The increasing number of available Web Application Programming Interfaces (APIs) in various service sharing communities have enabled software developers to develop their interested multimedia mashups quickly and conveniently. In this situation, a multimedia mashup with complex functionalities could be achieved by composing a set of pre-selected Web APIs by software developers. However, due to the APIs diversity in terms of development organization, programming language, invocation interface, etc, it is often difficult to determine the compatibility between the APIs selected by multimedia mashup developers beforehand especially when the developers have little background knowledge of APIs, which significantly decreases the successful rate of subsequent multimedia mashup development. In response to this challenge, we propose a subgraph matching-based compatible API’s composition recommendation method, called SubMC_WACR. The advantage of SubMC_WACR is that it can directly search for the API’s subgraphs that not only meet the functional requirements of the multimedia mashup but also are compatible with each other, thus boosting the effectiveness of multimedia mashup development. Through extensive experiments on a real dataset crawled from the Web API sharing platform ProgrammableWeb.com, we have demonstrated that our proposed recommendation method achieves significant improvements in terms of recommendation precision and compatibility compared with other competitive API recommendation methods.

In the filed of connectomics, reconstructing an accurate and complete connectome requires considerable manpower, financial resources, and time. Efficient management of reconstruction projects to conserve resources and enable rapid reconstruction poses a significant challenge. This study views individual annotators as decision-making units from a microlevel perspective and uses data envelopment analysis to establish productivity and performance analysis model of annotators. By introducing advanced Artificial Intelligence (AI) algorithms to empower intelligent management of connectome reconstruction, we can mine users’ effective outputs in a more reliable and robust way. Edge computing performance is improved by embedding intelligent algorithms and data collection systems into user devices. Through the analysis of the inputs and outputs in the production activities of annotators, the effectiveness of the proposed model has been validated, which helps to understand and optimize user performance. The proposed method can be used for efficient management in connectome reconstruction to allocate resources equitably and optimize human resources within the company.

The rapid expansion of Web APIs presents developers with significant challenges in selecting optimal API compositions. To address this issue, keyword-based API composition recommendation techniques have been proposed. However, these methods often suffer from popularity bias due to the influence of historical datasets and recommendation models. This bias leads to the disproportionate recommendation of popular APIs over less popular ones, potentially causing the Matthew effect and impeding the balanced development of the API ecosystem. Although several studies have identified and attempted to mitigate popularity bias, they have largely relied on static analysis without accounting for the dynamic nature of API recommendations. In this paper, we introduce a dynamic simulation framework for API composition recommendations, designed to explore the evolution of popularity bias within recommendation results, and propose a debiasing method for dynamic recommendations by combining the enhanced API correlation graph with the Determinantal Point Process (DPP) method. Finally, extensive experiments on real datasets show that the algorithm effectively alleviates the popularity bias problem while guaranteeing high recommendation accuracy.