随着移动互联网的发展，用户拥有了更多商品服务的选择，与此同时用户也会迷失在丰富的商品空间中，无法有效的找到自己喜欢的物品。推荐系统是帮助用户解决这一问题的强有力手段，推荐系统的核心任务就是关联用户和信息，保留用户、防止用户流失，达到用户和电商双赢的目的。本文着重介绍并分析了几种常用的个性化推荐技术，推荐系统的模块结构，并对推荐技术的发展方向进行了展望。

声纹识别是生物特征识别领域的一个重要分支。它采用语言数据自动地鉴定测试者身份。本文研究复杂背景下的声纹识别系统的设计与实现,首先,利用正交小波滤波器组来对信号进行预滤波,对语言信号的每个频率段进行细粒度去噪,提取出各频段小波系数,重构出语音信号;其次,在特征提取阶段,利用倒谱法计算出基音周期参数,通过Mel滤波器组将小波系数转换成Mel倒谱系数(MFCC),将得出的两种参数组成一个特征矢量作为声纹特征;最后声纹识别阶段,每一个说话人都由一个GMM表示,计算出特征矢量序列的每个似然函数,找到其中最大的说话人模型,即判定为说话人。

The paper introduced a implementations th at centralized supervisory of UNIX system servers under the UNIX servers widely used currently,through the establishment of the pipes and the daemon,centralized transmitted running state of UNIX servers to the monitoring host real-time,to achieve cross-platform portability of system monitoring,and reflect intuitive to the user,to allow customers to more easily grasp the system operation,and do not take up more system resources.

Aiming at the problem of excess information,for consideration the binary relation of the users and item,mining local similarity model,proposes a biclustering technology to carry out clustering of user and item simultaneously,design a biclustering model recommendation engine,a off-line calculation and online recommended,improve response speed in system.

Aiming at the high failure rate of the current business intelligence,a system structure of business intelligence based on virtual warehouse and ODS is researched,the cycle of the implementation is short and high real-time data.The implementation of the business intelligence platform is succeed in the enterprise,and can help the enterprise leadership to make the right decision on time.

To facilitate the calculation of the number of weeks,using object-oriented methodologies implemented a basic week calculator,analyzed of the architecture of the software,and using C #,VB.NET and XML technology achieved the program.

Background Multi-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets. Results This paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions. Conclusions The proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.

BACKGROUND:Newly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the post-genomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem.RESULTS:Based on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner.CONCLUSIONS:The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence.

Multi-objective optimization (MOP) a fast growing area of research. Bioinformatics data sets come mostly from DNA microarray experiments. The analysis of microarray data sets can provide valuable information on the biological relevance of genes and correlations among them. Biclustering methods allow us to identify genes with similar behavior with respect to different conditions. A single bicluster represents a given subset of genes in a given subset of conditions. For solving multiple objectives optimization, ant colony optimization algorithms have been shown to be very effective for MOP. This paper proposes online Multiple Objective Ant Colony Optimization biclustering algorithm to solve patterns mining problem of microarray dataset. During optimization, the size of ant population is dynamically changed to quicken the convergence of the algorithm. Experimental analysis on two real dataset shows that the proposed algorithm achieves good performance in the diversity of solution and the time complexity of the algorithm.

Biclustering of DNA microarray data that can mine significant patterns to help in understanding gene regulation and interactions. This is a classical multi-objective optimization problem (MOP). Recently, many researchers have developed stochastic search methods that mimic the efficient behavior of species such as ants, bees, birds and frogs, as a means to seek faster and more robust solutions to complex optimization problems. The particle swarm optimization(PSO) is a heuristics-based optimization approach simulating the movements of a bird flock finding food. The shuffled frog leaping algorithm (SFLA) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. This paper introduces SFL algorithm to solve biclustering of microarray data, and proposes a novel multi-objective shuffled frog leaping biclustering(MOSFLB) algorithm to mine coherent patterns from microarray data. Experimental results on two real datasets show that our approach can effectively find significant biclusters of high quality.

In this paper,based on the advantages of Microsoft's Hyper-V R2 virtualization architecture,Put forward the principles of qualitative analysis of resource allocation,quantitative calculation rules and the method of dynamic resource allocation to consolidate physical servers.First,compared the bare-metal virtualization architecture of the current three companies VMware vSphere,Microsoft Hyper-V and Citrix Xen Server,Concluded the advantages of Microsoft's Hyper-V virtualization architecture,then from the qualitative analysis and quantitative resource calculated the methods of resource allocation,And with performance and resource optimization(PRO) monitor to achieve dynamic resource allocation,Finally,demonstrated the effect of these resource allocation methods through practice,the results show that the method can meet the needs of enterprise customers with highly flexibility and superior performance.

This paper formulates the protein function prediction into a typical LPU.Aiming at imbalance or over-fitting from LPU with few positive examples,it proposes a method creating synthetic examples to enlarge the set of positive examples based on the nearest neighbor and convex combination,and meanwhile modifies the procedure learning optimal classifier for the classic LPU algorithm by using one-class SVM(support vector machine) to identify the most probable negative examples,running iteratively SVM to move the classification hyperplane to a suitable place and obtaining representative negative examples through cross validation.For the yeast genomic data,the experiments show that our algorithm outperforms several classic prediction methods,particularly,for function classes with few positive examples.

Most of optimization problems have more than one objective function. As a heuristic search technique, particle swarm optimization (PSO) simulates the movements of a flock of birds which aim to find food. The success of PSO has motivated researchers to extend the use of population-based technique to multi-objective optimization. Rapid development of the DNA microarray technology make it very possible to study the transcriptional response of a complete genome to different experimental conditions. Biclustering technique has successfully used to analysis those gene expression data. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective modeling is suitable for solving biclustering problem. Based on dynamic population, this paper proposes a novel dynamic multi-objective particle swarm optimization biclustering (DMOPSOB) algorithm to mine coherent patterns from microarray data. Experimental results on real datasets show that our approach can effectively find significant biclusters of high quality.

Many bioinformatics data sets come from DNA microarray experiments. Biclustering of gene expression data can identify genes with similar behaviour with respect to different conditions. Ant Colony Optimisation (ACO) algorithms have been shown to be effective problem solving strategies for a wide range of problem domains. Multiple Objective Ant Colony Optimisation (MOACO) mainly focuses on solving the multiple objective combinatorial optimisation problems. This paper incorporates crowding update technology into MOACOB and proposes crowding MOACO biclustering algorithm to mine biclusters from gene expression data. Experimental results are shown for biclustering algorithm on two real gene expression data.

During the last few years, Kernel methods have gained considerable attention for analyzing biological data for protein function prediction. Based on biological processes annotation of Yeast and GO(gene ontology), we constructed a kernel matrix to predict protein functions. We used measurement method about semantic similarity on GO and adaptive Hausdorff distance to successfully obtain protein similarity matrix, and furthermore, transformed protein similarity matrix to a undirected graph. Then, We developed a novel method that can learn optimal diffusion kernel from graph by maximizing kernel-target alignment. Experimental results illustrate that the kernel matrix generated by our formula has larger AUC value than ordinary diffusion kernel and those proposed before. Our method can even learn a common optimal kernel matrix for multiple predict tasks at one run. Furthermore, it can also be directly used to learn from various biolobical networks.

BACKGROUND:High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.RESULTS:In this work we present the CMOPSOB (Crowding distance based Multi-objective Particle Swarm Optimization Biclustering), a novel clustering approach for microarray datasets to cluster genes and conditions highly related in sub-portions of the microarray data. The objective of biclustering is to find sub-matrices, i.e. maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a subset of conditions. Since these objectives are mutually conflicting, they become suitable candidates for multi-objective modelling. Our approach CMOPSOB is based on a heuristic search technique, multi-objective particle swarm optimization, which simulates the movements of a flock of birds which aim to find food. In the meantime, the nearest neighbour search strategies based on crowding distance and -dominance can rapidly converge to the Pareto front and guarantee diversity of solutions. We compare the potential of this methodology with other biclustering algorithms by analyzing two common and public datasets of gene expression profiles. In all cases our method can find localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner.CONCLUSION:The proposed CMOPSOB algorithm is successfully applied to biclustering of microarray dataset. It achieves a good diversity in the obtained Pareto front, and rapid convergence. Therefore, it is a useful tool to analyze large microarray datasets.

High throughput technologies yield large-scale datasets on genomic variation in diverse populations, allowing the study of these variations and their association with disease and their complex traits. Systematic functional characterization of genes identified in the genome sequencing projects is urgently needed in the post-genomic era. Biclustering, which searches for subsets of individuals that are coherent in their behavior across a subset of the features, is a very useful data mining technique in microarray data analysis and has presented its advantages in many applications. This paper proposes a novel multi-objective immune biclustering (MOIB) algorithm, based on the immune response principle of the immune system, to mine biclusters from microarray data.In the algorithm, we extends ε-dominance and performs the mechanism of crowding computation to obtain many Pareto optimal solutions distributed onto the Pareto front. Experimental results on real datasets show that our approach can effectively find more significant biclusters than other biclustering algorithms.

Predicting gene function is usually formulated as binary classification problem. However, we only know which gene has some function while we are not sure that it doesn't belong to a function class, which means that only positive examples are given. Therefore, selecting a good training example set becomes a key step. In this paper, we cluster the genes on integrated weighted graph by generalizing the cluster coefficient of unweighted graph to weighted one, and identify the reliable negative samples based on distance between a gene and centroid of positive clusters. Then, the tri-training algorithm is used to learn three classifiers from labeled and unlabeled examples to predict the gene function by combining three prediction result. The experiment results show that our approach outperforms several classic prediction methods.

Latest microarray technique can measure the expression levels of thousands of genes under a set of conditions, and generates some large-scale microarray datasets. Biclustering can perform clustering of rows and columns of those dataset simultaneously, allowing the mining of additional information from microarray datasets which is important in bioinformatics research and biomedical applications. Since the biclustering problem is combinatorial, and multi-objective ant optimization systems present several advantages during dealing with this kind of problem. This paper proposes a novel multi-objective ant colony optimization biclustering algorithm to mine biclusters from microarray dataset. Experimental results on real dataset show that our approach can find significant biclusters of high quality.

3D(three-dimensional) clusters mining from gene-sample-time(simply GST) microarray data can identify the samples corresponding to some phenotypes,such as diseases,and find the candidate genes correlated to phenotypes.When mining 3D clusters in 3D microarray data matrix,several objectives have to be optimized simultaneously,and often these objective are in conflict with each other.Therefore,it is very available to use a multi-objective evolutionary algorithms(MOEA) for finding 3D clusters in GST data.Based on ∈-dominance and sigma select strategy,this paper proposes a novel multi-objective evolutionary 3D clustering algorithm to mine 3D cluster from 3D microarray data.Experimental results on yeast cell cycle dataset show that our approach can find significant 3D clusters of high quality.