Forthcoming and Online First Articles

International Journal of Web Engineering and Technology

International Journal of Web Engineering and Technology (IJWET)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Web Engineering and Technology (3 papers in press)

Regular Issues

  • Service Recommendation Method based on Text View and Interaction View   Order a copy of this article
    by Shuaijia Lin, Ting Yu, Yaqi Wang, Jie Xu, Fangying Cheng, Tian Liang 
    Abstract: With the increasing prosperity of web service-sharing platforms, more and more software developers are reusing web services when developing applications. Existing web service recommendation systems often face two challenges. Firstly, developers discover services by inputting requirements, but the user's input is arbitrary and it cannot fully reflect the user's intention. Secondly, the application-service interaction records are too sparse, making it particularly difficult to find services that meet the requirements. To address the above challenges, in this paper, we propose a service recommendation method based on text and interaction views (SRTI). Firstly, SRTI employs graph neural network to deeply mine the features of applications and services. Secondly, SRT uses transformer and fully connected neural networks to deeply mine the matching degree between candidate services and requirements. Finally, we integrate the above two to obtain the final service list. Extensive experiments on real-world datasets have shown that SRTI outperforms several state-of-the-art methods.
    Keywords: service recommendation; text view; interaction view; application; recommendation algorithm.
    DOI: 10.1504/IJWET.2024.10064249
  • Practice of College Music Intangible Cultural Heritage Based on Clustering Improved Distance Beat Tracking Algorithm   Order a copy of this article
    by Xiaolei Liu 
    Abstract: The survival, inheritance, and development of intangible cultural heritage of music face serious challenges. Traditional point-to-point inheritance has limitations and is likely to lead to cultural loss. It is crucial to introduce music intangible cultural heritage into university classrooms and innovate music creation forms based on youth groups. The study employs pulse coding modulation encoding and end-point intensity curve extraction to achieve beat tracking through a maximum and minimum distance clustering method of BPM features in signal time and frequency domain analysis. An improved musical beat tracking model is created based on clustering. Experimental results showed that the model accurately tracked the musical beat (average P-Score = 61.719, Cemgil = 48.640, CMLc = 20.174, AML t = 49.862). This research model is significant for protecting music intangible cultural heritage. This study explores the practical application of the distance beat tracking algorithm based on cluster improvement to introduce music intangible cultural heritage into college classes. The study provides effective methods and ideas for the inheritance and protection of music intangible cultural heritage, and contributes to the innovation of teaching modes in college music classes. The findings have significant implications for the protection of music intangible cultural heritage.
    Keywords: protection of intangible cultural heritage; BPM; clustering method; music class.
    DOI: 10.1504/IJWET.2024.10064806
  • A Cluster-based Approach for Distributed Anonymization of Vertically Partitioned Data   Order a copy of this article
    by Antonios Xenakis, Z. Chen, George Karabatis 
    Abstract: In modern organisations, data is often spread across different sites, posing challenges for effective analysis. Transferring data to a centralised server may jeopardise privacy and leak sensitive/proprietary information. Therefore, organisations hesitate adopting this solution despite its potential to fully utilise, and analyse the data, for better decision making. Current approaches concentrate on distributed privacy-preserving techniques for data analysis, where data does not leave each site, but incurs substantial computational and communication overhead. This paper focuses on distributed data that is anonymised on site, then merged and sent to a centralised server for analysis. Two new approaches on cluster-based distributed anonymisation are introduced for vertically partitioned data, one based on distributed coordinated anonymisation, and the other based on top-down distributed anonymisation, resulting in low initial onsite anonymisation overhead. Experiments show these approaches preserve data privacy with very minor loss of utility of anonymised data and impose minimal computational overhead.
    Keywords: privacy; distributed anonymisation; differential privacy; K-anonymity; cluster-based anonymisation.
    DOI: 10.1504/IJWET.2024.10064904