Shengjia Chang, Baojiang Cui^*, Shaocong Feng

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4345-4374, 2025, DOI:10.32604/cmc.2025.070195 - 23 October 2025

Abstract Binary Code Similarity Detection (BCSD) is vital for vulnerability discovery, malware detection, and software security, especially when source code is unavailable. Yet, it faces challenges from semantic loss, recompilation variations, and obfuscation. Recent advances in artificial intelligence—particularly natural language processing (NLP), graph representation learning (GRL), and large language models (LLMs)—have markedly improved accuracy, enabling better recognition of code variants and deeper semantic understanding. This paper presents a comprehensive review of 82 studies published between 1975 and 2025, systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence (AI) techniques. Particular… More >

A-Seong Moon¹, Seungyeon Jeong¹, Donghee Kim¹, Mohd Asyraf Zulkifley², Bong-Soo Sohn^3,*, Jaesung Lee^1,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2851-2872, 2025, DOI:10.32604/cmc.2025.067103 - 23 September 2025

Abstract Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems. The current study introduces an audio-visual recognition framework designed to address performance degradation caused by environmental interference, such as background noise, overlapping speech, and visual obstructions. The proposed framework employs a structured fusion approach, combining early-stage feature-level integration with decision-level coordination guided by temporal attention mechanisms. Audio data are transformed into mel-spectrogram representations, and visual data are represented as raw frame sequences. Spatial and temporal features are extracted through convolutional and transformer-based encoders, allowing the framework to capture… More > Graphic Abstract

Yu-Shiuan Tsai^*, Zhen-Rong Wu, Jian-Zhi Liu

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3431-3457, 2025, DOI:10.32604/cmc.2025.066509 - 03 July 2025

Abstract Few-shot learning has emerged as a crucial technique for coral species classification, addressing the challenge of limited labeled data in underwater environments. This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection. The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity, effectively capturing both feature magnitude and directional relationships. This approach achieves a notable accuracy of 71.8% under a 5-way 5-shot evaluation, outperforming state-of-the-art models such as Prototypical Networks, FEAT, and ESPT by up to 10%. Notably, the model demonstrates high… More >

Jiling Wan, Lifeng Cao^*, Jinlong Bai, Jinhui Li, Xuehui Du

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4775-4794, 2025, DOI:10.32604/cmc.2025.061964 - 19 May 2025

Abstract Blockchain platforms with the unique characteristics of anonymity, decentralization, and transparency of their transactions, which are faced with abnormal activities such as money laundering, phishing scams, and fraudulent behavior, posing a serious threat to account asset security. For these potential security risks, this paper proposes a hybrid neural network detection method (HNND) that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain. In HNND, the Temporal Transaction Graph Attention Network (T2GAT) is first designed to learn biased aggregation representation of multi-attribute transactions among More >

Sarah M. Kamel^1,*, Mai A. Fadel², Lamiaa Elrefaei^1,3, Shimaa I. Hassan^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 373-411, 2025, DOI:10.32604/cmes.2025.062837 - 11 April 2025

Abstract Visual question answering (VQA) is a multimodal task, involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer. In this paper, we propose a VQA system intended to answer yes/no questions about real-world images, in Arabic. To support a robust VQA system, we work in two directions: (1) Using deep neural networks to semantically represent the given image and question in a fine-grained manner, namely ResNet-152 and Gated Recurrent Units (GRU). (2) Studying the role of the utilized multimodal bilinear… More >

Zhen-Yu Chen¹, Feng-Chi Liu², Xin Wang³, Cheng-Hsiung Lee¹, Ching-Sheng Lin^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4287-4300, 2025, DOI:10.32604/cmc.2025.061661 - 06 March 2025

Abstract In the domain of knowledge graph embedding, conventional approaches typically transform entities and relations into continuous vector spaces. However, parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations. In particular, resource-intensive embeddings often lead to increased computational costs, and may limit scalability and adaptability in practical environments, such as in low-resource settings or real-world applications. This paper explores an approach to knowledge graph representation learning that leverages small, reserved entities and relation sets for parameter-efficient embedding. We introduce a hierarchical attention network designed to refine More >

Mei Chen, Yunsheng Xu, Nan Wu, Ying Pan^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5307-5324, 2025, DOI:10.32604/cmc.2024.060993 - 06 March 2025

Abstract With the development of the Semantic Web, the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex, understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task. At present, the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently. Therefore, we propose an Ontology Matching Method Based on the Gated Graph Attention Model (OM-GGAT). Firstly, the semantic knowledge related… More >

Naikang Zhong¹, Xiao Lin^1,2,3,4,*, Wen Du⁵, Jin Shi⁶

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5285-5306, 2025, DOI:10.32604/cmc.2025.059102 - 06 March 2025

Abstract Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images. Obtaining class-specific precise representations at different scales is a key aspect of feature representation. However, existing methods often rely on the single-scale deep feature, neglecting shallow and deeper layer features, which poses challenges when predicting objects of varying scales within the same image. Although some studies have explored multi-scale features, they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales. To address these issues, we propose… More >

Ming Liu, Qichao Yang, Wenqing Wang, Shengli Liu^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2985-3004, 2025, DOI:10.32604/cmc.2024.059417 - 17 February 2025

Abstract The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable challenge to cybersecurity. Traditional machine learning and deep learning techniques often fall short in identifying encrypted malicious traffic due to their inability to fully extract and utilize the implicit relational and positional information embedded within data packets. This limitation has led to an unresolved challenge in the cybersecurity community: how to effectively extract valuable insights from the complex patterns of traffic packet transmission. Consequently, this paper introduces the… More >

Jian Feng^*, Tian Liu, Cailing Du

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2895-2909, 2024, DOI:10.32604/cmc.2024.056434 - 18 November 2024

Abstract Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph representation learning to eliminate the dependence of labels. However, existing studies neglect positional information when learning discrete snapshots, resulting in insufficient network topology learning. At the same time, due to the lack of appropriate data augmentation methods, it is difficult to capture the evolving patterns of the network effectively. To address the above problems, a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic graphs. Firstly, the global snapshot is built based on the historical snapshots… More >