Zhe Wang¹, Yu Yan², Junqi Tong¹, Yijun Lin¹, Dechun Yin^1,*, Xiaoliang Zhao¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081155 - 15 June 2026

Abstract As software applications grow increasingly large and complex, traditional code vulnerability detection methods struggle with performance and efficiency. Although code visualization-based algorithms have demonstrated effectiveness in capturing sparse features and complex workflows in large-scale source code, their capacity to extract global semantic information and intricate long-range dependencies remains limited. Recent large language model (LLM)-based approaches have shown promising accuracy by leveraging rich contextual information, but their high computational cost often limits practical efficiency. To address these challenges, we propose VulSCP, a new framework that integrates sequential convolution with a parallel attention mechanism. Specifically, VulSCP first… More >

Aihua Wu, Chenlu Huang^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078692 - 15 June 2026

Abstract Three-dimensional (3D) point cloud semantic segmentation is a core task in indoor scene understanding, providing detailed semantic information about spatial structures and object categories in indoor environments. Although methods based on deep learning have made steady progress in recent years, accurately segmenting complex indoor scenes remains challenging due to the unordered nature of point clouds and variations across large scales. Most existing networks have limited capability for multi-scale feature aggregation and struggle to balance local geometric details with global semantic context. These issues are further exacerbated by hierarchical downsampling, which often leads to the loss… More >

Minhyeok Choi¹, Imran Ahsan², Hyunwook Yu¹, Taeyoung Choe¹, Mucheol Kim^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079504 - 08 May 2026

Abstract Large language models (LLMs) are increasingly integrated into recommender systems to support semantic reasoning, natural language understanding, and user-adaptive personalization. However, their reliance on static parametric knowledge and fixed representations limits robustness in dynamic environments, particularly under long-tail and cold-start conditions. Retrieval-augmented architectures have emerged to address these limitations by grounding LLMs in external, non-parametric knowledge sources. This systematic literature review synthesizes 138 peer-reviewed studies published between 2023 and 2025 in conferences and journals, focusing on retrieval-augmented and LLM-enhanced recommendation. We analyze these works through a three-dimensional framework covering: (i) domain application, (ii) semantic feature… More >

Choongwon Kang^1,2, Haein Lee^3,4, Jang Hyun Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078997 - 08 May 2026

Abstract With the rapid growth of social media and frequent anonymous interactions, hate speech has become widespread. As users express diverse opinions in digital spaces, the need for effective detection remains crucial. To address this, we propose a framework applicable to diverse hate speech types, combining sentence-level semantic representation vectors from the pre-trained Bidirectional Encoder Representations from Transformers (BERT) with sentiment score vectors from the Linguistic Inquiry and Word Count (LIWC) dictionary and the Valence Aware Dictionary for sEntiment Reasoning (VADER). This semantic-sentiment fusion integrates three deep learning models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), More >

Dong Zhang¹, Lianhe Shao^2,*, Weijie Xu³, Xihan Wang^1,*, Quanli Gao²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078074 - 08 May 2026

Abstract With the popularization of multimodal content on social media, accurately identifying sarcastic intent is of great significance for understanding public attitudes and grasping public opinion trends. However, sarcastic expressions rely on context, exhibit inconsistencies in multimodal information, and have implicitly contradictory semantics. These characteristics pose challenges to traditional single-text modality methods. Existing multimodal methods, due to their default assumption of symmetric modal interactions and difficulty in capturing the subtlety of sarcasm and modal contradictions, yield limited detection performance. Therefore, this paper proposes a quantum-inspired complex-valued fusion framework to optimize the intra-modal semantics and inter-modal fusion… More >

Lan Xiong, Liang Wan^*, Jingxia Ren

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076986 - 09 April 2026

Abstract The semantic complexity of large-scale malicious payloads in modern network traffic severely limits the robustness and generalization of existing Intrusion Detection Systems (IDS). This limitation presents a major challenge to network security. This paper proposes a dual-branch intrusion detection method called CPS-IDS. This method fuses deep semantic features with statistical features. The first branch uses the DeBERTav2 module. It performs deep semantic modeling on the session payload. This branch also incorporates a Time Encoder. The Time Encoder models the temporal behavior of the packet arrival interval time series. A Cross-Attention mechanism achieves the joint modeling… More >

Adam Dudáš^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076507 - 12 March 2026

Abstract It is known that correlation does not imply causality. Some relationships identified in the analysis of data are coincidental or unknown, and some are produced by real-world causality of the situation, which is problematic, since there is a need to differentiate between these two scenarios. Until recently, the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data. This has changed with the advance of large language models, which are often utilized as surrogates for such human experts, making the process automated and readily… More >

Xi Cai, Xiaoqiang Wang, Huiying Zhao, Guang Han^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075756 - 12 March 2026

Abstract Low-light image enhancement (LLIE) remains challenging due to underexposure, color distortion, and amplified noise introduced during illumination correction. Existing deep learning–based methods typically apply uniform enhancement across the entire image, which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction. To overcome these limitations, we propose a Semantic-Guided Visual Mamba Network (SGVMNet) that unifies semantic reasoning, state-space modeling, and mixture-of-experts routing for adaptive illumination correction. SGVMNet comprises three key components: (1) a semantic modulation module (SMM) that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant (LLaVA) and… More >

Suman Sukhavasi, Thinagaran Perumal^*, Norwati Mustapha, Razali Yaakob

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075613 - 12 March 2026

Abstract The Internet of Things (IoT) ecosystem is inherently heterogeneous, comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange. However, as the number of service requests grows, existing approaches suffer from increased discovery time and degraded Quality of Service (QoS). Moreover, the massive data generated by heterogeneous IoT devices often contain redundancy and noise, posing challenges to efficient data management. To address these issues, this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management. The architecture employs Modified-Ordered Points to Identify the Clustering Structure (M-OPTICS)… More >

Weimin Shi^1,*, Kuntao Lv¹, Chang Xuan¹, Ji Wu²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075203 - 12 March 2026

Abstract The application of deep learning in fabric defect detection has become increasingly widespread. To address false positives and false negatives in fabric roll seam detection, and to improve automation efficiency and product quality, we propose the Multi-scale Context DeepLabV3+ (MSC-DeepLabV3+), a semantic segmentation network designed for fabric roll seam detection, based on DeepLabV3+. The model improvements include enhancing the backbone performance through optimization of the UIB-MobileNetV2 network; designing the Dynamic Atrous and Sliding-window Fusion (DASF) module to improve adaptability to multi-scale seam structures with dynamic dilation rates and a sliding-window mechanism; and utilizing the Progressive… More >