In order to extend the practicality of Augmented Reality (AR), a method based on Temporal Psycho Visual Modulation (TPSM) technology and digital camera to realize AR effect was proposed. First, the AR tags were embedded in the digital screen of the media. Based on the principle difference between the human eye to identify the perception and the digital camera to capture the image formed in the digital screen or projector, the digital camera equipment was used to obtain the digital screen image with AR tags which are not easily to be detected by human eye. Finally, the AR effect was displayed on the smart device that gets the AR tags. Simulation results show that the combination of AR and TPVM technology can accurately identify the AR tags in the image and achieve AR effect, while the human eye can not detect the AR tags. Through the mobile phone instead of 3D glasses and other extra equipment, the use restrictions of AR are reduced, and the practicality of AR is also expanded.

In order to solve the problem of slighty imbalanced classification in Web spam detection, three ensemble classifiers based on random under-sampling techniques were proposed, including Random Under-Sampling once without replacement (RUS-once), Random Under-Sampling multiple times without replacement (RUS-multiple) and Random Under-Sampling with replacement (RUS-replacement). At first, the unbalanced training dataset was converted into several balanced datasets by using one of the under-sampling techniques. Secondly, the Classification And Regression Tree (CART) classifiers were trained based on the balanced datasets. Finally, an ensemble classifier was constructed with all of the CART classifiers based on simple voting rule and used to classify the test samples. The experimental results show that the three kinds of random under-sampling based ensemble classifiers achieve good classification results, the performance of RUS-multiple and RUS-replacement are better than RUS-once. Compared with CART, Bagging with CART and Adaboost with CART, the AUC values of RUS-multiple and RUS-replacement increase about 10% on WEBSPAM UK-2006 and about 25% on WEBSPAM UK-2007; compared with several state-of-the-art baseline classification models, RUS-multiple and RUS-replacement achieve the optimal results in AUC value.

To solve the problem of "curse of dimensionality" and imbalance classification, a binary classifier algorithm based on immune clonal feature selection and Under-Sampling (US) ensemble was proposed to detect Web spam. Firstly, major samples in training dataset were sampled into several sample subsets, which were combined with minor samples to generate several balanced training sample subsets. Then an immune clonal algorithm was proposed to select several optimal feature subsets. The balanced training subsets were projected to multiple views based on the optimal feature subsets. Finally, several Random Forest (RF) classifiers were trained by these views of the training sample subsets to classify the testing samples. The testing samples' classifications were determined by voting. The experimental results on the WEBSPAM UK-2006 dataset show that the ensemble classifier algorithm outperforms these algorithms like RF, Bagging with RF and AdaBoost with RF, and its accuracy, F1-Measure, AUC (Area Under ROC Curve) are increased by more than 11% respectively. Compared with several state-of-the-art baseline classification models, the F1-Measure is increased by 2% and the AUC reaches the optimum result using the ensemble classifier.

In order to solve the problem of imbalance classification and "curse of dimensionality", a binary classifier algorithm based on Random Forest (RF) and under-sampling ensemble was proposed to detect Web spam. Firstly, majority samples in training dataset were sampled into several sub sample sets, each of them was combined with minority samples and several balanced training sample sub sets were generated; then several RF classifiers were trained by these training sample sub sets to classify the testing samples; finally, the testing samples' classifications were determined by voting. Experiments on the WEBSPAM UK-2006 dataset show that the ensemble classifier outperformed RF, Bagging with RF and Adaboost with RF etc., and its accuracy, F1-measure, AUC increased by at least 14%, 13% and 11%. Compared with the winners of Web spam challenge 2007, the ensemble classifier increased F1-measure by at least 1% and reached to the optimum result in AUC.