Chinese stock market prediction based on multifeature fusion and TextCNN

Lai, Shanyan; Jiang, Hongyu; Ye, Chunyang; Zhou, Hui

doi:10.1109/icss53362.2021.00017

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…TextCNN is a simple model with a fast training speed, which can achieve promising results in semantic analysis 36 and text classification 37 . Lai et al 38 used TextCNN as the feature extractor to predict stock trends and achieved high improvement in the prediction accuracy. Besides, Xu et al 39 found that the TextCNN model is effective for detecting the hardware Trojans.…”

Section: Introductionmentioning

confidence: 99%

A Token‐based Compilation Error Categorization and Its Applications

Wang

Liu

et al. 2022

J Software Evolu Process

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Compilation errors are unavoidable during the debugging process of novice students. Compiler error messages can help novices to localize and remove errors, but these messages are difficult to understand for students. Previous studies have investigated the compilation error categorization by analyzing compiler error messages, but the categorization cannot cover all kinds of errors, which limits the evaluation of compilation error studies. Therefore, a comprehensive categorization for compilation errors is needed for evaluating the performance of models or tools related to compilation errors. In this study, we first propose a new compilation error categorization, which is based on the smallest unit of the program, tokens. The experiments on 29,573 programs from three datasets show that our proposed compilation error categorization can cover more types of errors and the distribution of the error categorization are significantly different between the datasets. Then, based on our proposed categorization, we develop a neural network model CLACER (CLAssification of Compilation ERrors) for predicting the compilation errors. The results indicate that CLACER can improve the compiler's error localization accuracy and predicts the compilation error effectively. Moreover, based on the proposed categorization, we conduct empirical studies to evaluate the performance of three repairing tools (i.e., DeepFix, RLAssist, and MACER). The comparison results illustrate that DeepFix and RLAssist can fix more errors in the category of delimiter than errors in other categories. Furthermore, MACER performs better than DeepFix and RLAssist because it has a sufficient repairing pattern set for the errors. We also provide some suggestions for improving the repairing tools in the future.

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