Shuya Dong | Searcharxiv

arXiv2026

Automatic Association of Cloud Security Controls and Quantifiable Metrics for Certification

The draft candidate European Cybersecurity Certification Scheme for Cloud Services (EUCS) defines security controls that must be associated with measurable metrics to assess compliance. This association process is currently manual, time-consuming, and prone to inconsistencies. In this paper, we propose an automated approach based on Sentence Transformers to associate cloud security controls with quantifiable metrics by leveraging semantic similarity between their textual descriptions. We evaluate our method on a dataset of 70 controls derived from the EUCS framework. The proposed approach outperforms a FastText-based baseline, achieving a conditional Normalized Discounted Cumulative Gain at rank 10 score of 0.640 (+0.146) and improving the standard nDCG@10 score from 0.275 to 0.504. These results demonstrate that contextual embedding models significantly enhance both the likelihood of retrieving relevant metrics and their ranking quality. Our findings highlight the potential of transformer-based methods to support automated, scalable, and more reliable compliance processes in cloud cybersecurity certification.

cs.CR↗

arXiv2025

KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns

Accurate determination of crystal structures is central to materials science, underpinning the understanding of composition-structure-property relationships and the discovery of new materials. Powder X-ray diffraction is a key technique in this pursuit due to its versatility and reliability. However, current analysis pipelines still rely heavily on expert knowledge and slow iterative fitting, limiting their scalability in high-throughput and autonomous settings. Here, we introduce a physics-guided contrastive learning framework termed as XCCP. It aligns powder diffraction patterns with candidate crystal structures in a shared embedding space to enable efficient structure retrieval and symmetry recognition. The XRD encoder employs a dual-expert design with a Kolmogorov-Arnold Network projection head, one branch emphasizes low angle reflections reflecting long-range order, while the other captures dense high angle peaks shaped by symmetry. Coupled with a crystal graph encoder, contrastive pretraining yields physically grounded representations. XCCP demonstrates strong performance across tasks, with structure retrieval reaching 0.89 and space group identification attains 0.93 accuracy. The framework further generalizes to compositionally similar multi principal element alloys and demonstrates zero-shot transfer to experimental patterns. These results establish XCCP as a robust, interpretable, and scalable approach that offers a new paradigm for X-ray diffraction analysis. XCCP facilitates high-throughput screening, rapid structural validation and integration into autonomous laboratories.

cond-mat.mtrl-sci↗