Yongbo Jiang, Shengnan Huang, Tao Feng, Baofeng Duan^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.069423 - 10 November 2025

Abstract In the context of modern software development characterized by increasing complexity and compressed development cycles, traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates. This paper proposes a Syntax-Aware Hierarchical Attention Network (SAHAN) model, which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms. The SAHAN model first generates Syntax Independent Units (SIUs), which slices the code based on Abstract Syntax Tree (AST) and predefined grammar rules, retaining vulnerability-sensitive contexts. Following this, through More >

Haixia Wang, Huan Zhang, Xiuling Wang, Xule Xin, Zhiguo Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069340 - 10 November 2025

Abstract Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision, with applications ranging from crowd counting to various other object counting tasks. To address this, we propose HUANNet (High-Resolution Unified Attention Network), a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework, while optimizing computational distribution across parallel branches. HUANNet introduces three core modules: the High-Resolution Attention Module (HRAM), which enhances feature extraction by optimizing multi-resolution feature fusion; the Unified Multi-Scale Attention Module (UMAM), which integrates spatial, channel, and More >

Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

Qiang Zhang¹, Shuo Feng¹, Shanxiong Chen², Teng Wan¹, Ying Qi^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.068247 - 10 November 2025

Abstract Human motion modeling is a core technology in computer animation, game development, and human-computer interaction. In particular, generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge. Existing methods typically rely on dense keyframe inputs or complex prior structures, making it difficult to balance motion quality and plausibility under conditions such as sparse constraints, long-term dependencies, and diverse motion styles. To address this, we propose a motion generation framework based on a frequency-domain diffusion model, which aims to better model complex motion distributions and enhance generation… More >

Muhammad Asif Khan¹, Dildar Hussain², Bhuyan Kaibalya Prasad³, Irfan Ullah⁴, Inayat Khan⁵, Jawad Khan^6,*, Yeong Hyeon Gu^2,*, Pavlos Kefalas⁷

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5451-5468, 2025, DOI:10.32604/cmc.2025.069134 - 23 October 2025

Abstract Dialogue State Tracking (DST) is a critical component of task-oriented spoken dialogue systems (SDS), tasked with maintaining an accurate representation of the conversational state by predicting slots and their corresponding values. Recent advances leverage Large Language Models (LLMs) with prompt-based tuning to improve tracking accuracy and efficiency. However, these approaches often incur substantial computational and memory overheads and typically address slot extraction implicitly within prompts, without explicitly modeling the complex dependencies between slots and values. In this work, we propose PUGG, a novel DST framework that constructs schema-driven prompts to fine-tune GPT-2 and utilizes its tokenizer… More >

Jun Li^#,*, Kai Xu^#,*, Baozhu Chen, Xiaohan Yang, Mengting Sun, Guojun Li, HaoJie Du

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3349-3368, 2025, DOI:10.32604/cmc.2025.067316 - 23 September 2025

Abstract Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving, social robotics, and intelligent surveillance systems. Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents. These interactions are critical to trajectory prediction accuracy. While prior studies have employed Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) to model such interactions, these methods fail to distinguish varying influence levels among neighboring pedestrians. To address this, we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions. Specifically,… More >

Lang Ding, Qinmu Wu^*, Jiaheng Li, Tao Hong, Linqing Bian

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1999-2020, 2025, DOI:10.32604/cmc.2025.066423 - 29 August 2025

Abstract Trajectory prediction is a critical task in autonomous driving systems. It enables vehicles to anticipate the future movements of surrounding traffic participants, which facilitates safe and human-like decision-making in the planning and control layers. However, most existing approaches rely on end-to-end deep learning architectures that overlook the influence of driving style on trajectory prediction. These methods often lack explicit modeling of semantic driving behavior and effective interaction mechanisms, leading to potentially unrealistic predictions. To address these limitations, we propose the Driving Style Guided Trajectory Prediction framework (DSG-TP), which incorporates a probabilistic representation of driving style… More >

Fatma S. Alrayes¹, Syed Umar Amin^2,*, Nada Ali Hakami², Mohammed K. Alzaylaee³, Tariq Kashmeery⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 581-614, 2025, DOI:10.32604/cmes.2025.065188 - 31 July 2025

Abstract The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks, there is a demand for better techniques to improve detection reliability. This study introduces a new method, the Deep Adaptive Multi-Layer Attention Network (DAMLAN), to boost the result of intrusion detection on network data. Due to its multi-scale attention mechanisms and graph features, DAMLAN aims to address both known and unknown intrusions. The real-world NSL-KDD dataset, a popular choice among IDS researchers, is used to… More >

Dang Hung Tran, Van Tinh Nguyen^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4679-4698, 2025, DOI:10.32604/cmc.2025.063228 - 19 May 2025

Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… More >

Shaoming Qiu, Xinchen Huang^*, Liangyu Liu, Bicong E, Jingfeng Ye

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5657-5678, 2025, DOI:10.32604/cmc.2025.062482 - 19 May 2025

Abstract Most existing knowledge graph relationship prediction methods are unable to capture the complex information of multi-relational knowledge graphs, thus overlooking key details contained in different entity pairs and making it difficult to aggregate more complex relational features. Moreover, the insufficient capture of multi-hop relational information limits the processing capability of the global structure of the graph and reduces the accuracy of the knowledge graph completion task. This paper uses graph neural networks to construct new message functions for different relations, which can be defined as the rotation from the source entity to the target entity… More >