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Haoran Xie
Lingnan University

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Natural Language Processing
Structural topic modeling
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Short Biography

Prof. Haoran Xie is an Associate Professor at the Department of Computing and Decision Sciences, Lingnan University, Hong Kong. He received his Ph.D. in Computer Science from the City University of Hong Kong. His research interest includes artificial intelligence, big data, and educational technology. He has published 243 research publications, including 115 journal articles. Among all 115 journal articles, there are 90 SCI/SSCI indexed and 13 SCOPUS indexed. He has obtained 14 research awards, including the Golden Medal and the special award from International Invention Innovation Competition in Canada and so on, and five best paper awards from WI 2020, ICBL 2020, DASFAA 2017, ICBL 2016 and SECOP 2015. Prof. Xie is the Editor-in-Chief of Computers & Education: Artificial Intelligence, Associate Editors of Array Journal, Australasian Journal of Educational Technology, Advances in Computational Intelligence, and International Journal of Mobile Learning and Organisation. He has successfully obtained more than 40 research grants; the total amount of these grants is more than HK$27 million. He is the Senior Member of IEEE and ACM, and the Life Member of AAAI.

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Conference paper
Published: 03 August 2021 in Lecture Notes in Computer Science
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This research investigated the perceptions of a group of students in a university in Hong Kong concerning peer assessment enhanced collaborative learning in a virtual learning environment. A total of 31 Chinese learners of English participated in the project and conducted online collaborative learning and peer assessment in Moodle, ZOOM, and Flipgrid. They were interviewed afterwards and reported their perceptions of the learning process, the challenges that they encountered, and the strategies that they applied to overcome the challenges. The results showed that the students’ perceptions of this approach to teaching and learning were overall positive. Concerning the main challenges, the students indicated that when they received massive amounts of feedback, they felt overwhelmed and did not know where to start for further improvement. They also found that peer suggestions were inconsistent sometimes, and when they received similar critical feedback repeatedly, they felt frustrated. To overcome these challenges, the students reported that they managed to prioritize the areas that they need to improve and started with the most important ones. They also asked follow-up questions concerning the controversial suggestions to figure out why their peers’ suggestions were different and discussed within groups to decide which suggestions to follow. Moreover, they attempted to focus more on providing constructive feedback, rather than critical feedback. These results suggested that peer assessment enhanced collaborative online learning was an effective approach to active learning.

ACS Style

Di Zou; Haoran Xie; Fu Lee Wang. Peer-Assessment Enhanced Collaborative Learning in a Virtual Learning Environment. Lecture Notes in Computer Science 2021, 132 -141.

AMA Style

Di Zou, Haoran Xie, Fu Lee Wang. Peer-Assessment Enhanced Collaborative Learning in a Virtual Learning Environment. Lecture Notes in Computer Science. 2021; ():132-141.

Chicago/Turabian Style

Di Zou; Haoran Xie; Fu Lee Wang. 2021. "Peer-Assessment Enhanced Collaborative Learning in a Virtual Learning Environment." Lecture Notes in Computer Science , no. : 132-141.

Conference paper
Published: 03 August 2021 in Lecture Notes in Computer Science
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Under the influence of COVID-19, online learning has become the primary way for students to continue their education. At all stages of online learning, active learning is a useful strategy promoting optimal understanding. However, there is a lack of relevant research on how to evaluate students’ active learning performance. This paper presents an online active learning assessment framework based on the learning pyramid and learning dimension theory. After the division of course modules according to the learning pyramid theory, the active learning assessment is performed from five dimensions: (1) positive attitudes and perceptions about learning; (2) acquiring and integrating knowledge; (3) extending and refining knowledge; (4) using knowledge meaningfully, and (5) productive habits of mind. By identifying patterns from each online course module’s weblog data, instructors can assess students’ active learning conveniently from the beginning to the end of the online course. This study helps instructors understand learners’ learning situations and adopt corresponding strategies to adjust teaching activities to ensure high-quality teaching activities. Simultaneously, learners can also actively change their learning status according to active learning assessment to improve the learning effect.

ACS Style

Caixia Liu; Di Zou; Wai Hong Chan; Haoran Xie; Fu Lee Wang. An Assessment Framework for Online Active Learning Performance. Lecture Notes in Computer Science 2021, 338 -350.

AMA Style

Caixia Liu, Di Zou, Wai Hong Chan, Haoran Xie, Fu Lee Wang. An Assessment Framework for Online Active Learning Performance. Lecture Notes in Computer Science. 2021; ():338-350.

Chicago/Turabian Style

Caixia Liu; Di Zou; Wai Hong Chan; Haoran Xie; Fu Lee Wang. 2021. "An Assessment Framework for Online Active Learning Performance." Lecture Notes in Computer Science , no. : 338-350.

Journal article
Published: 31 July 2021 in Information Fusion
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Emotion recognition is a crucial application in the human–computer interaction. It is usually performed by using facial expressions as the main modality, which may not be reliable. In this study, we proposed a multimodal approach that uses 2-channel electroencephalography (EEG) signals and the eye modality in addition to the face modality to enhance the recognition performance. We also studied the use of facial images versus facial depth as the face modality and adapt the common arousal-valence model of emotions and the convolutional neural network, which can model the spatiotemporal information from the modality data for emotion recognition. Extensive experiments have been conducted on the modality and emotion data, the results of which showed that our system has high accuracies 67.8% and 77.0% in the valence recognition and arousal recognition, respectively. The proposed method outperformed most of the state-of-the-art systems that use similar but fewer modalities. Moreover, the use of facial depth outperformed the use of facial images. The proposed method of emotion recognition has great potential to be integrated into various educational applications.

ACS Style

Wang Kay Ngai; Haoran Xie; Di Zou; Kee-Lee Chou. Emotion recognition based on convolutional neural networks and heterogeneous bio-signal data sources. Information Fusion 2021, 77, 107 -117.

AMA Style

Wang Kay Ngai, Haoran Xie, Di Zou, Kee-Lee Chou. Emotion recognition based on convolutional neural networks and heterogeneous bio-signal data sources. Information Fusion. 2021; 77 ():107-117.

Chicago/Turabian Style

Wang Kay Ngai; Haoran Xie; Di Zou; Kee-Lee Chou. 2021. "Emotion recognition based on convolutional neural networks and heterogeneous bio-signal data sources." Information Fusion 77, no. : 107-117.

Review
Published: 17 June 2021 in Neurocomputing
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Knowledge graph as a research topic is increasingly popular to represent structural relations between entities. Recent years have witnessed the release of various open-source and enterprise-supported knowledge graphs with dramatic growth in applying knowledge representation and reasoning into different areas like natural language processing and computer vision. This study aims to comprehensively explore the status and trends – particularly the thematic research structure – of knowledge graphs. Specifically, based on 386 research articles published from 1991 to 2020, we conducted analyses in terms of the (1) visualization of the trends of annual article and citation counts, (2) recognition of major institutions, countries/regions, and publication sources, (3) visualization of scientific collaborations of major institutions and countries/regions, and (4) detection of major research themes and their developmental tendencies. Interest in knowledge graph research has clearly increased from 1991 to 2020 and is continually expanding. China is the most prolific country in knowledge graph research. Moreover, countries/regions and institutions that have higher levels of international collaboration are more impactful. Several widely studied issues such as knowledge graph embedding, search and query based on knowledge graphs, and knowledge graphs for intangible cultural heritage are highlighted. Based on the results, we further summarize perspective directions and suggestions for researchers, practitioners, and project managers to facilitate future research on knowledge graphs.

ACS Style

Xieling Chen; Haoran Xie; Zongxi Li; Gary Cheng. Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades. Neurocomputing 2021, 1 .

AMA Style

Xieling Chen, Haoran Xie, Zongxi Li, Gary Cheng. Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades. Neurocomputing. 2021; ():1.

Chicago/Turabian Style

Xieling Chen; Haoran Xie; Zongxi Li; Gary Cheng. 2021. "Topic Analysis and Development in Knowledge Graph Research: A Bibliometric Review on Three Decades." Neurocomputing , no. : 1.

Research article
Published: 14 June 2021 in Computer Assisted Language Learning
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Spaced repetition has been widely implemented and examined in mobile-assisted word learning as an important learning strategy. However, the nature of spaced repetition by commercial word-learning apps and the factors leading to the favoured mobile-assisted spaced repetition have yet to be investigated in authentic contexts. In this study, we coded the spaced repetition patterns and methods of nine apps and interviewed 72 Chinese English learners about their perceptions of spaced repetition for word learning. The results showed three major repetition patterns at three knowledge levels (i.e. the word is unknown, familiar-but-unsure, and known to the learner). The three most common repetition methods were using text-plus-audio for multimedia learning, conducting retrieval practice through flashcards and multiple-choice questions, and integrating game elements such as Goals/Rules and Rewards/Points into learning. Concerning learner preferences, they preferred to have (a) six or seven learning sessions for ‘unknown’ words, three or four sessions for ‘familiar-but-unsure’ words, and two or three sessions for ‘known’ words over ten- to fourteen- day periods, (b) gradually longer intervals between learning sessions, (c) text-plus-audio-plus-image as multimedia, (d) two or three innovative formats of retrieval practice, and (e) integration of Goals/Rules, Rewards/Points, and Time Limits. The results indicate that teachers, researchers, and app designers ought to consider both learning effectiveness and learner perceptions when applying, designing, and developing spaced repetition patterns and methods for mobile-assisted word learning.

ACS Style

Ruofei Zhang; Di Zou; Haoran Xie. Spaced repetition for authentic mobile-assisted word learning: nature, learner perceptions, and factors leading to positive perceptions. Computer Assisted Language Learning 2021, 1 -34.

AMA Style

Ruofei Zhang, Di Zou, Haoran Xie. Spaced repetition for authentic mobile-assisted word learning: nature, learner perceptions, and factors leading to positive perceptions. Computer Assisted Language Learning. 2021; ():1-34.

Chicago/Turabian Style

Ruofei Zhang; Di Zou; Haoran Xie. 2021. "Spaced repetition for authentic mobile-assisted word learning: nature, learner perceptions, and factors leading to positive perceptions." Computer Assisted Language Learning , no. : 1-34.

Journal article
Published: 27 May 2021 in Knowledge-Based Systems
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Emotion distribution learning aims to annotate unlabeled instances with a set of emotion categories and their strengths. Non-negative Matrix Tri-Factorization (NMTF) introduces an association matrix between document clusters and word clusters to help the domain adaptation task in emotion distribution learning. Nevertheless, many prior cross-domain emotion distribution learning methods had two major deficiencies. First, they hypothesize that there is a one-to-one correspondence between document clusters and emotion labels. In their experiments, the number of document clusters depends on the number of labels. Second, the prior work does not endow models with adequate constraints. In the real scenario of cross-domain emotion distribution learning, there are potential constraints that may improve the performance of such models. In order to address these problems, we propose a constrained optimization approach based on NMTF for cross-domain emotion distribution learning. In our model, the relationship between document clusters and emotion labels is not always one-to-one. A novel content-based constraint is also endowed based on the hypothesis that documents belonging to the same clusters must have similar content. We solve the optimization problem by an alternately iterative algorithm and show the proof of convergence. Experiments on 12 real-world cross-domain emotion distribution learning tasks validate the effectiveness of our method.

ACS Style

Xiaorui Qin; Yufu Chen; Yanghui Rao; Haoran Xie; Man Leung Wong; Fu Lee Wang. A constrained optimization approach for cross-domain emotion distribution learning. Knowledge-Based Systems 2021, 227, 107160 .

AMA Style

Xiaorui Qin, Yufu Chen, Yanghui Rao, Haoran Xie, Man Leung Wong, Fu Lee Wang. A constrained optimization approach for cross-domain emotion distribution learning. Knowledge-Based Systems. 2021; 227 ():107160.

Chicago/Turabian Style

Xiaorui Qin; Yufu Chen; Yanghui Rao; Haoran Xie; Man Leung Wong; Fu Lee Wang. 2021. "A constrained optimization approach for cross-domain emotion distribution learning." Knowledge-Based Systems 227, no. : 107160.

Journal article
Published: 21 May 2021 in Sustainability
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This research discusses the potential of using big data for vocabulary learning from the perspective of learner-generated pictorial annotations. Pictorial annotations lead to effective vocabulary learning, the creation of which is however challenging and time-consuming. As user-generated annotations promote active learning, and in the big data era, data sources in social media platforms are not only huge but also user-generated, the proposal of using social media data to establish a natural and semantic connection between pictorial annotations and words seems feasible. This research investigated learners’ perceptions of creating pictorial annotations using Google images and social media images, learners’ evaluation of the learner-generated pictorial annotations, and the effectiveness of Google pictorial annotations and social media pictorial annotations in promoting vocabulary learning. A total of 153 undergraduates participated in the research, some of whom created pictorial annotations using Google and social media data, some evaluated the annotations, and some learned the target words with the annotations. The results indicated positive attitudes towards using Google and social media data sets as resources for language enhancement, as well as significant effectiveness of learner-generated Google pictorial annotations and social media pictorial annotations in promoting both initial learning and retention of target words. Specifically, we found that (i) Google images were more appropriate and reliable for pictorial annotations creation, and therefore they achieved better outcomes when learning with the annotations created with Google images than images from social media, and (ii) the participants who created word lists that integrate pictorial annotations were likely to engage in active learning when they selected and organized the verbal and visual information of target words by themselves and actively integrated such information with their prior knowledge.

ACS Style

Di Zou; Haoran Xie. Vocabulary Learning Based on Learner-Generated Pictorial Annotations: Using Big Data as Learning Resources. Sustainability 2021, 13, 5767 .

AMA Style

Di Zou, Haoran Xie. Vocabulary Learning Based on Learner-Generated Pictorial Annotations: Using Big Data as Learning Resources. Sustainability. 2021; 13 (11):5767.

Chicago/Turabian Style

Di Zou; Haoran Xie. 2021. "Vocabulary Learning Based on Learner-Generated Pictorial Annotations: Using Big Data as Learning Resources." Sustainability 13, no. 11: 5767.

Journal article
Published: 10 May 2021 in Australasian Journal of Educational Technology
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Information literacy (IL) is important for university students. In this research, we developed a digital role-playing game to enhance students’ learning of IL and investigated the effects of gameplay modes on their learning performance, motivation, self-efficacy and flow experiences. A total of 90 students participated in the study and played the game in collaborative, competitive and solo modes. Their IL knowledge was measured through a post-test after they completed the game and associated exercises. Their motivation, self-efficacy and flow experiences were evaluated through a questionnaire survey. The results indicated statistically significant effects of the gameplay modes on the students’ learning performance, motivation, self-efficacy and flow experiences. The solo mode was inferior to the other two in all four aspects. The collaborative mode significantly outperformed the competitive mode in terms of enhancing learning performance and flow experience, while the competitive mode was significantly better in terms of promoting self-efficacy. These two modes were similarly effective in the dimension of motivation. Based on the results, we suggest that students play games in the collaborative or competitive modes when conditions permit. We also advise teachers to provide students with rich opportunities for discussion, collaboration and interaction and believe that an appropriate competitive atmosphere is important.

ACS Style

Di Zou; Ruofei Zhang; Haoran Xie; Fu Lee Wang. Digital game-based learning of information literacy: Effects of gameplay modes on university students’ learning performance, motivation, self-efficacy and flow experiences. Australasian Journal of Educational Technology 2021, 37, 152 -170.

AMA Style

Di Zou, Ruofei Zhang, Haoran Xie, Fu Lee Wang. Digital game-based learning of information literacy: Effects of gameplay modes on university students’ learning performance, motivation, self-efficacy and flow experiences. Australasian Journal of Educational Technology. 2021; 37 (2):152-170.

Chicago/Turabian Style

Di Zou; Ruofei Zhang; Haoran Xie; Fu Lee Wang. 2021. "Digital game-based learning of information literacy: Effects of gameplay modes on university students’ learning performance, motivation, self-efficacy and flow experiences." Australasian Journal of Educational Technology 37, no. 2: 152-170.

Article
Published: 05 May 2021 in Cognitive Computation
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Research on sentic computing has received intensive attention in recent years, as indicated by the increased availability of academic literature. However, despite the growth in literature and researchers’ interests, there are no reviews on this topic. This study comprehensively explores the current research progress and tendencies, particularly the thematic structure of sentic computing, to provide insights into the issues addressed during the past decade and the potential future of sentic computing. We combined bibliometric analysis and structural topic modeling to examine sentic computing literature in various aspects, including the tendency of annual article count, top journals, countries/regions, institutions, and authors, the scientific collaborations between major contributors, as well as the major topics and their tendencies. We obtained interesting and meaningful findings. For example, sentic computing has attracted growing interest in academia. In addition, Cognitive Computation and Nanyang Technological University were found to be the most productive journal and institution in publishing sentic computing studies, respectively. Moreover, important issues such as cyber issues and public opinion, deep neural networks and personality, financial applications and user profiles, and affective and emotional computing have been commonly addressed by authors focusing on sentic computing. Our study provides a thorough overview of sentic computing, reveals major concerns among scholars during the past decade, and offers insights into the future directions of sentic computing research.

ACS Style

Xieling Chen; Haoran Xie; Gary Cheng; Zongxi Li. A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis. Cognitive Computation 2021, 1 -24.

AMA Style

Xieling Chen, Haoran Xie, Gary Cheng, Zongxi Li. A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis. Cognitive Computation. 2021; ():1-24.

Chicago/Turabian Style

Xieling Chen; Haoran Xie; Gary Cheng; Zongxi Li. 2021. "A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis." Cognitive Computation , no. : 1-24.

Review
Published: 26 April 2021 in Sustainability
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Computers in Human Behavior (CHB) is a well-established source with a wide range of audiences in the field of human interactions with computers and has been one of the most widely acknowledged and leading venues with significant scientific impact for more than 35 years. This review provides an overview of the status, trends, and particularly the thematic structure of the CHB by adopting bibliometrics and structural topic modeling on 5957 studies. Specifically, we analyzed the trend of publications, identified major institutions and countries/regions, detected scientific collaboration patterns, and uncovered important topics. Significant findings were presented. For example, the contribution of the USA and Open University of Netherlands was highlighted. Important research topics such as e-commerce, social interactions and behaviors, public opinion and social media, cyberbullying, online sexual issues, and game and gamification were identified. This review contributes to the CHB community by justifying the interest in human behavior issues concerning computer use and identifying future research lines on this topic.

ACS Style

Xieling Chen; Di Zou; Haoran Xie; Gary Cheng. A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics. Sustainability 2021, 13, 4859 .

AMA Style

Xieling Chen, Di Zou, Haoran Xie, Gary Cheng. A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics. Sustainability. 2021; 13 (9):4859.

Chicago/Turabian Style

Xieling Chen; Di Zou; Haoran Xie; Gary Cheng. 2021. "A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics." Sustainability 13, no. 9: 4859.

Review
Published: 21 April 2021 in Sustainability
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This paper provided a systematic review of previous Augmented Reality (AR) and Virtual Reality (VR) studies on language learning. A total of 88 articles were selected and analyzed from five perspectives: their ways of integrating AR or VR tools in language learning; main users of AR and VR technologies; major research findings; why AR and VR tools are effective in promoting language learning; and the implications. It was found that (1) immersing learners into virtual worlds is the main approach to language learning in AR and VR studies; (2) university students were the main users of AR/VR technologies; (3) the major research findings concerning the benefits of AR and VR included improvement of students’ learning outcomes, enhancement of motivation, and positive perceptions towards using AR and VR; (4) AR and VR tools promoted language learning through providing immersive learning experience, enhancing motivation, creating interaction, and reducing learning anxiety; and (5) implications identified from previous research include the need of providing training for teachers, enlarging sample sizes, and exploring learner factors such as learner engagement and satisfaction.

ACS Style

Xinyi Huang; Di Zou; Gary Cheng; Haoran Xie. A Systematic Review of AR and VR Enhanced Language Learning. Sustainability 2021, 13, 4639 .

AMA Style

Xinyi Huang, Di Zou, Gary Cheng, Haoran Xie. A Systematic Review of AR and VR Enhanced Language Learning. Sustainability. 2021; 13 (9):4639.

Chicago/Turabian Style

Xinyi Huang; Di Zou; Gary Cheng; Haoran Xie. 2021. "A Systematic Review of AR and VR Enhanced Language Learning." Sustainability 13, no. 9: 4639.

Review
Published: 17 April 2021 in Asia Pacific Education Review
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Massive Open Online Courses (MOOCs) have become a popular learning mode in recent years, especially since the outbreak of COVID-19 in late 2019, which had resulted in a significant increase in associated research. This paper presents a bibliometric review of 1078 peer-reviewed MOOC studies between 2008 and 2019. These papers are extracted from three influential databases, the Web of Science (WOS), Scopus, and the Education Resources Information Center (ERIC). The MOOC literature analysis with a bibliometric approach identified the research trends, journals, countries/regions, and institutions with high H-index, scientific collaborations, research topics, topic distributions of the prolific countries/regions and institutions, and annual topic distributions, after which the representative research and research implications were discussed. This review gives researchers a deep and comprehensive understanding of current MOOC research and identifies potential research topics and collaborative partners, which supports MOOC-related future research.

ACS Style

Caixia Liu; Di Zou; Xieling Chen; Haoran Xie; Wai Hong Chan. A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019). Asia Pacific Education Review 2021, 1 -20.

AMA Style

Caixia Liu, Di Zou, Xieling Chen, Haoran Xie, Wai Hong Chan. A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019). Asia Pacific Education Review. 2021; ():1-20.

Chicago/Turabian Style

Caixia Liu; Di Zou; Xieling Chen; Haoran Xie; Wai Hong Chan. 2021. "A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019)." Asia Pacific Education Review , no. : 1-20.

Article
Published: 22 February 2021 in Cognitive Computation
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Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters can represent a combination of meaning, structure, and pronunciation. However, existing embedding learning methods focus on the structure and meaning of Chinese characters. In this study, we aim to develop an embedding learning method that can make complete use of the information represented by Chinese characters, including phonology, morphology, and semantics. Specifically, we propose a pronunciation-enhanced Chinese word embedding learning method, where the pronunciations of context characters and target characters are simultaneously encoded into the embeddings. Evaluation of word similarity, word analogy reasoning, text classification, and sentiment analysis validate the effectiveness of our proposed method.

ACS Style

Qinjuan Yang; Haoran Xie; Gary Cheng; Fu Lee Wang; Yanghui Rao. Pronunciation-Enhanced Chinese Word Embedding. Cognitive Computation 2021, 13, 688 -697.

AMA Style

Qinjuan Yang, Haoran Xie, Gary Cheng, Fu Lee Wang, Yanghui Rao. Pronunciation-Enhanced Chinese Word Embedding. Cognitive Computation. 2021; 13 (3):688-697.

Chicago/Turabian Style

Qinjuan Yang; Haoran Xie; Gary Cheng; Fu Lee Wang; Yanghui Rao. 2021. "Pronunciation-Enhanced Chinese Word Embedding." Cognitive Computation 13, no. 3: 688-697.

Journal article
Published: 22 February 2021 in IEEE Transactions on Instrumentation and Measurement
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Mesh denoising is a fundamental component of many disparate reverse engineering applications of measurement surfaces. This article presents a cascaded normal filtering neural network (termed a CNF-Net) for geometry-aware mesh denoising of measurement surfaces. CNF-Net leverages the geometry domain knowledge (GDK) that, a mesh approximates to its underlying surface compactly if all mesh facets at most lie on the surface intersections while not crossing them. Benefiting from the well-estimated underlying geometry of noisy mesh facets, a multiscale guidedly filtered normal descriptor (M-GFND) is formulated, and multiple height maps are constructed from the M-GFND. The height maps can be effectively fed into CNF-Net for learning the transformation matrices between the M-GFND and the ground-truth facet normal. CNF-Net can automatically handle meshes with multiscale geometric features yet corrupted by the noise of different distributions, while existing learning-based wisdoms commonly pursue an overall normal estimation accuracy yet fail to preserve surface significant features. Both visual and numerical evaluations on synthetic and real noise data sets consistently show the clear improvements of CNF-Net over the state-of-the-arts.

ACS Style

Dingkun Zhu; Yingkui Zhang; Zhiqi Li; Weiming Wang; Haoran Xie; Mingqiang Wei; Gary Cheng; Fu Lee Wang. Cascaded Normal Filtering Neural Network for Geometry-Aware Mesh Denoising of Measurement Surfaces. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -13.

AMA Style

Dingkun Zhu, Yingkui Zhang, Zhiqi Li, Weiming Wang, Haoran Xie, Mingqiang Wei, Gary Cheng, Fu Lee Wang. Cascaded Normal Filtering Neural Network for Geometry-Aware Mesh Denoising of Measurement Surfaces. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-13.

Chicago/Turabian Style

Dingkun Zhu; Yingkui Zhang; Zhiqi Li; Weiming Wang; Haoran Xie; Mingqiang Wei; Gary Cheng; Fu Lee Wang. 2021. "Cascaded Normal Filtering Neural Network for Geometry-Aware Mesh Denoising of Measurement Surfaces." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-13.

Journal article
Published: 23 January 2021 in Computers and Education: Artificial Intelligence
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Top-N personalized recommendation has been extensively studied in assisting learners in finding interesting courses in MOOCs. Although existing Top-N personalized recommendation methods have achieved comparable performance, these models have two major shortcomings. First, these models seldom learn an explicit representation of the structural relation of items. Second, most of these models typically obtain a user’s general preference and neglect the recency of items. This paper proposes a Top-N personalized Recommendation with Graph Neural Network (TP-GNN) in the Massive Open Online Course (MOOCs) as a solution to tackle this problem. We explore two different aggregate functions to deal with the user’s sequence neighbors and then use an attention mechanism to generate the final item representations. The experiments on a real-world course dataset demonstrated that TP-GNN could improve the performances. Furthermore, the system developed based on our method obtains positive feedback from the participants, which denotes that our method effectively predicts learners’ preferences and needs.

ACS Style

Jingjing Wang; Haoran Xie; Fu Lee Wang; Lap-Kei Lee; Oliver Tat Sheung Au. Top-N personalized recommendation with graph neural networks in MOOCs. Computers and Education: Artificial Intelligence 2021, 2, 100010 .

AMA Style

Jingjing Wang, Haoran Xie, Fu Lee Wang, Lap-Kei Lee, Oliver Tat Sheung Au. Top-N personalized recommendation with graph neural networks in MOOCs. Computers and Education: Artificial Intelligence. 2021; 2 ():100010.

Chicago/Turabian Style

Jingjing Wang; Haoran Xie; Fu Lee Wang; Lap-Kei Lee; Oliver Tat Sheung Au. 2021. "Top-N personalized recommendation with graph neural networks in MOOCs." Computers and Education: Artificial Intelligence 2, no. : 100010.

Journal article
Published: 22 January 2021 in Pattern Recognition
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Targeting at boosting business revenue, purchase prediction based on user behavior is crucial to e-commerce. However, it is not a well-explored topic due to a lack of relevant datasets. Specifically, no public dataset provides both price and discount information varying on time, which play an essential role in the user’s decision making. Besides, existing learn-to-rank methods cannot explicitly predict the purchase possibility for a specific user-item pair. In this paper, we propose a two-step graph-based model, where the graph model is applied in the first step to learn representations of both users and items over click-through data, and the second step is a classifier incorporating the price information of each transaction record. To evaluate the model performance, we propose a transaction-based framework focusing on the purchased items and their context clicks, which contain items that a user is interested in but fails to choose after comparison. Our experiments show that exploiting the price and discount information can significantly enhance prediction accuracy.

ACS Style

Zongxi Li; Haoran Xie; Guandong Xu; Qing Li; Mingming Leng; Chi Zhou. Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information. Pattern Recognition 2021, 113, 107824 .

AMA Style

Zongxi Li, Haoran Xie, Guandong Xu, Qing Li, Mingming Leng, Chi Zhou. Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information. Pattern Recognition. 2021; 113 ():107824.

Chicago/Turabian Style

Zongxi Li; Haoran Xie; Guandong Xu; Qing Li; Mingming Leng; Chi Zhou. 2021. "Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information." Pattern Recognition 113, no. : 107824.

Journal article
Published: 07 January 2021 in Neural Computing and Applications
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The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009–2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that “electroencephalogram,” “brain–computer interface,” “classification,” “support vector machine,” “electroencephalography,” and “signal” were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain–machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.

ACS Style

Xieling Chen; Xiaohui Tao; Fu Lee Wang; Haoran Xie. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Computing and Applications 2021, 1 -39.

AMA Style

Xieling Chen, Xiaohui Tao, Fu Lee Wang, Haoran Xie. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Computing and Applications. 2021; ():1-39.

Chicago/Turabian Style

Xieling Chen; Xiaohui Tao; Fu Lee Wang; Haoran Xie. 2021. "Global research on artificial intelligence-enhanced human electroencephalogram analysis." Neural Computing and Applications , no. : 1-39.

Journal article
Published: 31 December 2020 in Neural Networks
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The success of neural network based methods in named entity recognition (NER) is heavily relied on abundant manual labeled data. However, these NER methods are unavailable when the data is fully-unlabeled in a new domain. To address the problem, we propose an unsupervised cross-domain model which leverages labeled data from source domain to predict entities in unlabeled target domain. To relieve the distribution divergence when transferring knowledge from source to target domain, we apply adversarial training. Furthermore, we design an entity-aware attention module to guide the adversarial training to reduce the discrepancy of entity features between different domains. Experimental results demonstrate that our model outperforms other methods and achieves state-of-the-art performance.

ACS Style

Qi Peng; Changmeng Zheng; Yi Cai; Tao Wang; Haoran Xie; Qing Li. Unsupervised cross-domain named entity recognition using entity-aware adversarial training. Neural Networks 2020, 138, 68 -77.

AMA Style

Qi Peng, Changmeng Zheng, Yi Cai, Tao Wang, Haoran Xie, Qing Li. Unsupervised cross-domain named entity recognition using entity-aware adversarial training. Neural Networks. 2020; 138 ():68-77.

Chicago/Turabian Style

Qi Peng; Changmeng Zheng; Yi Cai; Tao Wang; Haoran Xie; Qing Li. 2020. "Unsupervised cross-domain named entity recognition using entity-aware adversarial training." Neural Networks 138, no. : 68-77.

Conference paper
Published: 17 December 2020 in Communications in Computer and Information Science
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University students must acquire good academic reading skills in undergraduate study. One of the effective skills is to insert annotations in the process of reading. With the advance of web technologies, students can jointly annotate an online article. However, the effectiveness of using online collaborative annotation platforms largely depends on the integration of instructional support. This paper elaborates on the design and effectiveness of using a peer assessment of online collaborative annotation approach in enhancing academic reading skills. Seventy-five first-year undergraduates were invited to annotate an article collaboratively in an online platform. They were requested to carry out peer assessment on the appropriateness of the annotations. Results from the data collected by a questionnaire suggest that the students considered the online platform enhanced collaborative learning, and the peer assessment component helped engage learning. They expressed their preferences to use the online annotation platform collaboratively in their future learning.

ACS Style

Wing Shui Ng; Haoran Xie; Gary Cheng. Enhancing Academic Reading Skills Using a Peer Assessment of Online Collaborative Annotation Approach. Communications in Computer and Information Science 2020, 281 -292.

AMA Style

Wing Shui Ng, Haoran Xie, Gary Cheng. Enhancing Academic Reading Skills Using a Peer Assessment of Online Collaborative Annotation Approach. Communications in Computer and Information Science. 2020; ():281-292.

Chicago/Turabian Style

Wing Shui Ng; Haoran Xie; Gary Cheng. 2020. "Enhancing Academic Reading Skills Using a Peer Assessment of Online Collaborative Annotation Approach." Communications in Computer and Information Science , no. : 281-292.

Review
Published: 13 November 2020 in Computer Assisted Language Learning
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This paper presents a systematic review of the literature on flipped language classrooms from the perspectives of theoretical foundations, learning activities, tools, research topics and findings based on an analysis of 34 published articles. The results indicate that various research methods (e.g., tests, surveys, and interviews) were applied in the selected studies and different types of e-tools (e.g., video-watching tools, online learning platforms, online discussion tools, and video-making tools) were used in the flipped language classrooms. The findings also reveal that the flipped language classroom not only improved students’ academic performance and cultivated their learning motivation but also developed their self-regulation, confidence, and higher-order thinking skills. Other research topics in the reviewed articles included the effects of external and learner factors on the flipped learning approach, students’ readiness and technology acceptance, the flipped learning process, students' interactions, and teacher perceptions.

ACS Style

Di Zou; Shuqiong Luo; Haoran Xie; Gwo-Jen Hwang. A systematic review of research on flipped language classrooms: theoretical foundations, learning activities, tools, research topics and findings. Computer Assisted Language Learning 2020, 1 -27.

AMA Style

Di Zou, Shuqiong Luo, Haoran Xie, Gwo-Jen Hwang. A systematic review of research on flipped language classrooms: theoretical foundations, learning activities, tools, research topics and findings. Computer Assisted Language Learning. 2020; ():1-27.

Chicago/Turabian Style

Di Zou; Shuqiong Luo; Haoran Xie; Gwo-Jen Hwang. 2020. "A systematic review of research on flipped language classrooms: theoretical foundations, learning activities, tools, research topics and findings." Computer Assisted Language Learning , no. : 1-27.