Luong Vuong Nguyen^1,*, Hoang Tran², Thuy-Trang Pham³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082406 - 23 July 2026

Abstract In the era of information overload, cross-domain recommendations offer a promising solution by leveraging user preferences across domains to improve recommendation accuracy and relevance. This study proposes a novel approach to cross-domain recommendations based on cognitive similarity derived from user-based features. We construct comprehensive user profiles across multiple domains by defining cognitive similarity based on user interaction data, including ratings, reviews, and genre preferences. We employ advanced feature extraction techniques, including TF-IDF for textual data and matrix factorization for latent factors, to quantify similarities in user preferences across domains. These cognitive similarity measures are then More >

Seung-su Lee¹, Young-Been Noh¹, HwaYoung Jeong², Kwang-il Hwang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081251 - 23 July 2026

Abstract Robust motion similarity retrieval from monocular 2D pose sequences is challenged by body-scale variation, viewpoint inconsistency, translation drift, and temporal misalignment. Existing contrastive skeleton learning methods primarily address action recognition and rarely integrate explicit geometric canonicalization for retrieval-oriented metric learning. This paper proposes a spatial-temporal normalized contrastive embedding framework that unifies structured nuisance suppression with scalable similarity representation learning. A four-stage normalization pipeline—torso-scale normalization, pelvis-centered alignment, posture-axis alignment, and phase-synchronized temporal resampling—removes geometric and temporal distortions prior to embedding. The normalized sequences are encoded using an acausal dilated temporal convolutional network trained with a hybrid More >

Fengqi Hao^1,2,3, Yawen Hou^2,3, Conghui Gao^2,3, Jinqiang Bai^2,3, Gang Liu⁴, Hoiio Kong^1,*, Xiangjun Dong^1,2,3

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077252 - 09 April 2026

Abstract Field–road classification, a fine-grained form of agricultural machinery operation-mode identification, aims to use Global Navigation Satellite System (GNSS) trajectory data to assign each trajectory point a semantic label indicating whether the machine is performing field work or travelling on roads. Existing methods struggle with highly imbalanced class distributions, noisy measurements, and intricate spatiotemporal dependencies. This paper presents AgroGeoDB-Net, a unified framework that combines a residual BiLSTM backbone with two tightly coupled innovations: (i) a Density-Aware Local Interpolator (DALI), which balances the minority road class via density-aware interpolation while preserving road-segment structure; and (ii) a geometry-aware… More >

Mohammed A. Ahmed¹, Jian Dong^2,*, Ronghua Shi², Ammar Nassr³, Hani Almaqtari³, Ala A. Alsanabani³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075616 - 09 April 2026

Abstract Facial Expression Recognition (FER) is an essential endeavor in computer vision, applicable in human-computer interaction, emotion assessment, and mental health surveillance. Although Convolutional Neural Networks (CNNs) have proven effective in Facial Emotion Recognition, they encounter difficulties in capturing long-range connections and global context. To address these constraints, we propose Spatial Quad-Similarity Network (SQSNet), an innovative hybrid framework that integrates the local feature extraction capabilities of CNNs with the global contextual modeling efficacy of Swin Transformers via a cohesive fusion technique. SQSNet introduces the Spatial Quad-Similarity (SQS) module, a feature refinement approach that amplifies discriminative characteristics… More >

Somia A. Abd El-Mottaleb¹, Amira G. Mohamed², Mehtab Singh³, Hassan Yousif Ahmed⁴, Medien Zeghid⁴, Abu Sufian A. Osman^5,*, Sami Mourou⁵

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075669 - 12 March 2026

Abstract This paper presents an image encryption scheme for underwater optical wireless communication (UOWC) systems based on dynamically generated hyperchaotic S-boxes, aiming to enhance both data security and transmission performance in underwater environments. The proposed encryption approach provides strong confusion and diffusion properties and is evaluated over five Jerlov water types with different optical attenuation characteristics. Security analysis demonstrates that the encrypted images achieve information entropy values close to the ideal value of 8 (7.9925–7.9993), with very low correlation coefficients in horizontal, vertical, and diagonal directions, as well as the system achieves high values in key… More >

Huiying Liu^1,2, Wenjin Wu^1,2,3,*, Yaqi Geng^1,2,3, Zhiqu Liu⁴, Xiulai Xiao⁴, Huadong Guo^1,2,3

Revue Internationale de Géomatique, Vol.35, pp. 121-130, 2026, DOI:10.32604/rig.2026.075844 - 04 March 2026

Abstract Moon-based Synthetic Aperture Radar (SAR) is particularly suitable for monitoring polar regions because of its consistent and continuous imaging. It has promising applications in the observation of sea ice by capturing rapid freeze-thaw cycles in the Arctic and Antarctic. However, the long synthetic aperture time inherent in Moon-based SAR may lead to image defocusing due to water fluctuations. Additionally, large incidence angles during observations in polar regions can result in weak backscatter from sea ice, thereby affecting the signal-to-noise ratio and ice–water discrimination. In this study, a ground-based experiment was conducted to evaluate the impact More >

Jinfeng Ji¹, Geunseok Yang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071733 - 12 January 2026

Abstract Automated Program Repair (APR) techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects. Despite considerable progress in APR methodologies, existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code. In this paper, we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework, explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis. Our approach begins with an innovative preprocessing pipeline, where code segments and stack traces are… More >

Karim Boudjebbour^1,2, Abdelkader Belkhir¹, Hamza Kheddar^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071532 - 12 January 2026

Abstract Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties. Although several community detection methods have been proposed, many are unsuitable for social networks due to significant limitations. Specifically, most approaches depend mainly on user–user structural links while overlooking service-centric, semantic, and multi-attribute drivers of community formation, and they also lack flexible filtering mechanisms for large-scale, service-oriented settings. Our proposed approach, called community discovery-based service (CDBS), leverages user profiles and their interactions with consulted web services. The method introduces a novel similarity measure, global similarity interaction profile (GSIP), which… More >

Ran Wei^*, Hui Shu

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069562 - 10 November 2025

Abstract Transformer-based models have significantly advanced binary code similarity detection (BCSD) by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings. Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code, existing techniques predominantly depend on inserting artificial instructions, which incur high computational costs and offer limited diversity of perturbations. To address these limitations, we propose AIMA, a novel gradient-guided assembly instruction relocation method. Our method decouples the detection model into tokenization, embedding, and encoding layers to enable efficient gradient computation. Since token IDs of instructions are… More >

Damodharan Palaniappan¹, Tan Kuan Tak², K. Vijayan³, Balajee Maram⁴, Pravin R Kshirsagar⁵, Naim Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4107-4127, 2025, DOI:10.32604/cmes.2025.073387 - 23 December 2025

Abstract A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy (DR) stratification. A curated cohort of 1198 patients (2915 UWF_RI and 17,854 UWF_FA images) with strict registration quality supports training across three angiographic phases (initial, mid, final). The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature, and is evaluated using MAE, PSNR, SSIM, and MS-SSIM on held-out pairs. Quantitatively, the mid phase achieves the lowest MAE (98.76 ± 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s More >