To assure justice and science of scientific and technological project evaluation, avoiding the corrupt transaction in the process of project evaluation, it is necessary to evaluation the experts' performance with a scientific method. The main factors that affect the experts' performance evaluation were analyzed. To avoid the effect of individual subjective judgment and favoritism on the result of...
One of the major problems in target recognition is that targets may be changed with translation, rotation, scale and intensity. A numerals recognition model based on PCNN (pulse-coupled neural networks) and FPF (fractional-power filter) is proposed in this paper, which use inherent ability of PCNN to extract feature and capability of FPF allowing invariance to be built into can recognize numerals...
To improve efficiency and quality of case retrieval in case-based reasoning system, a case retrieval model based on the artificial neural network (ANN) and nearest neighbor (NN) algorithm is presented. Firstly, the indexes of cases are created in order to shrink the case-searching range, and the BP neural network is applied to memorize the product cases that are indexed. Secondly, the similar cases,...
Extracting attributes from network traffic is the first step of network intrusion detection. However, the question of what attributes are most effective for the detection still remains. In this paper, we employed information gain, wrapper with Bayesian networks (BN) and decision trees (C4.5) respectively to select key subsets of attributes for network intrusion detection based on KDD Cup 1999 data...
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...
An adaptive neural network control of a novel type of translational meshing motor with model uncertainties is considered. Owing to its nonlinear characteristic, a model reference control system which consists of two neural networks is used. The torque model is identified based on BP neural network, and then a RBF neural network works as the controller. The model reference control system is trained...
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