Hybrid scheduling model and analysis of performance for switched industrial Ethernet

The fact that outburst traffic in industrial Ethernet was focused on that would bring self-similar phenomenon leading to the delay increase of the cyclical data, and a hybrid priority queue schedule model was proposed in which the outburst data was given the highest priority. Some properties of the self-similar outburst data were proved by network calculus, and its service curve scheduled by the switch was gained. And then the performance of the scheduling algorithm was obtained. The simulation results are close to those calculated by using network calculus model. Some results are of actual significance to the construction of switched industrial Ethernet.

Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining organizations.This paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware variants.This model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors.The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware behavior.From these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under scrutiny.The outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final decision.The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of features.The efficacy of our proposed APIbased hybrid model is evident in its performance metrics.It outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

A new clustering algorithm for large datasets

The Circle algorithm was proposed for large datasets.The idea of the algorithm is to find a set of vertices that are close to each other and far from other vertices.This algorithm makes use of the connection between clustering aggregation and the problem of correlation clustering.The best deterministic approximation algorithm was provided for the variation of the correlation of clustering problem,and showed how sampling can be used to scale the algorithms for large datasets.An extensive empirical evaluation was given for the usefulness of the problem and the solutions.The results show that this method achieves more than 50% reduction in the running time without sacrificing the quality of the clustering.

Forecasting of Stock Returns by Using Manifold Wavelet Support Vector Machine

An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.

Analytical Determination of Interwell Connectivity Based on Interwell Influence

Interwell connectivity, an important element in reservoir characterization, especially for water flooding,is used to make decisions for better oil production. The existing methods in literature directly use related data of wells to infer interwell connectivity, but they ignore the influence between different wells. The connection of one well to more than two wells(as is often true in the oil field well pattern) will impact the accuracy of the connectivity analysis. To address this challenge, this paper proposes the Particle Swarm Optimization-based CatBoost for Interwell Connectivity(PSOC4IC) based on relative features to analyze interwell connectivity with the combination of joint mutual information maximization-based denoising sparse autoencoder for inter-feature construction and extraction and PSO-based CatBoost(PSO-CatBoost) for connectivity prediction with high-dimensional noise data.The experimental results show that the PSOC4IC improves analysis accuracy.

An Approximating Algorithm on Reconstruction of Complicated Curved Surface

An approximating algorithm on handling 3-D points cloud data was discussed for reconstruction of complicated curved surface. In this algorithm, the coordinate information of nodes both in internal and external regions of partition interpolation was used to realize minimized least squares approximation error of surface fitting. The changes between internal and external interpolation regions are continuous and smooth. Meanwhile, surface shape has properties of local controllability, variation reduction, and convex hull. The practical example shows that this algorithm possesses a higher accuracy of curved surface reconstruction and also improves the distortion of curved surface reconstruction when typical approximating algorithms and unstable operation are used.