SCChOA:Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy.However,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search process.Therefore,the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm(SCChOA)to address the feature selection problem.In this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm(SCA)and the Chimp Optimization Algorithm(ChOA),enabling a more effective search in the objective space.Secondly,an S-shaped transfer function is introduced to perform binary transformation on SCChOA.Finally,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection method.To evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are considered.Meanwhile,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for comparison.Experimental results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection problem.It is capable of maximizing the reduction in the number of selected features while maintaining a high classification accuracy.Furthermore,the results of statistical tests also confirm the significant effectiveness of this method.

An OpenFlow-Based Load Balancing Strategy in SDN

In today’s datacenter network,the quantity growth and complexity increment of traffic is unprecedented,which brings not only the booming of network development,but also the problem of network performance degradation,such as more chance of network congestion and serious load imbalance.Due to the dynamically changing traffic patterns,the state-of the-art approaches that do this all require forklift changes to data center networking gear.The root of problem is lack of distinct strategies for elephant and mice flows.Under this condition,it is essential to enforce accurate elephant flow detection and come up with a novel load balancing solution to alleviate the network congestion and achieve high bandwidth utilization.This paper proposed an OpenFlow-based load balancing strategy for datacenter networks that accurately detect elephant flows and enforce distinct routing schemes with different flow types so as to achieve high usage of network capacity.The prototype implemented in Mininet testbed with POX controller and verify the feasibility of our load-balancing strategy when dealing with flow confliction and network degradation.The results show the proposed strategy can adequately generate flow rules and significantly enhance the performance of the bandwidth usage compared against other solutions from the literature in terms of load balancing.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.

Comparison of formation and evolution of radiation-induced defects in pure Ni and Ni-Co-Fe medium-entropy alloy

High-entropy alloys(HEAs)and medium-entropy alloys(MEAs)have attracted a great deal of attention for developing nuclear materials because of their excellent irradiation tolerance.Herein,formation and evolution of radiation-induced defects in Ni Co Fe MEA and pure Ni are investigated and compared using molecular dynamics simulation.It is observed that the defect recombination rate of ternary Ni Co Fe MEA is higher than that of pure Ni,which is mainly because,in the process of cascade collision,the energy dissipated through atom displacement decreases with increasing the chemical disorder.Consequently,the heat peak phase lasts longer,and the recombination time of the radiation defects(interstitial atoms and vacancies)is likewise longer,with fewer deleterious defects.Moreover,by studying the formation and evolution of dislocation loops in Ni-Co-Fe alloys and Ni,it is found that the stacking fault energy in Ni-Co-Fe decreases as the elemental composition increases,facilitating the formation of ideal stacking fault tetrahedron structures.Hence,these findings shed new light on studying the formation and evolution of radiation-induced defects in MEAs.

Conversion between Vickers hardness and nanohardness by correcting projected area with sink-in and pile-up effects

The Vickers hardness test has been widely used for neutron-irradiated materials and nanoindentation for ion-irradiated materials.Comparing the Vickers hardness and nanohardness of the same materials quantitatively and establishing a correlation between them is meaningful.In this study,five representative materials—pure titanium(Ti),nickel(Ni),tungsten(W),304 coarse-grained stainless steel(CG-SS)and 304 nanocrystalline austenitic stainless steel(NG-SS)—are investigated for comparison.The results show that the relationship between Vickers hardness and nanohardness does not conform to a mathematical geometric relationship because of sink-in and pile-up effects confirmed by finite element analysis(FEA)and the results of optical microscopy.Finally,one new method was developed by excluding the effects of sink-in and pile-up in materials.With this improved correction in the projected area of the Vickers hardness and nanohardness,the two kinds of hardness become identical.

Personalized tourist route recommendation model with a trajectory understanding via neural networks

Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features,for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning,a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically,a trajectory encoding module is designed to mine the semantic information of trajectory data,and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular,a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai,and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.

“Swimming Ducks Forecast the Coming of Spring”—The predictability of aggregate insider trading on future market returns in the Chinese market

This study systematically examines the ability of aggregate insider trading to predict future market returns in the Chinese A-share market. After controlling for the contrarian investment strategy, aggregate executive(large shareholder)trading conducted over the past six months can predict 66%(72.7%) of market returns twelve months in advance. Aggregate insider trading predicts future market returns very accurately and is stronger for insiders who have a greater information advantage(e.g., executives and controlling shareholders).Corporate governance also affects the predictability of insider trading. The predictability of executive trading is weakest in central state-owned companies,probably because the "quasi-official" status of the executives in those companies effectively curbs their incentives to benefit from insider trading.The predictive power of large shareholder trading in private-owned companies is higher than that in state-owned companies, probably due to their stronger profit motivation and higher involvement in business operations. This study complements the literature by examining an emerging market and investigating how the institutional context and corporate governance affect insider trading.

岈屿意象——荣昌在地设计实践

2018年10月至2020年5月,笔者在荣昌岈屿山进行在地设计实践。在学习当地制陶技艺叩造的基础上,通过保留制作过程产生的拍打痕迹,实验匣钵熏烧的火痕色彩变化,结合驻留所在地岈屿山的日常景观等三个向度,以意象九境的艺术手法,探究"意象美学"的时间性和在场性。