Focused on the issue that the attribute reduction algorithm based on neighborhood rough set only considers the influence of a single attribute on the decision attribute, and fails to consider the correlation among different attributes, a Neighborhood Rough Set attribute reduction algorithm based on Chi-square test (ChiS-NRS) was proposed. Firstly, the Chi-square test was used to calculate the correlation, and the influence between the related attributes was considered when selecting the important attributes, making the time complexity reduced and the classification accuracy improved. Then, the improved algorithm and the Gradient Boosting Decision Tree (GBDT) algorithm were combined to establish a classification model and the model was verified on UCI datasets. Finally, the proposed model was applied to predict the occurrence of microvascular invasion in hepatocellular carcinoma. The experimental results show that the proposed algorithm has the highest classification accuracy on some UCI datasets compared with the reduction algorithm without reduction and neighborhood rough set reduction algorithm. In the prediction of microvascular invasion in hepatocellular carcinoma, compared with Convolution Neural Network (CNN), Support Vector Machine (SVM) and Random Forest (RF) prediction models, the proposed model has the prediction accuracy of 88.13% in test set, the sensitivity, specificity and the Area Under Curve (AUC) of Receiver Operating Curve (ROC) of 88.89%, 87.5% and 0.90 respectively are the best. Therefore, the prediction model proposed can better predict the occurrence of microvascular invasion in hepatocellular carcinoma and assist doctors to make more accurate diagnosis.

To combat low efficiency of Artificial Bee Colony (ABC) algorithm on solving combinatorial problem, a parallel ABC optimization algorithm based on Spark was presented. Firstly, the bee colony was divided into subgroups among which broadcast was used to transmit data, and then was constructed as a resilient distributed dataset. Secondly, a series of transformation operators were used to achieve the parallelization of the solution search. Finally, gravitational mass calculation was used to replace the roulette probability selection and reduce the time complexity. The simulation results in solving the Traveling Salesman Problem (TSP) prove the feasibility of the proposed parallel algorithm. The experimental results show that the proposed algorithm provides a 3.24x speedup over the standard ABC algorithm and its convergence speed is increased by about 10% compared with the unimproved parallel ABC algorithm. It has significant advantages in solving high dimensional problems.

The binary descriptor based on Learned Arrangements of Three Patch Codes (LATCH) lacks of scale invariance and its rotation invariance depends upon feature detector, so a new feature detection and description algorithm was proposed based on Oriented fast and Rotated Binary robust independent elementary feature (ORB) and LATCH. Firstly, the Features from Accelerated Segment Test (FAST) was adopted to detect corner feature on the scale space of image pyramid. Then, the intensity centroid method of ORB was used to obtain orientation compensation. Finally, the LATCH was used to describe the feature. The experimental results indicate that, the proposed algorithm has the characteristics of low computational complexity, high real-time performance, rotation invariance and scale invariance. Under the same accuracy, the recall rate of the proposed algorithm is better than ORB and HARRIS-LATCH algorithm, the matching inner rate of the proposed algorithm is higher than ORB algorithm by 4.2 percentage points. In conclusion, the proposed algorithm can reduce the performance gap with histogram based algorithms such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) while maintaining the real-time property, and it can deal with image sequence in real-time quickly and exactly.

The Spark computing framework has been adopted as the framework for big data analysis by an increasing number of enterprises. However, the complexity of the system is increased due to the characteristic that it is typically deployed in distributed and cloud environments. Therefore, it is always considered to be difficult to monitor the performance of the Spark framework and finding jobs that lead to performance degradation. In order to solve this problem, a real-time monitoring and analysis method for Spark performance in distributed container environment was proposed and compiled. Firstly, the resource consumption information of jobs at runtime was acquired and integrated through the implantation of code in Spark and monitoring of Application Program Interface (API) files in Docker containers. Then, the Gaussian Mixture Model (GMM) was trained based on job history information of Spark. Finally, the trained model was used to classify the resource consumption information of Spark jobs at runtime and find jobs that led to performance degradation. The experimental results show that, the proposed method can detect 90.2% of the abnormal jobs and it only introduces 4.7% degradation to the performance of Spark jobs. The proposde method can lighten the burden of error checking and help users find the abnormal jobs of Spark in a shorter time.

Aiming at improving the users' high viscosity in social networks, an algorithm based on user loyalty in social network system was proposed. In the proposed algorithm, double Recency Frequency Monetary (RFM) model was used for mining the different loyalty kinds of users. Firstly, according to the double RFM model, the users' consumption value and behavior value were calculated dynamically and the loyalty in a certain time was got. Secondly, the typical loyal users and disloyal users were found out by using the founded standard curve and similarity calculation. Lastly, the potential loyal and disloyal users were found out by using modularity-based community discovery and independent cascade propagation model. On some microblog datasets of a social network, the quantitative representation of user loyalty was confirmed in Social Network Service (SNS), thus the users could be distinguished based on users' loyalty. The experimental results show that the proposed algorithm can be used to effectively dig out different loyalty kinds of users, and can be applied to personalized recommendation, marketing, etc. in the social network system.

Visual Background extractor (ViBe)model for moving target detection cannot avoid interference caused by irregular flicker pixels noise in dynamic outdoor scenes. In order to solve the issue, a flicker pixels noise-suppression method based on ViBe model algorithm was proposed. In the initial stage of background model, a fixed standard deviation of background model samples was used as the threshold value to limit the range of background model samples and get suitable background model samples for each pixel. In the foreground detection stage, an adaptive detection threshold was applied to improve the accuracy of detection result. Edge inhibition of image edge background pixels was executed to avoid error background sample values updating to the background model in the background model update process. On the basis of above, morphological operation was added to fix connected components to get more complete foreground images. Finally, the proposed method was compared with the original ViBe algorithm and the ViBe's improvement with morphology post-processing on the results of multiple video sequences. The experimental results show that the flicker pixels noise-suppression method can suppress flicker pixels noise effectively and get more accurate results.

A big data benchmark is needed eagerly by customers, industry and academia, to evaluate big data systems, improve current techniques and develop new techniques. A number of prominent works in last several years were reviewed. Their characteristics were introduced and the shortcomings were analyzed. Based on that, some suggestions on building a new big data benchmark are provided, including: 1) component based benchmarks as well as end-to-end benchmarks should be used in combination to test different tools inside the system and test the system as a whole, while component benchmarks are ingredients of the whole big data benchmark suite; 2) workloads should be enriched with complex analytics to encompass different application requirements, besides SQL queries; 3) other than performance metrics (response time and throughput), some other metrics should also be considered, including scalability, fault tolerance, energy saving and security.

Focusing on the drawback that Discovering Maximum Frequent Itemsets Algorithm (DMFIA) has to generate lots of maximum frequent candidate itemsets in each dimension when given datasets with many candidate items and each maximum frequent itemset is not long, an improved Algorithm for mining Maximum Frequent Itemsets based of Frequent-Pattern tree (FP-MFIA) for mining maximum frequent itemsets based on FP-tree was proposed. According to Htable of FP-tree, this algorithm used bottom-up searches to mine maximum frequent itemsets, thus accelerated the count of candidates. Producing infrequent itemsets with lower dimension according to conditional pattern base of every layer when mining, cutting and reducing dimensions of candidate itemsets can largely reduce the amount of candidate itemsets. At the same time taking full advantage of properties of maximum frequent itemsets will reduce the search space. The time efficiency of FP-MFIA is at least two times as much as the algorithm of DMFIA and BDRFI (algorithm for mining frequent itemsets based on dimensionality reduction of frequent itemset) according to computational time contrast based on different supports. It shows that FP-MFIA has a clear advantage when candidate itemsets are with high dimension.

Focusing on the issue that the prediction model for task accuracy is deficient in the relationship of speed-accuracy tradeoff in human computer interaction, a method of predictive model for accuracy based on temporal constraint was proposed. The method studied the relationship between task accuracy and specified temporal constraint when users tried to complete the task with a specified amount of time in the computer user interface by controlled experiments, which was used to measure the human performance in temporal constraint tasks. A series of steering tasks with temporal constraint were designed in the experiment, which manipulated the tunnel amplitude, tunnel width and specified movement time. The dependent variable in the experiment was the task accuracy, which was quantifiable as lateral deviation of the trajectory. It was pointed out that the task accuracy was linearly related to tunnel width and steering speed (indicated as specified movement time divided by tunnel amplitude) by analyzing the experimental data from 30 participants. Finally, a quantitative model was established to predict the task accuracy based on the least-square regression in steering tasks with temporal constraint. The proposed model has a good fit with the real dataset, the goodness of fit is 0.857.

To solve the low running speed problem of Knuth39 random number generator, a Knuth39 parallelization method based on Many Integrated Core (MIC) platform was proposed. Firstly, the random number sequence of Knuth39 generator was divided into subsequences by regular interval. Then, the random numbers were generated by every thread from the corresponding subsequence's starting point. Finally, the random number sequences generated by all threads were combined into the final sequence. The experimental results show that the parallelized Knuth39 generator successfully passed 452 tests of TestU01, the results are the same as those of Knuth39 generator without parallelization. Compared with single thread on Central Processing Unit (CPU), the optimal speed-up ratio on MIC platform is 15.69 times. The proposed method improves the running speed of Knuth39 generator effectively, ensures the randomness of the generated sequences, and it is more suitable for high performance computing.

To improve the accuracy of bird sounds recognition in low Signal-to-Noise Ratio (SNR) environment, a new bird sounds recognition technology based on Radon Transform (RT) and Translation Invariant Discrete Wavelet Transform (TIDWT) from spectrogram after the noise reduction was proposed. First, an improved multi-band spectral subtraction method was presented to reduce the background noise. Second, short-time energy was used to detect silence of clean bird sound, and the silence was removed. Then, the bird sound was translated into spectrogram, RT and TIDWT were used to extract features. Finally, classification was achieved by Support Vector Machine (SVM) classifier. The experimental results show that the method can achieve better recognition effect even the SNR belows 10dB.

Isometric feature mapping (Isomap) algorithm is topologically unstable if the input data are distorted. Therefore, an improved Isomap algorithm was proposed. In the improved algorithm, Image Euclidean Distance (IMED) was embedded into Isomap algorithm. Firstly, the authors transformed images into image Euclidean Distance (ED) space through a linear transformation by introducing metric coefficients and metric matrix; then, Euclidean distance matrix of images in the transformed space was calculated to find the neighborhood graph and geodesic distance matrix; finally, low-dimensional embedding was constructed by MultiDimensional Scaling (MDS) algorithm. Experiments with the improved algorithm and nearest-neighbor classifier were conducted on ORL and Yale face database. The results show that the proposed algorithm outperforms Isomap with average recognition rate by 5.57% and 3.95% respectively, and the proposed algorithm has stronger robustness for face recognition with small changes.

The Self-Organizing Map (SOM) was modified in this paper: the number of the neurons did not change with time and the neurons collectively maintained their mean to be the mean of the data point in the training phase. After training, every city was associated with a label of a neuron. Then there may be a problem that one or more than one cities have the same neuron. In order to avoid that, a dot labels index was adopted instead of the integer index. The virtue of this scheme is that different city has different index. Then the label would contribute to make sure the order of the city in the tour. Then the algorithm was applied to solve problems taken from a Traveling Salesman Problem Library (TSPLIB). The experimental results show that the proposed algorithm is feasible and effective.

As for six Degree Of Freedom (DOF) platform, its inspection and control system should not only meet the requirement for real-time control, but also has powerful graphic interface. Because of the disadvantages of the existing system, this paper designed a real-time inspection and control system which can meet both requirements in an industrial computer based on INtime. In this system, the method of direct operating on data acquisition and control cards was adopted by INtime process, to obtain real-time performance; in the meantime, nonreal-time tasks were handled by Windows process. The test results of real running demonstrate that the system has high real-time performance, and the animation of platform in user interface is rendered fluently, and verifies the feasibility and effectiveness of the system.

Kernel Discriminant Anlaysis (KDA) and Kernel Principal Component Analysis (KPCA) are the nonlinear extensions of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) respectively. In this paper, we presented a feature extraction algorithm by combing KDA and KPCA to extract reliable and robust features for recognition. Furthermore, a generalized nearest feature line (GNFL) method was also presented for constructing powerful classifier. The performance of the proposed method was demonstrated through real data.

After the characteristics of the mainstream P2P network models were analyzed, a new hierarchical P2P network model called SHDN (Supernode based Hierarchical DHT Network) was designed. It features load balance, extensibility, dynamic character and higher efficiency. SHDN can offer the basal information services such as precise location of the users, administration by clusters, files transmission and so on. The design of the SHDN network model was described, and the rationality and the routing efficiency of SHDN were tested by emulation.

The obstacles of the typical CIMS in the age of knowledge management were discussed,the design thinking and frame structure of the CIMS system of an enterprise based on knowledge were put forward.Realizing work-flow management and the integrated skill of knowledge management were one of key skills.The detail design,actualized project and economy benefit of CIMS were pointed out.

Traditional wavelet-based algorithm has a common effect on the images of light nonuniformity and scarcity. Aiming at the shortcoming, a new wavelet-based algorithm for image enhancement was proposed. The image was first decomposed into multi-level wavelet to obtain the scaling coefficients and the multi-level wavelet coefficients. Then, every level of wavelet coefficients was enhanced by different algorithms, and the scaling coefficients were processed by MSR(Multiscale Retinex). Finally, the image of enhancement was obtained via the inverse wavelet transform. Experiments show that the algorithm excels conventional algorithms in the effect of enhancement and the abatement of noise, at the same time, it has an excellent effect on the images of light nonuniformity and scarcity.