Lanze Zhang, Yijun Gu^*, Jingjie Peng

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1701-1731, 2024, DOI:10.32604/cmes.2023.045129

Abstract Graph Neural Networks (GNNs) play a significant role in tasks related to homophilic graphs. Traditional GNNs, based on the assumption of homophily, employ low-pass filters for neighboring nodes to achieve information aggregation and embedding. However, in heterophilic graphs, nodes from different categories often establish connections, while nodes of the same category are located further apart in the graph topology. This characteristic poses challenges to traditional GNNs, leading to issues of “distant node modeling deficiency” and “failure of the homophily assumption”. In response, this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks (SFA-HGNN), which integrates adaptive embedding mechanisms for… More >

Adeeb M. Alhomoud^*

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 69-88, 2021, DOI:10.32604/iasc.2021.014773

Abstract This research article provides an up-to-date review of spatial-domain steganography. Maintaining the communication as secure as possible when transmitting secret data through any available communication channels is the target of steganography. Currently, image steganography is the most developed field, with several techniques are provided for different image formats. Therefore, the general image steganography including the fundamental concepts, the terminology, and the applications are highlighted in this paper. Further, the paper depicts the essential characteristics between information hiding and cryptography systems. In addition, recent well-known techniques in the spatial-domain steganography, such as LSB and pixel value differencing, are discussed in detail… More >