Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

Shuai Zhao, Zhiwei Zhang^*, Junkai Wang, Ye Yuan, Guoren Wang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073315 - 12 January 2026

Abstract With the development of sharded blockchains, high cross-shard rates and load imbalance have emerged as major challenges. Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates. Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads. Meanwhile, because of the coupling between consensus and execution, a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution. However, we observe that transaction partitioning and subsequent consensus do not require… More >

Tai Liu^1,2, Mao Ye^2,*, Feng Wu³, Chao Zhu², Bo Chen², Guoyan Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072692 - 12 January 2026

Abstract With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these… More >

Md Minhazul Islam^1,2, Yunfei Yin^1,2,*, Md Tanvir Islam^1,2, Zheng Yuan^1,2, Argho Dey^1,2

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072550 - 12 January 2026

Abstract Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes, where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions. To address these issues, we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder, guided multimodal fusion, and deep supervision. The framework is built upon the synergistic combination of cross-attention, gated fusion, and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation, enabling efficient… More >

Zixuan Guo¹, Houbin Wang², Sameer Kumar^1,*, Yuanfang Chen³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072064 - 12 January 2026

Abstract With the rapid development of digital culture, a large number of cultural texts are presented in the form of digital and network. These texts have significant characteristics such as sparsity, real-time and non-standard expression, which bring serious challenges to traditional classification methods. In order to cope with the above problems, this paper proposes a new ASSC (ALBERT, SVD, Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model. Based on the framework of TextRCNN, the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding. Combined with the dual attention mechanism, the… More >

Haoxuanye Ji^*, Zhiliang Chen, Pengfei Jiang, Ziyue Wang, Ting Yu, Wei Zhang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071880 - 12 January 2026

Abstract Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems. Previous approaches have primarily utilized convolutional neural networks (CNNs) to effectively integrate spatial and semantic information. However, the performance of CNN-based methods remains limited in classification accuracy, primarily due to insufficient exploration of local image characteristics. Unlike CNNs, Vision Transformer (ViT) captures discriminative features by modeling relationships between local image patches. However, such methods typically require a large number of training samples to perform effectively. In the context of foreign body classification on coal conveyor belts, the limited availability… More >

Musheng Chen^1,2, Qiang Wen¹, Xiaohong Qiu^1,2, Junhua Wu^1,*, Wenqing Fu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071207 - 12 January 2026

Abstract In multi-modal emotion recognition, excessive reliance on historical context often impedes the detection of emotional shifts, while modality heterogeneity and unimodal noise limit recognition performance. Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions. To address these issues, we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition. A dynamic gating mechanism is applied across unimodal encoding, cross-modal alignment, and emotion transfer modeling, substantially improving noise robustness and feature alignment. First, we construct a unimodal encoder More >

Sandra Kałużna^1,*, Monika Świerczewska^1,2, Sylwia Ciesiółka¹, Małgorzata Partyka¹, Michał Nowicki¹, Karolina Wojtowicz¹

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070356 - 30 December 2025

Abstract The last research focuses on the role of exosomes in cancer treatment. Exosomes are extracellular vesicles. They can be secreted by cancer cells, and they can modulate chemotherapy sensitivity. Determining exosomal content opens the possibility for guiding treatment strategies for cancer diseases. Exosomal microRNA are considered one of the prime candidates for exosomal biomarkers. Exosomal circular RNAs represent excellent biomarkers for liquid biopsy because of their stability in many types of cancer. Exosomal proteins remain reliable biomarkers also. Exosomes have emerged as promising therapeutic candidates. Their biological properties render them ideal vectors for drug delivery.… More >

Hongyu Wang¹, Wenwu Cui¹, Kai Cui¹, Zixuan Meng^2,*, Bin Li², Wei Zhang¹, Wenwen Li¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069576 - 27 December 2025

Abstract To achieve low-carbon regulation of electric vehicle (EV) charging loads under the “dual carbon” goals, this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multi-objective optimization. First, a dual-convolution enhanced improved Crossformer prediction model is constructed, which employs parallel 1 × 1 global and 3 × 3 local convolution modules (Integrated Convolution Block, ICB) for multi-scale feature extraction, combined with an Adaptive Spectral Block (ASB) to enhance time-series fluctuation modeling. Based on high-precision predictions, a carbon-electricity cost joint optimization model is further designed to balance economic, environmental, and grid-friendly objectives.… More > Graphic Abstract

Shaojie Min¹, Yaxiao Luo¹, Kebing Liu¹, Qingyuan Gong², Yang Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.073175 - 09 December 2025

Abstract User identity linkage (UIL) across online social networks seeks to match accounts belonging to the same real-world individual. This cross-platform mapping enables accurate user modeling but also raises serious privacy risks. Over the past decade, the research community has developed a wide range of UIL methods, from structural embeddings to multimodal fusion architectures. However, corresponding adversarial and defensive approaches remain fragmented and comparatively understudied. In this survey, we provide a unified overview of both mapping and anti-mapping methods for UIL. We categorize representative mapping models by learning paradigm and data modality, and systematically compare them… More >