A Corpus-Based Behavioral Profile Study of the Near-Synonyms: Serious, Severe, Grave, Grievous

Using Corpus of Contemporary American English as the source data,this paper carries out a corpus-based behavioral profile study to investigate four near-synonymous adjectives(serious,severe,grave,and grievous),focusing on their register and the types of nouns they each modify.Although sharing core meaning,these adjectives exhibit variations in formality levels and usage patterns.The identification of fine-grained usage differences complements the current inadequacies in describing these adjectives.Furthermore,the study reaffirms the effectiveness of the corpus-based behavioral profile approach in examining synonym differences.

The Study of Fueling Method and Particle Confinement Improvement

Several high performance tokamak operation regimes have been achieved experimentally in the experiments with the peaked density profiles. The regimes include the improved Ohmic confinement in ASDEX, the pellet enhanced performance mode in Alcator-C, and the super-shot mode in TFTR. In these regimes, peaked core density profiles are always existent, and almost always go with the internal transport barriers, these barriers generally produced by sheared radial electric field. In addition to enhance confinement, the peaked density profile is also needed for the optimized fusion reaction rate and alpha heating power in tokamak plasma, and combined peaked density profile and peaked temperature profile, would make the ignition condition easy obtained. It is desirable to seek and analyze the density profile control schemes that effectively lead to density profile peaking in particle transports experimental investigation.

A Novel Approach for Android Malware Detection Based on Intelligent Computing

Detecting malware on mobile devices using the Android operating system has become a critical challenge in the field of cybersecurity,in the context of the rapid increase in the number of malware variants and the frequency of attacks targeting Android devices.In this paper,we propose a novel intelligent computational method to enhance the effectiveness of Android malware detection models.The proposed method combines two main techniques:(1)constructing a malware behavior profile and(2)extracting features from the malware behavior profile using graph neural networks.Specifically,to effectively construct an Android malware behavior profile,this paper proposes an information enrichment technique for the function call graph of malware files,based on new graph-structured features and semantic features of the malware’s source code.Additionally,to extract significant features from the constructed behavior profile,the study proposes using the GraphSAGE graph neural network.With this novel intelligent computational method,a variety of significant features of the malware have been effectively represented,synthesized,and extracted.The approach to detecting Android malware proposed in this paper is a new study and has not been explored in previous research.The experimental results on a dataset of 40,819 Android software indicate that the proposed method performs well across all metrics,with particularly impressive accuracy and recall scores of 99.03%and 99.19%,respectively,which outperforms existing state-of-the-art methods.