Deep-learning and machine-learning algorithms have recently become a prominent research topic for forecasting the price of cryptocurrencies. Some research indicates that deep learning models are incapable of accuratel...
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With the advancement of service computing technology, the Internet has witnessed an exponential proliferation of Web APIs. However, the selection of suitable APIs from this vast pool for Mashup creation poses a great ...
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ISBN:
(纸本)9798350368567;9798350368550
With the advancement of service computing technology, the Internet has witnessed an exponential proliferation of Web APIs. However, the selection of suitable APIs from this vast pool for Mashup creation poses a great challenge for users. Various Web API recommendation methods have been proposed to address this issue, aiming to simplify the complex selection process. Despite these efforts, limited studies have been conducted on complementary function recommendation. In this context, a general complementary Web API recommendation framework based on a learning model, named CoWAR, is designed to recommend complementary Web APIs tailored for Mashup creation, based on the user's selected Web APIs. Specifically, we propose a data labeling algorithm to generate the labeled dataset based on Mashup-API interactions derived from historical Mashups and Web APIs. Additionally, we employ BERT model to generate representation vectors of Web APIs based on the functionality description documents. Subsequently, we utilize SANFM (Self-Attentional Neural Factorization machines) to train the complementary Web API recommendation model with the labeled sample dataset based on representation vectors of Web APIs. To the best of our knowledge, this is the first work addressing the complementary function recommendation problem with a learning model. By conducting a set of experiments over a real-world dataset, the effectiveness of the proposed approach is validated. The experimental results demonstrate that the learning model outperforms the traditional machinelearning-based models and several deep learning-based models.
At present, most scheduling problems of resource-constrained projects are static scheduling, and there are few studies on dynamic scheduling of resource-constrained projects with random construction periods. Therefore...
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Choroiditis is a group of inflammatory eye diseases where inflammation occurs in the eye’s choroid, and Fundus images help in its diagnosis and tracking over time. Inflammatory eye diseases are one of the significant...
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Software architects and developers face challenges while trying to explain the complex functional relationships between actors and systems in the process of system design. The field of natural language processing (NLP...
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Tons of android apps are being developed every day. Therefore, there is a growing need to determine the security of an effective and accurate tool to detect hidden threats in some of these applications. The objective ...
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Recently, Monkeypox has spread to numerous nations, causing a life-threatening health concerns to both humans as well as animals. This sudden appearance of Monkeypox cases in different countries has shuttered the enti...
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In distributed environments, data for machinelearning (ML) applications may be generated from numerous sources and devices, and traverse a cloud-edge continuum via a variety of protocols, using multiple security sche...
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ISBN:
(纸本)9798350303223
In distributed environments, data for machinelearning (ML) applications may be generated from numerous sources and devices, and traverse a cloud-edge continuum via a variety of protocols, using multiple security schemes and equipment types. While ML models typically benefit from using large training sets, not all data can be equally trusted. In this work, we examine data trust as a factor in creating ML models, and explore an approach using annotated trust metadata to contribute to data weighting in generating ML models. We assess the feasibility of this approach using well-known datasets for both linear regression and classification problems, demonstrating the benefit of including trust as a factor when using heterogeneous datasets. We discuss the potential benefits of this approach, and the opportunity it presents for improved data utilisation and processing.
Human Activity Recognition with Smartphones (HAR) leverages the ubiquitous nature of smartphones equipped with sensor technology to identify and classify daily human activities from the generated data. This paper expl...
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