Short Text Topic Learning Using Heterogeneous Information Network

Wang, Qingren; Zhu, Chengcheng; Zhang, Yiwen; Zhong, Hong; Zhong, Jinqin; Sheng, Victor S.

doi:10.1109/tkde.2022.3147766

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…Support vector machine (SVM) 53,54 is a supervised learning model with relevant learning algorithms which is used to analyze data for the classification task. Random forest is an ensemble of decision trees, with each decision tree trained on a subset of the training data 55 .…”

Section: Resultsmentioning

confidence: 99%

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Huang

2023

Concurrency and Computation

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SummaryWith the development of smartphones, mobile applications play an irreplaceable role in our daily life, which characteristics often commit code changes to meet new requirements. This characteristic can introduce defects into the software. To provide immediate feedback to developers, previous researchers began to focus on just‐in‐time (JIT) software defect prediction techniques. JIT defect prediction aims to determine whether code commits will introduce defects into the software. It contains two scenarios, within‐project JIT defect prediction and cross‐project JIT defect prediction. Regardless of whether within‐project JIT defect prediction or cross‐project JIT defect prediction all need to have enough labeled data (within‐project JIT defect prediction assumes that have plenty of labeled data from the same project, while cross‐project JIT defect prediction assumes that have sufficient labeled data from source projects). However, in practice, both the source and target projects may only have limited labeled data. We propose the MTL‐DNN method based on multi‐task learning to solve this question. This method contains the data preprocessing layer, input layer, shared layers, task‐specific layers, and output layer. Where the common features of multiple related tasks are learned by sharing layers, and the unique features of each task are learned by the task‐specific layers. For verifying the effectiveness of the MTL‐DNN approach, we evaluate our method on 15 Android mobile apps. The experimental results show that our method significantly outperforms the state‐of‐the‐art single‐task deep learning and classical machine learning methods. This result shows that the MTL‐DNN method can effectively solve the problem of insufficient labeled training data for source and target projects.

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Section: Resultsmentioning

confidence: 99%

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Huang

2023

Concurrency and Computation

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“…It requires a proper definition of individual performance measures and prediction targets [6]. Third, the variable space to describe the characteristics of context, students, team and teaching activities is high-dimensional [7][8][9]. Moreover, there are a lot of complex relationships among them.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Analysis of the Effect of Team Competition on College Students’ Academic Performance

Zhang,

Wu,

et al. 2023

Preprint

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In the field of Mobile Edge Computing (MEC), machine learning techniques present a promising avenue for intelligent integration and processing of data from MEC terminals. Our study delves into the intersection of Machine Learning with MEC terminal data, exploring the complexity of team competition mechanisms based on social identity and competition theory. This exploration aims to enhance student participation and enthusiasm within university classrooms. However, despite of its potential benefit, there are still many unresolved issues: What type of students and teams benefit more from team competition? In what teaching context is team competition more effective? Which competition design methods better increase student academic performance? To answer these questions, we first de-sign a randomized field experiment among freshmen enrolled in college English course. Then, we collected data using mobile devices and analyzed the observational data to predict the individual treatment effect of academic performance in college English through linear and nonlinear machine learning models. Finally, by carefully investigating features of teams and individual student, we reduce the prediction error by up to 30%. In addition, through interpreting the predictive models, we discover some valuable insights regarding the practice of team competition in college classrooms.

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Enhancing Heterogeneous Graph-based Short Text Topic Learning

Wang

Cui

2022

2022 IEEE Smartworld, Ubiquitous Intelligence &Amp; Computing, Scalable Computing &Amp; Communications, Digital Twin, Privacy C

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Short Text Topic Learning Using Heterogeneous Information Network

Cited by 8 publications

References 42 publications

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps

Machine Learning based Analysis of the Effect of Team Competition on College Students’ Academic Performance

Enhancing Heterogeneous Graph-based Short Text Topic Learning

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