Yuwei Lu^1,2, Jia Liu^1,2,*, Qiya Wang^1,2, Yujie Liu^1,2, Peng Luo^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084506 - 13 August 2026

Abstract Existing deep-learning-based steganography methods are typically designed for single-modality cover data and often rely on modality-specific network structures, which limits their cross-modal adaptability. To address this limitation, this paper proposes a multimodal implicit neural representation (INR) steganographic framework based on a point-cloud intermediate representation. The framework first fits the cover data as a carrier INR and samples the fitted carrier into a noisy point cloud. A pre-shared noise seed and secret key are then used to reproduce the carrier-derived point cloud and select a key-dependent point subset as the secret point cloud. Finally, a separate… More >

Khamza Eshankulov¹, Bahodir Muminov², Robiya Farmonova¹, Dilnavoz Sodikova³, Bakhriddin Bozorov⁴, Zavqiddin Temirov⁵, Rashid Nasimov^6,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081803 - 23 July 2026

Abstract Cloud-based Business Intelligence (BI) systems operate under highly dynamic analytical workloads, including bursty OLAP queries, concurrent aggregations, and real-time microservice interactions, where static resource allocation leads to latency spikes and inefficient resource utilization. This paper proposes a decentralized adaptive Pareto-based multi-agent decision model for real-time resource coordination in cloud BI microservice environments. The agent placement problem is formulated as a multi-criteria decision process that minimizes service response latency, improves computational resource utilization, and preserves Quality-of-Service (QoS) stability. Instead of constructing a centralized global optimization policy, the proposed framework relies on decentralized locally Pareto-efficient decisions combined More >

Zhengshun Fei^1,*, Qiao Sun¹, Chuang Yang², Siranee Nuchitprasitchai³, Yongping Zheng¹, Xinjian Xiang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.078696 - 23 July 2026

Abstract Path planning is a critical component for enabling autonomous navigation in mobile robots. Sampling-based planners are widely adopted due to their strong generality, yet they rely heavily on uniform sampling, which often leads to unstable performance and high computational cost in complex environments. To address this issue, recent studies feed free-space point clouds into neural networks to infer a set of guidance states near the optimal path, thereby enabling non-uniform sampling; however, the accuracy of the guidance set becomes a key bottleneck for further improvement. In this paper, we propose an improved point-cloud neural RRT*… More >

Ping Zhang¹, Chungang Li², Jian Song¹, Jianjun Tan¹, Han Tian¹, Le Chen³, Yongkun Song^3,*

Journal on Artificial Intelligence, Vol.8, pp. 323-334, 2026, DOI:10.32604/jai.2026.080463 - 02 July 2026

Abstract Accurate segmentation of coal pile point clouds is essential for automated inventory management and safety supervision in industrial coal sheds. With the widespread adoption of LiDAR technology, massive point cloud data are available, yet traditional clustering algorithms often fail to distinguish physically adhering coal piles in high-density storage scenarios, resulting in severe under-segmentation. To address this challenge, this paper proposes an automated point cloud segmentation framework tailored for complex industrial environments. The proposed framework consists of three core stages: data preprocessing, ground segmentation, and instance segmentation. First, relative elevation mapping and selective statistical filtering are… More >

Haotian Cao^1,2, Qingsheng Zhu^1,2,3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081695 - 15 June 2026

Abstract Point Cloud Registration (PCR) is a basic task in computer vision, mobile robotics, and autonomous driving. PCR primarily faces challenges, including insufficient registration performance in low-overlap scenarios and high computational resource consumption in large-scale point cloud scenarios. Most recent PCR methods are transformer-based. Methods like transformers have quadratic computational complexity $𝒪(n^{2}d)$, leading to rapid increases in computational cost with large-scale point cloud data. To address these problems, an iterative PCR method named Attention and Mamba Based Iterative Registration Network (AMBIR) is proposed, overcoming the shortcomings of the current PCR method on low-overlap and large-scale scenarios. Specifically, an… More >

Chao Zhang¹, Weidong Zhong¹, Xu An Wang¹, Weiwei Jiang^2,*, Ziteng Wang², Miao Tian¹, Jianhong Ling¹, Hangjiang Du¹, Yunhui Duan¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081399 - 15 June 2026

Abstract The integrity risks posed by data outsourcing in cloud storage have driven the development of remote data integrity auditing (RDIA) technologies. However, traditional schemes rely on trusted third-party auditors (TPAs), leading to potential collusion and single-point failure vulnerabilities. The integration of blockchain alleviates these issues through decentralization and transparency, yet existing blockchain-based certificateless auditing schemes still suffer from security flaws in the tag generation phase. Addressing the tag forgery vulnerability in Miao et al.’s scheme, which stems from the absence of random parameters in the hash function input, this paper proposes a lightweight enhancement mechanism: More >

Aihua Wu, Chenlu Huang^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078692 - 15 June 2026

Abstract Three-dimensional (3D) point cloud semantic segmentation is a core task in indoor scene understanding, providing detailed semantic information about spatial structures and object categories in indoor environments. Although methods based on deep learning have made steady progress in recent years, accurately segmenting complex indoor scenes remains challenging due to the unordered nature of point clouds and variations across large scales. Most existing networks have limited capability for multi-scale feature aggregation and struggle to balance local geometric details with global semantic context. These issues are further exacerbated by hierarchical downsampling, which often leads to the loss… More >

Mohammed Saad Javeed¹, MD AL Rafi², Arifa Akter Eva³, Muhammad Firoz Mridha³, Qiangfu Zhao^4,*, Jungpil Shin^4,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.076825 - 15 June 2026

Abstract Hybrid and multi-cloud infrastructures make IP address management (IPAM) difficult, especially when IP and Domain Name System (DNS) records must stay consistent across on-premises networks and cloud platforms. Traditional IPAM tools often lack deep automation and cross-platform visibility, which leads to DNS drift, IP conflicts, and configuration errors. This paper proposes a unified, Application Programming Interface (API)-driven IPAM framework that integrates Infoblox Network Identity Operating System (NIOS) with Amazon Web Services (AWS) Route53 and Azure DNS using Infrastructure-as-Code and CI/CD pipelines. We generate an IPAM event log from Infoblox API simulations and fuse it with More >

Pengcheng Kang¹, Yuanyuan Tian¹, Ying Liu¹, Qian Xu¹, Yuting Chen¹, Lixin Jia², Shuge Guo², Heng Rong², Taolong Xu^2,*

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.077643 - 18 May 2026

Abstract Accurate assessment of gas explosion risks in urban underground culverts is often hindered by the decoupling of leakage diffusion and explosion mechanics. This study develops a high-fidelity numerical framework by implementing a one-way coupling strategy, where the steady-state methane concentration field simulated in FLUENT is mapped into ANSYS/LS-DYNA as the initial material status. Unlike traditional models assuming uniform gas distribution, this approach captures the realistic impact of complex culvert geometries on explosion precursors. A multi-material coupled model involving the confined space, road surface, and surrounding air was established to investigate shock-wave propagation and structural response. The More >

Jinchuan Pei¹, Yuxiang Hu^1,2,3,4,*, Hongtao Yu¹, Zihao Wang¹, Menglong Li^1,2,3,4

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079684 - 08 May 2026

Abstract In the cloud-edge collaborative network, advanced persistent threats (APTs) pose a serious security risk to critical network assets. Although network deception defense can mislead attackers’ cognition, its effectiveness depends on dynamically selecting appropriate rotation timings of the deception defense. However, the deployment of deception resources and state updates is not completed instantaneously, and existing methods ignore the state transition delay and the dynamic interaction between the attackers and defenders during the real attack and defense process. To address this, we propose a deception defense timing selection method based on the time-delayed FlipIt game. Firstly, a… More >