Earticle

Existing metadata management methods bring about lower access efficiency in solving the problem of renaming directory. This paper proposes a metadata management method based on directory path redirection (named as DPRD) which includes the data distribution method based on directory path and the directory renaming method based on directory path redirection. Experiments show that DPRD effectively solves the lower access efficiency caused by the renaming directory.

With expansion in the innovation, the requests of the individuals are expanding as individuals are more intrigued by the web offices with higher information rate. Despite the fact that 3G advances convey essentially higher bit rates than 2G innovations, "LTE" (3GPP Long Term Evolution) got a blast in the field of advancements with new offices like Internet applications or versatile broadband ( Voice over IP (VoIP), feature spilling, music downloading, portable TV and numerous others material) all around. It is very much necessary to extract the useful information of the user using LTE technology to understand he performance and durability of this new technology. In this paper we present a novel architecture for mining the data for extracting useful information regarding the success rates of various handoff procedures in LTE technology.

Word sense disambiguation (WSD) plays an important role in natural language processing fields. Semantic category is semantic knowledge and part-of-speech is syntax knowledge. In this paper, word window is opened to get semantic category and part-of-speech of left and right adjacent words around an ambiguous word. A new approach of determining true meanings of ambiguous words based on support vector machine (SVM) is given. The training corpus in SemEval-2007: Task#5 is applied to optimize SVM and the optimized SVM is tested. Experimental results show that the performance of the proposed method is improved.

As a generalization of intuitionistic fuzzy sets, neutrosophic sets (NSs) can be better handle the incomplete, indeterminate and inconsistent information, which have attracted the widespread concerns for researchers. In this paper, some new aggregation operators are proposed under single-valued neutrosophic environment. Firstly, the definition and operational laws of single-valued neutrosophic numbers (SVNNs) are introduced. Then, the single-valued neutrosophic power average (SVNPA) operator and the single- valued neutrosophic power weighted average (SVNPWA) operator are developed, and some properties of SVNPWA operator are also analyzed. Furthermore, a method for solving multi-criteria decision-making (MCDM) problems is explored based on the power aggregation operators and cosine similarity measures. Finally, an illustrative example is shown to verify the effectiveness and practicality of the proposed method.

In this work detailed research and analysis of the challenges of integration and interoperability in education information systems is presented. The integration methods and techniques are examined, as well as interoperability frameworks and challenges in the last 15 years. The work is also driven by sharing of assessment data for the purpose of efficient personalization of learning environments.

To improve the performance of BLAS 2 GEMV subroutine under the latest instruction set, Intel AVX, this paper presents a new approach to analyze the new generation instruction set and enhance the efficiency of current data-oriented math subroutines. The whole optimizing process involves memory access optimization, SIMD optimization and parallel optimization. Also, this paper shows the comparison between the traditional SSE instruction set and the AVX instruction set. Experiments show that the optimized GEMV function has obtained considerable increase on performance. Compared with the Intel MKL, GotoBLAS, ATLAS, this optimized GEMV exceeds these BLAS implementations from 5% to 10%.

This study investigated the university students’ resilience through online interaction’s view within a social cognition framework, in China. 525 college students were asked to complete a questionnaire consists of The Online Interaction Level Scale, the Online Social Support Scale of Adolescence and the Conner-Davidson Resilience Scale. Research Data was analyzed by using the structural equation modeling (SEM) method. The results indicated as follows: (1) The total average score of college students’ online interaction, online social support and resilience level were higher than medium critical value; (2) There was no difference in male and female’s resilience. (3) College students’ online interaction and online social support were all the positive predictor of resilience; (4) The effect of resilience of online interaction was fully mediated by online social support. By introducing the perspective of online interaction into the research of resilience, meanwhile adding new complement to the traditional structure, this study provides a new view and approach to the resilience construction research.

In this paper, fast image search with efficient additive kernels and kernel locality-sensitive hashing has been proposed. As to hold the kernel functions, recent work has probed methods to create locality-sensitive hashing , which guarantee our approach’s linear time, however existing methods still do not solve the problem of locality-sensitive hashing (LSH) and indirectly sacrifice the loss in accuracy of search results in order to allow fast queries. To improve the search accuracy, we show how to apply explicit feature maps into the homogeneous kernels, which help in feature transformation and combine it with kernel locality-sensitive hashing. We prove our method on several large datasets, and illustrate that it improve the accuracy relative to commonly used methods and make the task of object classification, content-based retrieval more fast and accurate.

Previous panoramic studies focused on image stitching and the panoramic camera development. In recent years, people began to focus on panoramic scalability issues, the existed methods adopt traditional photogrammetric methods for data processing based on a multi-directional panorama images, using a large number of control points and combined bundle adjustment. Indeed, panorama itself is a kind modeling method, it is completely unnecessary to reconstruct the panoramic entire scene for measurement. So in this paper, we focus on a kind of panorama measurement from spherical projective geometry we studied and constructed the relative, absolute orientation and measurable algorithms based on spherical stereo pair panorama in order to achieve panoramic measurement in the process of panorama roaming. The experiment shows that the approach is much validated, and something useful is obtained.

In this paper Semantic-Enhanced Entity Relationship (SEER) and Semantic –Relational Database (SRD) diagrams are proposed. SEER and SRD are extensions of Enhanced Entity Relationship (EER) and Relational Database (RD) diagrams respectively that are based on dealing with Semantic Integrity Constraints. EER and RD do not take semantic constraints in matter. A lots of information are lost because of the shortage of documentations. The proposed SEER and SRD models embed the semantic constraints using a new notations and pseudo code to give a full diagram fo

The Particle deposition position and subside curve equation was studied. Particle depositing Para-curve formula was generated through the dust particle dynamic analysis in the pipe. Settlement nephogram and the trajectory curve of dust particle were obtained by FLUENT numerical simulation. Depositing velocity of particle was simulated and the simulation results, the calculated results and the experimental result were compared and analyzed. The particle motion was reflected by coordinate equation and parabolic curves were drawn for the particle No.65 in the z direction. Various depositing velocities in horizontal rectangle pipe under different particle diameter and flow velocity circumstances were compared to summarize motion patterns.

This paper proposes an effective ensemble classifier, named PCAenSVM, which consists of ten weak Support Vector Machine classifiers based on different Principal Component Analysis thresholds. Those ten base Support Vector Machine classifiers are made up to fulfill classification tasks using Majority Voting strategy. Experiments are made on four UCI data sets and a data set from the Uppsala University to evaluate the performances of PCAenSVM. The results of PCAenSVM are compared with that of LibSVM and EnsembleSVM. Experimental results show that PCAenSVM has better classification accuracy than other two algorithms. Moreover, PCAenSVM has the same confidence level with the LibSVM, and its confidences of accuracy and sensitivity on those five data sets outperform that of the EnsembleSVM.

With the development of network technology and database, network investigation will become one of the main ways of statistical survey. The Internet has provided an unprecedented low cost, fast information channel, also provided convenient information feedback for the information dissemination, and these are the advantages of network investigation. In this paper, we test the impact of college sanda teaching to students' mental health by using network questionnaires. The result shows that Sanda teaching can greatly improve the physical function of college students and improve the quality of college students. Through the big data and network platform, network teaching can optimize the teaching of college physical education; improve the students' interest in sanda.

Online customer review is considered as a significant informative resource which is useful for both potential customer and product manufacturers. As a result, it is one of the most challenging tasks to mine customer reviews automatically and to provide users with opinion summary. Product features and opinion word play the most important roles in the customers’ opinions mining. In this paper, we dedicate our work to opinion word mining. We proposed an approach for opinion word identification based on the association rule mining algorithm. The method makes full use of co-occurrence syntactic characteristic between product features and opinion word. Firstly, the product feature is identified by two-stage filtering scheme, and secondly the opinion word is extracted through association rule mining. The final experiment results show that the proposed method could not only obtain the product features related to domain characteristics, but identify the opinion word effectively. Meanwhile, our approach possesses much higher precision and recall than Hu’s work.

To find the fascinating example, distinguish web client conduct, enhance the business procedure, anticipate web client desire, we are utilizing web utilization mining making utilization of affiliation standard mining To find the relation between the data item, we are using the association rule mining, which is an important field of data mining. Information is assembled on the web server as web log record in web usage mining.A different number of website visitor access the website that is why accessing of web logs and identifying relationships among these logs becomes a complex task because of rapid growth of web log files. To observe the relationship between the log records before applying the affiliation lead some preprocessing works are expected to diminish the uproarious information of web log documents. Multiple researchers done the different type of works on the WCM and WUM to enhance the competence of the website by providing innovative methods and and this paper gives a review about the current works done on WUM by the scientists.

With the rapid growth of personal website influence, the advertisement placing has become an important investment in personal websites. But in order to accurate the advertisement placing, the specific quest for the specific users with their specific interesting need to be concerned. Acquiring, preprocessing and classifying consumption intention of the released information that published in the personal websites is the main task of this essay. We regard consumption intention mining as a binary classification problem, and extract multi-dimensional features from the raw corpus. Finally, we propose models based on SVM, Naïve Bayes and deep learning to solve the consumption intention classification problem. The experimental result shows that the deep learning based method achieves the highest F-measure.

Traditional data management is usually based on relational databases, which are capable of managing small amounts of data. But relational databases have some difficulty in inquiry, management, and analysis of large amounts of data and magnanimity data. The method of effective management of magnanimity data is a problem deserving of study. In this paper, traditional relational databases are moved to Hadoop, in order to implement query and analysis on Hadoop. This paper changes the amount of data record and number of nodes in clusters, and records the query time in different conditions. Advantages and disadvantages of query on Hadoop can be analyzed by comparing the statistics with the query time on relational database Oracle. The factors affecting the time of query on Hadoop can be found by analysis. Furthermore, the result is also a reference material of future research and data managements on cloud platforms.

Though clustering Analysis has been developed for many years with many clustering methods come into application, image clustering is still a difficult problem. One of the most fundamental problems is that there are many kinds of image representations, and the distinguish ability of each feature is different, so their cluster effects are also different. To decide cluster priority level of different images features on a specific image dataset, the distinguish ability of three typical image features are analyzed, and a cluster discriminant index is present, which called Simplified Overall Cluster Quality is composed of cluster compaction and cluster separation. Experimental results showed the feature with best distinguish ability also possessed best discriminant index. So this index can be used to decide the priority of features for clustering images or the best feature for image cluster.

Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue. Firstly, to solve the problem of computational complexity, spectral clustering algorithm is imported to select efficient bands for subsequent classification task. Secondly, due to lack of labeled training sample points, this paper proposes a new algorithm that combines support vector machines and Bayesian classifier to create a discriminative/generative hyperspectral image classification method using the selected features. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.

Association rule mining which is the most significance and use is one of a relevant approach for data mining. The fundamental of the association rule mining approach have been Apriori and introduce many access with changes in the apriori but though main idea continue to be the same that is use of support and confidence threshold (s). Conforming to the theory it is well know that no work has been done in the domain of Enhancing pruning step of Apriori. This paper introduces a new algorithm M-APRIORI. This algorithm advances to Enhance the Apriori algorithm by using mean support (supmean) rather than minimum support (supmin), to produce probable item-set instead of large item-set and Artificial bee Colony technique used to optimization the rules. In this paper Apriroi and M-Apriori are based On Artificial Bee Colony.

In this paper, we address the problem of unbalanced training dataset for source camera identification, namely, there are fewer training examples for some camera models compared to other camera models. A new source camera identification approach is proposed to alleviate the influence of imbalanced training dataset. In the proposed approach, firstly, we treat source camera identification as a multi-class classification problem, and decompose it into binary classification problems. After decomposing, the problem of imbalanced training dataset for multiclass classification is transformed to the problem of imbalanced training dataset for binary classification. Then, we incorporate SMOTE and AdaBoost algorithms to construct SVM ensemble to address the issue of imbalanced training dataset for binary classification. A number of experiments show the proposed approach can deal with the imbalanced training dataset effectively.

This paper proposes an improved Markov prediction, according to the temperature control problem of hot blast stove alternate supply air in the operation of blast furnace operation, namely, implement clustering for waiting to be processed data, used kernel fuzzy c-means clustering based on the pairwise constraints. The supply air temperature of hot blast stove is seen as without aftereffect things in this method, introduce semi-supervised learning mechanism in traditional fuzzy clustering to deal with the basic data, and using the kernel effectiveness index improved the FCM algorithm. Experiments show that the improved clustering algorithm is superior to other algorithms in accuracy and performance, at the same time, the improved prediction model comparison with the traditional values of temperature prediction, which has obvious advantages in defined temperature range and the fit of the temperature value, the guiding significance was significantly enhanced in industrial field.

Wireless Sensor & Actuator networks take actuation decisions based on the data collected by the deployed set of sensor nodes. The method of data acquisition, leading to decision making, could be semi-automated or fully automated. In either case, the reliable delivery of information assumes critical importance since it has a direct impact on the decision making process for subsequent action by the actuator network. This paper presents in details a novel methodology “Layer Based Time Constrained Reliable Data Acquisition Mechanism” LTCRDM, which can be utilized for reliable delivery of information, sensed periodically or in response to a query, by the sensors deployed over a geographical area to a centralized sink where the decision for eventual actuation is taken. Since the latency and reliability requirements in a WSAN are stringent, the mechanism detailed attempts delivery of maximum packets with minimum latency to ensure that estimation of the sensed event is accurate leading to correct decision making. The methodology ensures relatively low packet loss as compared to standard packet delivery mechanisms with latency time constraints. The algorithm for dissemination of query (LQDM) in the deployed nodes is also presented. Authors have provided detailed algorithm, results of simulation and observations using IEEE 802.15.4 PHY & MAC as underlying layers. Experimental results over a test-bed are also presented. A critical analysis of the results is presented for comparison against the standard methodologies in vogue.

Multi-granularity linguistic information has been studied by researchers in many disciplines, in which the scale of linguistic term sets is usually restricted in domain [0, 1]. In this paper, we consider the multiple attribute group decision making (MAGDM) problems, in which the evaluation of each alternative with respect to each attribute is provided by several experts of the corresponding field. In order to convey the preferences of different experts exactly and to describe the characteristics of different attributes, linguistic term sets with different granularities and different scales have been proposed to express their evaluation values. Moreover, the performance values given by the experts take the form of modified proportional 2-tuples. In this process, the modified proportional 2-tuple, its comparative rules as well as several aggregation operators have been proposed. A new method has been proposed to achieve a basic modified linguistic term set (BMLTS) and a transformation function has been defined to make the performance values uniform. An example is given to illustrate the applicability and flexibility of the method.