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Among the approaches used to analyze biomedical signals, e.g. electroencephalogram, there are methods based on fractal dimension (FD) calculation. One of the main drawbacks of these methods is the requirement for long duration of the analyzed signal. To analyze events of brief duration in real-time mode and to apply the results obtained directly in the time domain, thus providing an easier interpretation...
Optimization problems are ubiquitous and consequential. In fact every sphere of human activity that can be quantified can be formulated as an optimization problem. The focus of this work is on Global Optimization which is not only desirable but also necessary in many cases. In the past few decades several Global optimization algorithms have been suggested in literature out of which stochastic, population...
In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. Aiming at learning from partial feedback of online classification results, i.e. ??true?? when the predicting label is right or ??false?? when the predicting label is wrong, this new kind of problems arouses much of researchers' interest due to its close relations...
In this paper an improved genetic algorithm is proposed to solve optimal problems applying fixed-point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on an J1 subdivision of searching space and generates the integer labels at the vertices, and then crossover operators and mutation operators relying on the integer labels are designed. In this case, whether every individual...
Type analysis is an important part of decompilation, which has a great impact on the readability and the veracity of the output of decompilation. In this paper we discussed a method of type analysis which was based on dataflow analysis .We inference the type of data during the calculating of the dataflow equations, by constructing the type of multiplicate kinds data of the hierarchical structure for...
As the agglomerative clustering algorithm is widely used in data mining, image processing, bioinformatics and pattern recognition. it has attracted great interests from both academical and industrial communities. However, existing studies neglect the decisive factor of the efficiency of the agglomerative clustering algorithm for large complex networks and usually use criterion functions which lead...
Recent studies have demonstrated that language-based static analysis is capable of finding hundreds of bugs in complex real systems. Such static analysis allows users to specify properties in a specification language on demand. Paths in control flow graphs are explored exhaustively against user-defined properties. To avoid the potential path explosion problem, many techniques have been used in practice...
Mining frequent closed itemsets provides complete and condensed information for frequent pattern mining. Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we first present a general methodology to identify closed itemsets over data streams, using concept lattice theory. Using this methodology, we then proposed a...
Reinforcement learning suffers scalability problems due to the state space explosion and the temporal credit assignment problem. Knowledge-based approaches have received a significant attention in the area. Reward shaping is a particular approach to incorporate domain knowledge into reinforcement learning. Theoretical and empirical analysis of this paper reveals important properties of this principle,...
Radio frequency identification (RFID) is an advanced tracking technology that can be used to study the spatio-temporal behavior of customers in a supermarket. The aim of this work is to build a new RFID-based autonomous system to follow individuals' spatio-temporal activity, a tool not currently available, and to develop new methods for automatic data mining. Here, we study how to transform these...
An ontology mapping algorithm was proposed for calculating the mapping relations between ontology which had been automatically created from the data-bases. For rapid information integration, ontology should be automatically created from the database of the heterogeneous systems. The algorithm was designed for the ontology which had been created from the data-base, and the data characteristics of the...
Nowadays, how to efficiently compose Web services has become a hotspot. In this paper, we introduce a method of recommending an optimal service sequence based on the original service sequence for a composite service. This method uses a Bayesian-based approach and selects the service sequence that has the largest probability as the best choice. Compared with existing methods, this method has two advantages:...
Bisimilarity is one of the most important relations for comparing the behaviour of formal systems in concurrency theory. Decision algorithms for bisimilarity in finite state systems are usually classified into two kinds: global algorithms are generally efficient but require to generate the whole state spaces in advance, and local algorithms combine the verification of a system's behaviour with the...
Biclustering algorithms are an important tool for the analysis of gene expression data. Research on analysis of gene expression data includes identification of groups of genes with similar expression patterns. Standard clustering methods for the analysis of gene expression data only identify the global similarity while missing the local patterns of expression similarity, i.e. genes could behave similar...
Text summarization is a meaningful part of the research of natural language document understanding, and it is an important branch of natural language processing. In this paper, on the basis of the research status quo of the researchers and experts both home and abroad, two text summarization algorithms are proposed. And one algorithm is rule-based, and the other is based on statistics.
Evolutionary algorithms have been widely used to solve difficult constrained optimization problems. However, evolutionary algorithms are intrinsically unconstrained optimization techniques. Constraint handling is mostly incorporated additionally and its choice has great bearing on the quality of the solution. Stochastic ranking was introduced as an improvement over feasibility rules for handling constraints...
In modern society, especially on the Internet, you might have found you are having more and more usernames or IDs and passwords, which contains your private information. There are too many for you to remember and it is unsafe to write them down on you notebook. To solve this problem, this paper made a design and implementation of Passwords Management System (PMS), by which you can manage your usernames...
Based on the analysis of defects of traditional evolutionary algorithms in solving global optimization of non-linear or multi-modal function, a novel evolutionary algorithm called multi-stage evolutionary algorithm (MSEA) is proposed. MSEA has many new features. It develops some new operators such as multi-parent crossover operator with elite-preservation, dynamical mutation operator, space contraction...
The QR decomposition of matrix is an especial tool for matrix calculation, although its decomposition process is very complex. Some specified n-1 components of the n-dimensional nonzero vector x need to be transformed into zeros by using householder transformation many times. Or a special kind of given transformation would be used repeatedly to transform one specified component of the vector x into...
A massively parallel computer involves a large number of routers that are independent of each other. Adaptive routing methods offer high levels of flexibility in packet routing, but their performance is limited due to their locality nature. The Cross-Line method handles quasi-global information of congestion to improve performance. This paper introduces two new ideas of adaptation level and evaluation...
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