On the English translation of the network new and hot words from the perspective of lexicology

From the initial sparks of fire to the present independent school, the network language has become the research object of many language scholars. With the rapid development of the lntemet, a large number of the network new words and hot words have a rapid impact on people＇s social communication and their life. This kind of the vocabularies truly reflects the different life attitudes and cultures mainly with the words as the carrier, and then shows the network cultures, mentalities and life styles of the Chinese people, and especially the young people in different stages. From the perspective of the English vocabulary, take the reverse into consideration of the reaction of the network new and hot words on the present situations of the life of young people, the latest social phenomena and the importance and particularity of the new things, and we can find out the roles and values of the network new and hot words in the intercultural communication.

On C-E Translation of Network Hot Words from the Perspective of Translator's Subjectivity

As a special kind of vocabularies originated from the network language,the network hot words are the manifcstation of the diversification of thec nctwork cra as well as of people's social consciousncss,To improve the translation quality of Chinese network hot words is not only conducive to promoting the Chinese network culure,but also an important way for foreigner to better understand China.This paper starts from the cases of network hot words translation,making the specific analysis of those words and the relationship among the translator,original writer and target readers.It also analyzes the factors that limit the translator's subjectivity and the translation skills of the network hot words,aiming at further clarifying the translator's subjectivity in the translation of network hot words.Through this research,this paper expectes to have a positive impact on the translation of Chinese network hot words and the promotion of Chinese culture.

A Novel Immune System Model and Its Application to Network Intrusion Detection

Based on analyzing the techniques and architecture of existing network Intrusion Detection System (IDS), and probing into the fundament of Immune System (IS), a novel immune model is presented and applied to network IDS, which is helpful to design an effective IDS. Besides, this paper suggests a scheme to represent the self profile of network. And an automated self profile extraction algorithm is provided to extract self profile from packets. The experimental results prove validity of the scheme and algorithm, which is the foundation of the immune model.

A Distributed Routing and Wavelength Assignment Algorithm for Online Multicast in All-Optical WDM Networks

Routing and wavelength assignment for online real-time multicast connection setup is a difficult task due to the dynamic change of availabilities of wavelengths on links and the consideration of wavelength conversion delay in WDM networks. This paper presents a distributed routing and wavelength assignment scheme for the setup of real-time multicast connections. It integrates routing and wavelength assignment as a single process, which greatly reduces the connection setup time. The proposed routing method is based on the Prim’s MST (Minimum Spanning Tree) algorithm and the K-restricted breadth-first search method, which can produce a sub-minimal cost tree under a given delay bound. The wave-length assignment uses the least-conversion and load balancing strategies. Simulation results show that the proposed algorithm is suitable for online multicast connection establishment in WDM networks.

GACP:graph neural networks with ARMA flters and a parallel CNN for hyperspectral image classification

In recent years,the use of convolutional neural networks(CNNs)and graph neural networks(GNNs)to identify hyperspectral images(HSIs)has achieved excellent results,and such methods are widely used in agricultural remote sensing,geological exploration,and marine remote sensing.Although many generalization classification algorithms are designed for the purpose of learning a small number of samples,there is often a problem of a low utilization rate of position information in the empty spectral domain.Based on this,a GNN with an autoregressive moving average(ARMA)-based smoothingfilter samples the node information in the null spectral domain and then captures the spatial information at the pixel level via spatial feature convolution;then,the null spectral domain position information lost by the CNN is located by a coordinate attention(CA)mechanism.Finally,autoregressive,spatial convolution,and CA mechanisms are combined into multiscale features to enhance the learning capacity of the network for tiny samples.Experiments conducted on the widely used Indian Pines(IP)dataset,the Botswana(BS)dataset,Houton 2013(H2013),and the WHU-Hi-HongHu(WHU)benchmark HSI dataset demonstrate that the proposed GACP technique can perform classification work with good accuracy even with a small number of training examples.