With the rise of Web 2.0 and microservices, the increasing availability of Web APIs has intensified the need for effective recommendation systems. Existing approaches are generally categorized into two methods: recommendation-type methods, which classify APIs using labels, and match-type methods, which retrieve APIs through matching with mashups. However, three significant challenges remain: 1) semantic ambiguities in comparing API and mashup descriptions, 2) a lack of progressive semantic refinement between mashup requirements and individual API descriptions, and 3) computational inefficiency of exhaustive mashup-API comparisons in large-scale repositories. To tackle these challenges, we propose WARBERT, a hierarchical model based on BERT for Web API recommendation. WARBERT utilizes dual-component feature fusion and attention mechanisms to create accurate semantic representations. It consists of WARBERT(R) for initial candidate filtering using recommendation methods, and WARBERT(M), which focuses on refined similarity matching. The final likelihood of an API-mashup pairing combines predictions from both components, with WARBERT(R) further enhanced by an auxiliary task of predicting mashup categories. Experiments conducted on the ProgrammableWeb dataset demonstrate WARBERT outperforms existing baselines, achieving notable improvements in both accuracy and efficiency.

Multiple-secret visual cryptography scheme (MVCS) and fully incrementing visual cryptography scheme (FIVCS) have the same functionality that different secrets are gradually revealed by stacking different numbers of shadows. In essence, MVCS and FIVCS are the same. However, both of the two schemes suffer from large pixel expansion and deteriorated reconstructed image quality. In addition, MVCS and FIVCS require intensive computations to create base matrices. In this research, we exploit the capacity of sharing multiple secrets in XOR-based VCS (XVCS). First of all, three efficient base matrix constructions are proposed for realizing the$(k, n)$non-monotonic XVCS (NXVCS), where the secret image is only revealed by XOR-ing exact$k$shadows. The$(k, n)$-NXVCS is adopted to constitute the multiple-secret XVCS (MXVCS). Theoretical analysis on the proposed constructions is provided. Extensive experiments and comparisons are conducted to illustrate that the pixel expansion, the visual quality of recovered image and the efficiency of generating base matrices are significantly improved by the proposed MXVCS, while comparing to MVCS and FIVCS.

An almost unexplored research topic in visual secret sharing (VSS), i.e., sharing different visual secrets among various groups, is investigated in this article. Generally, multiple groups are cooperated together to encrypt multiple visual secrets, where every group is correlated with a VSS scheme to share one secret and each participant receives only one shadow. Such a cooperative system is referred to as cooperative VSS (CVSS). In this paper, we develop a general construction for CVSS with improved computational overhead. The basis matrices for generating the shadows are easily obtained. To improve the recovered image quality, an efficient XOR-based CVSS (XCVSS) is given. Furthermore, to fulfill the requirement of implementing complex sharing policy, the XCVSS is extended to collaborate general access structure (GAS) VSS schemes together. Theoretical and experimental results and comparisons are illustrated to demonstrate the advantages of the proposed techniques.