Abstract With the rapid development of the Internet, XML (Extensible Markup Language) is increasingly gaining acceptance as a medium for integrating and exchanging data. Meanwhile, object-oriented database has a strong ability to store data, XML can benefit greatly and specifically from database support and object-oriented database management system. Consequently, it is significant to exchange data from object-oriented database to XML. In particular, taking spatiotemporal information existing in many practical applications... With the rapid development of the Internet, XML (Extensible Markup Language) is increasingly gaining acceptance as a medium for integrating and exchanging data. Meanwhile, object-oriented database has a strong ability to store data, XML can benefit greatly and specifically from database support and object-oriented database management system. Consequently, it is significant to exchange data from object-oriented database to XML. In particular, taking spatiotemporal information existing in many practical applications into consideration, we study the spatiotemporal data models and the problem of exchanging spatiotemporal data from object-oriented database to XML. In order to find an XML Schema which is the best description of the existing object-oriented database, we propose a comprehensive approach to focus on a set of mapping functions to accomplish the transformation of spatiotemporal data. Read More Read Less

Abstract Nutritional deficiencies in coffee plants affect production and therefore it is important its early identification. The current research is focused on the automatic identification of nutritional deficiencies of Boron (B), Calcium (Ca), Iron (Fe) and Potassium (K), by using shape and texture descriptors in images of coffee tree leaves. After the acquisition of images containing coffee tree leaves, they are subjected to a segmentation process using Otsu's... Nutritional deficiencies in coffee plants affect production and therefore it is important its early identification. The current research is focused on the automatic identification of nutritional deficiencies of Boron (B), Calcium (Ca), Iron (Fe) and Potassium (K), by using shape and texture descriptors in images of coffee tree leaves. After the acquisition of images containing coffee tree leaves, they are subjected to a segmentation process using Otsu's method. Afterwards, for the resulting images they are applied the descriptors Blurred Shape Model (BSM) and Gray-Level Co-occurrence Matrix (GLCM) for extracting characteristics of shape and texture. Finally, the obtained image representation is used for training KNN, Naïve Bayes and Neural Network classifiers by using the extracted features, in order to infer the type of deficiency presented in each analyzed image. The experimental results show that the developed procedure has a high accuracy, being the better results associated to the identification of Boron (B) and Iron (Fe) deficiencies. Read More Read Less

Abstract Researchers in the field of cloud forensics need to move away from insisting on acquiring all data - as has historically been the case in computer forensicsand yet still be able to prove the accuracy, sufficiency and soundness of partially acquired data. Virtualization is considered to be one of the main pillars in providing cloud services. In some cases, investigators might end up having to rely on... Researchers in the field of cloud forensics need to move away from insisting on acquiring all data - as has historically been the case in computer forensicsand yet still be able to prove the accuracy, sufficiency and soundness of partially acquired data. Virtualization is considered to be one of the main pillars in providing cloud services. In some cases, investigators might end up having to rely on suspect Virtual Machine (VM) snapshots in the form of memory dumps and user activity logs. Then, in these cases the main challenge is to analyse these memory dumps without altering the evidence. In this paper, after assessing static and live forensics tool in examining cloud based snapshot, we propose a forensic process model based on the NIST model to examine the private cloud based VM snapshots (e.g. XenServer). Moreover, we examined snapshots using existing digital forensic tools and were able to successfully acquire data without the need to transform the snapshot files. Read More Read Less

Abstract At present, the traditional convolutional neural network (CNN) can easily cause overfitting in the training process, thereby resulting in an invalid training model. Thus, this study proposed a novel CNN regularization method to avoid overfitting in the training process and to increase the image classification accuracy of CNN. The proposed method uses failure probability as the theoretical basis. First, the failure probability density (FPD) function of image pixel... At present, the traditional convolutional neural network (CNN) can easily cause overfitting in the training process, thereby resulting in an invalid training model. Thus, this study proposed a novel CNN regularization method to avoid overfitting in the training process and to increase the image classification accuracy of CNN. The proposed method uses failure probability as the theoretical basis. First, the failure probability density (FPD) function of image pixel points was deduced, and the FPD prediction model of pixel points in image neighborhood was established. Second, this method evaluated the FPD of image eigenvalues, selected the pixel points with the minimum FPD in the image neighborhood as the retention characteristics, and extracted excellent features. Finally, this method conducted a labeling classification experiment on three image classification datasets (MNIST, CIFAR-10, and CIFAR-100) and compared the image classification accuracy of the three existing popular pooling methods (Max, Average, and Stochastic pooling). Results demonstrated that the proposed regularization method based on FPD can increase the classification accuracy of the image data. The classification accuracies gained by the training datasets are all higher than 90%, and the classification accuracies gained by the testing datasets are all higher than 85%. The proposed CNN regularization method possesses good generalization capability and engineering applicability. Read More Read Less

Abstract This paper establishes the impact factors framework of Marine technology innovation in Zhejiang Province, China. According to the impact factors, we do questionnaire survey on 30 marine science and technology enterprises of Zhejiang Province. A structural equation model is established to make an empirical study on influence factors of marine technology innovation based on the objective environment of Zhejiang Province, China. The conclusion... This paper establishes the impact factors framework of Marine technology innovation in Zhejiang Province, China. According to the impact factors, we do questionnaire survey on 30 marine science and technology enterprises of Zhejiang Province. A structural equation model is established to make an empirical study on influence factors of marine technology innovation based on the objective environment of Zhejiang Province, China. The conclusion indicates that marine technology human resources, Marine science and technology policy, financing environment, infrastructure and Marine technology platform, Marine cultural environment, marketization degree, natural resources, R&D spending , industrial structure optimization, have an effect on marine technology innovation. And, the most effective factor is the marine science and technology policy. It is the affected by the delay of the planned economy. The tilt of the policy can effectively promote the Marine science and technology progress, and promote the technology commercialization. Read More Read Less