Ran Wei^*, Hui Shu

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069562 - 10 November 2025

Abstract Transformer-based models have significantly advanced binary code similarity detection (BCSD) by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings. Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code, existing techniques predominantly depend on inserting artificial instructions, which incur high computational costs and offer limited diversity of perturbations. To address these limitations, we propose AIMA, a novel gradient-guided assembly instruction relocation method. Our method decouples the detection model into tokenization, embedding, and encoding layers to enable efficient gradient computation. Since token IDs of instructions are… More >

Faraj M. Zaid^1,2, Thaar M. Aljuwaya^3,4,*, Muthanna H. Al-Dahhan^1,3,5,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.10, pp. 2443-2471, 2025, DOI:10.32604/fdmp.2025.067557 - 30 October 2025

Abstract This study presents a detailed experimental evaluation of a newly developed mechanistic scale-up methodology for gas-solid fluidized beds. Traditional scale-up approaches typically rely on matching global dimensionless groups, which often fail to ensure local hydrodynamic similarity. In contrast, the new mechanistic method aims to achieve scale-up by matching the radial profiles of gas holdup between geometrically similar beds at corresponding dimensionless axial positions (z/D_c). This approach is based on the premise that when gas holdup profiles align, other key hydrodynamic parameters—such as solids holdup and particle velocity—also become similar. To validate this methodology, experiments were conducted More >

Shengjia Chang, Baojiang Cui^*, Shaocong Feng

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4345-4374, 2025, DOI:10.32604/cmc.2025.070195 - 23 October 2025

Abstract Binary Code Similarity Detection (BCSD) is vital for vulnerability discovery, malware detection, and software security, especially when source code is unavailable. Yet, it faces challenges from semantic loss, recompilation variations, and obfuscation. Recent advances in artificial intelligence—particularly natural language processing (NLP), graph representation learning (GRL), and large language models (LLMs)—have markedly improved accuracy, enabling better recognition of code variants and deeper semantic understanding. This paper presents a comprehensive review of 82 studies published between 1975 and 2025, systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence (AI) techniques. Particular… More >

Fatima Abu Siryeh^*, Hussein Alrammahi, Abdullahi Abdu İbrahim

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5021-5046, 2025, DOI:10.32604/cmc.2025.065206 - 30 July 2025

Abstract Biometric template protection is essential for finger-based authentication systems, as template tampering and adversarial attacks threaten the security. This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication. The system was tested against NIST SD4 and Anguli fingerprint datasets, wherein 10,000 watermarked fingerprints were employed for training. The designed approach recorded a tamper detection rate of 98.3%, performing 3–6% better than current DCT, SVD, and DWT-based watermarking approaches. The false positive rate (≤1.2%) and false negative rate (≤1.5%) were much lower compared to previous… More >

Yu-Shiuan Tsai^*, Zhen-Rong Wu, Jian-Zhi Liu

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3431-3457, 2025, DOI:10.32604/cmc.2025.066509 - 03 July 2025

Abstract Few-shot learning has emerged as a crucial technique for coral species classification, addressing the challenge of limited labeled data in underwater environments. This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection. The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity, effectively capturing both feature magnitude and directional relationships. This approach achieves a notable accuracy of 71.8% under a 5-way 5-shot evaluation, outperforming state-of-the-art models such as Prototypical Networks, FEAT, and ESPT by up to 10%. Notably, the model demonstrates high… More >

Adéla Hamplová^*, Tomáš Novák, Miroslav Žáček, Jiří Brožek

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3329-3349, 2025, DOI:10.32604/cmes.2025.066025 - 30 June 2025

Abstract This study proposes a new component of the composite loss function minimised during training of the Super-Resolution (SR) algorithms—the normalised structural similarity index loss , which has the potential to improve the natural appearance of reconstructed images. Deep learning-based super-resolution (SR) algorithms reconstruct high-resolution images from low-resolution inputs, offering a practical means to enhance image quality without requiring superior imaging hardware, which is particularly important in medical applications where diagnostic accuracy is critical. Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity, visual artefacts may persist, making the design of… More >

Ali Hamid Farea^1,*, Iman Askerzade^1,2, Omar H. Alhazmi³, Savaş Takan⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1457-1484, 2025, DOI:10.32604/cmc.2025.064872 - 09 June 2025

Abstract Feature selection (FS) is a pivotal pre-processing step in developing data-driven models, influencing reliability, performance and optimization. Although existing FS techniques can yield high-performance metrics for certain models, they do not invariably guarantee the extraction of the most critical or impactful features. Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features. However, the challenge of discerning the most relevant and influential features persists, particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial… More >

Muhammad Hameed Siddiqi^1,*, Menwa Alshammeri¹, Jawad Khan^2,*, Muhammad Faheem Khan³, Asfandyar Khan⁴, Madallah Alruwaili¹, Yousef Alhwaiti¹, Saad Alanazi¹, Irshad Ahmad⁵

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5345-5371, 2025, DOI:10.32604/cmc.2025.062340 - 19 May 2025

Abstract As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that… More >

Wenchang Yu¹, Xiaoqin Ma^1,2, Zheqing Zhang¹, Qinli Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1195-1218, 2025, DOI:10.32604/cmc.2025.060833 - 26 March 2025

Abstract Feature selection methods rooted in rough sets confront two notable limitations: their high computational complexity and sensitivity to noise, rendering them impractical for managing large-scale and noisy datasets. The primary issue stems from these methods’ undue reliance on all samples. To overcome these challenges, we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection algorithm. Firstly, we construct a robust fuzzy relation by introducing a truncation parameter. Then, based on this fuzzy relation, we propose the concept of cross-similarity, which emphasizes the sample-to-sample similarity relations… More >

Jian Feng^*, Yifan Guo, Cailing Du

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5135-5151, 2025, DOI:10.32604/cmc.2025.059610 - 06 March 2025

Abstract Graph similarity learning aims to calculate the similarity between pairs of graphs. Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies, which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs. To address these issues, we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning. First, to tackle the problem of random augmentation disrupting the semantics and structure of the graph, we design a learnable augmentation method to selectively choose nodes and… More >