Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression

Federated learning has become a popular tool in the big data era nowadays. It trains a centralized model based on data from different clients while keeping data decentralized. In this paper, we propose a federated sparse sliced inverse regression algorithm for the first time. Our method can simultaneously estimate the central dimension reduction subspace and perform variable selection in a federated setting. We transform this federated high-dimensional sparse sliced inverse regression problem into a convex optimization problem by constructing the covariance matrix safely and losslessly. We then use a linearized alternating direction method of multipliers algorithm to estimate the central subspace. We also give approaches of Bayesian information criterion and hold-out validation to ascertain the dimension of the central subspace and the hyper-parameter of the algorithm. We establish an upper bound of the statistical error rate of our estimator under the heterogeneous setting. We demonstrate the effectiveness of our method through simulations and real world applications

Online Kernel Sliced Inverse Regression

Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other methods have been studied in depth, but as far as we know, online supervised nonlinear dimension reduction methods have not been fully studied. In this article, an online kernel sliced inverse regression method is proposed. By introducing the approximate linear dependence condition and dictionary variable sets, we address the problem of increasing variable dimensions with the sample size in the online kernel sliced inverse regression method, and propose a reduced-order method for updating variables online. We then transform the problem into an online generalized eigen-decomposition problem, and use the stochastic optimization method to update the centered dimension reduction directions. Simulations and the real data analysis show that our method can achieve close performance to batch processing kernel sliced inverse regression

Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods

What affect energy poverty in China? A path towards sustainable development

Despite the crucial role of investment in energy under public–private partnership (I.E.P.P.P.) in abating environmental pollution and reducing energy poverty (E.P.), the existing literature offers less information about the nexus between I.E.P.P.P. and E.P. In order to identify the E.P. gap based on accessibility, affordability, and availability dimensions, this study investigates the factors influencing E.P., and examines the impact of I.E.P.P.P., globalisation (G.L.O.), output (G.D.P.), risk, technological innovation (T.I.) and renewable energy consumption (R.E.C.) on E.P. in China during the period of 1990 to 2019. The causal relationship between E.P. with its determinants is also examined. Utilising fully modified ordinary least squares (F.M.O.L.S.) econometric approach, we find that investment in energy with a public–private partnership, T.I., and gross domestic product (G.D.P.) bridge the gap for E.P., whereas R.E.C., composite risk index (C.R.I.), and G.L.O. increase the E.P. gap in China. In addition, frequency Domain Causality test reveals that unidirectional causation from I.E.P.P.P., G.D.P., T.I., G.L.O., risk, and R.E.C. to E.P. in the short run to long run

Effects of Self-Monitoring Intervention on Independent Completion of a Daily Living Skill for Children with Autism Spectrum Disorders in China

The purpose of this study was to investigate the effects of a self-monitoring intervention on the independent completion of dishwashing for three boys with autism (age 6, 7, and 8) in China. The self-monitoring intervention included visual task analysis, in vivo modeling, self-recording, video self-feedback, and reinforcement. A multiple probe across subjects design was used. Prior to the study, the children had limited or no dishwashing skills, nor did they receive any training on self-monitoring. All three children acquired dishwashing and performed the task independently without supervision one week after the intervention. Their parents were very satisfied with this intervention