Haoran Xie

Haoran Xie
Lingnan University · Department of Computing and Decision Sciences

BEng, MSc, PhD (Computer Science) and EdD (Digital Learning), SrMACM, SrMIEEE

About

352
Publications
99,058
Reads
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8,498
Citations
Citations since 2016
304 Research Items
8347 Citations
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201620172018201920202021202205001,0001,5002,000
201620172018201920202021202205001,0001,5002,000
Additional affiliations
July 2020 - present
Elsevier
Position
  • Editor-in-Chief
January 2020 - present
Lingnan University
Position
  • Professor (Associate)
July 2016 - January 2020
The Education University of Hong Kong
Position
  • Professor (Assistant)

Publications

Publications (352)
Article
Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we pro...
Article
In this study, the trends and developments of technology-enhanced adaptive/personalized learning have been studied by reviewing the related journal articles in the recent decade (i.e., from 2007 to 2017). To be specific, we investigated many research issues such as the parameters of adaptive/personalized learning, learning supports, learning outcom...
Article
Sentiment strength detection is an essential task in sentiment analysis, wherein the sentiment strength of subjective text is automatically determined. Sentiment analysis has numerous applications in different sectors, including business and social domains. In this study, we present a model to effectively extract the features and strength of sentim...
Article
Personalized recommendation systems have solved the information overload problem caused by large volumes of Web data effectively. However, most existing recommendation algorithms are weak in handling the problem of rating data sparsity that characterizes most recommender systems and results in deteriorated recommendation accuracy. The results in th...
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With the fast progress in information technologies and artificial intelligence (AI), smart health-care has gained considerable momentum. By using advanced technologies like AI, smart healthcare aims to promote human beings' health and well-being throughout their life. As smart healthcare develops, big healthcare data are produced by various sensors...
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Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and s...
Article
In this study, sentiment classification and emotion distribution learning across domains are both formulated as a semi-supervised domain adaptation problem, which utilizes a small amount of labeled documents in the target domain for model training. By introducing a shared matrix that captures the stable association between document clusters and wor...
Preprint
LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing methods to fuse data from the two cross sensors, making them complementary for quality 3D object detection (3OD). We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sen...
Preprint
There is a trend to fuse multi-modal information for 3D object detection (3OD). However, the challenging problems of low lightweightness, poor flexibility of plug-and-play, and inaccurate alignment of features are still not well-solved, when designing multi-modal fusion newtorks. We propose PointSee, a lightweight, flexible and effective multi-moda...
Preprint
Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in deep layers. To this end, we propose iSmallNet, a multi-stream densely nested network with label decoupling for infrared small object detection. On the one hand, to full...
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We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD). Beyond the traditional wisdom of PCD, to fully exploit the geometric information of point clouds, we define two kinds of surface normals, one is called Real Normal (RN), and the other is Virtual Normal (VN). RN preserves the local details of...
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Researchers and practitioners are paying increasing attention to blockchain’s potential for resolving trust, privacy, and transparency-related issues in smart education. Research on educational blockchain is also becoming an active field of research. Based on 206 studies published from 2017 to 2020, we identify contributors, collaborators, applicat...
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Despite the popularity of trend-following strategies in financial markets, they often lack adaptability to the emerging varied markets. Recently, deep learning (DL) methods demonstrate the effectiveness in stock-market analysis. Thus, the application of DL methods to enhance trend-following strategies has received substantial attention. However, th...
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Large imbalance often exists between the foreground points (i.e., objects) and the background points in outdoor LiDAR point clouds. It hinders cutting-edge detectors from focusing on informative areas to produce accurate 3D object detection results. This paper proposes a novel object detection network by semantical point-voxel feature interaction,...
Article
The maturity of 5G and artificial intelligence has promoted the XRED (X Reality in Education)'s application and implementation. XRED involves the application of X Reality (i.e., augmented reality, virtual reality, or mixed reality) technologies in the process of instruction and learning. Learning assisted by XR technologies can facilitate students'...
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Peer, teacher, and self-feedback have been widely applied in English writing courses in higher education. However, few studies have used technology to activate the potential of feedback in project-based collaborative learning or discussed how technology-enhanced peer, teacher and self-feedback may assist students’ writing, promote their critical th...
Preprint
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question - if the combination of image restoration and object detection, can boost the performance of cutting-edge det...
Preprint
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex entanglement of semantic structures and trivial details. However, current methods neglect two important facts in smoothing: 1) n...
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Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, either abandon considerable local features during pooling operations or ignore many global features in the whole scene context. This paper explores new modules to simultaneou...
Preprint
Large imbalance often exists between the foreground points (i.e., objects) and the background points in outdoor LiDAR point clouds. It hinders cutting-edge detectors from focusing on informative areas to produce accurate 3D object detection results. This paper proposes a novel object detection network by semantical point-voxel feature interaction,...
Article
Full-text available
Image deraining aims to restore the clean scenes of rainy images, which facilitates a number of outdoor vision systems, such as autonomous driving, unmanned aerial vehicles and surveillance systems. This paper proposes a high-resolution detail-recovering image deraining network (HDRD-Net) to effectively remove rain streaks and recover lost details,...
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In the process of training convolutional neural networks, the training data is often insufficient to obtain ideal performance and encounters the overfitting problem. To address this issue, traditional data augmentation (DA) techniques, which are designed manually based on empirical results, are often adopted in supervised learning. Essentially, tra...
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Image decomposition is a useful operation that benefits a number of low-level vision tasks. However, this conventional wisdom is not well studied in deep learning, and almost no existing deep learning-based methods consider the fact that the extracted feature map from a convolution layer consists of different frequency information. We propose an en...
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As an important task in Asian language information processing, Chinese word embedding learning has attracted much attention recently. Based on either Skip-gram or CBOW, several methods have been proposed to exploit Chinese characters and sub-character components for learning Chinese word embeddings. Chinese characters are combinations of meaning, s...
Article
Using deep learning to automatically and quickly extract faults from seismic images is of practical significance. An improved U-Net algorithm is proposed by reducing convolutional layers, designing skip connections, enforcing deep supervision, and improving the loss function and learning rate to build a new model. In the operation, the feature map...
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Secure deduplication aims to efficiently eliminate redundant data in cloud storage system, where convergent encryption (CE) is widely-used to provide the data confidentiality. As the number of convergent keys (CKs) in CE will increase dramatically with enlarging data, there is a critical issue that how to safely manage the CKs. Previous works usual...
Preprint
How will you repair a physical object with some missings? You may imagine its original shape from previously captured images, recover its overall (global) but coarse shape first, and then refine its local details. We are motivated to imitate the physical repair procedure to address point cloud completion. To this end, we propose a cross-modal shape...
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The diversity and advance of information, communication, and analytical technologies and their increasing adoption to assist instruction and learning give rise to various technology-driven conferences (e.g., artificial intelligence in education) in educational technology. Previous reviews on educational technology commonly focused on journal articl...
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Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners’ perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC course reviews to understand f...
Preprint
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity. In this paper, we present a robust semantic segmentation...
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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 E...
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Technology-enhanced collaborative writing (TECW) for second language development is receiving increasing research attention from educators and teachers. However, there have been few review studies investigating how teachers implement this activity, how they use technology for the implementation, and what challenges they have. To better prepare prac...
Article
Can you find me? By simulating how humans to discover the so-called 'perfectly'-camouflaged object, we present a novel boundary-guided separated attention network (call BSA-Net). Beyond the existing camouflaged object detection (COD) wisdom, BSA-Net utilizes two-stream separated attention modules to highlight the separator (or say the camouflaged o...
Chapter
Recommendation system facilitates users promptly obtaining the information they need in this age of data explosion. Research on recommendation models have recognized the importance of integrating user historical behavior sequence into the model to alleviate the matrix sparsity. Although deep learning algorithm with attentive mechanism exhibits comp...
Article
Understanding vasculatures is important for endovascular intervention simulation (EIS). A recent trend attempts to represent vasculatures with three-dimensional (3D) surface meshes by multiview image and graphics-based techniques used in optical and laser scanners. Conversion from image volume data to 3D surface meshes, however, suffers from stairc...
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This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects. To achieve this, we formulate a d...
Chapter
This study focuses on understanding classroom interaction using epistemic and social network analysis. Based on the classroom interaction data concerning an online course named Justice with 12 episodes, we demonstrate epistemic and social network analyses’ advantages in evaluating the quality of classroom interaction, instructors’ performance in pr...
Chapter
Peer assessment is a common strategy in English as a Second or Foreign Language (ESL or EFL) writing classrooms. Researchers and practitioners generally consider peer feedback useful for the improvement of students’ writing performance. However, little research has been conducted to investigate the effects of peer assessment on students’ academic w...
Chapter
Creativity and collaboration are considered core competencies of contemporary students in different education levels and disciplines. Existing research mainly focuses on the theoretical framework for computer-supported collaborative learning, and the dialogic content analysis is mainly based on expert annotating. Consequently, there is a vacuum in...
Preprint
Snow is one of the toughest adverse weather conditions for object detection (OD). Currently, not only there is a lack of snowy OD datasets to train cutting-edge detectors, but also these detectors have difficulties learning latent information beneficial for detection in snow. To alleviate the two above problems, we first establish a real-world snow...
Article
As one of the prevalent topic mining methods, neural topic modeling has attracted a lot of interests due to the advantages of low training costs and strong generalisation abilities. However, the existing neural topic models may suffer from the feature sparsity problem when applied to short texts, due to the lack of context in each message. To allev...
Preprint
While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world ha...
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Digital games have become increasingly popular. However, many teachers may not have relevant knowledge and experience of designing and implementing digital game-based teaching in formal classrooms. This study proposed a collaborative design approach to facilitate pre-service teachers’ abilities of designing for learning with a digital game. To eval...
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Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions. We observe that: (i) rain is a mixture of rain streaks and rainy haze; (ii) the scene depth determines the intensity of rain streaks and the transformation in...
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The TPACK (technological pedagogical and content knowledge) framework is an influential theoretical foundation for teaching with technology research. This analysis of 1,608 empirical research studies of TPACK identifies trends and research topics from 2000 to 2020 using structural topic modelling and bibliometrics. The results showed that academic...
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Learning analytics (LA) has become an increasingly active field focusing on leveraging learning process data to understand and improve teaching and learning. With the explosive growth in the number of studies concerning LA, it is significant to investigate its research status and trends, particularly the thematic structure. Based on 3900 LA article...
Preprint
The intricacy of rainy image contents often leads cutting-edge deraining models to image degradation including remnant rain, wrongly-removed details, and distorted appearance. Such degradation is further exacerbated when applying the models trained on synthetic data to real-world rainy images. We raise an intriguing question -- if leveraging both a...
Preprint
Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting-edge learning-based techniques for shape analysis and generation. In this paper, we propose a normal refinement network, called Refine-Net, to predict accurate no...
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Full-text available
The recent development of deep learning-based natural language processing (NLP) methods has fostered many downstream applications in various fields. As one of the applications in the financial industry, fine-grained financial sentiment analysis (FSA) aims to understand the sentimental orientation, i.e., bullish or bearish, of financial texts by pre...
Preprint
Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks. In practical scenarios, users have to tweak parameters multiple times to obtain satisfied results. This inconvenience heavily discounts the efficiency and user experience. We propose basis composition learning on singl...
Chapter
This book brings together some thought-provoking papers around the theme of “Smart CALL.” The term “smart” nowadays means “connected to and exchanging information with other devices.” The contributions in this volume focus on a more human-centered perspective, namely the definition of smartness in terms of three qualities or dimensions: personaliza...
Article
Automated writing evaluation (AWE) plays an important role in writing pedagogy and has received considerable research attention recently; however, few reviews have been conducted to systematically analyze the recent publications arising from the many studies in this area. The present review aims to provide a comprehensive analysis of the literature...
Article
Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting-edge learning-based techniques for shape analysis and generation. In this paper, we propose a normal refinement network, called Refine-Net, to predict accurate no...
Article
Most of the existing object detection methods generate poor glass detection results, due to the fact that the transparent glass shares the same appearance with arbitrary objects behind it in an image. Different from traditional deep learning-based wisdoms that simply use the object boundary as an auxiliary supervision, we exploit label decoupling t...
Article
Three-dimensional (3D) imaging devices (e.g., depth cameras and optical and laser scanners) are frequently used to measure outdoor/indoor scenes. The measurement data represented by 3D point clouds is, however, usually noisy and should be denoised to facilitate subsequent applications. Existing point cloud denoising methods typically perform 1) poi...
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Full-text available
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...