Zehan Li¹, Yingyi Li^2,*, Zhiwei Tang³, Xuemeng Zhai³, Jiandong Liang¹, Guangmin Hu³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086118 - 13 August 2026

Abstract Social network platforms have become primary channels for information dissemination, yet they are increasingly exploited by anomalous users such as bots, fake accounts, and coordinated disinformation spreaders. These malicious actors manipulate public opinion, spread misinformation and undermine platform integrity, posing severe threats to the security of the online ecosystem. Accurate detection of such users is challenging because they often organize into sophisticated high-order connection patterns that extend beyond local neighborhoods. Existing methods address this by either injecting predefined motifs as handcrafted features, which lack flexibility to discover unknown patterns, or employing higher-order Graph neural networks… More >

Jinshuo Ma, Yang Li^*, Can Guo, Wen Gao, Ruiming Zhang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085999 - 13 August 2026

Abstract Underwater image restoration is severely hindered by a tightly coupled degradation process: wavelength-dependent spectral distortion combined with non-uniform, multi-scale spatial scattering. Standard Convolutional Neural Networks (CNNs) and rigid physical priors frequently fail in these dynamic environments, limited by restricted receptive fields, overlooked inter-channel spectral correlations, and severe over-enhancement in photon-starved regions. To break this bottleneck, we propose the Phased Feature Rectification Network (PFR-Net), a decoupled architecture that transforms the ill-posed restoration task into a sequential global spectral calibration and deep semantic refinement paradigm. In the first phase, an efficient Multi-Layer Perceptron (MLP)-based Color Mapping (MLP-CM)… More >

Jie Li, Fuyuan Song^*, Qin Jiang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085946 - 13 August 2026

Abstract Mobile crowdsensing enables large-scale sensing tasks through smart devices carried by users and has been widely applied in intelligent transportation and environmental monitoring. With the increasing complexity of sensing tasks, many tasks require the collaboration of multiple workers with different skills. However, both task-required skills and worker skills are privacy-sensitive, and directly exposing them to the platform may reveal task intentions and workers’ capability profiles. To address this issue, this paper proposes Dual-Fog Privacy-Preserving Multi-skill Task Allocation (DPMTA), a privacy-preserving task allocation scheme for multi-skill collaborative tasks. DPMTA adopts a dual-fog architecture to separately protect… More >

Maksim Iavich¹, Nursulu Kapalova², Kunbolat Algazy^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085904 - 13 August 2026

Abstract Lattice-based post-quantum cryptographic standards such as Module-Lattice Key Encapsulation Mechanism (ML-KEM) and Module-Lattice-Based Digital Signature Algorithm (ML-DSA) have demonstrated documented susceptibility to power-based side-channel attacks even when protected by higher-order arithmetic masking. Concurrently, hash-based and Verkle-tree digital signature schemes lack a systematic analysis of their physical-layer attack surface. This paper closes both gaps by introducing a Verkle-tree digital signature scheme incorporating multiple complementary countermeasures: (i) arithmetic masking of lattice-based Short Integer Solution (SIS) vector commitments, (ii) a counter-mode deterministic random bit generator (CTR_DRBG) seeded by a hardware quantum random number generator (QRNG), and (iii) an… More >

Hyunbum Kim^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085773 - 13 August 2026

Abstract Recently, unmanned aerial vehicles (UAVs), or drones, have attracted considerable research interest across diverse fields, encompassing public and private domains, industrial and academic fields, transportation areas, disaster and harsh environments, reliable delivery services, digital twin-enabled space, and smart cities. In particular, UAVs play a critical role in surveillance and security applications. In this paper, we investigate recent advances in surveillance and intelligent security applications using UAVs. This study covers a wide range of practical tasks and missions including intelligent traffic monitoring, disaster environments, forests and national parks, large-scale events and patrols in public circumstances. Also, More >

Jinlin Chen^1,2, Yiquan Wu^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085761 - 13 August 2026

Abstract Accurate waterline detection is critical for automated ship draft monitoring but remains challenging due to weak textures, low contrast, and dynamic maritime interferences. This paper presents TGNet, a task-guided framework that jointly optimizes character recognition and waterline keypoint localization. TGNet introduces a triple attention network (TAnet) with channel, spatial, and texture attention modules to enhance discriminative feature extraction. Crucially, a task-to-task guidance mechanism leverages detected draft characters to spatially constrain and crop feature maps, focusing the keypoint detection head on the most relevant waterline region. Extensive experiments on three large-scale aerial datasets show that TAnet More >

Rui Yan¹, Botao Fan², Xueqi Qu², Cuihuan Ren^1,*, Xu Zhou^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085749 - 13 August 2026

Abstract Accurate prediction of coal spontaneous combustion (CSC) temperatures is crucial for safe coal mine production. To further improve the accuracy of CSC temperature prediction and the interpretability of the model, this study proposes an interpretable Chebyshev chaotic mapping-Lévy flight-Enhanced pinhole imaging inverse learning-Adaptive weighted Osprey Optimization Algorithm optimized Bidirectional Long Short-Term Memory (CLEA-OOA-BiLSTM) framework for predicting CSC temperatures. First, we optimized the Osprey Optimization Algorithm (OOA) by incorporating the Chebyshev chaotic map, Lévy flights, enhanced pinhole imaging backpropagation, and an adaptive weighting strategy, thereby developing the CLEA-OOA algorithm. Through comparative experiments using eight benchmark test… More >

Yufei Wang¹, Jiayi Shang¹, Fang Liu^1,*, Jun Liu²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085708 - 13 August 2026

Abstract Ship detection is an effective way of sea area supervision, which has important research value in both military and civil fields. For small ship targets in the sea scene, the deep feature map is difficult to effectively capture their subtle features, resulting in the decline of small target detection accuracy and the increase of the missing detection rate. To solve this problem, this paper proposes a detection algorithm called YOLO-MARALight, which adds a small target detection layer in the head network, uses a larger scale feature map to retain the details, and improves the discrimination… More > Graphic Abstract

Yufa Shi^1,#, Jiaxing Hu^2,#, Lipeng Wang^1,3,*, Rui Ma^1,3,*, Mengyao Wang¹, Zhijuan Jia^1,3

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085633 - 13 August 2026

Abstract AI agents frequently access external files, databases, and application programming interfaces (APIs) through the Model Context Protocol (MCP). However, these external resources typically lie outside the security boundary of the agent. During data circulation, attackers can not only tamper with the external data but also manipulate critical metadata, such as access permissions, validity periods, and authorization scopes. Even when the underlying data remains intact, such attacks can cause proxies to ingest expired or policy-violating resources, leading to severe privacy breaches and risks of unauthorized execution. To address these challenges, we propose SecuAudit, a privacy-enhancing decentralized… More >

Yitao Yang^1,2, Peng Wu^1,2,*, Xiaoming Zhang^3,*, Renjie Xu³, Yong Zhang³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085505 - 13 August 2026

Abstract Existing graph-enhanced sequential recommendation methods typically adopt a unidirectional information flow, in which graph embeddings are injected into the sequential encoder only at the input stage, after which the graph signal is progressively diluted through multiple layers of deep processing. In this paper, the graph signal dilution phenomenon is analyzed systematically across three levels—the input, representation, and prediction layers—and the GSPRec model is proposed to address this issue. The core of GSPRec is the Graph-Sequence Collaborative Injection (GSCI) module, comprising three lightweight components: the Graph Confidence Gate (GCG) controls GCN smoothing via dimension-wise bounded interpolation;… More >