41 citations · 118 across the 21 of their papers we have counts for
21 papers
An auto-locking ultra-stable laser with sub-hertz linewidth
D. Jiao, G. Xu, J. Gao +7
We report in detail the design process and performance of an auto-locking ultra-stable laser with sub-hertz linewidth at the first time. The laser frequency is automatically stabil…
A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics
Hongru Liang, Haozheng Wang, Qian Li +5
Learning and analyzing rap lyrics is a significant basis for many web applications, such as music recommendation, automatic music categorization, and music information retrieval, d…
Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection
Jun Yin, Qian Li, Shaowu Liu +2
Much recent research has shed light on the development of the relation-dependent but content-independent framework for social spammer detection. This is largely because the relatio…
Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia
Jianlong Zhou, Hamad Zogan, Shuiqiao Yang +3
The recent COVID-19 pandemic has caused unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, st…
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Hamad Zogan, Imran Razzak, Xianzhi Wang +2
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction. However, most of the existing machine learning m…
Causality Learning: A New Perspective for Interpretable Machine Learning
Guandong Xu, Tri Dung Duong, Qian Li +2
Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation…
TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search
Wenhua Wang, Yuqun Zhang, Zhengran Zeng +1
Code summarization and code search have been widely adopted in sofwaredevelopmentandmaintenance. However, fewstudieshave explored the efcacy of unifying them. In this paper, we pro…
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
Yue Wang, Chenwei Zhang, Shen Wang +4
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future link…
Multi-Modal Attention Network Learning for Semantic Source Code Retrieval
Yao Wan, Jingdong Shu, Yulei Sui +4
Code retrieval techniques and tools have been playing a key role in facilitating software developers to retrieve existing code fragments from available open-source repositories giv…
Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation
Peipei Wang, Lin Li, Yi Yu +1
Frequent group activities of human beings have become an indispensable part in their daily life. Group recommendation can recommend satisfactory activities to group members in the…
Event Detection in Twitter Stream using Weighted Dynamic Heartbeat Graph Approach
Zafar Saeed, Rabeeh Ayaz Abbasi, Muhammad Imran Razzak +1
Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a nov…
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
Yao Wan, Zhou Zhao, Min Yang +4
Code summarization provides a high level natural language description of the function performed by code, as it can benefit the software maintenance, code categorization and retriev…
Target-Independent Active Learning via Distribution-Splitting
Xiaofeng Cao, Ivor W. Tsang, Xiaofeng Xu +1
To reduce the label complexity in Agnostic Active Learning (A^2 algorithm), volume-splitting splits the hypothesis edges to reduce the Vapnik-Chervonenkis (VC) dimension in version…
A Structured Perspective of Volumes on Active Learning
Xiaofeng Cao, Ivor W. Tsang, Guandong Xu
Active Learning (AL) is a learning task that requires learners interactively query the labels of the sampled unlabeled instances to minimize the training outputs with human supervi…
A Divide-and-Conquer Approach to Geometric Sampling for Active Learning
Xiaofeng Cao
Active learning (AL) repeatedly trains the classifier with the minimum labeling budget to improve the current classification model. The training process is usually supervised by an…
An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering
Hongzhi Zhang, Guandong Xu, Xiao Liang +2
Relation detection plays a crucial role in Knowledge Base Question Answering (KBQA) because of the high variance of relation expression in the question. Traditional deep learning m…
Coupling Analysis Between Twitter and Call Centre
Fangfang Li, Yanchang Zhao, Klaus Felsche +2
Social media has been contributing many research areas such as data mining, recommender systems, time series analysis, etc. However, there are not many successful applications rega…
CSAL: Self-adaptive Labeling based Clustering Integrating Supervised Learning on Unlabeled Data
Fangfang Li, Guandong Xu, Longbing Cao
Supervised classification approaches can predict labels for unknown data because of the supervised training process. The success of classification is heavily dependent on the label…
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Liang Wu, Hui Xiong, Liang Du +6
The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been…
Coupled Item-based Matrix Factorization
Fangfang Li, Guandong Xu, Longbing Cao
The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF),…
Coupled Matrix Factorization within Non-IID Context
Fangfang Li, Guandong Xu, Longbing Cao
Recommender systems research has experienced different stages such as from user preference understanding to content analysis. Typical recommendation algorithms were built on the fo…