Xiaolei Yin, Zihan Wang, Sanfeng Zhang^*, Shouwei Li^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088083 - 15 September 2026

Abstract On-chain risky address identification is an important task in blockchain security analysis and digital asset risk management. In practical on-chain risk control, however, risky addresses are usually far fewer than benign ones. Fund flows also follow complex propagation paths and strict temporal orders, which makes it difficult for existing methods to handle class imbalance, structural semantic modeling, and information leakage under temporal split settings at the same time. To address these challenges, this paper proposes AMLHunter, an on-chain risky address identification method based on temporally consistent transaction semantic constraints and generative graph augmentation. AMLHunter first… More >

Maryam Almarwani^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086887 - 15 September 2026

Abstract OAuth authorization ecosystems contain a large and diverse collection of capabilities distributed across multiple cloud platforms. Although previous studies have investigated OAuth security, privacy, and authorization management, the structural organization of authorization capabilities across providers has received limited attention. This study presents a cross-provider structural analysis of OAuth capabilities from seven major authorization platforms. A unified capability dictionary is constructed by normalizing publicly documented OAuth scopes into a common semantic representation. The normalized capabilities are transformed into an undirected semantic topology in which nodes represent capabilities and edges represent deterministic semantic relationships. Standard network analysis… More >

Hadeel Alsolai¹, Muhammad Waqas Ahmed², Bayan Alabdullah¹, Fatimah Alhayan¹, Mohammed Alonazi³, Ahmad Jalal^4,5, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085838 - 15 September 2026

Abstract Robust scene-level classification and semantic understanding from aerial and disaster-related imagery are essential for intelligent vision systems deployed in emergency response, UAV-based monitoring, and safety-critical environments. However, existing deep learning approaches, including convolutional neural networks and Vision Transformers (ViTs), often struggle to simultaneously capture fine-grained local object characteristics and global semantic scene context, while also lacking reliable uncertainty estimation mechanisms for trustworthy decision-making. To address these limitations, this paper proposes MS-SLCA-ViT, a novel multi-scale scene–local cross-attention Vision Transformer framework for robust and uncertainty-aware image scene understanding. The proposed architecture introduces three major contributions. First, a… More >

Gobinda Bhattacharjee¹, Joy Dey¹, Tanjim Mahmud^1,*, Mohammad Shahadat Hossain^2,3, Karl Andersson³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085032 - 15 September 2026

Abstract Log parsing is a fundamental prerequisite for automated system monitoring, anomaly detection, and root cause analysis in large-scale distributed environments. However, existing parsing approaches often rely on heuristic rules, manually engineered features, or fixed similarity thresholds, limiting their adaptability to heterogeneous and evolving log structures. To address these challenges, this study presents SemBERT, a fully unsupervised log parsing framework that integrates semantic BERT embeddings, Incremental Principal Component Analysis (IPCA), HDBSCAN clustering, and adaptive centroid-based cluster merging for robust template mining. Unlike conventional methods that employ fixed merging criteria, SemBERT adaptively determines semantic merging thresholds according… More >

Lingtao Zhan¹, Jiale Zhu¹, Tingting Wang¹, Xiongbai Cao¹, Xiaoyu Hao^1,2, Cesare Grazioli³, Quanzheng Zhang¹, Huixia Yang¹, Teng Zhang^1,*, Yeliang Wang¹

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083413 - 15 September 2026

Abstract Scanning tunneling microscopy (STM) images are frequently affected by low-frequency vibrations, substrate-induced background variations, and bias-dependent contrast changes, which degrade molecular feature responses and hinder reliable segmentation. To address this challenge, we develop an STM-oriented Feature Pyramid Network (FPN) + Dual-Path Intensity Calibration (DPIC) framework by adapting a DPIC module, originally derived from a cloud-noise calibration mechanism, to the specific characteristics of STM molecular images. In this framework, DPIC is reformulated as a noise-aware feature calibration module that suppresses low-response background interference while preserving foreground molecular contours. We integrated DPIC into the FPN architecture and… More >

Ti-Lun Miao¹, Hsien-Tsung Chang^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086762 - 28 August 2026

Abstract Personalized fashion recommendation requires models that can capture visual compatibility, textual semantics, structured attributes, and user-specific preferences. However, existing multimodal approaches often rely on static word embeddings and shallow text encoders, limiting their ability to represent nuanced fashion descriptions. This study proposes a multimodal recommendation framework enhanced by large language models (LLMs) that integrates visual features, contextual textual representations, and structured attribute features for personalized outfit matching. A Japanese pretrained BERT encoder is used to replace the conventional Word2Vec and convolutional neural network (CNN)-based text pipeline, while GPT-4o is employed to extract fine-grained fashion attributes… More >

Masood Karamoozian¹, Zarrin Mahdavipour², Mohammed Ameen³, Faisal Binzagr⁴, Abdolraheem Khader^2,*, Ahmed Hamza Osman³, Ali Ahmed⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085421 - 28 August 2026

Abstract Achieving net-zero carbon emissions in the building sector requires computational tools that are simultaneously efficient, physically rigorous, and scalable to complex multi-domain design spaces. Traditional physics-based simulators such as EnergyPlus and finite-element analysis (FEM) packages deliver high fidelity but are computationally prohibitive for large-scale parametric exploration and real-time multi-objective optimization. This study introduces a hybrid Physics-Informed Graph Neural Network (PI-GNN) surrogate that directly leverages semantic Building Information Modeling (BIM) data to enable rapid, accurate, and physically consistent multi-physics prediction and optimization of prefabricated modular buildings. Building components and their physical and functional relationships are encoded… 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 >

Xiaosong Chang, Liang Shi^*, Ao Zhang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085087 - 13 August 2026

Abstract To ensure the reliability of biometric authentication, Face Anti-Spoofing (FAS) models must accurately detect presentation attacks. However, due to the highly complex distribution shifts caused by variations in style, cross-domain generalization remains a significant challenge. Test-Time Domain Generalization (TTDG) has recently surfaced as an innovative framework, facilitating the adaptation of unseen samples to source-domain characteristics through the strategic utilization of learned style bases. Nevertheless, existing TTDG methods optimize randomly initialized style bases solely through statistical objectives, leaving a critical research gap: the lack of explicit semantic constraints inevitably leads to hierarchical semantic inconsistency and weakens… More >

Manaswi Kulahara¹, Khadija Parwez², Faisal Alhwikem^3,*, Fawwad Hassan Jaskani⁴

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084877 - 13 August 2026

Abstract Large Language Models (LLMs) have recently advanced in real-world commonsense reasoning, including understanding everyday object behaviors and inferring their attributes from text. However, they remain limited in reasoning about the real-world consequences of events, such as how object failures, obstructions, or structural changes affect the surrounding environment-especially without visual or sensorimotor input. Existing works like PIQA and NEWTON evaluate narrow sub-skills, such as whether an object action makes sense and whether object properties can be inferred, providing valuable benchmarks for commonsense and physical reasoning but offering limited evaluation of how events alter environmental functionality and downstream… More >