Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users' raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user's GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user's present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change pointbased segmentation method, an inference model and a postprocessing algorithm based on conditional probability. The change point-based segmentation method was compared with two baselines including uniform duration based and uniform length based methods. Meanwhile, four different inference models including Decision Tree, Bayesian Net, Support Vector Machine (SVM) and Conditional Random Field (CRF) are studied in the experiments. We evaluated the approach using the GPS data collected by 45 users over six months period. As a result, beyond other two segmentation methods, the change point based method achieved a higher degree of accuracy in predicting transportation modes and detecting transitions between them. Decision Tree outperformed other inference models over the change point based segmentation method.
Abstact An agronomic gene pool of wheat (Triticum aestivum L.) was constructed through recurrent selection. In present research, 24 wheat SSR markers determining 25 loci on 14 different chromosomes were used to evaluate the gene pool. Thirty parents used as original materials in recurrent selection were also assessed. In total, 115 alleles were detected in gene pool with an average of 4.6, ranging from 2 to 9 alleles per locus. Statistical test showed that genetic diversities had no significant difference between the gene pool and the 30 parents. Principle coordinates analysis revealed that the individuals of the gene pool were mainly divided into three groups, which was consistent with the result of cluster analysis based on genetic distance matrix of the gene pool. Cluster analysis was carried out based on Euclidian distance calculated upon five morphological trait values and the results showed that most individuals were in a group while the others scattered. Correlation analysis of genetic distance matrix and Euclidian distance matrix showed no significant correlation between two matrices. The results suggest that the gene pool is improved after several cycles of selection, while genetic variation is still maintained. Therefore, the gene pool is suitable for further breeding program.
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