FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment

The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning models.Owing to the massive traffic,massive CU resource requests are sent to FCPs,and appropriate service recommendations are sent by FCPs.Currently,the FourthGeneration(4G)-Long Term Evolution(LTE)network faces bottlenecks that affect end-user throughput and latency.Moreover,the data is exchanged among heterogeneous stakeholders,and thus trust is a prime concern.To address these limitations,the paper proposes a Blockchain(BC)-leveraged rank-based recommender scheme,FedRec,to expedite secure and trusted Cloud Service Provisioning(CSP)to the CU through the FCP at the backdrop of base 5G communication service.The scheme operates in three phases.In the first phase,a BCintegrated request-response broker model is formulated between the CU,Cloud Brokers(BR),and the FCP,where a CU service request is forwarded through the BR to different FCPs.For service requests,Anything-as-aService(XaaS)is supported by 5G-enhanced Mobile Broadband(eMBB)service.In the next phase,a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender(RBR)model based on the CU requests.In the final phase,based on the matching recommendations between the CU and the FCP,Smart Contracts(SC)are executed,and resource provisioning data is stored in the Interplanetary File Systems(IPFS)that expedite the block validations.The proposed scheme FedRec is compared in terms of SC evaluation and formal verification.In simulation,FedRec achieves a reduction of 27.55%in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks.For the IPFS,we have achieved a bandwidth improvement of 17.91%.In the RBR models,the maximum obtained hit ratio is 0.9314 at 200 million CU requests,showing an improvement of 1.2%in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.7688 at the flow request 1.088 and at granted service price of USD 1.559 million to FCP for provided services.The obtained results indicate the viability of the proposed scheme against traditional approaches.

Deep Rank-Based Average Pooling Network for Covid-19 Recognition

(Aim)To make a more accurate and precise COVID-19 diagnosis system,this study proposed a novel deep rank-based average pooling network(DRAPNet)model,i.e.,deep rank-based average pooling network,for COVID-19 recognition.(Methods)521 subjects yield 1164 slice images via the slice level selection method.All the 1164 slice images comprise four categories:COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control.Our method firstly introduced an improved multiple-way data augmentation.Secondly,an n-conv rankbased average pooling module(NRAPM)was proposed in which rank-based pooling—particularly,rank-based average pooling(RAP)—was employed to avoid overfitting.Third,a novel DRAPNet was proposed based on NRAPM and inspired by the VGGnetwork.Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis.(Results)Our DRAPNet achieved a micro-averaged F1 score of 95.49%by 10 runs over the test set.The sensitivities of the four classes were 95.44%,96.07%,94.41%,and 96.07%,respectively.The precisions of four classes were 96.45%,95.22%,95.05%,and 95.28%,respectively.The F1 scores of the four classes were 95.94%,95.64%,94.73%,and 95.67%,respectively.Besides,the confusion matrix was given.(Conclusions)The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases.The RAP gives better results than four other methods:strided convolution,l2-norm pooling,average pooling,and max pooling.

A Note on the Relationship between the Pearson Product-Moment and the Spearman Rank-Based Coefficients of Correlation

This note derives the relationship between the Pearson product-moment coefficient of correlation and the Spearman rank-based coefficient of correlation for the bivariate normal distribution. This new derivation shows the relationship between the two correlation coefficients through an infinite cosine series. A computationally efficient algorithm is also provided to estimate the relationship between the Pearson product-moment coefficient of correlation and the Spearman rank-based coefficient of correlation. The algorithm can be implemented with relative ease using current modern mathematical or statistical software programming languages e.g. R, SAS, Mathematica, Fortran, et al. The algorithm is also available from the author of this article.

An intuitive general rank-based correlation coefficient

Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient.We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric： independence from sample size,value between-1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration.Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman＇s rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria.

Investigation Effects of Selection Mechanisms for Gravitational Search Algorithm

The gravitational search algorithm (GSA) is a population-based heuristic optimization technique and has been proposed for solving continuous optimization problems. The GSA tries to obtain optimum or near optimum solution for the optimization problems by using interaction in all agents or masses in the population. This paper proposes and analyzes fitness-based proportional (rou- lette-wheel), tournament, rank-based and random selection mechanisms for choosing agents which they act masses in the GSA. The proposed methods are applied to solve 23 numerical benchmark functions, and obtained results are compared with the basic GSA algorithm. Experimental results show that the proposed methods are better than the basic GSA in terms of solution quality.

Testing High-Dimensional Nonparametric Behrens-Fisher Problem

For high-dimensional nonparametric Behrens-Fisher problem in which the data dimension is larger than the sample size,the authors propose two test statistics in which one is U-statistic Rankbased Test(URT)and another is Cauchy Combination Test(CCT).CCT is analogous to the maximumtype test,while URT takes into account the sum of squares of differences of ranked samples in different dimensions,which is free of shapes of distributions and robust to outliers.The asymptotic distribution of URT is derived and the closed form for calculating the statistical significance of CCT is given.Extensive simulation studies are conducted to evaluate the finite sample power performance of the statistics by comparing with the existing method.The simulation results show that our URT is robust and powerful method,meanwhile,its practicability and effectiveness can be illustrated by an application to the gene expression data.