English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

End-users often have to enter the same information to various services (e.g., websites and mobile applications) repetitively. To save end-users from typing redundant information, it becomes more convenient for an end-user if the previous inputs of the end-user can be pre-filled to applications based on end-user’s contexts. The existing pre-filling approaches have poor accuracy of pre-filling information, and only provide limited support of reusing user inputs within one application and propagating the inputs across different applications. The existing approaches do not distinguish parameters, however different user input parameters can have very varied natures. Some parameters should be pre-filled and some should not. In this paper, we propose an ontology model to express the common parameters and the relations among them and an approach using the ontology model to address the shortcomings of the existing pre-filling techniques. The basis of our approach is to categorize the input parameters based on their characteristics. We propose categories for user inputs parameters to explore the types of parameters suitable for pre-filling. Our empirical study shows that the proposed categories successfully cover all the parameters in a representative corpus. The proposed approach achieves an average precision of 75% and an average recall of 45% on the category identification for parameters. Compared with a baseline approach, our approach can improve the existing pre-filling approach, i.e., 19% improvement on precision on average.

An Empirical Study on Categorizing User Input Parameters for User Inputs Reuse