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The purpose of this paper is to make a theoretical comprehensive efficiency evaluation of a nonlinear analysis method based on the Woodbury formula from the efficiency of the solution of linear equations in each incremental step and the selected iterative algorithms.

First, this study employs the time complexity theory to quantitatively compare the efficiency of the Woodbury formula and the LDLT factorization method which is a commonly used method to solve linear equations. Moreover, the performance of iterative algorithms also significantly effects the efficiency of the analysis. Thus, the three-point method with a convergence order of eight is employed to solve the equilibrium equations of the nonlinear analysis method based on the Woodbury formula, aiming to improve the iterative performance of the Newton–Raphson (N–R) method.

First, the result shows that the asymptotic time complexity of the Woodbury formula is much lower than that of the LDLT factorization method when the number of inelastic degrees of freedom (IDOFs) is much less than that of DOFs, indicating that the Woodbury formula is more efficient for local nonlinear problems. Moreover, the time complexity comparison of the N–R method and the three-point method indicates that the three-point method is more efficient than the N–R method for local nonlinear problems with large-scale structures or a larger ratio of IDOFs number to the DOFs number.

This study theoretically evaluates the efficiency of nonlinear analysis method based on the Woodbury formula, and quantitatively shows the application condition of the comparative methods. The comparison result provides a theoretical basis for the selection of algorithms for different nonlinear problems.

The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters.

The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases.

Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic.

Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.

The multiple factors of intelligence measurement are critical in intelligent science. The intelligence measurement is typically built as a model based on multiple factors. The different agent is generally difficult to measure because of the uncertainty between multiple factors. The purpose of this paper is to solve the problem of uncertainty between multiple factors and propose an effective method for universal intelligence measurement for the different agents.

In this paper, the authors propose a universal intelligence measurement method based on meta-analysis for crowd network. First, the authors get study data through keywords in the database and delete the low-quality data. Second, they compute the effect value by odds ratio, relative risk and risk difference. Then, they test the homogeneity by Q-test and analyze the bias by funnel plots. Third, they select the fixed effect and random effect as a statistical model. Finally, through the meta-analysis of time, complexity and reward, the weight of each factor in the intelligence measurement is obtained and then the meta measurement model is constructed.

This paper studies the relationship among time, complexity and reward through meta-analysis and effectively combines the measurement of heterogeneous agents such as human, machine, enterprise, government and institution.

This paper provides a universal intelligence measurement model for crowd network. And it can provide a theoretical basis for the research of crowd science.

Mesh Parameterization is central to reverse engineering, tool path planning, etc. This work synthesizes parameterizations with un-constrained borders, overall minimum angle plus area distortion. This study aims to present an assessment of the sensitivity of the minimized distortion with respect to weighed area and angle distortions.

A Mesh Parameterization which does not constrain borders is implemented by performing: isometry maps for each triangle to the plane Z = 0; an affine transform within the plane Z = 0 to glue the triangles back together; and a Levenberg–Marquardt minimization algorithm of a nonlinear F penalty function that modifies the parameters of the first two transformations to discourage triangle flips, angle or area distortions. F is a convex weighed combination of area distortion (weight: α with 0 ≤ α ≤ 1) and angle distortion (weight: 1 − α).

The present study parameterization algorithm has linear complexity [𝒪(n), n = number of mesh vertices]. The sensitivity analysis permits a fine-tuning of the weight parameter which achieves overall bijective parameterizations in the studied cases. No theoretical guarantee is given in this manuscript for the bijectivity. This algorithm has equal or superior performance compared with the ABF, LSCM and ARAP algorithms for the Ball, Cow and Gargoyle data sets. Additional correct results of this algorithm alone are presented for the Foot, Fandisk and Sliced-Glove data sets.

The devised free boundary nonlinear Mesh Parameterization method does not require a valid initial parameterization and produces locally bijective parameterizations in all of our tests. A formal sensitivity analysis shows that the resulting parameterization is more stable, i.e. the UV mapping changes very little when the algorithm tries to preserve angles than when it tries to preserve areas. The algorithm presented in this study belongs to the class that parameterizes meshes with holes. This study presents the results of a complexity analysis comparing the present study algorithm with 12 competing ones.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The web provides its users with abundant information. Unfortunately, when a web search is performed, both users and search engines must deal with an annoying problem: the presence of spam documents that are ranked among legitimate ones. The mixed results downgrade the performance of search engines and frustrate users who are required to filter out useless information. To improve the quality of web searches, the number of spam documents on the web must be reduced, if they cannot be eradicated entirely. This paper aims to present a novel approach for identifying spam web documents, which have mismatched titles and bodies and/or low percentage of hidden content in markup data structure.

The paper shows that by considering the degree of similarity among the words in the title and body of a web docuemnt D, which is computed by using their word‐correlation factors; using the percentage of hidden context in the markup data structure within D; and/or considering the bigram or trigram phase‐similarity values of D, it is possible to determine whether D is spam with high accuracy

By considering the content and markup of web documents, this paper develops a spam‐detection tool that is: reliable, since we can accurately detect 84.5 percent of spam/legitimate web documents; and computational inexpensive, since the word‐correlation factors used for content analysis are pre‐computed.

Since the bigram‐correlation values employed in the spam‐detection approach are computed by using the unigram‐correlation factors, it imposes additional computational time during the spam‐detection process and could generate higher number of misclassified spam web documents.

The paper verifies that the spam‐detection approach outperforms existing anti‐spam methods by at least 3 percent in terms of F‐measure.

Existing shape‐based fuzzy clustering algorithms are all designed to explicitly segment regular geometrically shaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily shaped objects. The purpose of this paper is to introduce a new detection and separation of generic‐shaped object algorithm.

With the aim of separating arbitrary‐shaped objects in an image, this paper presents a new detection and separation of generic‐shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling.

Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects.

The proposed FKG algorithm can be highly used in applications where object segmentation is necessary. Likewise, this algorithm can be applied in Moving Picture Experts Group‐4 for real object segmentation that is already applied in synthetic object segmentation.

Filter design plays an essential role in most communication standards. The essential element of the software-defined radio is a channelizer that comprises several channel filters. Designing filters with lower complexity, minimized area and enhanced speed is a demanding task in currently prevailing communication standards. This study aims to propose an efficient reconfigurable residue number system (RNS)-based multiply-accumulate (MAC) channel filter for software radio receivers.

RNS-based pipelined MAC module for the realization of channel finite impulse response (FIR) filter architecture is considered in this work. Further, the use of a single adder and single multiplier for realizing the filter architecture regardless of the number of taps offers effective resource sharing. This design provides significant improvement in speed of operation as well as a reduction in area complexity.

In this paper, two major tasks have been considered: first, the RNS number conversion is performed in which the integer is converted into several residues. These residues are processed in parallel and are applied to the MAC-FIR filter architecture. Second, the MAC filter architecture involves pipelining, which enhances the speed of operation to a significant extent. Also, the time-sharing-based design incorporates a single partial product-based shift and add multiplier and single adder, which provide a low complex design. The results show that the proposed 16-tap RNS-based pipelined MAC sub-filter achieves significant improvement in speed as well as 89.87% area optimization when examined with the conventional RNS-based FIR filter structure.

The proposed MAC-FIR filter architecture provides good performance in terms of complexity and speed of operation because of the use of the RNS scheme with pipelining and partial product-based shift and adds multiplier and single adder when examining with the conventional designs. The reported architecture can be used in software radios.