Aiming at the data noise filtering problem and the importance problem of different POIs in POI (Point-Of-Interest)recommendation research, a POI recommendation algorithm, named RecSI (Recommendation by Spatiotemporal information and POI Importance), was proposed. First, the geographic information and the mutual attraction between the POIs were used to filter out the data noise, so as to narrow the range of candidate set. Second, the user’s preference score was calculated by combining the user’s preference on the POI category at different time periods of the day and the popularities of the POIs. Then, the importances of different POIs were calculated by combining social information and weighted PageRank algorithm. Finally, the user’s preference score and POI importances were linearly combined in order to recommend TOP- K POIs to the user. Experimental results on real Foursquare sign-in dataset show that the precision and recall of the RecSI algorithm are higher than those of baseline GCSR (Geography-Category-Socialsentiment fusion Recommendation) algorithm by 12.5% and 6% respectively, which verify the effectiveness of RecSI algorithm.

A new feature selection algorithm using forest optimization algorithm was proposed, which aimed at solving the problems of the traditional feature selection using forest optimization algorithm in the stages of initialization, candidate forest generation and updating. In the algorithm, Pearson correlation coefficient and L₁ regularization method were used to replace the random initialization strategy in the initialization stage, the methods of separating good and bad trees and fulfilling the difference were used to solve the problems of incompletion of good and bad trees in the candidate forest generation stage, and trees having the same precision but different dimension with the optimal tree were added to the forest in the updating stage. In the experiments, with the same experimental data and experimental parameters, the proposed algorithm and the traditional feature selection using forest optimization algorithm were used to test the small, medium and large dimension data respectively. The experimental results show that the proposed algorithm is better than the traditional feature selection using forest optimization algorithm in the classification performance and dimension reduction ability on two medium and two large dimension data. The experimental results prove the effectiveness of the proposed algorithm in solving feature selection problems.

The sparse data of Web services Quality of Service (QoS) which is invoked by service users in Web service recommendation may lead to low recommendation quality. In order to solve the problem, a collaborative filtering based Web service Recommendation algorithm for User Group (WRUG) was proposed. Firstly, personalized similar user group was constructed for each service user according to user similarity matrix. Secondly, instead of the group, the center of similar user group was employed to compute the user group similarity matrix. Finally, Web service recommendation equation with user group was defined and missing QoS values of Web service were predicted for target user. And a dataset was used for experiments which included 1.97 million real-world Web QoS invocation records. Compared with Traditional Collaborative Filtering algorithm (TCF) and Collaborative Filtering recommendation algorithm Based on User Group Influence (CFBUGI), the mean absolute error of the proposed WRUG was decreased by 28.9% and 4.57% respectively, and the coverage rate of WRUG was increased by 110% and 22.5% separately. The experimental results show that the proposed WRUG can not only achieve better prediction accuracy of Web service recommendation system, but also noticeably enhance the percentage of valuable predicted QoS values under the same experimental settings.

The traditional Web services selection algorithms were analyzed and the problems existing in dynamic environment were pointed out. A personalized Web services selection method based on collaborative filtering was proposed to address these problems. And a personalized Web service selection framework was designed, which used the collaborative filtering to predict the Quality of Service (QoS) and selected the best service that met users' requirements. About 1.5 million real world QoS data were employed to evaluate the proposal with other four methods and the experimental results demonstrate that the proposed method is a feasible manner and it provides better prediction results.

To solve the low performance and efficiency issues of the traditional centralized index when processing large-scale unstructured knowledge, the authors proposed the retrieval algorithm based on text clustering. The algorithm used text clustering algorithm to improve the existing index distribution method, and reduced the search range by judging the query intent through the distance of query and clusters. The experimental results show that the proposed scheme can effectively alleviate the pressure of indexing and retrieval in handling large-scale data. It greatly improves the performance of distributed retrieval, and it still maintains relatively high accuracy rate and recall rate.

The paper proposed an efficient algorithm of collision detection by using Bounding Volume Hierarchy (BVH) in order to improve the real-time performance in virtual surgery. The main contribution of this work was to use the technology of mixed bounding volume hierarchy to represent different objects according to different topology structure. First, surgical instruments and objects were represented as hierarchy tree. Then the intersection test was implemented between sphere and oriented bounding box for eliminating disjoint parts fast. After that more accurate triangle collision test was used to determine the contact status in overlapping parts. Experimental results show that our algorithm achieves higher speed compared to the algorithm of single bounding box.

Hiding capacity, robustness and invisibility are some of the key parameters in information hiding system. Moreover, these parameters are seriously impacted by the difference of Discrete Cosine Transform (DCT) coefficients in DCT domain hiding algorithm. Embedded capacity, which is impacted by the mutual interference of visual perception of different DCT coefficients and the reverse DCT, was analyzed in this paper. Furthermore, the relation between hiding capacity and coefficients-chosen was given out in DCT domain hiding algorithm. A conclusion that there is no correlation between embedded position and robustness against compression of embedded information was put forward, and embedded capacity could be improved in reference to this conclusion. The experimental results show that this conclusion is correct even if the system is disturbed by noise.

By clustering of traffic flow time series, the typical traffic fluctuation patterns can be found. Generally, the euclidean distance and K-means algorithm can be used to clustering the time series, but it is hard to separate the time series with great different variability well. To solve this problem, fluctuation similarity measure, such as dynamic time warping and gray relation grade, and the hierarchical clustering algorithm were used to further separate the traffic flow time series. The experiments show that the proposed method can work and the gray relation grade measure is better suited for the problem than the dynamic time warping measure.

This paper studied the supply chain optimal algorithm based on manufacturing resource limits, defined the mathematical model, established the first-order necessary conditions of Karush-Kuhn-Tucker (KKT) optimality for it. Considering the Lagrangian theorem, the iterative solution approaches in difference conditions were presented. Our computational testing indicates that both algorithms converge in a few iterations and are very efficient.