Applied Mechanics and Materials Vols. 284-287

Abstract: Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this project will study the issues related to two types of data. First, we will focus on the content filtering on single-attribute streams, such as sensor data. Second, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

Abstract: Similarity computing of ontological concept has made rapid progress in the field of data mining, information processing and artificial intelligence and becoming one of the hot research field of information technology, particularly the idea of the semantic Web was proposed in 2000, the concept of semantic similarity has gotten more attention, while also facilitating its further development and application in information retrieval. Considering the deficiencies of existing concept similarity algorithm, this paper design the method to reduce the candidate set of domain concept, and put forward a similarity calculation model based on the concept name, instances, properties, and semantic structure of domain ontology. Integrated several main influencing factors, the experiments show the proposed algorithm can express the impact of various factors on the similarity in the calculation concept similarity of domain ontology. By comparing with the traditional similarity method and expertise experience value, the experiment result shows that the effectiveness and correctness of the concept similarity calculation model.

Abstract: Reversible data hiding techniques can completely recover the cover images after extracting the secret message from the stego images and become a hot research topic recently. The histogram shift-based steganography, which is a kind of reversible data hiding technique, has good performance on imperceptibility. However, it also produces obvious features in the histograms of stego images. In this paper, we propose a steganalysis method based on the payload invariant features to detect the histogram shift-based steganography proposed by Ni et al. In the proposed steganalysis method, the minimum of the sum of the proposed features is first obtained, which is then compared with a predefined threshold to determine an image is a stego or a cover one. Experimental results show that the proposed steganalysis method can provide very high detection accuracy (about 98%) in various payload cases. Compared with the other steganalysis method, the proposed method can provide better detection performance under different embedding ratios and, therefore, is more reliable for detection of the histogram sift-based steganography.

Abstract: Recently bilinear pairings have found various applications in cryptosystems. However, a natural open question is to construct a secure and efficient pairing system without the MapToPoint hash function. The new scheme offers a less order of security and efficiency than the existing other signature schemes based on discrete logarithm. Furthermore, our scheme upholds all desirable properties of previous blind signature schemes, and requires general cryptographic hash function instead of MapToPoint hash function that is inefficient and probabilistic. It can apply in more critical systems like e-voting, e-commerce and e-payment systems that need higher security against sophisticated attacks and can preserve participants’ anonymity. The security and efficiency of the proposed method is analyzed and presented. We believe that the proposed idea can be applicable to all other usages for all blind signatures.

Abstract: Cloud computing platforms offer a convenient solution for addressing challenges of processing large-scale data in both academia and industry, beyond what could be achieved with traditional on-site clusters. There are a great number of on-line cloud services, and in the meantime, the security issue is getting more and more significant for cloud users. Whereas Hadoop-based cloud platform is currently a well-known service framework, our goal in this paper is to investigate the mechanisms of authentication and encryption of Hadoop. We try to construct a highly secure Hadoop platform with small deployment cost, robust attacking prevention, and less performance degradation. We also conduct a number of simulations to evaluate the performance under different parametric settings and cryptographic algorithms. Simulation results reveal the feasibility of security mechanisms, and find that the more important thing to construct cloud platforms with appropriate security mechanisms is to consider the application requirements, which could be a better trade-off between security and user requirement.

Abstract: Enterprise Application Integration is a new business solution by combining the existing applications. Most enterprises consider data integration is the first and most critical step of integrating information systems within the organization. This research proposes a multi-cloud framework that integrates the enterprise data of various sources through a message brokering mechanism. The major objective of this framework is to provide remote database access and data consistency among enterprise applications through the messaging mechanism base on enterprise data. To analyze the feasibility of the multi-clouds framework for integrating enterprise data, we implemented the proposed architecture and evaluated with simulation data. The simulation results present the completeness and correctness of the data integration process. Through this proposed framework, the enterprise information can integrate in a more efficient and effective way.

Abstract: Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.

Abstract: The IP-CAM plays a major role in the context of digital video surveillance systems. The function of face detection can add extra value and can contribute towards an intelligent video surveillance system. The cascaded AdaBoost-based face detection system proposed by Viola can support real-time detection with a high detection rate. The performance of the Alt2 cascade (from OpenCV) in an IP-CAM video is worse than that with regard to static images because the training data set in the Alt2 cannot consider the localized characters in the special IP-CAM video. Therefore, this study presents an enhanced training method using the Adaboost algorithm which is capable of obtaining the localized sampling optimum (LSO) from a local IP-CAM video. In addition, we use an improved motion detection algorithm that cooperates with the former face detector to speed up processing time and achieve a better detection rate on video-rate processing speed. The proposed solution has been developed around the cascaded AdaBoost approach, using the open-CV library, with a LSO from a local IP-CAM video. An efficient motion detection model is adopted for practical applications. The overall system performance using 30% local samples can be improved to a 97.9% detection rate and reduce detection time by 54.5% with regard to the Alt2 cascade.

Abstract: L-System has been very useful in modeling the development of biological plant. It has also been applied to music rendering and the L-systems plant model has also been used to represent musical note. Nonetheless, most of the tools available for L-System modeling require strong backgrounds in programming as well as L-Systems. In this paper, we describe an implementation of a simple and yet useful visual language framework for rapid generation of musical sounds which requires no such backgrounds. The framework is realized by designing and developing a simplified icon-based visual language framework. The system, called Visual Language Music Rendering provides an expressive music environment for L-System music rendering. In the framework, visual language grammar models for L-System music rendering are fed into the system for more effective transformation of plant model to music rendering. The framework also includes using mutated stochastic and context-sensitive L-Systems visually which results in better melody and enhanced musical sounds. In the evaluation, the system was compared with an existing music rendering tool and it was found that Visual Language Music Rendering is the preferred system and individuals who are interested in music rendering can easily use it even though one has no experience in music or L-System.

Abstract: This study uses quantitative methods to rate content and teaching on a phonics program delivered by computer assisted language learning (CALL). Data was obtained by Questionnaire and analysed using GM (0,N) model of Grey Theory. Results showed a high effectiveness rating and underlined the value of computer assisted language learning in the continuing education programs in the subject University.