Earticle

The unascertained clustering is a new clustering method, which combines unascertained theory and clustering theory to construct the unascertained measure, and uses the unascertained measure as set membership to indicate the membership relation between the samples with the different classes. It overcomes the disadvantage of means clustering algorithm, that a sample definitely belongs to a class, which made greater progress than -means clustering. There are complex non-linear relationship between the coal industry competitiveness and various factors. The article established the evaluation influencing factors system of coal industry international competitiveness. 6 unascertained clustering method to cluster competitiveness. It found out each class center, and gave the membership degree of the samples belong to each class, which better resolved the problem of classifying the coal industry international competitiveness.

Technological innovation is the foundation for high-tech enterprises to enhance competitiveness and achieve sustainable development. However, there are some uncertain factors existing in the process of technological innovation, which bring about risks. In the light of this, first, based on relevant studies and an expert questionnaire survey, an index system for evaluating the risks is constructed. Then, a two-tuple-linguistic-information-based evaluation model is proposed, wherein a two-tuple group-decision method and its aggregation operators are introduced and the specific evaluation steps are given. At last, an empirical study of technological innovation risk evaluation is conducted on an enterprise of Nanchang high-tech development zone. The result indicates that by applying this method, high-tech enterprises can easily learn the overall risk level of their technological innovation projects, and then weaken or control the risks.

Though MapReduce programming model simplifies the development of parallel program, ordinary users have difficulties in setting up the development environment for MapReduce. The online integrated development environment for MapReduce programming can solve this problem, thus users need not build the environment themselves, only need to focus on the logical design of the parallel program. During the software construction, the problem of independent space setting and naming conflict of the file in the multi-user environment, and the problem of online compiling, execution and instant feedback message to client are solved. The software has been deployed and tested in Hadoop cluster, and can meet users’ basic requirements for the development of MapReduce.

This paper deals with machine learning through interaction with a person. We developed an interactive learning system (LTIP) by concentrating on a specific situation where a user and a robot cooperatively find a lost object in real world. The LTIP consists of a gathering candidate images process, a self-categorizing process based on SOM, and a cooperative and repeatable narrow down process so that the robotic system effectively learns the appearance of the target object that the user is looking for. We conducted several experiments to evaluate and analyze the process of interactive learning. We confirmed that preliminarily determining the effective features set for categorization is difficult. So our proposed LTIP scheme is useful for such a practical processes such as finding a lost object.

The existing method of task mapping between process models is mostly based on the similarity of labels to get the similarity between tasks, which is affected by a single factor and easy to cause errors. This paper proposes a task mapping method for Artifact-Centric business process analyzes the data operation in business process execution. At first we add the description of data operation to EZ-Flow model. Secondly select the similar artifact attributes of the two business artifact by whole artifacts similarity calculation and based them to calculate two tasks’ similarity by label similarity, artifact operation similarity and context similarity calculations. Finally, the task mapping comes from the optimal selection of tasks’ similarities. Experimental results verify the effectiveness of the method, and show that the method reflects the data operation characteristics of business process model, as artifact task mapping of the process model provides a feasible method.

With the rapid development of social media, video sharing sites like YouTube are getting more and more attention. Discovering the view growth pattern have become interesting topics for researchers as well as advertisers, media companies. In this paper, we analyze two aspects about video view growth pattern of YouTube videos. Firstly, the pattern of aggregated view is studied. It is found that the aggregated view rate peaks in the first few days, and falls quickly in the following days, and then decrease slowly during the consecutive weeks. Finally, the view rate tends to be a constant on the long run. The aggregated view count after a period of two months can be fitted with a linear line. Secondly, the view growth pattern of individual video is explored. The results indicate that the majority of videos peak at the very beginning of videos’ lifetime, and the category of view sources causes the peak is different. The view count of individual video and the view count from each source item also stabilize after a period of two months, and we finally show the referring time and active period of each source item.