Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software engineering including program analysis...
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ISBN:
(纸本)9781665412193
Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software engineering including program analysis, type inference, and code representation. In this paper, we introduce GraphGallery, a platform for fast benchmarking and easy development of GNNs based software. GraphGallery is an easy-to-use platform that allows developers to automatically deploy GNNs even with less domain-specific knowledge. It offers a set of implementations of common GNN models based on mainstream deep learning frameworks. In addition, existing GNNs toolboxes such as PyG and DGL can be easily incorporated into the platform. Experiments demonstrate the reliability of implementations and superiority in fast coding. The official source code of GraphGallery is available at https://***/EdisonLeeeee/GraphGallery and a demo video can be found at https://***/mv7Zs1YeaYo.
Dear editor,Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. GUI design plays an important role in offering a smooth user experience. The complexity of the apps ofte...
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Dear editor,Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. GUI design plays an important role in offering a smooth user experience. The complexity of the apps often depends on the user interface (UI)[1]with minor data processing or data processing delegates to the backend component.
Patients with Learning Disabilities (LD) have substantial and unmet healthcare needs, and previous studies have highlighted that they face both health inequalities and worse outcomes than the general population. Prima...
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ISBN:
(数字)9783030348854
ISBN:
(纸本)9783030348854;9783030348847
Patients with Learning Disabilities (LD) have substantial and unmet healthcare needs, and previous studies have highlighted that they face both health inequalities and worse outcomes than the general population. Primary care practitioners are often the first port-of-call for medical consultations, and one issue faced by LD patients in this context is the very limited time available during consultations - typically less than ten minutes. In order to alleviate this issue, we propose a digital communication aid in the form of an ontology-based medical questionnaire that can adapt to a patient's medical context as well as their accessibility needs (physical and cognitive). The application is intended to be used in advance of a consultation so that a primary care practitioner may have prior access to their LD patients' self-reported symptoms. This work builds upon and extends previous research carried out in the development of adaptive medical questionnaires to include interactive and interface functionalities designed specifically to cater for patients with potentially complex accessibility needs. A patient's current health status and accessibility profile (relating to their impairments) is used to dynamically adjust the structure and content of the medical questionnaire. As such, the system is able to significantly limit and focus questions to immediately relevant concerns while discarding irrelevant questions. We propose that our ontology-based design not only improves the relevance and accessibility of medical questionnaires for patients with LDs, but also provides important benefits in terms of medical knowledge-base modularity, as well as for software extension and maintenance.
An activity constantly engaged by most programmers in coding is to search for appropriate application programming interfaces(APIs). Contextual information is widely recognized to play a crucial role in effective API r...
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An activity constantly engaged by most programmers in coding is to search for appropriate application programming interfaces(APIs). Contextual information is widely recognized to play a crucial role in effective API recommendation, but it is largely overlooked in practice. In this paper, we propose contextaware API recommendation using tensor factorization(CARTF), a novel API recommendation approach in considering programmers' working context. To this end, we use tensors to explicitly represent the queryAPI-context triadic relation. When a new query is made, CARTF harnesses word embeddings to retrieve similar user queries, based on which a third-order tensor is constructed. CARTF then applies non-negative tensor factorization to complete missing values in the tensor and the Smith-Waterman algorithm to identify the most matched context. Finally, the ranking of the candidate APIs can be derived based on which API sequences are recommended. Our evaluation confirms the effectiveness of CARTF for class-level and method-level API recommendations, outperforming state-of-the-art baseline approaches against a number of performance metrics, including SuccessRate, Precision, and Recall.
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