Jianbing Xiahou

Integrated project based learning in software engineering education

This paper presents an education framework to effectively develop crucial software engineering skills in students of software Engineering major at National Exemplary Software School (NESS), Xiamen University. The goal is to describe a systematic approach towards integrating project based learning in software engineering major, both inside and outside the classroom. An essential part of Software Engineering Education is practical training in principles, methods and procedures under conditions similar to developing real software products. This paper describes the different conventional and traditional approaches at length for Software Engineering Education and proposes integrated project based learning approach is more effective and interesting for teaching and learning SE as compared to the lecture based approach.

Multi-kernel learning for multivariate performance measures optimization

AbstractIn this paper, we investigate the problem of optimizing complex multivariate performance measures to learn classifiers for pattern classification problems. For the first time, the multi-kernel learning is considered to construct a classifier to optimize a given nonlinear and non-smooth multivariate classifier performance measure. We estimate and optimize the upper bound of the given multivariate performance measure, instead of optimizing it directly. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The learning of the classifier parameter and the kernel weight are unified in a single objective function considering minimizing the upper bound of the given multivariate performance measure. The objective function is optimized with regard to classifier parameter and kernel weight alternately in an iterative algorithm. The developed algorithm is evaluated on two different pattern classification methods with regard to various multivariate performance measure optimization problems. The experiment results show the proposed algorithm outperforms the competing methods.

Multi-datacenter cloud storage service selection strategy based on AHP and backward cloud generator model

Abstract This paper proposed the Cloud Storage Service Selection Strategy under the cross-datacenter environment. Due to the dynamic network environment and the independence between the data centers, this paper presented Cloud Storage Service Selection Strategy across the data center based on AHP–backward cloud generator algorithm. The strategy combines the theory of analytic hierarchy process (AHP) analysis and uncertainty reasoning of cloud method by means of collecting cloud storage providers’ quantitative performance data and inferring qualitative classification of service capability, to select Cloud Storage Service Selection Strategy across the data center. Simulation results show that the strategy has a great advantage in system load balance, replica access rate, and data reliability.

TCM clinic records data mining approaches based on weighted-LDA and multi-relationship LDA model

As an important part of traditional medicine, TCM (Traditional Chinese Medicine) has unique and distinct clinical effects in the aspect of disease diagnosis and treatment. Thousands of years of TCM treatment has accumulated abundant clinical data and medical literatures, including valued TCM theories and clinical practice rules. Researchers have conducted various methods such as clustering analysis, association rules and regression analysis to study TCM theory. However, none of them could reflect well the semantic complexity of TCM and systemic characteristics of TCM treatment. This paper conducted a research on the inherent rules of TCM clinic records with a topic model. On the basis of LDA model, weighted mechanism was adopted for each feature word to improve the distinguishing ability and interpretability between the topics. Meanwhile, the modeled topic is taken as the feature for the classification of SVM (Support Vector Machine) to improve the classification accuracy. The topic number of LDA topic model is confirmed by the KL distance and similarity between the topics. After analyzing the relationship between topic model and TCM differentiation and treatment, MULTI-RELATIONSHIP Topics LDA MODEL was proposed on the basis of LDA model and Author-topic model to automatically extract the topic structures between the four parties and explore the relationship of the multiple parties with clinical significance. In the meantime, relevancy between the parties and the feature word weighted mechanism are used to improve the MULTI-RELATIONSHIP Topics LDA MODEL and the classification accuracy of the topics. The experiments showed that analysis of clinical data with topic model can extract TCM treatment rules and provide a novel theoretical method for TCM clinical research.

Loading and Rendering XFile in DirectX

Using C ++, we implement XFile loading based on DirectX, then generate three-dimensional mesh with the information of XFile and render it at last. And with further research on progressive mesh, we achieve controlling the precision of the three dimensional mesh.

A new approach of parametric auto-generation on polyline

In this paper, a new approach of parametric polyline generation is presented following the architectural principle and the plane geometry theory of polyline. The arcs of polyline are classified and the geometry property is investigated based on the parametric definition of it. The method of parametric auto-generation on polyline is obtained through the auto-calculation of polyline bugle. The experimental results on parametric 3D modeling show that the proposed algorithm is efficient.

High-order covariate interacted Lasso for feature selection

High-order covariates interaction is considered into Lasso-type variable selection.We evaluate the significance of feature by considering their neighborhood dependency.Having too few features in not necessarily a good feature selection result.Some interactive features may be lost in the process of removing redundancy. Lasso-type feature selection has been demonstrated to be effective in handling high dimensional data. Most existing Lasso-type models over emphasize the sparsity and overlook the interactions among covariates. Here on the other hand, we devise a new regularization term in the Lasso regression model to impose high order interactions between covariates and responses. Specifically, we first construct a feature hypergraph to model the high-order relations among covariates, in which each node corresponds to a covariate and each hyperedge has a weight corresponding to the interaction information among covariates connected by that hyperedge. For the hyperedge weight, we use multidimensional interaction information (MII) to measure the significance of different covariate combinations with respect to response. Secondly, we use the feature hypergraph as a regularizer on the covariate coefficients which can automatically adjust the relevance measure between a covariate and the response by the interaction weights obtained from hypergraph. Finally, an efficient alternating direction method of multipliers (ADMM) is presented to solve the resulting sparse optimization problem. Extensive experiments on different data sets show that although our proposed model is not a convex problem, it outperforms both its approximately convex counterparts and a number of state-of-the-art feature selection methods.

Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data

The improved differential evolutionary algorithm (EA) discussed in this paper is used to solve high-dimensional big data. Specifically, the algorithm improves population diversity by expanding the searching scope of the population, prevents premature deaths of the population through wider and more specific searches, and aims to solve the high-dimensional issue. To achieve this improvement goal, the paper suggests a multilayer hierarchical architecture on the basis of the above-mentioned heuristic mechanism. In each layer of the hierarchical architecture in the dynamic subpopulation, individuals who are more suitable for isolated evolution can better coexist with the original main population. We propose a new multiobjective optimization algorithm based on nondominated sorting and bidirectional local search (NSBLS). The algorithm takes the local beam search as the main body. NSBLS outputs the nondominated solution set through a continuous iterative search when the iteration termination condition is satisfied. It is worthy to note that the iteration of NSBLS is similar to the generation of the EA; therefore, this paper uses generation to represent the iterations. An algorithm introduces a new distribution maintaining strategy based on the sampling theory to combine with the fast nondominated sorting algorithm in order to select a new population into the next iteration. NSBLS will compare with three classical algorithms: NSGA-II, MOEA/D-DE, and MODEA through a series of bi-objective test problems. The proposed nondominated sorting and local search is able to find a better spread of solutions and better convergence to the true Pareto-optimal front compared to the other four algorithms. The outstanding performance of the proposed technology was proven in well-known benchmark problems.

Customer profitability analysis of automobile insurance market based on data mining

Data mining technology is an interdiscipline using theory and technology of artificial intelligence, machine learning, statistics and other fields. It can extract implicit but useful information and knowledge from vast amount of historical data for the enterprise, and provide solid support for the decision of company. Combining with the rate reform of domestic automobile insurance industry, this paper discusses the application of data mining technology to the customer profitability, finds out the rule of classification before and after the rate reform, and shows the progress of customer profitability analysis by using decision tree.

Integrated Approach of Dynamic Human Eye Movement Recognition and Tracking in Real Time

The states and movements of human eyes contain a lot of useful information, in which can be applied in real-time HCI systems. This paper introduces an integrated human eye movement recognition and tracking approach. The threshold based eye recognition method identifies eye elements, such as iris and pupils of human eyes. In addition, the eye movement tracking method is present by analyses motion feature of eyes including translation and velocity. In experiments, real-time tests of different peoples eyes recognition and tracking are present, and NUI based application scenarios is designed to show the potential application of eye interaction systems.