206603798
A Fuzzy Contrast Model to Measure Semantic Similarity Between OWL DL Concepts
On the basis of psychological studies about similarity, we propose a model, called the fuzzy contrast model, to measure the semantic similarity between concepts expressed by OWL DL. By transforming an OWL DL concept to a set of axioms in description logic $\mathcal {S}\mathcal {H}\mathcal {O}\mathcal {I}\mathcal {N}(\mathcal {D})$, the fuzzy contrast model computes the similarity of concepts from their semantic descriptions in $\mathcal {S}\mathcal {H}\mathcal {O}\mathcal {I}\mathcal {N}(\mathcal {D})$. In order to imitate human perception of sameness and difference, fuzzy set is introduced to built intersection and set difference of feature set in our model. An iterative method is proposed to compute the similarity of concepts. Two experimental results are provided to show the effectiveness of fuzzy contrast model.
8340253
UGE4B: An Universal Grid Environment for Bioinformatics Research
Recent years, grid technology has been used widely in the fields of scientific research. In this paper, we present a universal grid environment for bioinformatics research (UGE4B). Although there have been several similar projects successfully constructed in the world, we need to meet some unique requirements from bioinformatics research in China. UGE4B consists of 6 components, including portal, task management, data management, information server, security and computing node. As a national-wide bioinformatics research platform, UGE4B is built through interconnecting multiple sub grids provided by each bioinformatics institutes. Besides provides a universal portal to end users for bioinformatics computing and data accessing, UGE4B also supported some special bioinformatics applications
26669443
Bottom-up discovery of frequent rooted unordered subtrees
In the past decade, XML has emerged as the standard language for information exchanging over the Internet. Due to its tree-structure paradigm, XML is superior for its capability of storing, querying, and manipulating complex data. Therefore, discovering frequent tree patterns over tree-structured data has become an interesting topic for XML data management. In this paper, we propose a tree mining algorithm, named BUXMiner, for finding a special class of frequent trees, called rooted unordered trees, from a tree-structured database. BUXMiner employs an efficient bottom-up approach to enumerate all candidate trees over a compact global tree guide and computes the frequent trees based on the tree guide. In addition to BUXMiner, we also propose a mining approach called BUMXMiner to discover the maximal frequent rooted unordered trees. We compare BUXMiner with previous tree-structure mining algorithms, namely XQPMinerTID and FastXMiner, which were also proposed to discover rooted unordered trees. The experimental results show that our algorithm outperforms XQPMinerTID and FastXMiner in terms of efficiency. The performance results from real-world applications also indicate the usefulness of our proposed tree mining algorithms in a variety of web applications, such as analysis of web page access patterns and mining frequent XML query patterns for caching.
8051159
A Web-based Framework For On-line Collaborative Learning
This paper presents a framework for design of online courses and collaborative learning based on Web technologies. It provides the following features: construction of structured learning materials in a distributed environment; personalization of course contents in the run-time that adapts to the progress of individual students; it can supply relevant Web resources to the students by analyzing the learning history dynamically; students in a group could exchange their learning experiences and discover relevant knowledge via the communication channels provided by the framework. The proposed techniques could be deployed to a variety of online computer-assisted modules to improve the effectiveness of Web-based e-learning
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
116588251
On-line evolutionary exponential family mixture
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.
7031383
Image retrieval based on Sugeno fuzzy integral
In this paper, we propose a fuzzy similarity measure based on the Sugeno fuzzy integral and apply it to the content-based image retrieval. Fuzzy measure is used to formulate the subjective feedback information. Experimental results show that the proposed method can improve the efficiency and reliability of image retrieval system. Our method of formulating the subjective feedback information performs better than the weighted average operator (WAO) and a Choquet-integral-based similarity measure.
6370123
Full integration of subtyping and if-expression
The combination of full subsumption and conditional expression is a challenging problem, because in such a system, a term might not have a type which is a representative of all types of the term. Therefore, the traditional type checking technique fails. Due to such a difficulty, Java typing rule for if-expression uses only a restricted form of subtyping. In this paper, we propose a type system which includes both of the above mentioned features and enjoys decidable type checking. We also show that the system has the subject reduction property. It is expected that this technique could be used to improve the type system of Java with full subsumption.
15543221
A Research on Chaos Forecast with the Capital Market Structure Model
To study the process of pricing in capital market and look for a new way for decision support in financial market, according to the mechanism of artificial neural network, Capital Market Structure Model is developed in this case due to the influence between bargainer and price of asset in market. During proof-test, result of chaos forecast under the capital market structure model shows that chaos forecast capability of the Capital Market Structure Model is good for decision support. In financial market, the bargainer always concentrated in same place and the information spread quickly, meanwhile, bargainer and price are affected by each other. This means that the bargainer will influence the asset price in financial market when they trading. As we know, the decision of trading always after the changing of asset price in market. The new price also will influence the new decision of trading. So we can conclude that the bargainer and the price of asset in financial market are also influenced by each other continually. It is very important to study the principle of the influence before predict the tendency of asset price in financial market, because we need to know the process of price changing to support our decision. According to related papers in recent years, most of the researches about the asset forecast make use of the Artificial Neural Network (ANN). In the cases of positive research about forecast by using of the Artificial Neural Network, theories of chaos and fuzzy are apply to improve the data processing. In addition, wavelet theory is applies in forecast. According to the fundamental concepts of dynamical reconstruction theory and multi resolution analysis, YangYiwen,LiuGuizhong and ZhangZongping [1] take the reconstructed state vectors as multiple inputs to neural network to predict stock index trend of Shanghai Stock Exchange (SSE) in future multiple trading days. We can get high accuracy in imitating nonlinear curve through ANN, but we don’t know the relationship among input variables in ANN. So when ANN is applies to forecast in capital market, we can’t explain the process of pricing, which means we get the result without reason. The way of price forecast can’t explain the process of pricing and will not be variable in economics, and also will not make people know much more about the market. This is the key problem people faced in price forecast when ANN is applies to it. People can’t explain the meaning of weights and biases of ANN in real market. Until now, except researches of forecast by using ANN or wavelet theory directly, no research of forecast due to the principle of the influence among bargainers and prices in financial market has been reported. The purpose of this study is to fund a structure model of capital market due to principle of influence among bargainers and prices in capital market according to the idea by which ANN is founded. This structure model applies to forecast the future price in capital market. To insure the validity of forecast, chaos theory is applied to account the available length of non-periodic cycle of stock price.
24570447
Java implementation of an object-oriented software metrics assessment tool
37994970
Image coding quality assessment using fuzzy integrals with a three-component image model
Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.