Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.

Canonical Correlation Analysis of Agronomic Characters of Brassica juncea in Western China

[Objective] The study aimed at exploring the relationship among the agronomic characters of B. juncea in western China, in order to provide scientific basis for the breeding of B. juncea in western China. [Method] 39 B. juncea materials from western China were used for the canonical correlation analysis, and canonical correlations between each pair of the four ecological character （containing 18 variables） were verified, including yield characters （5 variables）, caulis characters （6 variables）, branch characters （3 variables） and pod characters （3 variables）. [Result] Yield per plant of B. juncea in western China suffered a tremendous influence from effective pod number per plant while was not significantly affected by the total pod number per plant, seed number per pod and 1 000-seed weight; the most important character related with the yield character of B. juncea in western China was caulis character, followed by the branch character and pod character; yield characters, caulis characters, branch characters and pod characters of B. juncea in western China were closely correlated. [Conclusion] In order to improve the yield characters of B. juncea in western China, caulis characters should be focused on, followed by branch characters and pod characters; rapeseed varieties with high performance in total pod number per plant and effective pod number per plant should be chosen through the perspectives of effective branch number, plant height, pod number of main inflorescence, fruit stalk number of main inflorescence and other traits, while rapeseed varieties with high performance in seed number per pod and 1 000-seed weight should be chosen through the perspectives of beak length and other traits.

Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis

Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.

Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis

Distributed denial of service(DDoS)attacks launch more and more frequently and are more destructive.Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense.Most DDoS feature extraction methods cannot fully utilize the information of the original data,resulting in the extracted features losing useful features.In this paper,a DDoS feature representation method based on deep belief network(DBN)is proposed.We quantify the original data by the size of the network flows,the distribution of IP addresses and ports,and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified values.Two feedforward neural networks(FFNN)are initialized by the trained deep belief network,and one of the feedforward neural networks continues to be trained in a supervised manner.The canonical correlation analysis(CCA)method is used to fuse the features extracted by two feedforward neural networks per layer.Experiments show that compared with other methods,the proposed method can extract better features.

Study on soil water characteristics of tobacco fields based on canonical correlation analysis

In order to identify the principal factors influencing soil water characteristics （SWC） and evaluate SWC effectively, the multivariate-statistical canonical correlation analysis （CCA） method was used to study and analyze the correlation between SWC and soil physical and chemical properties. Twenty-two soil samples were taken from 11 main tobacco-growing areas in Guizhou Province in China and the soil water characteristic curves （SWCC） and basic physical and chemical properties of the soil samples were determined. The results show that： （1） The soil bulk density, soil total porosity and soil capillary porosity have significant effects on SWC of tobacco fiels. Bulk density and total porosity are positively correlated with soil water retention characteristics （SWRC）, and soil capillary porosity is positively correlated with soil water supply characteristics （SWSC）. （2） Soil samples from different soil layers at the same soil sampling point show similarity or consistency in SWC. Inadequate soil water supply capability and imbalance between SWRC and SWSC are problems of tobacco soil. （3） The SWC of loamy clay are generally superior to those of silty clay loam.

SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

Canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), i.e.LR and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially correlated.However , for a multi-resolution scenario , there are no related works.Therefore , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution faces.Furthermore , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.

Canonical correlation analysis to land-use structure and its driving forces——Taking Yulin Prefecture as an example

In this paper, one of the most classical statistical methods, Canonical Correlation Analysis (CCA) is applied to identify quantitatively the driving forces of landuse structure in Yulin Prefecture. The main analysis is carried out through the software SPSS with the data on the level of towns and townships in 1992. The results indicate that landuse structure is determined by comprehensive action of different factors. Landuse structure with rural characteristics is mainly determined by geographical factors such as the elevation, temperature and precipitation, while the landuse structure with urban characteristics is mainly determined by demographic and socioeconomic conditions. At the same time, tests were carried out through the canonical correlation coefficient and redundancy analysis.

ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

Multiple moving sources passive location based on multiset canonical correlation analysis

To solve the problem of multiple moving sources passive location, a novel blind source separa- tion （BSS） algorithm based on the muhiset canonical correlation analysis （MCCA） is presented by exploiting the different temporal structure of uncorrelated source signals first, and then on the basis of this algorithm, a novel multiple moving sources passive location method is proposed using time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements. The key technique of this location method is TDOA and FDOA joint estimation, which is based on BSS. By blindly separating mixed signals from multiple moving sources, the multiple sources location problem can be translated to each source location in turn, and the effect of interference and noise can also he removed. The simulation results illustrate that the performance of the MCCA algorithm is very good with relatively light computation burden, and the location algorithm is relatively simple and effective.

Canonical Correlation Analysis and climate research

Correlation analysis as used by meteorologists and oceanographers is a tool for the analysisof the spacial or temporal variability of physical fields. In his notes, Dr. Hasselmann pro-posed to combine correlation analysis and linear regression analysis in climate prediction re-search. The main idea is to decompose the physical field into its principal oscillation patterns.

A NOVEL ALGORITHM FOR VOICE CONVERSION USING CANONICAL CORRELATION ANALYSIS

A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.

A Novel 3D Gait Model for Subject Identification Robust against Carrying and Dressing Variations

Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3DGait)represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model.The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing.The 3DGait recognitionmethod involves 2-dimensional(2D)to 3DGaitdata learningbasedon3Dvirtual samples,a semantic gait parameter estimation Long Short Time Memory(LSTM)network(3D-SGPE-LSTM),a feature fusion deep model based on a multi-set canonical correlation analysis,and SoftMax recognition network.First,a sensory experiment based on 3D body shape and pose deformation with 3D virtual dressing is used to fit 3DGait onto the given 2D gait images.3Dinterpretable semantic parameters control the 3D morphing and dressing involved.Similarity degree measurement determines the semantic descriptors of 2D gait images of subjects with various shapes,poses and styles.Second,using the 2D gait images as input and the subjects’corresponding 3D semantic descriptors as output,an end-to-end 3D-SGPE-LSTM is constructed and trained.Third,body shape,pose and external gait factors(3D-eFactors)are estimated using the 3D-SGPE-LSTM model to create a set of interpretable gait descriptors to represent the 3DGait Model,i.e.,3D intrinsic semantic shape descriptor(3DShape);3D skeleton-based gait pose descriptor(3D-Pose)and 3D dressing with other 3D-eFators.Finally,the 3D-Shape and 3D-Pose descriptors are coupled to a unified pattern space by learning prior knowledge from the 3D-eFators.Practical research on CASIA B,CMU MoBo,TUM GAID and GPJATK databases shows that 3DGait is robust against object carrying and dressing variations,especially under multi-cross variations.

A Novel CCA-NMF Whitening Method for Practical Machine Learning Based Underwater Direction of Arrival Estimation

Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.

Influences of Institutional Pressures on Corporate Social Performance： Empirical Analysis on the Panel Data of Chinese Power Generation Enterprises

Institutional theory has proved the influence of institutional pressures on organization practices and structures. Meanwhile, with the soaring use of corporate social performance （CSP）, more researchers are focusing on exploring the relationship between institution pressures and CSP which is still not completely understood yet. Against this background, the paper aims to fill the gap through generally hypothesizing that different types of institutional pressures individually and collectively affect CSP via the mediating effect of corporate environmental strategy. First, based on the previous and extensive literature review, the theoretical framework and research hypotheses are constructed. Next, canonical correlation analysis about the panel data of 51 Chinese large-scale power generation enterprises from 2004 to 2009 is made to test the relevant hypotheses. Finally, based on the data analysis results, the study draws some conclusions and policy implications for promoting the CSP of Chinese enterprises, including enhancing the steering function of government policies and industry regulations and emphasizing the intermediary role of media.

A Method of SSVEP Signal Identification Based on Improved eCAA

Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.

Facial expression recognition based on fuzzy-LDA/CCA

A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data.

Aggregate Characteristics During Natural Revegetation on the Loess Plateau

Field investigations and laboratory analysis were conducted to study the characteristics of soil water-stable aggregates during vegetation rehabilitation in typical grassland soils of the hilly-gullied loess area. The relationship between water- stable aggregates and other soil properties was analyzed using canonical correlation analysis and principal component analysis. The results show that during the natural revegetation, the aggregates 〉 5 mm dominated and constituted between 50% and 80% of the total soil water-stable aggregates in most of the soil layers. The 2-5 mm aggregate class was the second main component. The mean value of water-stable aggregates 〉 5 mm within the 0-2 m soil profile under different plant communities decreased in the following order： Stipa grandis 〉 Stipa bungeana Trin. 〉 Artemisia sacrorum Ledeb. 〉 Thymus mongolicus Ronn. 〉 Hierochloe odorata （L.） Beauv. Clay, organic matter, and total N were the key factors that influenced the water stability of the aggregates. Total N and organic matter were the main factors that affected the water stability of the aggregates 〉 5 mm and 0.5-1 mm in size. The contents of Fe2O3, Al2O3, and physical clay （〈 0.01 mm） were the main factors which affected the water stability of the 1-2 and 0.25-0.5 mm aggregates.

Spatial Heterogeneity of Soil Mineral Oxide Components in Depression Between Karst Hills,Southwest China

In karst regions,the spatial heterogeneity of soil mineral oxides and environmental variables is still not clear.We investigated the spatial heterogeneity of SiO2,Al2O3,Fe2O3,CaO,MgO,P2O5,K2O,and MnO contents in the soils of slope land,plantation forest,secondary forest,and primary forest,as well as their relationships with environmental variables in a karst region of Southwest China.Geostatistics,principal component analysis(PCA),and canonical correlation analysis(CCA)were applied to analyze the field data.The results show that SiO2was the predominant mineral in the soils(45.02%–67.33%),followed by Al2O3and Fe2O3.Most soil mineral oxide components had a strong spatial dependence,except for CaO,MgO,and P2O5in the plantation forest,MgO and P2O5in the secondary forest,and CaO in the slope land.Dimensionality reduction in PCA was not appropriate due to the strong spatial heterogeneity in the ecosystems.Soil mineral oxide components,the main factors in all ecosystems,had greater influences on vegetation than those of conventional soil properties.There were close relationships between soil mineral oxide components and vegetation,topography,and conventional soil properties.Mineral oxide components affected species diversity,organic matter and nitrogen levels.

Convergence rate of kernel canonical correlation analysis

Kernel canonical correlation analysis(CCA) is a nonlinear extension of CCA,which aims at extract-ing the information shared by two random variables. It has wide applications in many fields,such as information retrieval. This paper gives the convergence rate analysis of kernel CCA under some approximation conditions and some suggestions on how to choose the regularization parameter. The result shows that the convergence rate only depends on two parameters:the rate of regularization parameter and the decay rate of eigenvalues of compact operator VY X,and it gives better understanding of kernel CCA.

Multi-view dimensionality reduction via canonical random correlation analysis

Canonical correlation analysis （CCA） is one of the most well-known methods to extract features from multi- view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take discriminant information into account. In this paper, we add discriminant information into CCA by using random cross- view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis （RCA）. In RCA, two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique. Furthermore, kernel RCA （KRCA） is proposed to extract nonlinear correlations between different views. Experiments on several multi-view data sets show the effectiveness of the proposed methods.