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Currently, Thinking Maps(TM) which includes eight kinds of sub graphs, has been a set of popular visual tools for learners to achieve the tasks of inductive and deductive thinking, dialogical thinking, metaphorical thinking and systemic dynamic thinking. But many of the researches focus on building these sub graphs manually. In this paper, after analyzing the features of e-Learners' behaviors, a method...
In order to improve detection efficiency of on-line web news stream, we propose a new method to accomplish detection task with window-adding, named entity recognition and suffix tree clustering. In our method, we make full use of informative elements of news stream(such as date, place, person and so on) to help detection process, and this method decreases text similarity computation greatly. Experimental...
Similarity analysis plays a key role in clustering of time series. Normalized longest common subsequence (NLCS) is a similarity measurement widely used in comparing character sequences. In this paper, we developed the NLCS and present a novel algorithm to precisely calculate the similarity of time series. The algorithm used the sum of all common subsequence instead of longest common subsequence which...
The study object of the analysis of sentence emotional tendency is sentences appearing in a particular context, its task is analyzing and extracting a variety of subjective information of the sentences and judging the emotional tendency of the sentences. Existing methods of analysis usually tend to divide the emotional tendency of sentences into positive and negative emotion categories, not further...
As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance inefficient. Recently the development of network and distributed technology makes cloud computing a reality in the implementation...
On shared memory multiprocessors, synchronization often turns out to be a performance bottleneck and the source of poor fault-tolerance. By avoiding locks, the significant benefit of lock (or wait)-freedom for real-time systems is that the potentials for deadlock and priority inversion are avoided. The lock-free algorithms often require the use of special atomic processor primitives such as CAS (compare...
In the field of image segmentation, because of the exist of the noise dot and misjudgment, hollows and the unexpected image are unavoidable. In order to get a high quality segmented image the process of connected component analysis is necessary. This paper proposed a new algorithm that used a kind of tree-structure named max-tree, the experiment shows this method has a high practical value.
The significant benefit of lock (or wait)-freedom for real-time systems is that by avoiding locks the potentials for deadlock and priority inversion are avoided. The lock-free algorithms often require the use of special atomic processor instructions such as CAS (compare and swap) or LL/SC(load linked/store conditional). However, many machine architectures support either CAS or LL/SC with restricted...
The classical union-find algorithm is the basis for many graph algorithms and for dealing with equality. The easiest way to implement concurrent objects is by means of classical software solutions, but this leads to blocking when the process that holds exclusive access to the object is delayed or stops functioning. Thus we require our solutions to the data structure problem have the lock-free property...
Community structure is an important property of the complex networks. How to detect the communities is significant to understand the network structure and analyze the network properties. Many algorithms, such as K-L and GN, have been proposed to detect the community structure in complex networks. However, the communities detected by these algorithms are always not overlapping. According to daily experience,...
In this paper, a new local algorithm of community detection which requires mostly local information while detecting communities in networks is proposed. The algorithm is available in both unweighted and weighted networks. It starts from vertices with low intensity and extracts communities from networks. Three real-world networks are used to test the performance of algorithm proposed, the experimental...
An important property of complex networks is community structure. Community detection is significant to understand the network structure and analyze the network properties. In recent years, lots of algorithms have been developed to find community structure in complex networks. These algorithms, however, are based on unweighted networks, which limits their applications. An unweighted network is only...
In this paper, we propose a new efficient algorithm which makes a good thing out of the betweenness centrality and the local information to detect community structures in complex networks. When being tested on some typical real world networks, our algorithm demonstrates excellent community partition results and very fast processing performance, much faster than the existing classical community detecting...
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