75 citations · 473 across the 72 of their papers we have counts for
21 papers · 1 filter
Data-Driven State Estimation for Light-Emitting Diode Underwater Optical Communication
Yingquan Li, Zhenwen Liang, Ibrahima N'Doye +3
Light-Emitting Diodes (LEDs) based underwater optical wireless communications (UOWCs), a technology with low latency and high data rates, have attracted significant importance for…
Meta Cross-Modal Hashing on Long-Tailed Data
Runmin Wang, Guoxian Yu, Carlotta Domeniconi +1
Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor s…
Cross-modal Zero-shot Hashing by Label Attributes Embedding
Runmin Wang, Guoxian Yu, Lei Liu +3
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search. Most CMH solutions ideally assume the labels of training and test…
Open-Set Crowdsourcing using Multiple-Source Transfer Learning
Guangyang Han, Guoxian Yu, Lei Liu +3
We raise and define a new crowdsourcing scenario, open set crowdsourcing, where we only know the general theme of an unfamiliar crowdsourcing project, and we don't know its label s…
Crowdsourcing with Meta-Workers: A New Way to Save the Budget
Guangyang Han, Guoxian Yu, Lizhen Cui +2
Due to the unreliability of Internet workers, it's difficult to complete a crowdsourcing project satisfactorily, especially when the tasks are multiple and the budget is limited. R…
Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST
Hind Alamro, Manal Alshehri, Basma Alharbi +4
This paper provides an overview of the Arabic Sentiment Analysis Challenge organized by King Abdullah University of Science and Technology (KAUST). The task in this challenge is to…
AppQ: Warm-starting App Recommendation Based on View Graphs
Dan Su, Jiqiang Liu, Sencun Zhu +3
Current app ranking and recommendation systems are mainly based on user-generated information, e.g., number of downloads and ratings. However, new apps often have few (or even no)…
FARF: A Fair and Adaptive Random Forests Classifier
Wenbin Zhang, Albert Bifet, Xiangliang Zhang +2
As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning…
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
Zhenwen Liang, Jipeng Zhang, Lei Wang +4
Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutio…
Attack Transferability Characterization for Adversarially Robust Multi-label Classification
Zhuo Yang, Yufei Han, Xiangliang Zhang
Despite of the pervasive existence of multi-label evasion attack, it is an open yet essential problem to characterize the origin of the adversarial vulnerability of a multi-label l…
Scientific Dataset Discovery via Topic-level Recommendation
Basmah Altaf, Shichao Pei, Xiangliang Zhang
Data intensive research requires the support of appropriate datasets. However, it is often time-consuming to discover usable datasets matching a specific research topic. We formula…
Socially-Aware Self-Supervised Tri-Training for Recommendation
Junliang Yu, Hongzhi Yin, Min Gao +3
Self-supervised learning (SSL), which can automatically generate ground-truth samples from raw data, holds vast potential to improve recommender systems. Most existing SSL-based me…
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling
Tong Chen, Hongzhi Yin, Xiangliang Zhang +3
As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manu…
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
Tong Chen, Hongzhi Yin, Jie Ren +3
With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable. Recent…
Fast-adapting and Privacy-preserving Federated Recommender System
Qinyong Wang, Hongzhi Yin, Tong Chen +3
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem. Rece…
Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation
Lei Guo, Hongzhi Yin, Tong Chen +2
In this work, we study group recommendation in a particular scenario, namely Occasional Group Recommendation (OGR). Most existing works have addressed OGR by aggregating group memb…
Graph Embedding for Recommendation against Attribute Inference Attacks
Shijie Zhang, Hongzhi Yin, Tong Chen +3
In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be natur…
Does Head Label Help for Long-Tailed Multi-Label Text Classification
Lin Xiao, Xiangliang Zhang, Liping Jing +2
Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label f…
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Guanhua Ye, Hongzhi Yin, Tong Chen +3
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of det…
Combat Data Shift in Few-shot Learning with Knowledge Graph
Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang +4
Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learn…
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Junliang Yu, Hongzhi Yin, Jundong Li +3
Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models explo…