An efficient semi-supervised multi-level intrusion detection algorithm was proposed to solve the problems existing in present intrusion detection algorithms such as difficulty of collecting a lot of tagged data for supervised learning-based algorithms, low accuracy of unsupervised learning-based algorithms and low detection rate on R2L (Remote to Local) and U2L (User to Root) of both types of algorithms. Firstly, according to Kd-tree (K-dimension tree) index structure, weighted density was used to select initial clustering centers of K-means algorithm in high-density sample region. Secondly, the data after clustering were divided into three clusters. Then, weighted voting rule was utilized to expand the labeled dataset by means of Tri-training from the unlabeled clusters and mixed clusters. Finally, a hierarchical classification model with binary tree structure was designed and experimental verification was performed on NSL-KDD dataset. The results show that the semi-supervised multi-level intrusion detection model can effectively improve detection rate of R2L and U2R attacks by using small amount of tagged data, the detection rates of R2L and U2R attacks reach 49.38% and 81.14% respectively, thus reducing the system's false negative rate.

Focused on the problem that the convergence speed of traditional iterative learning control algorithm used in linear regular systems is slow, a kind of fast iterative learning control algorithm was designed for a class of linear regular systems. Compared with the traditional P-type iterative learning control algorithm, the algorithm increases tracking error at neighboring two iterations generated from last difference signal and present difference signal. And the convergence of the algorithm was proven by using Yong inequality of convolutional inference in the sense of Lebesgue- p norm. The results show the tracking error of the system will converge to zero with infinite iterations. The convergence condition is also given. Compared with P-type iterative learning control, the proposed algorithm can fasten the convergence and avoid the shortcomings of using λ norm to measure the tracking error. Simulation further testifies the validity and effectiveness.

It is difficult for entity search and question answering over text corpora to join cues from multiple documents to process relationship-centric search tasks, although structured querying over knowledge base can resolve such problem, but it still suffers from poor recall because of the heterogeneity and incompleteness of knowledge base. To address these problems, the knowledge graph was extended with information from textual corpora and a corresponding triple pattern with textual phrases was designed for uniform query of knowledge graph and textual corpora. Accordingly, a model for automatic query relaxation and scoring query answers (tuples of entities) was proposed, and an efficient top- k query processing strategy was put forward. Comparison experiments were conducted with two classical methods on three different benchmarks including entity search, entity-relationship search and complex entity-relationship queries using a combination of the Yago knowledge graph and the entity-annotated ClueWeb '09 corpus. The experimental results show that the entity-relationship search system with query relaxation over extended knowledge base outperforms the comparison systems with a big margin, the Mean Average Precision (MAP) are improved by more than 27%, 37%, 64% respectively on the three benchmarks.

The current software reliability prediction model has big prediction accuracy fluctuation and poor adaptability in field data of reliability with strong randomness and dynamics. In order to solve the problems, a software reliability prediction model based on grey Elman neural network was proposed. First, the grey GM (1,1) model was used to predict the failure data and weaken its randomness. Then the Elman neural network was utilized to build the model for predicting the residual produced by GM (1,1), and catch the dynamic change rules. Finally, the prediction results of GM (1,1) and Elman neural network residual were combined to get the final prediction outcomes. The simulation experiment was conducted by using field failure data set produced by the flight inquiry system. The gray Elman neural network model was compared with Back-Propagation (BP) neural network model and Elman neural network model, the corresponding Mean Squared Error (MSE) and Mean Relative Error (MRE) of the three models were respectively 105.1, 270.9, 207.5 and 0.0011, 0.0021, 0.0016. The errors of gray Elman neural network prediction model were the minimum. The experimental results show that the proposed gray Elman neural network prediction model has higher prediction accuracy.

Federated search is a widely-used technique to find information on Deep Web. Given a user query, one of the challenges for a federated search system is to select a set of resources that are most likely to return relevant results for the query. Most existing resource selection methods are based on text-matching between the sample documents of the resource and the query, which typically suffer the problem of missing vocabulary or incomplete information. To alleviate the problem of incomplete information, Latent Dirichlet Allocation (LDA) topic model approach for resource selection was proposed. First, topic probability distributions for resources and query were inferred using LDA topic model approach. Then the similarities between the topic distributions of resources and query were calculated to rank the resources. By mapping both resources and the query into the low dimensional topic space, the problem of missing information caused by the sparsity of high dimensional word space was alleviated. Experiments were conducted on the test sets of TREC FedWeb 2013 and 2014 Tracks, and the results were compared with that of other participants in the Tracks. The experimental results on the TREC FedWeb 2013 Track show that the LDA based approach outperforms the best result of other participants by 24%; and the results on the TREC FedWeb 2014 Track show that it outperforms the best results of the traditional text-matching-based resource selection methods using either small-or big-document strategies by 22% for small-document methods and 43% for big-document methods respectively. In addition, using sampled snippets rather than documents to generate big-document representation for resources can significantly improve the efficiency of the system, thus enables the proposed approach more feasible and applicable in practice.

In this paper a Graphic Processing Unit (GPU)-based and seamless method for terrain rendering was proposed. The method utilized binary tree to construct the terrain Level Of Detail (LOD) which was represented by row-column templates. In this method, the elevation data was transformed to elevation-texture and then the elevation-texture was used to extract elevation for rendering by Vertex Texture Fetch (VTF). And the whole process was handled by GPU so that the data accessing efficiency was greatly improved. The force-split method in Real-time Optimally Adapting Meshes (ROAM) was used in this method which solved terrain cracks by limiting the level of adjacent terrain blocks. The TriangleStrip primitive was used for terrain rendering which avoided transferring the same vertices' coordinates of adjacent triangles to GPU. Finally the paper tested the efficiency of the new method by two terrain datasets, and compared the frame rates between Clipmap and the new method. The results show that the new proposed method solves the terrain crack effectively and fulfills the demand of interactive terrain rendering.