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  • Open Access

    ARTICLE

    VPAFL: Verifiable Privacy-Preserving Aggregation for Federated Learning Based on Single Server

    Peizheng Lai1, Minqing Zhang1,2,*, Yixin Tang1, Ya Yue1, Fuqiang Di1,2

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2935-2957, 2025, DOI:10.32604/cmc.2025.065887 - 03 July 2025

    Abstract Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing. However, its reliance on a server introduces critical security vulnerabilities: malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results. Consequently, achieving verifiable aggregation without compromising client privacy remains a critical challenge. To address these problem, we propose a reversible data hiding in encrypted domains (RDHED) scheme, which designs joint secret message embedding and extraction mechanism. This approach enables clients to embed secret messages… More >

  • Open Access

    ARTICLE

    Optimizing Sentiment Integration in Image Captioning Using Transformer-Based Fusion Strategies

    Komal Rani Narejo1, Hongying Zan1,*, Kheem Parkash Dharmani2, Orken Mamyrbayev3,*, Ainur Akhmediyarova4, Zhibek Alibiyeva4, Janna Alimkulova5

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3407-3429, 2025, DOI:10.32604/cmc.2025.065872 - 03 July 2025

    Abstract While automatic image captioning systems have made notable progress in the past few years, generating captions that fully convey sentiment remains a considerable challenge. Although existing models achieve strong performance in visual recognition and factual description, they often fail to account for the emotional context that is naturally present in human-generated captions. To address this gap, we propose the Sentiment-Driven Caption Generator (SDCG), which combines transformer-based visual and textual processing with multi-level fusion. RoBERTa is used for extracting sentiment from textual input, while visual features are handled by the Vision Transformer (ViT). These features are More >

  • Open Access

    REVIEW

    3D Printing of Plant Fiber-Based Materials and Quality Evaluation of Their Products: A Review

    Weili Liu1, Fayi Hao1, Jiangping Yuan1,2,*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1951-1979, 2025, DOI:10.32604/cmc.2025.065836 - 03 July 2025

    Abstract Additive manufacturing (AM) and Three-dimensional (3D) printing build complex structures layer by layer, greatly expanding design possibilities. Traditional thermoplastics like Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), and Polyethylene Terephthalate Glycol (PETG) are widely used in 3D printing, but their non-renewable nature and limited biodegradability have driven research into plant fiber-based materials. These materials, mainly cellulose and lignin, come from sources like wood and agricultural waste, offering renewability, biodegradability, and biocompatibility. This paper reviews recent advances in plant fiber-based materials for 3D printing, covering their development from raw materials to applications. It highlights the sources,… More >

  • Open Access

    ARTICLE

    Linguistic Steganography Based on Sentence Attribute Encoding

    Lingyun Xiang*, Xu He, Xi Zhang, Chengfu Ou

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2375-2389, 2025, DOI:10.32604/cmc.2025.065804 - 03 July 2025

    Abstract Linguistic steganography (LS) aims to embed secret information into normal natural text for covert communication. It includes modification-based (MLS) and generation-based (GLS) methods. MLS often relies on limited manual rules, resulting in low embedding capacity, while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency. To address this, we propose a sentence attribute encoding-based MLS method that enhances extraction efficiency while maintaining strong performance. The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information. When the attribute values of the cover sentence differ… More >

  • Open Access

    ARTICLE

    Unleashing the Potential of Metaverse in Social IoV: An Authentication Protocol Based on Blockchain

    Tsu-Yang Wu1,2,3, Haozhi Wu3, Maoxin Tang1,2, Saru Kumari4, Chien-Ming Chen1,2,*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3175-3192, 2025, DOI:10.32604/cmc.2025.065717 - 03 July 2025

    Abstract As a model for the next generation of the Internet, the metaverse—a fully immersive, hyper-temporal virtual shared space—is transitioning from imagination to reality. At present, the metaverse has been widely applied in a variety of fields, including education, social entertainment, Internet of vehicles (IoV), healthcare, and virtual tours. In IoVs, researchers primarily focus on using the metaverse to improve the traffic safety of vehicles, while paying limited attention to passengers’ social needs. At the same time, Social Internet of Vehicles (SIoV) introduces the concept of social networks in IoV to provide better resources and services… More >

  • Open Access

    ARTICLE

    DRL-AMIR: Intelligent Flow Scheduling for Software-Defined Zero Trust Networks

    Wenlong Ke1,2,*, Zilong Li1, Peiyu Chen1, Benfeng Chen1, Jinglin Lv1, Qiang Wang2, Ziyi Jia2, Shigen Shen1

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3305-3319, 2025, DOI:10.32604/cmc.2025.065665 - 03 July 2025

    Abstract Zero Trust Network (ZTN) enhances network security through strict authentication and access control. However, in the ZTN, optimizing flow control to improve the quality of service is still facing challenges. Software Defined Network (SDN) provides solutions through centralized control and dynamic resource allocation, but the existing scheduling methods based on Deep Reinforcement Learning (DRL) are insufficient in terms of convergence speed and dynamic optimization capability. To solve these problems, this paper proposes DRL-AMIR, which is an efficient flow scheduling method for software defined ZTN. This method constructs a flow scheduling optimization model that comprehensively considers… More >

  • Open Access

    ARTICLE

    Physics-Informed Gaussian Process Regression with Bayesian Optimization for Laser Welding Quality Control in Coaxial Laser Diodes

    Ziyang Wang1, Lian Duan1,2,*, Lei Kuang1, Haibo Zhou1, Ji’an Duan1

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2587-2604, 2025, DOI:10.32604/cmc.2025.065648 - 03 July 2025

    Abstract The packaging quality of coaxial laser diodes (CLDs) plays a pivotal role in determining their optical performance and long-term reliability. As the core packaging process, high-precision laser welding requires precise control of process parameters to suppress optical power loss. However, the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise. To address this challenge, a physics-informed (PI) and data-driven collaboration approach for welding parameter optimization is proposed. First, thermal-fluid-solid coupling finite element method (FEM) was employed to quantify the sensitivity of welding parameters to physical characteristics, including… More >

  • Open Access

    ARTICLE

    Chinese DeepSeek: Performance of Various Oversampling Techniques on Public Perceptions Using Natural Language Processing

    Anees Ara1, Muhammad Mujahid1, Amal Al-Rasheed2,*, Shaha Al-Otaibi2, Tanzila Saba1

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2717-2731, 2025, DOI:10.32604/cmc.2025.065566 - 03 July 2025

    Abstract DeepSeek Chinese artificial intelligence (AI) open-source model, has gained a lot of attention due to its economical training and efficient inference. DeepSeek, a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step, demonstrates remarkable reasoning capabilities of performing a wide range of tasks. DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries. Users possess divergent viewpoints regarding advanced models like DeepSeek, posting both their merits and shortcomings across several social media platforms. This research presents a new framework for predicting… More >

  • Open Access

    ARTICLE

    Improved PPO-Based Task Offloading Strategies for Smart Grids

    Qian Wang1, Ya Zhou1,2,*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3835-3856, 2025, DOI:10.32604/cmc.2025.065465 - 03 July 2025

    Abstract Edge computing has transformed smart grids by lowering latency, reducing network congestion, and enabling real-time decision-making. Nevertheless, devising an optimal task-offloading strategy remains challenging, as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions. We cast the offloading problem as a Markov Decision Process (MDP) and solve it with Deep Reinforcement Learning (DRL). Specifically, we present a three-tier architecture—end devices, edge nodes, and a cloud server—and enhance Proximal Policy Optimization (PPO) to learn adaptive, energy-aware policies. A Convolutional Neural Network (CNN) extracts high-level features from system states, enabling More >

  • Open Access

    ARTICLE

    A Multi-Objective Joint Task Offloading Scheme for Vehicular Edge Computing

    Yiwei Zhang, Xin Cui*, Qinghui Zhao

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2355-2373, 2025, DOI:10.32604/cmc.2025.065430 - 03 July 2025

    Abstract The rapid advance of Connected-Automated Vehicles (CAVs) has led to the emergence of diverse delay-sensitive and energy-constrained vehicular applications. Given the high dynamics of vehicular networks, unmanned aerial vehicles-assisted mobile edge computing (UAV-MEC) has gained attention in providing computing resources to vehicles and optimizing system costs. We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption. We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm (DVCG-MWOA) to address this problem. A novel dynamic clustering algorithm is designed… More >

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