Author name cluster

Aoying Zhou

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

EdGCL: Disentangling Social and Cognitive Homophily in Graph-Based Educational Recommender Systems

Qingqing Liang
Chunyang Wang
Peiwei Xia
Yanan Zeng
Xin Liu
Xuesong Lu
Aoying Zhou

Educational recommendation systems have been a fundamental component for alleviating learning disorientation in self-paced learning. While existing studies mainly leverage cognitive theories to guide learning motivation modeling, they critically overlook the role of social influences. Through empirical analysis, we identify social homophily as an additional driver of learning behaviors, i.e., learners tend to adopt resources validated by their social cohort. However, two challenges impede effective social homophily modeling: (1) the absence and sparsity of predefined social relations in online education, and (2) the deep entanglement of social homophily with cognitive homophily in behavioral data. To tackle these challenges, we propose a graph-based framework EdGCL that explicitly disentangles social homophily and cognitive homophily. EdGCL infers implicit social relations from learners' social behaviors and encodes them via a graph transformer, generating social-view representations. Simultaneously, it constructs a heterogeneous learning graph to model cognitive homophily, which is enhanced by a type-aware aggregator and cognitive diagnosis loss. To ensure the semantic distinctiveness of dual-view homophily modeling, a cross-view contrastive disentanglement mechanism is designed to pull intra-view representations closer while pushing inter-view representations away. Evaluation on two real-world educational datasets demonstrates the superior recommendation performance of EdGCL, highlighting the necessity of dual homophily modeling for understanding the motivations behind learning behaviors.

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ICLR Conference 2025 Conference Paper

Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems

Jindong Tian
Yuxuan Liang 0002
Ronghui Xu 0001
Peng Chen 0038
Chenjuan Guo
Aoying Zhou
Lujia Pan
Zhongwen Rao

Air pollution significantly threatens human health and ecosystems, necessitating effective air quality prediction to inform public policy. Traditional approaches are generally categorized into physics-based and data-driven models. Physics-based models usually struggle with high computational demands and closed-system assumptions, while data-driven models may overlook essential physical dynamics, confusing the capturing of spatiotemporal correlations. Although some physics-guided approaches combine the strengths of both models, they often face a mismatch between explicit physical equations and implicit learned representations. To address these challenges, we propose Air-DualODE, a novel physics-guided approach that integrates dual branches of Neural ODEs for air quality prediction. The first branch applies open-system physical equations to capture spatiotemporal dependencies for learning physics dynamics, while the second branch identifies the dependencies not addressed by the first in a fully data-driven way. These dual representations are temporally aligned and fused to enhance prediction accuracy. Our experimental results demonstrate that Air-DualODE achieves state-of-the-art performance in predicting pollutant concentrations across various spatial scales, thereby offering a promising solution for real-world air quality challenges.

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AAAI Conference 2023 Conference Paper

Uncertainty-Aware Self-Training for Low-Resource Neural Sequence Labeling

Jianing Wang
Chengyu Wang
Jun Huang
Ming Gao
Aoying Zhou

Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc. However, the satisfying results achieved by traditional supervised-based approaches heavily depend on the large amounts of human annotation data, which may not be feasible in real-world scenarios due to data privacy and computation efficiency issues. This paper presents SeqUST, a novel uncertain-aware self-training framework for NSL to address the labeled data scarcity issue and to effectively utilize unlabeled data. Specifically, we incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation at the token level and then select reliable language tokens from unlabeled data based on the model confidence and certainty. A well-designed masked sequence labeling task with a noise-robust loss supports robust training, which aims to suppress the problem of noisy pseudo labels. In addition, we develop a Gaussian-based consistency regularization technique to further improve the model robustness on Gaussian-distributed perturbed representations. This effectively alleviates the over-fitting dilemma originating from pseudo-labeled augmented data. Extensive experiments over six benchmarks demonstrate that our SeqUST framework effectively improves the performance of self-training, and consistently outperforms strong baselines by a large margin in low-resource scenarios.

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TIST Journal 2021 Journal Article

Feature Grouping–based Trajectory Outlier Detection over Distributed Streams

Jiali Mao
Jiaye Liu
Cheqing Jin
Aoying Zhou

Owing to a wide variety of deployment of GPS -enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we focus on the issue of outlier detection over distributed trajectory streams, where the outliers refer to a few entities whose motion behaviors are significantly different from their local neighbors. In view of skewed distribution property and evolving nature of trajectory data, and on-the-fly detection requirement over distributed streams, we first design a high-efficiency outlier detection solution. It consists of identifying abnormal trajectory fragment and exceptional fragment cluster at the remote sites and then detecting abnormal evolving object at the coordinator site. Further, given that outlier detection accuracy would be damaged due to using inappropriate proximity thresholds or a few trajectory data not having sufficient neighbors at the remote sites, we extract proximity thresholds of different regions and spatial context relationship of each region from historical data to improve the precision. Built upon this is an improved version consisting of off-line modeling phase and on-line detection phase. During the on-line phase, the proximity thresholds that are derived from historical trajectories during the off-line phase are leveraged to assist in detecting abnormal trajectory fragments and exceptional fragment clusters at the remote sites. Additionally, at the coordinator site, the detection results of some remote sites can be refined by incorporating those of other remote sites with neighborhood relationship. Extensive experimental results on real data demonstrate that our proposed methods own high detection validity, less communication cost and linear scalability for online identifying outliers over distributed trajectory streams.

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IJCAI Conference 2020 Conference Paper

F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification

Xin Liang
Dawei Cheng
Fangzhou Yang
Yifeng Luo
Weining Qian
Aoying Zhou

The share prices of listed companies in the stock trading market are prone to be influenced by various events. Performing event detection could help people to timely identify investment risks and opportunities accompanying these events. The financial events inherently present hierarchical structures, which could be represented as tree-structured schemes in real-life applications, and detecting events could be modeled as a hierarchical multi-label text classification problem, where an event is designated to a tree node with a sequence of hierarchical event category labels. Conventional hierarchical multi-label text classification methods usually ignore the hierarchical relationships existing in the event classification scheme, and treat the hierarchical labels associated with an event as uniform labels, where correct or wrong label predictions are assigned with equal rewards or penalties. In this paper, we propose a neural hierarchical multi-label text classification method, namely F-HMTC, for a financial application scenario with massive event category labels. F-HMTC learns the latent features based on bidirectional encoder representations from transformers, and directly maps them to hierarchical labels with a delicate hierarchy-based loss layer. We conduct extensive experiments on a private financial dataset with elaborately-annotated labels, and F-HMTC consistently outperforms state-of-art baselines by substantial margins. We will release both the source codes and dataset on the first author's repository.

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AAAI Conference 2019 Conference Paper

Improving Hypernymy Prediction via Taxonomy Enhanced Adversarial Learning

Chengyu Wang
Xiaofeng He
Aoying Zhou

Hypernymy is a basic semantic relation in computational linguistics that expresses the “is-a” relation between a generic concept and its specific instances, serving as the backbone in taxonomies and ontologies. Although several NLP tasks related to hypernymy prediction have been extensively addressed, few methods have fully exploited the large number of hypernymy relations in Web-scale taxonomies. In this paper, we introduce the Taxonomy Enhanced Adversarial Learning (TEAL) for hypernymy prediction. We first propose an unsupervised measure U-TEAL to distinguish hypernymy with other semantic relations. It is implemented based on a word embedding projection network distantly trained over a taxonomy. To address supervised hypernymy detection tasks, the supervised model S-TEAL and its improved version, the adversarial supervised model AS-TEAL, are further presented. Specifically, AS-TEAL employs a coupled adversarial training algorithm to transfer hierarchical knowledge in taxonomies to hypernymy prediction models. We conduct extensive experiments to confirm the effectiveness of TEAL over three standard NLP tasks: unsupervised hypernymy classification, supervised hypernymy detection and graded lexical entailment. We also show that TEAL can be applied to non-English languages and can detect missing hypernymy relations in taxonomies.

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IS Journal 2015 Journal Article

Knowledge Engineering with Big Data

Xindong Wu
Huanhuan Chen
Gongqing Wu
Jun Liu
Qinghua Zheng
Xiaofeng He
Aoying Zhou
Zhong-Qiu Zhao

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation.

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