Reducing semantic bias of annotations for semantic web service discovery

In order to improve the effectiveness of semantic web service discovery, the semantic bias between an interface parameter and an annotation is reduced by extracting semantic restrictions for the annotation from the description context and generating refined semantic annotations, and then the semantics of the web service is refined. These restrictions are dynamically extracted from the parsing tree of the description text, with the guide of the restriction template extracted from the ontology definition. New semantic annotations are then generated by combining the original concept with the restrictions and represented via refined concept expressions. In addition, a novel semantic similarity measure for refined concept expressions is proposed for semantic web service discovery. Experimental results show that the matchmaker based on this method can improve the average precision of discovery and exhibit low computational complexity. Reducing the semantic bias by utilizing restriction information of annotations can refine the semantics of the web service and improve the discovery effectiveness.

An Approach to Unsupervised Character Classification Based on Similarity Measure in Fuzzy Model

This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...

Insights into Elastic and Thermodynamics Properties of Binary Intermetallics in Ni-AI Alloys under Extreme Condition： Full-Electronic Quasi-Harmonic Study

Atomistic modeling based on the accurate first-principles method is used to investigate the lattice parameter, elastic constant, elastic modulus including bulk modulus （B） and shear modulus （G）, Poisson＇s ratio, and elastic anisotropy of Al, NiAl and NiaAl under extreme condition. The elastic constants obtained from calculations meet their mechanical stability criteria. Both NiAl and Ni3Al exhibit ductile behavior due to their high bulk mudulus to shear modulus ratios of B/G ratios. Through the full-electronic quasi-harmonic approximation, in which the mobile electrons are considered, we successfully obtain the thermo-physical properties including the thermal expansion coefficient, bulk modulus, heat capacity and entropy at simultaneously high temperatures and high pressures. The calculated quantities agree well with the available results. Some silent results are also interpreted. Several interesting features in the thermodynamic properties can also be observed.

陆水电厂计算机监控系统改造设计

针对陆水电厂计算机监控系统存在的问题,提出了设计方案,并对该系统进行了全面升级,消除了安全隐患,对同类型计算机监控系统的改造具有一定的借鉴意义。

Threat Assessment Method Based on Intuitionistic Fuzzy Similarity Measurement Reasoning with Orientation

The aim of this paper is to propose a threat assessment method based on intuitionistic fuzzy measurement reasoning with orientaion to deal with the shortcomings of the method proposed in [Ying-Jie Lei et al., Journal of Electronics and Information Technology 29(9)(2007)2077-2081] and [Dong-Feng Chen et al., Procedia Engineering 29(5)(2012)3302-3306] the ignorance of the influence of the intuitionistic index's orientation on the membership functions in the reasoning, which caused partial information loss in reasoning process. Therefore, we present a 3D expression of intuitionistic fuzzy similarity measurement, make an analysis of the constraints for intuitionistic fuzzy similarity measurement, and redefine the intuitionistic fuzzy similarity measurement. Moreover, in view of the threat assessment problem, we give the system variables of attribute function and assessment index, set up the reasoning system based on intuitionistic fuzzy similarity measurement with orientation, and design the reasoning rules, reasoning algorithms and fuzzy-resolving algorithms. Finally, through the threat assessment, some typical examples are cited to verify the validity and superiority of the method.

Similarity measure on intuitionistic fuzzy sets

Study of fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) was proposed and analyzed. Unlike fuzzy set, IFSs contain uncertainty named hesitance, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of many entropy definitions. By considering different fuzzy entropy definitions, fuzzy entropy on IFSs is designed and discussed. Similarity measure was also presented and its usefulness was verified to evaluate degree of similarity.

Modeling and monitoring of nonlinear multi-mode processes based on similarity measure-KPCA

A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.

DETERMINGING BPA UNDER UNCERTAINTY ENVIRONMENTS AND ITS APPLICATION IN DATA FUSION

Dempster-Shafer (DS) theory of evidence has been widely used in many data fusion ap- plication systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, a new method to obtain Basic Probability Assignment (BPA) is proposed based on the similarity measure between generalized fuzzy numbers. In the proposed method, species model can be constructed by determination of the min, average and max value to construct a fuzzy number. Then, a new Radius Of Gravity (ROG) method to determine the similarity measure between generalized fuzzy numbers is used to calculate the BPA functions of each instance. Finally, the efficiency of the proposed method is illustrated by the classi- fication of Iris data.

Similarity measure design and similarity computation for discrete fuzzy data

The similarity computations for fuzzy membership function pairs were carried out.Fuzzy number related knowledge was introduced,and conventional similarity was compared with distance based similarity measure.The usefulness of the proposed similarity measure was verified.The results show that the proposed similarity measure could be applied to ordinary fuzzy membership functions,though it was not easy to design.Through conventional results on the calculation of similarity for fuzzy membership pair,fuzzy membership-crisp pair and crisp-crisp pair were carried out.The proposed distance based similarity measure represented rational performance with the heuristic point of view.Furthermore,troublesome in fuzzy number based similarity measure for abnormal universe of discourse case was discussed.Finally,the similarity measure computation for various membership function pairs was discussed with other conventional results.

SHAPE-BASED TIME SERIES SIMILARITY MEASURE AND PATTERN DISCOVERY ALGORITHM

Pattern discovery from time series is of fundamental importance. Most of the algorithms of pattern discovery in time series capture the values of time series based on some kinds of similarity measures. Affected by the scale and baseline, value-based methods bring about problem when the objective is to capture the shape. Thus, a similarity measure based on shape, Sh measure, is originally proposed, andthe properties of this similarity and corresponding proofs are given. Then a time series shape pattern discovery algorithm based on Sh measure is put forward. The proposed algorithm is terminated in finite iteration with given computational and storage complexity. Finally the experiments on synthetic datasets and sunspot datasets demonstrate that the time series shape pattern algorithm is valid.

First-principle investigation on electronic structures and elastic properties of Al-doped MoSi_2

The electronic structures and elastic properties of Al-doped MoSi2 were calculated using the plane wave pseudo-potential method based on the density functional theory,in which the generalized-gradient approximation(GGA) was used to describe the exchange-correlation potential.Starting from the elastic constants,bulk modulus,shear modulus,elastic modulus and Poisson ratio of Al-doped MoSi2 were obtained by using the Hill method.The results indicate that conductivity of Al-doped MoSi2 is improved to some extent in comparison with that of pure MoSi2 due to the orbit hybridization of Mo 4d,Al 3p and Si 3p electrons.In addition,calculations show that the elastic modulus and the brittleness of Al-doped MoSi2 are smaller than those of pure MoSi2,which implies that it is feasible to toughen MoSi2 by doping Al.The agreement of the conclusion with experiment shows that the present theory is reasonable.

Quantitative comparison of similarity measure and entropy for fuzzy sets

Comparison and data analysis with the similarity measures and entropy for fuzzy sets were carried out. The distance proportional value between the fuzzy set and the corresponding crisp set was considered by the fuzzy entropy. The relation between the similarity measure and the entropy for fuzzy set was also analyzed. The fuzzy entropy was reformulated as the dissimilarity measure. Furthermore, crisp set having the minimum uncertainty with respect to the corresponding fuzzy set was also proposed. Finally, derivation of a similarity measure from entropy with the help of total information property was derived. A simple example shows the relation between similarity measure and fuzzy entropy, in which the summation of similarity measure and fuzzy entropy represents a constant value.

Privacy-Preserving Top-k Keyword Similarity Search over Outsourced Cloud Data

In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.

Similarity measure design on overlapped and non-overlapped data

Similarity measure design on non-overlapped data was carried out and compared with the case of overlapped data.Unconsistant feature of similarity on overlapped data to non-overlapped data was provided by example.By the artificial data illustration,it was proved that the conventional similarity measure was not proper to calculate the similarity measure of the non-overlapped case.To overcome the unbalance problem,similarity measure on non-overlapped data was obtained by considering neighbor information.Hence,different approaches to design similarity measure were proposed and proved by consideration of neighbor information.With the example of artificial data,similarity measure calculation was carried out.Similarity measure extension to intuitionistic fuzzy sets(IFSs)containing uncertainty named hesitance was also followed.

Novel similarity measures for face representation based on local binary pattern

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.

First-order perturbation approximation for rock elastic moduli in transversely isotropic media

Seismic anisotropy is a relatively common seismic wave phenomenon in laminated sedimentary rocks such as shale and it can be used to investigate mechanical properties of such rocks and other geological materials. Young's modulus and Poisson's ratio are the most common mechanical properties determined in various rock engineering practices. Approximate and explicit equations are proposed for determining Young's modulus and Poisson's ratio in anisotropic rocks, in which the symmetry plane and symmetry axis of the anisotropy are derived from the constitutive equation of transversely isotropic rock. These equations are based on the media decomposition principle and seismic wave perturbation theory and their accuracy is tested on two sets of laboratory data. A strong correlation is found for Young's modulus in two principal directions and for Poisson's ratio along the symmetry plane. Further, there is an underprediction of Poisson's ratio along the symmetry axis, although the overall behavior follows the trend of the measured data. Tests on a real dataset show that it is necessary to account for anisotropy when characterizing rock mechanical properties of shale. The approximate equations can effectively estimate anisotropic Young's modulus and Poisson's ratio, both of which are critical rock mechanical data input for hydraulic fracturing engineering.

A modified simplified coherent potential approximation model of band gap energy of III-V ternary alloys

Based on the modification of the simplified coherent potential approximation （SCPA）, a model is developed to calculate the composition dependence of the band gap energy of Ⅲ-V ternary alloys with the same anion. The derived equation is used to fit the experimental band gap energy of InxAl1-xN, InxGa1-xN and A1xGal xN with x from 0 to 1. It is found that the fitting results are better than those done by using SCPA. The fitting results are also better than those obtained by using the formula with a small bowing coefficient, especially for InxAl1-xN. In addition, our model can also be used to describe the composition de- pendence of band gap energy of other Ⅲ-V ternary alloys.

A SELF-SIMILAR LOCAL NEURO-FUZZY MODEL FOR SHORT-TERM DEMAND FORECASTING

This paper proposes a selfsimilar local neurofuzzy （SSLNF） model with mutual informati onbased input selection algorithm for the shortterm electricity demand forecasting. The proposed self similar model is composed of a number of local models, each being a local linear neurofuzzy （LLNF） model, and their associated validity functions and can be interpreted itself as an LLNF model. The proposed model is trained by a nested local liner model tree （NLOLIMOT） learning algorithm which partitions the input space into axisorthogonal subdomains and then fits an LLNF model and its associated validity function on each subdomain. Furthermore, the proposed approach allows different input spaces for rule premises （validity functions） and consequents （local models）. This appealing property is employed to assign the candidate input variables （i.e., previous load and temperature） which influence shortterm electricity demand in linear and nonlinear ways to local models and validity functions, respectively. Numerical results from shortterm load forecasting in the New England in 2002 demonstrated the accuracy of the SSLNF model for the STLF applications.

Mechanical Anisotropic and Electronic Properties of Amm2-carbon under Pressure

Structural, electronic properties and mechanical anisotropy of Amm2-carbon are investigated utilizing tlrst-principles calculations by Oambridge Serial Total Energy Package （CASTEP） code. The work is performed with the generalized gradient approximation in the form of Perdew Burke-Ernzerhof （PBE）, PBEsol, Wu and Cohen （WC） and local density approximation in the form of Ceperley and Alder data as parameterized by Perdew and Zunger （CA-PZ）. The mechanical anisotropy eMculations show that Amm2-carbon exhibit large anisotropy in elastic moduli, such as Poisson＇s ratio, shear modulus and Young＇s modulus, and other anisotropy factors, such as the shear anisotropic factor and the universal anisotropic index AU. It is interestingly that the anisotropy in shear modulus and Young＇s modulus, universal anisotropic index and the shear anisotropie factor all increases with increasing pressure, but the anisotropy in Poisson＇s ratio decreases. The band structure calculations reveal that Amm2-carbon is a direct-band-gap semiconductor at ambient pressure, but with the pressure increasing, it becomes an indirect-band-gap semiconductor.

Corrected empirical likelihood for a class of generalized linear measurement error models

Generalized linear measurement error models, such as Gaussian regression, Poisson regression and logistic regression, are considered. To eliminate the effects of measurement error on parameter estimation, a corrected empirical likelihood method is proposed to make statistical inference for a class of generalized linear measurement error models based on the moment identities of the corrected score function. The asymptotic distribution of the empirical log-likelihood ratio for the regression parameter is proved to be a Chi-squared distribution under some regularity conditions. The corresponding maximum empirical likelihood estimator of the regression parameter π is derived, and the asymptotic normality is shown. Furthermore, we consider the construction of the confidence intervals for one component of the regression parameter by using the partial profile empirical likelihood. Simulation studies are conducted to assess the finite sample performance. A real data set from the ACTG 175 study is used for illustrating the proposed method.