Search Results - "He, Tieke"

A systemic framework for crowdsourced test report quality assessment by Chen, Xin, Jiang, He, Li, Xiaochen, Nie, Liming, Yu, Dongjin, He, Tieke, Chen, Zhenyu

“…In crowdsourced mobile application testing, crowd workers perform test tasks for developers and submit test reports to report the observed abnormal behaviors…”
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Negative-Free Self-Supervised Gaussian Embedding of Graphs by Liu, Yunhui, He, Tieke, Zheng, Tao, Zhao, Jianhua

“…Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations…”
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APPT: Boosting Automated Patch Correctness Prediction via Fine-Tuning Pre-Trained Models by Zhang, Quanjun, Fang, Chunrong, Sun, Weisong, Liu, Yan, He, Tieke, Hao, Xiaodong, Chen, Zhenyu

“…Automated program repair (APR) aims to fix software bugs automatically without human debugging efforts and plays a crucial role in software development and…”
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Reliable Node Similarity Matrix Guided Contrastive Graph Clustering by Liu, Yunhui, Gao, Xinyi, He, Tieke, Zheng, Tao, Zhao, Jianhua, Yin, Hongzhi

“…Graph clustering, which involves the partitioning of nodes within a graph into disjoint clusters, holds significant importance for numerous subsequent…”
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Automatic test report augmentation to assist crowdsourced testing by CHEN, Xin, JIANG, He, CHEN, Zhenyu, HE, Tieke, NIE, Liming

“…In crowdsourced mobile application testing, workers are often inexperienced in and unfamiliar with software testing. Meanwhile, workers edit test reports in…”
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Automated Evaluation for Performance Test Scripts by Ruijing Gao, Zhenyu Chen, Chunrong Fang, Tieke He, Peizhang Xie, Jungui Zhou

“…With the development of online education, more and more people learn knowledge about software engineering through online courses. Scoring homework has become a…”
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An empirical study on user-topic rating based collaborative filtering methods by He, Tieke, Chen, Zhenyu, Liu, Jia, Zhou, Xiaofang, Du, Xingzhong, Wang, Weiqing

“…User based collaborative filtering (CF) has been successfully applied into recommender system for years. The main idea of user based CF is to discover…”
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Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference by Liu, Yunhui, Gao, Xinyi, He, Tieke, Zhao, Jianhua, Yin, Hongzhi

“…Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in…”
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Negative-Free Self-Supervised Gaussian Embedding of Graphs by Liu, Yunhui, He, Tieke, Zheng, Tao, Zhao, Jianhua

“…Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations…”
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Summarizing the Crowdsourced Testing by Liang, Hong, He, Tieke

“…Testing a new Android application is hard, since the android platform has a lot of portability and compatibility issues. These issues are caused by its…”
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Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering by Liu, Yunhui, He, Tieke, Wu, Qing, Zheng, Tao, Zhao, Jianhua

“…Attributed graph clustering, which aims to group the nodes of an attributed graph into disjoint clusters, has made promising advancements in recent years…”
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Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning by Liu, Yunhui, Zhang, Huaisong, He, Tieke, Zheng, Tao, Zhao, Jianhua

“…Contrastive learning is a significant paradigm in graph self-supervised learning. However, it requires negative samples to prevent model collapse and learn…”
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ROIC-DM: Robust Text Inference and Classification via Diffusion Model by Yuan, Shilong, Yuan, Wei, Yin, Hongzhi, He, Tieke

“…While language models have made many milestones in text inference and classification tasks, they remain susceptible to adversarial attacks that can lead to…”
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Reliable Node Similarity Matrix Guided Contrastive Graph Clustering by Liu, Yunhui, Gao, Xinyi, He, Tieke, Zheng, Tao, Zhao, Jianhua, Yin, Hongzhi

“…Graph clustering, which involves the partitioning of nodes within a graph into disjoint clusters, holds significant importance for numerous subsequent…”
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Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures by Yuan, Wei, Nguyen, Quoc Viet Hung, He, Tieke, Chen, Liang, Yin, Hongzhi

“…Federated Recommender Systems (FedRecs) are considered privacy-preserving techniques to collaboratively learn a recommendation model without sharing user data…”
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APPT: Boosting Automated Patch Correctness Prediction via Fine-tuning Pre-trained Models by Zhang, Quanjun, Fang, Chunrong, Sun, Weisong, Liu, Yan, He, Tieke, Hao, Xiaodong, Chen, Zhenyu

“…Automated program repair (APR) aims to fix software bugs automatically without human debugging efforts and plays a crucial role in software development and…”
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Interaction-level Membership Inference Attack Against Federated Recommender Systems by Yuan, Wei, Yang, Chaoqun, Nguyen, Quoc Viet Hung, Cui, Lizhen, He, Tieke, Yin, Hongzhi

“…The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized…”
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Unified Question Generation with Continual Lifelong Learning by Yuan, Wei, Yin, Hongzhi, He, Tieke, Chen, Tong, Wang, Qiufeng, Cui, Lizhen

“…Question Generation (QG), as a challenging Natural Language Processing task, aims at generating questions based on given answers and context. Existing QG…”
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A Deep Learning Method for Judicial Decision Support by Chen, Baogui, Li, Yu, Zhang, Shu, Lian, Hao, He, Tieke

“…With the development and innovation of machine learning and deep learning technology, more and more fields try to apply artificial intelligence to practical…”
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Federated Unlearning for On-Device Recommendation by Yuan, Wei, Yin, Hongzhi, Wu, Fangzhao, Zhang, Shijie, He, Tieke, Wang, Hao

“…The increasing data privacy concerns in recommendation systems have made federated recommendations (FedRecs) attract more and more attention. Existing FedRecs…”
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