思想来自于视觉机制,是对信息进行抽象的过程。

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图池化是众多图神经网络(GNN)架构的核心组件。由于继承了传统的CNNs,大多数方法将图池化为一个聚类分配问题,将规则网格中的局部patch的思想扩展到图中。尽管广泛遵循了这种设计选择,但没有任何工作严格评估过它对GNNs成功的影响。我们以代表性的GNN为基础,并引入了一些变体,这些变体挑战了在补充图上使用随机化或聚类的局部保持表示的需要。引人注目的是,我们的实验表明,使用这些变体不会导致任何性能下降。为了理解这一现象,我们研究了卷积层和随后的池层之间的相互作用。我们证明了卷积在学习的表示法中起着主导作用。与通常的看法相反,局部池化不是GNNs在相关和广泛使用的基准测试中成功的原因。

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A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this paper, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.

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