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As the advancement of network technologies, the recruitment industry is also showing a trend of networking, but the current online recruitment lacks the application of data mining (DM) technology, and its analysis of data is limited to recruitment websites. Therefore, the study proposes a DM-based online recruitment technology that selects the best career candidate through correlation analysis of social media data. The study uses Scrapy crawler to obtain data and utilises an improved Apriori algorithm for correlation analysis. The research findings denote that the proposed algorithm has excellent convergence performance and training efficiency. The study is of experimental design type using experimental data for analysis. In contrast with the traditional Apriori and FP-growth algorithms, the fitting of the output results increases by 6.21% and 14.67%. In addition, the improved algorithm shows significant optimisation effects, with an average running time reduced by 2.44 s and 0.76 s, respectively, compared with the two algorithms, and is less affected by the minimum confidence level. In fit testing, the average error of this method is only 0.02. In summary, online recruitment technology based on DM has strong availability and high reliability. The improved algorithm has excellent performance, accurate output results, and can accurately apply data from social media to select the best job candidate.

Recently, the use of information hiding techniques to protect biometric data has been an active topic. This paper proposes a novel image hiding approach based on correlation analysis to protect network-based transmitted biometric image for identification. Firstly, the correlation between the biometric image and the cover image is analyzed using principal component analysis (PCA) and genetic algorithm (GA). The purpose of correlation analysis is to enable the cover image to represent the secret image in content as much as possible, not just as a carrier of hidden information. Then, the unrepresented part of the biometric image, as the secret image, is encrypted and hidden into the middle-significant-bit plane (MSB) of the cover image redundantly. Extensive experimental results demonstrate that the proposed hiding approach not only gains good imperceptibility, but also resists some common attacks validated by the biometric identification accuracy.

As short video social apps develop rapidly, feed has become the main approach or algorithm to present recommendation content to users in such apps. There are big differences in the way that video apps make use of feed flow based on artificial intelligence algorithm. Two kinds of short video social apps including DouYin and KuaiShou are studied with a user experiment in this paper. Several indicators are established to quantify the user experience differences of these two apps. The results are analyzed with correlation analysis to find out the relationship between user experience performance and content presentation mode of feed flow. The differences found from the results are explained from the perspectives of user cognition and behavior.

The current China State Grid network consists of a set of heterogeneous and distributed networks. In such a heterogeneous environment, it is a challenging task to provide efficient network alarm management, and further to analyze the correlation of alarms. Thus, an efficient and distributed network alarm analysis scheme is becoming necessary and indispensable. To this end, we have attached our emphasis on two aspects of the aforementioned problem and proposed the corresponding algorithms, respectively. Firstly, we introduced an intra-network alarm treatment process to manage the heterogeneous network alarms. Especially, we leverage on the fuzzy clustering method and fuse alarms within the same cluster into comprehensive alarms by utilizing the Dempster–Shafer theory. Secondly, we proposed an inter-network alarm analysis process to mine the correlation rules of alarms through a distributed scheme based on the Frequent Pattern-Growth (FP-Growth) association rule mining algorithm. Compared with the traditional centralized scheme and another Apriori-based distributed algorithm, our proposed scheme has a higher time efficiency for the effective management of network alarms. With the aid of such a two-step network alarm management scheme, it is easy for the network management system to make a better management of alarms in heterogeneous and distributed networks.

We introduce a new technique to associate a spanning tree to the average linkage cluster analysis. We term this tree as the Average Linkage Minimum Spanning Tree. We also introduce a technique to associate a value of reliability to the links of correlation-based graphs by using bootstrap replicas of data. Both techniques are applied to the portfolio of the 300 most capitalized stocks traded on the New York Stock Exchange during the time period 2001–2003. We show that the Average Linkage Minimum Spanning Tree recognizes economic sectors and sub-sectors as communities in the network slightly better than the Minimum Spanning Tree. We also show that the average reliability of links in the Minimum Spanning Tree is slightly greater than the average reliability of links in the Average Linkage Minimum Spanning Tree.

We have studied the performance of a Gunn oscillator (GO) based angle modulator–demodulator system in transmitting chaotic signals in the X-band microwave frequency channel. The principle of bias tuning of a GO is employed to implement the angle modulator used in the transmitter. The said GO is operated in a free running condition and then in a frequency synchronized condition to an external microwave signal, thus generating frequency modulated (FM) and phase modulated (PM) signals, respectively. The demodulator circuit is implemented with a GO phase synchronized to the incoming modulated signal, followed by a microwave mixer multiplying the input and the output signals of the GO and a low pass filter. The response of the system is analytically established, numerically examined and experimentally verified. The obtained results confirm that in the limit of low modulation index and in the linear range of operation of the bias tuned modulator and the phase synchronized detector, a chaos signal can be transmitted and recovered through a microwave channel using the technique of angle modulation.

Function signature recovery is vital for many binary analysis tasks, led by control-flow integrity enhancement. To minimize human effort, existing works attempt to replace rule-based methods with learning-based methods. These works put a lot of work into improving the system’s performance, but this had the unintended consequence of increasing resource usage. However, recovering the function signature is more about providing information for subsequent tasks, e.g. reverse engineering, so both efficiency and performance are significant.

To identify the fundamental factors that increase efficiency, we attempt to optimize data-driven systems throughout their lifecycle from a data perspective. To this end, we perform detailed data analysis on a carefully collected dataset. After analysis and exploration, selective input is adopted and a multi-task learning (MTL) structure is introduced for function feature recovery to make full use of mutual information, and the computing resource overhead is optimized based on the observation of information deviation and sub-task relationship. The resource usage of the entire process is significantly reduced by our suggested solution, named Nimbus++ for efficient function signature recovery, without sacrificing performance. Our test findings demonstrate that we even surpass the state-of-the-art method’s prediction accuracy across all function signature recovery tasks by about 1% with just about 12.5% of the processing time.

In this paper, we analyze factor uniqueness in the S&P 500 universe. The current theory of approximate factor models applies to infinite markets. In the limit of infinite markets, factors are unique and can be represented with principal components. If this theory would apply to realistic markets such as the S&P 500 universe, the quest for proprietary factors would be futile. We find that this is not the case: in finite markets of the size of the S&P 500 universe different factor models can indeed coexist. We compare three dynamic factor models: a factor model based on principal component analysis, a classical factor model based on industry, and a factor model based on cluster analysis. Dynamic behavior is represented by fitting vector autoregressive models to factors and using them to make forecasts. We analyze the uniqueness of factors using Procrustes analysis and correlation analysis. Forecasting performance of the factor models is analyzed by forming active portfolio strategies based on the forecasts for each model using sample data from the S&P 500 index in the 21-year period 1989–2010. We find that one or two factors which we can identify with global factors are common to all models, while the other factors for the factor models we analyzed are truly different. Models exhibit significant differences in performance with principal component analysis-based factor models appearing to behave better than the sector-based factor models.

Structural inherent dynamic characteristics would change when damage occurs, and this fact is the theoretical basis of damage detection method based on vibration features. As one of structural inherent properties, frequency response function (FRF) contains rich information and has more potential in damage detection. On the basis of crack detection researches, a new method based on the change of FRF is proposed in this paper. First, the single-degree-of-freedom (SDOF) system is used to illustrated the change of dynamic features caused by cracks represented by changing stiffness, and it is found that the change of stiffness leads to the obvious nonlinear variation of frequency response function curvature (FRFC) and frequency response function curvature differentiation (FRFCD) near the natural frequency. To describe this phenomenon, the Pearson correlation coefficient is employed to analyze correlation relationship of FRF, FRFC and FRFCD between the undamaged and the damaged system. The results demonstrate that absolute values of these coefficients approach from 1 to 0 with the increase of stiffness change. The rail with cracks is taken as the research object, and relationships between the crack size and the correlation coefficients of FRF, FRFC and FRFCD are investigated through the combination of finite element simulation and frequency response experiment. The correlation coefficients of FRF, FRFC and FRFCD are close to 1 when the structure is intact. When cracks occur, the absolute value of the correlation coefficient will gradually trend from 1 to 0, and the sensitivity of FRFCD correlation coefficient to crack is much higher than that of FRF and FRFC. The selection of calculated frequency bands containing different modes and the damping of the structure would affect the absolute value of FRFCD correlation coefficient, but will not change the characteristics of high sensitivity of FRFCD and the relationship between the coefficient and crack size. Besides, noise signals would enhance the nonlinear relationship between responses of intact structure and the structure with cracks, and the FRFCD correlation coefficient value is influenced accordingly.

The design and optimization of substructure of a floating offshore wind turbines (FOWT) is an extremely challengeable work. Before this work, it is necessary to investigate the correlation of design variables of the FOWT substructure. The correlation analysis can help to reduce the dimensions of design parameters and output results, and provide insight into the nature of the design space. The subsequent design and optimization workload can therefore be reduced. This paper aims to provide a framework of parametric modeling and correlation analysis of the substructure design variables for a single-column FOWT with a slack catenary mooring system. The NREL offshore 5 MW baseline wind turbine is selected in this study, and various floating foundation configurations are designed to adapt to this wind turbine. In order to evaluate the main dynamic performances of the FOWT well and quickly, a reduced-degree-of-freedom model of the FOWT is constructed and verified. Then, the substructure design parameters are parameterized to facilitate modeling, and simulations based on design of experiments are performed to save evaluation time and cost. In simulations, three sets of steady wind speeds are applied to eliminate the influence of controller dynamics. The results showed that the correlation of dynamic performances of the FOWT is significant, indicating that it is enough to consider several outputs to represent the dynamic behavior of the system. Regarding the designing parameters, the draft generates the most significant influence on the FOWT dynamic performances. The column radius has different influences for the cases with and without heave plates. Additionally, whether or not the heave plate is added, the mooring line length mainly influences the 95th percentile of platform horizontal surge displacement and standard deviation of fairlead tension of the downwind mooring line.

Long-span bridges are the key component of the human transportation system, linking communities over vast obstacles. To ensure the safe operation of long-span bridges, structural health monitoring (SHM) is perhaps the most effective solution. This study takes a 430m four-span continuous girder bridge as an example and systematically presents an implementation example of the SHM system on the bridge regarding monitoring items, sensor placement, and sensor parameters. The monitoring data including operational load and bridge response are analyzed and the statistical rules of these monitoring data are presented. Besides, the modal parameters of the girder are tracked from long-term vibration monitoring data. Furthermore, the correlations between structural temperature and bridge response are analyzed, and the correlation formulas between bridge modal frequency, static responses, and structural temperature are finally given. The analysis results reveal significant seasonal variations in the responses of the continuously monitored girder bridge, offering valuable insights for data-driven assessment and early warning systems for bridges.

In this paper, the optical properties of skin lesion are determined with the help of laser reflectometry. The result is compared with the phantom and simulation values to obtain an accurate result. Surface backscattering is determined by laser reflectometry. The tissue-equivalent phantom is prepared with the help of white paraffin wax mixed with various color pigments in multiple proportions. A familiar Monte Carlo simulation is used to analyze the optical properties of the tissue. The normalized backscattered intensity (NBI) signals from the tissue surface, measured by the output probes after digitization, are used to reconstruct the reflectance images of tissues in various layers below the skin surface. From NBI profiles measured at various locations of the tissues on the forearm, the corresponding optical parameters, the scattering (μ_s) and absorption (μ_a) coefficients, and the anisotropy parameter g are determined by matching these with profiles simulated by the Monte Carlo procedure. The correlation analysis between the lesion thickness and the diffuse reflectance gives the optical wavelengths which are selected for multispectral images of skin lesions. Comparison of results shows the presence of abnormal level in the tissue.

This study aims to explore the correlation between college students’ digital literacy and mental health and proposes a method based on Twin Support Vector Machines (TWSVMs) classification and chi-square validation correlation analysis. First, a group of college students’ digital literacy data was collected by designing and distributing questionnaires. The questionnaire covers multiple aspects such as digital skills, information literacy, and technology application, to comprehensively evaluate the students’ digital literacy level. The collected digital literacy data were classified using TWSVM to obtain the digital literacy assessment results. Next, the electroencephalogram (EEG) signals of the same group were collected, and the EEG signals were subjected to power spectral density (PSD) feature extraction and TWSVM classification model training to obtain the mental health identification results of each student. Finally, after obtaining the digital literacy assessment and mental health identification results, the chi-square validation method was used for correlation analysis to evaluate the linear relationship between the two. Through the analysis, we found that students with higher digital literacy were more likely to have good mental health. In comparison, students with lower digital literacy were more likely to have mental health problems. This study revealed a significant correlation between college students’ digital literacy and mental health, providing theoretical support and practical guidance for educators and mental health professionals. Improving students’ digital literacy will not only help their academic and career development but may also have a positive impact on their mental health, thereby promoting their overall development.

Assuming a covariance structure with blocked compound symmetry, it was showed that unbiased estimators for the covariance matrices are optimal under normality. In this paper, we derive the asymptotic distribution of the correlation matrix using unbiased estimators and discuss its use in hypothesis testing. The accuracy of the result is investigated through numerical simulation and the method is applied to real data.

This study investigates a new approach to determine the correlations between alpha ($\alpha$) electroencephalography (EEG) and other physiological parameters during Muslim prayer (Salat) utilizing the self organizing map (SOM). The powerfulness of SOM in visualizing, understanding, and exploring the complexity in multivariable data can be used to determine the relationships between the input variables. Thirty healthy Muslim male subjects were recruited in the study. Their electroencephalogram (EEG), electrocardiogram (ECG), respiration rate (RSP), and oxygen saturation (SPO2) were continuously recorded using computer-based data acquisition system (MP150, BIOPAC Systems Inc., Camino Goleta, California). Three maps were constructed to determine the correlations in pre-baseline, during Salat, and post-baseline conditions utilizing SOM. The visualized results during Salat indicated that, alpha power (P$\alpha$) showed significant positive correlation in the occipital and parietal electrodes with the normalized unit of high-frequency HF (n.u.) power of heart rate variability (HRV) components (as a parasympathetic index), heart rate (HR), and RSP. Significant negative correlation was also observed between P$\alpha$ with the normalized unit of low-frequency LF (n.u.) power and LF/HF of HRV (as sympathetic indices). SPO2 showed no correlation with P$\alpha$. While the results in pre-baseline and post-baseline conditions also did not show any correlation between the variables. The SOM proves that it can be successfully employed as a powerful technique in correlation analysis. The results were presented and compared with a previous study. Thus, it can be applied successfully in various biomedical engineering applications.

A wavefront sensing method is described which is based on comparison of two intensity patterns generated by a random intensity or phase screen in the near diffraction zone. This approach allows to determine the wavefront slope in any arbitrary aperture point with the resolution limited only by the statistical properties of the setup. Applications include wavefront sensing and optical shop testing.

For a long time, China’s transportation safety production situation has been generally stable. However, the situation is still grim, with frequent accidents, and the number of deaths and accidents in road traffic accidents is still high. Therefore, it will be of great use to analyze and study the causes of traffic accidents. The main work of this paper is to explore the correlation between accident factors and traffic accident severity. According to the relevant knowledge of machine learning, the influence and correlation of human, vehicle, road and environmental factors on the severity of traffic accidents are analyzed by using three correlation coefficients and the maximum information coefficient of statistics. The aim is to improve the current road safety situation and thus reduce the occurrence of traffic accidents. The results show that the severity of traffic accidents has the greatest correlation with the types of casualties and whether there is police intervention, and has a great correlation with pedestrians, the number of vehicles causing traffic accidents and the level of roads.

The following sections are included:

• INTRODUCTION

• POPULATION PARAMETERS AND THE REGRESSION MODELS
  • Data Description
  • Building the Population Regression Model
  • Sample Versus Population Regression Model

• THE LEAST-SQUARES ESTIMATION OF α AND β
  • Scatter Diagram
  • The Method of Least Squares
  • Estimation of Intercept and Slope

• STANDARD ASSUMPTIONS FOR LINEAR REGRESSION

• THE STANDARD ERROR OF ESTIMATE AND THE COEFFICIENT OF DETERMINATION
  • Variance Decomposition
  • Standard Error of Residuals (Estimate)
  • The Coefficient of Determination

• THE BIVARIATE NORMAL DISTRIBUTION AND CORRELATION ANALYSIS
  • The Sample Correlation Coefficient
  • The Relationship Between r and b
  • The Relationship Between r and R²

• Summary

• Appendix 13A Derivation of Normal Equations and Optimal Portfolio Weights

• Appendix 13B The Derivation of Equation 13.16

• Appendix 13C The Bivariate Normal Density Function
  • Using a Mathematics Aptitude Test to Predict Grade in Statistics

• Appendix 13D American Call Option and the Bivariate Normal CDF
  • Valuating American Option

• Questions and Problems

Removing the respiratory signal is a crucial topic to the high-quality ECG. But, not all models are available in the project. The use of digital filtering, signal averaging, adaptive processing and wavelet transform to remove the respiratory interference have some problems. The ICA algorithm for cancellation of respiratory interference is proposed. It is found that this method is more available to reconstruct high-quality ECG and de-noising artifacts comparing with the wavelet transform. Three steps are performed in the paper. From the simulation aspect, and from the evolution for the ability of de-noising the respiratory signal, as well as from reconstructing ECG, the comparisons between the results using ICA and that using wavelet transform are fulfilled. It is shown that the ICA algorithm is more powerful and more effective to de-noising the respiratory signal from ECG, almost not destroying the original ECG.

Hanjiang River basin is very rich in water resource which contains a total of more than 500 billion cubic meters of surface water. However, after years of development of cities along the river, the planning of seven hydropower stations and the construction and operation of the middle-lower reaches of eight hydropower stations will be a tremendous impact to the resources in Hanjiang River basin. The district's water area data is extracted by multitemporal remote sensing data from TM/ETM of LANDSAT. The correlation method is used to analysis the effect of temperature, precipitation, evaporation, water consumption and hydropower cascade development project to the water area changes of Hanjiang River basin. The results show that the water area of Hanjiang River basin has decreased from 3084.10 km² 1990 to 2422.12 km² 2010 and the hydropower cascade development project is a tremendous impact on the basin surface area change. The impact of cascade hydropower development will pay close attention in watershed management in Hanjiang River basin.