Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918 - 26 March 2024

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

Shuting Ge^1,2, Jin Ren^2,3,*, Yihua Shi⁴, Yujun Zhang¹, Shunzhi Yang², Jinfeng Yang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3215-3245, 2024, DOI:10.32604/cmc.2023.046746 - 26 March 2024

Abstract In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues,… More >

Shoukun Xu¹, Lujun Zhang¹, Guangqi Jiang¹, Yining Hua², Yi Liu^1,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3021-3039, 2024, DOI:10.32604/cmc.2023.045853 - 26 March 2024

Abstract This paper focuses on the task of few-shot 3D point cloud semantic segmentation. Despite some progress, this task still encounters many issues due to the insufficient samples given, e.g., incomplete object segmentation and inaccurate semantic discrimination. To tackle these issues, we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity, which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks (CapsNets) in the embedding network. Concretely, the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature… More >

Yuanle Chen¹, Haobo Wang¹, Chunyu Liu¹, Linyi Wang², Jiaxin Liu¹, Wei Wu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2985-3009, 2024, DOI:10.32604/cmes.2023.046837 - 11 March 2024

Abstract Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main More >

Pengchao Li^1,2,3,*, Fang Xu^1,2,3,4, Jintao Wang^1,2, Haibing Guo⁴, Mingmin Liu⁴, Zhenjun Du⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1755-1771, 2024, DOI:10.32604/cmc.2023.047057 - 27 February 2024

Abstract We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations. Initially, to enhance the capability of deep neural networks in extracting geometric attributes from depth images, we developed a novel deep geometric convolution operator (DGConv). DGConv is utilized to construct a deep local geometric feature extraction module, facilitating a more comprehensive exploration of the intrinsic geometric information within depth images. Secondly, we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network (FCN8) to establish a… More >

Afnan M. Alhassan^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427 - 27 February 2024

Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has… More >

Bin Luo^1,2,3, Liangguo Chen^1,2,3, Shuhua Ruan^1,2,3,*, Yonggang Luo^2,3,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1731-1754, 2024, DOI:10.32604/cmc.2023.045739 - 27 February 2024

Abstract Considering the stealthiness and persistence of Advanced Persistent Threats (APTs), system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host. Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks, and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection, which requires lots of manual efforts to locate attack entities. This paper proposes an APT-exploited process detection approach called ThreatSniffer, which constructs the benign provenance graph from attack-free audit… More >

Sitara Afzal¹, Imran Ullah Khan¹, Irfan Mehmood², Jong Weon Lee^1,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1443-1460, 2024, DOI:10.32604/cmc.2023.046707 - 30 January 2024

Abstract Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead. However, limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers, leading to accidents and fatalities. In this paper, we consider atrous convolution, a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image segmentation. This article explores the potential of seeing-through vehicles as a solution to enhance… More >

Wei Liu, Meijuan Yin^*, Jialong Zhang, Lunchong Cui

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 975-997, 2024, DOI:10.32604/cmc.2023.046475 - 30 January 2024

Abstract The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities, and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation. However, this method has some problems, such as relying on expert experience and poor portability. Inspired by the rule-based entity relation extraction method, this paper proposes a joint entity relation extraction model based on a relation semantic template automatically… More >

Ou Ye, Xinli Wei, Zhenhua Yu^*, Yan Fu, Ying Yang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1071-1093, 2024, DOI:10.32604/cmc.2023.046418 - 30 January 2024

Abstract In the video captioning methods based on an encoder-decoder, limited visual features are extracted by an encoder, and a natural sentence of the video content is generated using a decoder. However, this kind of method is dependent on a single video input source and few visual labels, and there is a problem with semantic alignment between video contents and generated natural sentences, which are not suitable for accurately comprehending and describing the video contents. To address this issue, this paper proposes a video captioning method by semantic topic-guided generation. First, a 3D convolutional neural network… More >