Social big data

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

https://tandfonline.com/doi/pdf/10.1080/17538947.2016.1239771

Big Data and cloud computing: innovation opportunities and challenges

There has been much effort to provide eco-friendly and biodegradable materials for the next generation of composite products owing to global environmental concerns and increased awareness of renewable green resources. An increase in the use of natural materials in composites has led to a reduction in greenhouse gas emissions and carbon footprint of composites. In addition to the benefits obtained from green materials, there are some challenges in working with them, such as poor compatibility between the reinforcing natural fiber and matrix and the relatively high moisture absorption of natural fibers. Green composites can be a suitable alternative for petroleum-based materials. However, before this can be accomplished, there are a number of issues that need to be addressed, including poor interfacial adhesion between the matrix and natural fibers, moisture absorption, poor fire resistance, low impact strength, and low durability. Several researchers have studied the properties of natural fiber composites. These investigations have resulted in the development of several procedures for modifying natural fibers and resins. To address the increasing demand to use eco-friendly materials in different applications, an up-do-date review on natural fiber and resin types and sources, modification and processing techniques, physical and mechanical behaviors, applications, life-cycle assessment, and other properties of green composites is required to provide a better understanding of the behavior of green composites. This paper presents such a review based on 322 studies published since 1978.

A review of natural fiber composites: properties, modification and processing techniques, characterization, applications

Inconel-718 has received an extensive using in mold industry. The selective laser melting (SLM) is providing an ideal means for manufacturing mold insert with complex geometrical features and internal architecture. During the manufacturing of high quality mold inserts with conformal cooling channel, the parameters play a vital role in the SLM process. In the study, the Inconel-718 alloys were manufactured by SLM with 2×2 mm 2 , 3×3 mm 2 , 5×5 mm 2 , and 7×7 mm 2 island scanning strategies. The microstructure, mechanical property, and residual stress were investigated by optical microscope, tensile test and Vickers micro-indentation, respectively. It can be found that the relative density increased with enlarging the island size; the results on the microstructure indicated that the cracks and more pores were detected in the 22-specimen; whilst the microstructures of all specimens were composed of fine dendritic grains, cellular, and columnar structures; the tensile testing suggested that the ultimate tensile strength and yield strength of all samples was similar; while the outcome of the residual stress showed that the value of residual stress was ranked in the following sequence: 22-specimen

Study on the microstructure, mechanical property and residual stress of SLM Inconel-718 alloy manufactured by differing island scanning strategy

A literature study for DEA applied to energy and environment

We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.

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Maximum neighborhood margin discriminant projection for classification

k-Nearest neighbor (KNN) rule is a very simple and powerful classification algorithm. In this article, we propose a new KNN-based classifier, called the local mean-based pseudo nearest neighbor (LMPNN) rule. It is motivated by the local mean-based k-nearest neighbor (LMKNN) rule and the pseudo nearest neighbor (PNN) rule, with the aim of improving the classification performance. In the proposed LMPNN, the k nearest neighbors from each class are searched as the class prototypes, and then the local mean vectors of the neighbors are yielded. Subsequently, we attempt to find the local mean-based pseudo nearest neighbor per class by employing the categorical k local mean vectors, and classify the unknown query patten according to the distances between the query and the pseudo nearest neighbors. To assess the classification performance of the proposed LMPNN, it is compared with the competing classifiers, such as LMKNN and PNN, in terms of the classification error on thirty-two real UCI data sets, four artificial data sets and three image data sets. The comprehensively experimental results suggest that the proposed LMPNN classifier is a promising algorithm in pattern recognition.

Improved pseudo nearest neighbor classification

The testing of Web services is an essential aspect of their quality assurance, however, because this testing often involves injecting only one mutant at one time, some vulnerability faults cannot be detected. To address this, the current paper presents a set of mutation operators that can be combined and defines the corresponding combinatorial strategies based on data perturbation and combinatorial testing. Based on this, multiple mutants can be injected at one time to help uncover interactive faults. To improve testing efficiency and effectiveness, a combinatorial testing approach focusing on Web service vulnerability is proposed: Firstly, initial test data are generated with perturbation techniques based on Web Services Description Language documents and Simple Object Access Protocol messages. Then, a combinatorial testing cases generation (CTCG) algorithm is used to generate the final combinatorial test data according to the proposed strategies. Furthermore, for some special Web services in which there is only one parameter or one method in service interface, a fuzzy mutation approach algorithm, as a complementary approach to CTCG, is also proposed. Finally, some testing experiments are conducted to verify the effectiveness of the proposed approaches in an integrated testing platform. The experiments show that proposed approaches are both feasible and effective: They can find more vulnerability faults than the traditional approaches.

Yongzhao Zhan

Papers

Maximum neighborhood margin discriminant projection for classification

Improved pseudo nearest neighbor classification

A Web services vulnerability testing approach based on combinatorial mutation and SOAP message mutation

A video semantic detection method based on locality-sensitive discriminant sparse representation and weighted KNN

Worst-input mutation approach to web services vulnerability testing based on SOAP messages