Ling Chen

AAAI Conference 2026 Conference Paper

AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting

• Binqing Wu
• Zongjiang Shang
• Shiyu Liu
• Jianlong Huang
• Jiahui Xu
• Ling Chen

Accurate air quality forecasting is essential for public health and environmental sustainability, but remains challenging due to the complex pollutant dynamics. Existing deep learning methods often model pollutant dynamics as an instantaneous process, overlooking the intrinsic delays in pollutant propagation. Thus, we propose AirDDE, the first neural delay differential equation framework in this task that integrates delay modeling into a continuous-time pollutant evolution under physical guidance. Specifically, two novel components are introduced: (1) a memory-augmented attention module that retrieves globally and locally historical features, which can adaptively capture delay effects modulated by multifactor data; and (2) a physics-guided delay evolving function, grounded in the diffusion-advection equation, that models diffusion, delayed advection, and source/sink terms, which can capture delay-aware pollutant accumulation patterns with physical plausibility. Extensive experiments on three real-world datasets demonstrate that AirDDE achieves the state-of-the-art forecasting performance with an average MAE reduction of 8.79% over the best baselines.

AAAI Conference 2026 Conference Paper

Investigating Social Bias Propagation in Federated Fine-tuning of Large Language Models

• Jiaxu Zhao
• Meng Fang
• Mingze Zhong
• Shunfeng Zheng
• Ling Chen
• Mykola Pechenizkiy

Large language models (LLMs) have achieved remarkable success in many domains, but concerns about data quality and privacy are growing. Federated Learning (FL) offers a privacy-preserving solution by training a model on local clients without sharing data. However, the impact of biased private data on LLMs fine-tuned through FL remains understudied. This work investigates how client-side biased data affects the global model during federated fine-tuning of LLMs. We simulate realistic scenarios where some clients possess datasets containing social biases (stereotypes, discriminatory language) while others have clean data through extensive experiments with popular FL algorithms (FedAvg, FedAdam and FedProx) and popular LLMs (LLaMA, Mistral, Phi-3 and Gemma) across datasets with varying bias proportions (33%, 66%, 100%). Our findings reveal that 1) FedAdam consistently shows the lowest bias propagation, reducing CrowS-Pairs scores by up to 15% compared to FedAvg; 2) Even small amounts of biased data (33%) can significantly influence global model bias; 3) Mixed biased and neutral data distributions lead to 5%-7% higher bias scores than segregated distributions. Additionally, we propose Bias-Aware Model Aggregation (BAMA), a novel debiasing method for federated fine-tuning that consistently reduces bias across various models and algorithms.

TIST Journal 2026 Journal Article

Mitigating Data Redundancy to Revitalize Transformer-Based Long-Term Time Series Forecasting System

• Mingjie Li
• Rui Liu
• Guangsi Shi
• Mingfei Han
• Changlin Li
• Lina Yao
• Xiaojun Chang
• Ling Chen

Long-term time series forecasting (LTSF) is fundamental to various real-world applications, where Transformer-based models have become the dominant framework due to their ability to capture long-range dependencies. However, these models often experience overfitting due to data redundancy in rolling forecasting settings, limiting their generalization ability particularly evident in longer sequences with highly similar adjacent data. In this work, we introduce CLMFormer, a novel framework that mitigates redundancy through curriculum learning and a memory-driven decoder. Specifically, we progressively introduce Bernoulli noise to the training samples, which effectively breaks the high similarity between adjacent data points. This curriculum-driven noise introduction aids the memory-driven decoder by supplying more diverse and representative training data, enhancing the decoder’s ability to model seasonal tendencies and dependencies in the time series data. To further enhance forecasting accuracy, we introduce a memory-driven decoder. This component enables the model to capture seasonal tendencies and dependencies in the time series data and leverages temporal relationships to facilitate the forecasting process. Extensive experiments on six real-world LTSF benchmarks show that CLMFormer consistently improves Transformer-based models by up to 30%, demonstrating its effectiveness in long-horizon forecasting.

AAAI Conference 2026 Conference Paper

MoCast: Learning Turbulent Motions Under Physical Guidance for Precipitation Nowcasting

• Binqing Wu
• Weiqi Chen
• Shiyu Liu
• Zongjiang Shang
• Haiou Wang
• Liang Sun
• Ling Chen

Precipitation nowcasting, a critical task for weather-sensitive applications, is highly challenging owing to the chaotic nature of atmospheric dynamics. Despite recent progress in deep learning, existing methods are limited in their capacity to model turbulent motions, one of the key drivers of precipitation evolution. Thus, we propose MoCast, the first work that incorporates turbulence knowledge to decompose turbulent motions into solvable components for precipitation nowcasting. Specifically, inspired by the continuity equation, MoCast introduces two core innovations: (1) a physics-guided motion module that learns turbulent motions from physically interpretable mean and fluctuating components based on Reynolds, Helmholtz, and Wavelet decomposition techniques, and (2) a motion-guided source-sink module that learns source-sink features considering the multi-scale impact from motions based on a mixture-of-experts architecture. Extensive experiments on three real-world datasets demonstrate that MoCast achieves the state-of-the-art performance. MoCast and its diffusion-based variant MoCast+ reduce CSI error by an average of 4.9% and 4.5% compared to the best deterministic and probabilistic baselines, respectively.

AAAI Conference 2026 Conference Paper

TawPipe: Topology-Aware Weight Pipeline Parallelism for Accelerating Long-Context Large Models Training

• Houming Wu
• Ling Chen

Training large language models (LLMs) is fundamentally constrained by limited device memory and costly inter-device communication. Although pipeline parallelism alleviates memory pressure by partitioning models across devices, it incurs activation communication overhead that scales linearly with sequence length, limiting efficiency in long-context training. Recent weight-passing approaches (e.g., WeiPipe) mitigate this by transmitting model weights instead of activations, but suffer from redundant peer-to-peer (P2P) transfers and underutilized intra-node bandwidth. We propose TawPipe—topology-aware weight pipeline parallelism, which exploits hierarchical bandwidth in distributed clusters for improved communication efficiency. TawPipe: (i) groups devices based on topology to optimize intra-node collective and inter-node P2P communication; (ii) assigns each device a fixed shard of model weights and gradients, avoiding redundant transfers; and (iii) overlaps communication with computation to hide latency. Unlike global collective operations used in fully sharded data parallelism (FSDP), TawPipe confines most communication within node boundaries, significantly reducing cross-node traffic. Extensive experiments on up to 24 GPUs with LLaMA‑style models show that TawPipe achieves superior throughput and scalability compared to state-of-the-art baselines.

AAAI Conference 2026 Conference Paper

Vision-Language Reasoning for Geolocalization: A Reinforcement Learning Approach

• Biao Wu
• Meng Fang
• Ling Chen
• Ke Xu
• Tao Cheng
• Jun Wang

Recent advances in vision-language models have opened up new possibilities for reasoning-driven image geolocalization. However, existing approaches often rely on synthetic reasoning annotations or external image retrieval, which can limit interpretability and generalizability. In this paper, we present Geo-R, a retrieval-free framework that uncovers structured reasoning paths from existing ground-truth coordinates and optimizes geolocation accuracy via reinforcement learning. We propose the Chain of Region, a rule-based hierarchical reasoning paradigm that generates precise, interpretable supervision by mapping GPS coordinates to geographic entities (e.g., country, province, city) without relying on model-generated or synthetic labels. Building on this, we introduce a lightweight reinforcement learning strategy with coordinate-aligned rewards based on Haversine distance, enabling the model to refine predictions through spatially meaningful feedback. Our approach bridges structured geographic reasoning with direct spatial supervision, yielding improved localization accuracy, stronger generalization, and more transparent inference. Experimental results across multiple benchmarks confirm the effectiveness of Geo-R, establishing a new retrieval-free paradigm for scalable and interpretable image geolocalization. To facilitate further research and ensure reproducibility, both the model and code will be made publicly available.

NeurIPS Conference 2025 Conference Paper

Beyond Node-Centric Modeling: Sketching Signed Networks with Simplicial Complexes

• Wei Wu
• Xuan Tan
• Yan Peng
• Ling Chen
• Fangfang Li
• Chuan Luo

Signed networks can reflect more complex connections through positive and negative edges, and cost-effective signed network sketching can significantly benefit an important link sign prediction task in the era of big data. Existing signed network embedding algorithms mainly learn node representation in the Graph Neural Network (GNN) framework with the balance theory. However, the node-wise representation learning methods either limit the representational power because they primarily rely on node pairwise relationship in the network, or suffer from severe efficiency issues. Recent research has explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by that, we propose EdgeSketch+, a simple and effective edge embedding algorithm beyond traditional node-centric modeling that directly represents edges as low-dimensional vectors without transitioning from node embeddings. The proposed approach maintains a good balance between accuracy and efficiency by exploiting the Locality Sensitive Hashing (LSH) technique to swiftly capture the higher-order information derived from the simplicial complex in a manner of no learning processes. Experiments show that EdgeSketch+ matches state-of-the-art accuracy while significantly reducing runtime, achieving speedups of up to $546. 07\times$ compared to GNN-based methods.

JBHI Journal 2025 Journal Article

Predicting Longitudinal Visual Field Progression With Class Imbalanced Data

• Ling Chen
• Chun-Hung Chen
• Wei Wang
• Da-Wen Lu
• Vincent S. Tseng

Glaucoma is the leading cause of irreversible blindness worldwide. The clinical standard for glaucoma diagnosis and progression tracking remains visual field (VF) testing via standard automated perimetry. One outstanding challenge of many ophthalmic prediction tasks is the issue of class imbalance, where the majority class outnumbers the minority class(es). Although this issue has been reported in several prior studies on the prediction of VF progression or glaucoma, it has not been addressed in the context of longitudinal VF data. In this work, we proposed, VF-Transformer, a transformer-based framework for VF progression prediction based on longitudinal VF examination results. In particular, we addressed the class imbalance issue by incorporating our proposed inverted class-dependent temperature (ICDT) loss and weight normalization. The proposed framework was developed and evaluated on a public VF dataset and further validated on an external hospital dataset, using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) as evaluation metrics. Extensive experiments and comparisons with existing state-of-the-art methods and class imbalance handling strategies confirmed the effectiveness of the proposed framework in predicting VF progression in the presence of class imbalance.

IJCAI Conference 2025 Conference Paper

Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

• Chaoxi Niu
• Hezhe Qiao
• Changlu Chen
• Ling Chen
• Guansong Pang

Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However, existing GAD methods are one-model-for-one-dataset approaches, i. e. , training a separate model for each graph dataset. This largely limits their applicability in real-world scenarios. To overcome this limitation, we propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight in UNPrompt is that i) the predictability of latent node attributes can serve as a generalized anomaly measure and ii) generalized normal and abnormal graph patterns can be learned via latent node attribute prediction in a properly normalized node attribute space. UNPrompt achieves a generalist mode for GAD through two main modules: one module aligns the dimensionality and semantics of node attributes across different graphs via coordinate-wise normalization, while another module learns generalized neighborhood prompts that support the use of latent node attribute predictability as an anomaly score across different datasets. Extensive experiments on real-world GAD datasets show that UNPrompt significantly outperforms diverse competing methods under the generalist GAD setting, and it also has strong superiority under the one-model-for-one-dataset setting. Code is available at https: //github. com/mala-lab/UNPrompt.

NeurIPS Conference 2024 Conference Paper

Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting

• Zongjiang Shang
• Ling Chen
• Binqing Wu
• Dongliang Cui

Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e. g. , rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4. 56%, 10. 38%, and 4. 97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https: //github. com/shangzongjiang/Ada-MSHyper.

NeurIPS Conference 2024 Conference Paper

DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach

• Qian Chen
• Ling Chen

Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in TKGs. To this end, we propose a D eep E volutionary C lustering jointed temporal knowledge graph R epresentation L earning approach ( DECRL ). Specifically, a deep evolutionary clustering module is proposed to capture the temporal evolution of high-order correlations among entities. Furthermore, a cluster-aware unsupervised alignment mechanism is introduced to ensure the precise one-to-one alignment of soft overlapping clusters across timestamps, thereby maintaining the temporal smoothness of clusters. In addition, an implicit correlation encoder is introduced to capture latent correlations between any pair of clusters under the guidance of a global graph. Extensive experiments on seven real-world datasets demonstrate that DECRL achieves the state-of-the-art performances, outperforming the best baseline by an average of 9. 53\%, 12. 98\%, 10. 42\%, and 14. 68\% in MRR, Hits@1, Hits@3, and Hits@10, respectively.

TIST Journal 2024 Journal Article

E 2 Storyline: Visualizing the Relationship with Triplet Entities and Event Discovery

• Yunchao Wang
• Guodao Sun
• Zihao Zhu
• Tong Li
• Ling Chen
• Ronghua Liang

The narrative progression of events, evolving into a cohesive story, relies on the entity-entity relationships. Among the plethora of visualization techniques, storyline visualization has gained significant recognition for its effectiveness in offering an overview of story trends, revealing entity relationships, and facilitating visual communication. However, existing methods for storyline visualization often fall short in accurately depicting the specific relationships between entities. In this study, we present E 2 Storyline, a novel approach that emphasizes simplicity and aesthetics of layout while effectively conveying entity-entity relationships to users. To achieve this, we begin by extracting entity-entity relationships from textual data and representing them as subject-predicate-object (SPO) triplets, thereby obtaining structured data. By considering three types of design requirements, we establish new optimization objectives and model the layout problem using multi-objective optimization (MOO) techniques. The aforementioned SPO triplets, together with time and event information, are incorporated into the optimization model to ensure a straightforward and easily comprehensible storyline layout. Through a qualitative user study, we determine that a pixel-based view is the most suitable method for displaying the relationships between entities. Finally, we apply E 2 Storyline to real-world data, including movie synopses and live text commentaries. Through comprehensive case studies, we demonstrate that E 2 Storyline enables users to better extract information from stories and comprehend the relationships between entities.

AIIM Journal 2024 Journal Article

EHR coding with hybrid attention and features propagation on disease knowledge graph

• Tianhan Xu
• Bin Li
• Ling Chen
• Chao Yang
• Yixun Gu
• Xiang Gu

TIST Journal 2024 Journal Article

FastRx: Exploring Fastformer and Memory-Augmented Graph Neural Networks for Personalized Medication Recommendations

• Nguyen Minh Thao Phan
• Ling Chen
• Chun-Hung Chen
• Wen-Chih Peng

Personalized medication recommendations aim to suggest a set of medications based on the clinical conditions of a patient. Not only should the patient’s diagnosis, procedure, and medication history be considered, but drug-drug interactions (DDIs) must also be taken into account to prevent adverse drug reactions. Although recent studies on medication recommendation have considered DDIs and patient history, personalized disease progression and prescription have not been explicitly modeled. In this work, we proposed FastRx, a Fastformer-based medication recommendation model to capture longitudinality in patient history, in combination with Graph Convolutional Networks (GCNs) to handle DDIs and co-prescribed medications in Electronic Health Records (EHRs). Our extensive experiments on the MIMIC-III dataset demonstrated superior performance of the proposed FastRx over existing state-of-the-art models for medication recommendation. The source code and data used in the experiments are available at https://github.com/pnmthaoct/FastRx.

AAAI Conference 2024 Conference Paper

Human-Guided Moral Decision Making in Text-Based Games

• Zijing Shi
• Meng Fang
• Ling Chen
• Yali Du
• Jun Wang

Training reinforcement learning (RL) agents to achieve desired goals while also acting morally is a challenging problem. Transformer-based language models (LMs) have shown some promise in moral awareness, but their use in different contexts is problematic because of the complexity and implicitness of human morality. In this paper, we build on text-based games, which are challenging environments for current RL agents, and propose the HuMAL (Human-guided Morality Awareness Learning) algorithm, which adaptively learns personal values through human-agent collaboration with minimal manual feedback. We evaluate HuMAL on the Jiminy Cricket benchmark, a set of text-based games with various scenes and dense morality annotations, using both simulated and actual human feedback. The experimental results demonstrate that with a small amount of human feedback, HuMAL can improve task performance and reduce immoral behavior in a variety of games and is adaptable to different personal values.

AAAI Conference 2024 Conference Paper

Large Language Models Are Neurosymbolic Reasoners

• Meng Fang
• Shilong Deng
• Yudi Zhang
• Zijing Shi
• Ling Chen
• Mykola Pechenizkiy
• Jun Wang

A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLMs) as symbolic reasoners. We focus on text-based games, significant benchmarks for agents with natural language capabilities, particularly in symbolic tasks like math, map reading, sorting, and applying common sense in text-based worlds. To facilitate these agents, we propose an LLM agent designed to tackle symbolic challenges and achieve in-game objectives. We begin by initializing the LLM agent and informing it of its role. The agent then receives observations and a set of valid actions from the text-based games, along with a specific symbolic module. With these inputs, the LLM agent chooses an action and interacts with the game environments. Our experimental results demonstrate that our method significantly enhances the capability of LLMs as automated agents for symbolic reasoning, and our LLM agent is effective in text-based games involving symbolic tasks, achieving an average performance of 88% across all tasks.

NeurIPS Conference 2024 Conference Paper

Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach

• Chaoxi Niu
• Guansong Pang
• Ling Chen
• Bing Liu

Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e. g. , node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i. e. , inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID prediction on graph data can be achieved by a Laplacian smoothing-based graph task profiling approach, in which each graph task is modeled by a task prototype based on Laplacian smoothing over the graph. It guarantees that the task prototypes of the same graph task are nearly the same with a large smoothing step, while those of different tasks are distinct due to differences in graph structure and node attributes. Further, to avoid the catastrophic forgetting of the knowledge learned in previous graph tasks, we propose a novel graph prompting approach for GCIL which learns a small discriminative graph prompt for each task, essentially resulting in a separate classification model for each task. The prompt learning requires the training of a single graph neural network (GNN) only once on the first task, and no data replay is required thereafter, thereby obtaining a GCIL model being both replay-free and forget-free. Extensive experiments on four GCIL benchmarks show that i) our task prototype-based method can achieve 100% task ID prediction accuracy on all four datasets, ii) our GCIL model significantly outperforms state-of-the-art competing methods by at least 18% in average CIL accuracy, and iii) our model is fully free of forgetting on the four datasets.

IJCAI Conference 2024 Conference Paper

WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction

• Binqing Wu
• Weiqi Chen
• Wengwei Wang
• Bingqing Peng
• Liang Sun
• Ling Chen

Due to insufficient local area information, numerical weather prediction (NWP) may yield biases for specific areas. Previous studies correct biases mainly by employing handcrafted features or applying data-driven methods intuitively, overlooking the complicated dependencies between weather factors and between areas. To address this issue, we propose WeatherGNN, a local NWP bias-correction method that utilizes Graph Neural Networks (GNNs) to exploit meteorological dependencies and spatial dependencies under the guidance of domain knowledge. Specifically, we introduce a factor GNN to capture area-specific meteorological dependencies adaptively based on spatial heterogeneity and a fast hierarchical GNN to capture dynamic spatial dependencies efficiently guided by Tobler's first and second laws of geography. Our experimental results on two real-world datasets demonstrate that WeatherGNN achieves the state-of-the-art performance, outperforming the best baseline with an average of 4. 75 % on RMSE.

NeurIPS Conference 2023 Conference Paper

Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction

• Zechuan Zhang
• Li Sun
• Zongxin Yang
• Ling Chen
• Yi Yang

Reconstructing 3D clothed human avatars from single images is a challenging task, especially when encountering complex poses and loose clothing. Current methods exhibit limitations in performance, largely attributable to their dependence on insufficient 2D image features and inconsistent query methods. Owing to this, we present the Global-correlated 3D-decoupling Transformer for clothed Avatar reconstruction (GTA), a novel transformer-based architecture that reconstructs clothed human avatars from monocular images. Our approach leverages transformer architectures by utilizing a Vision Transformer model as an encoder for capturing global-correlated image features. Subsequently, our innovative 3D-decoupling decoder employs cross-attention to decouple tri-plane features, using learnable embeddings as queries for cross-plane generation. To effectively enhance feature fusion with the tri-plane 3D feature and human body prior, we propose a hybrid prior fusion strategy combining spatial and prior-enhanced queries, leveraging the benefits of spatial localization and human body prior knowledge. Comprehensive experiments on CAPE and THuman2. 0 datasets illustrate that our method outperforms state-of-the-art approaches in both geometry and texture reconstruction, exhibiting high robustness to challenging poses and loose clothing, and producing higher-resolution textures. Codes are available at https: //github. com/River-Zhang/GTA.

AAAI Conference 2023 Conference Paper

Loan Fraud Users Detection in Online Lending Leveraging Multiple Data Views

• Sha Zhao
• Yongrui Huang
• Ling Chen
• Chunping Wang
• Shijian Li
• Lei Chen
• Gang Pan

In recent years, online lending platforms have been becoming attractive for micro-financing and popular in financial industries. However, such online lending platforms face a high risk of failure due to the lack of expertise on borrowers' creditworthness. Thus, risk forecasting is important to avoid economic loss. Detecting loan fraud users in advance is at the heart of risk forecasting. The purpose of fraud user (borrower) detection is to predict whether one user will fail to make required payments in the future. Detecting fraud users depend on historical loan records. However, a large proportion of users lack such information, especially for new users. In this paper, we attempt to detect loan fraud users from cross domain heterogeneous data views, including user attributes, installed app lists, app installation behaviors, and app-in logs, which compensate for the lack of historical loan records. However, it is difficult to effectively fuse the multiple heterogeneous data views. Moreover, some samples miss one or even more data views, increasing the difficulty in fusion. To address the challenges, we propose a novel end-to-end deep multiview learning approach, which encodes heterogeneous data views into homogeneous ones, generates the missing views based on the learned relationship among all the views, and then fuses all the views together to a comprehensive view for identifying fraud users. Our model is evaluated on a real-world large-scale dataset consisting of 401,978 loan records of 228,117 users from January 1, 2019, to September 30, 2019, achieving the state-of-the-art performance.

AAAI Conference 2023 Conference Paper

Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

• Sheng Xiang
• Mingzhi Zhu
• Dawei Cheng
• Enxia Li
• Ruihui Zhao
• Yi Ouyang
• Ling Chen
• Yefeng Zheng

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.

AAAI Conference 2023 Conference Paper

SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition

• Rong Hu
• Ling Chen
• Shenghuan Miao
• Xing Tang

In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the natural solution to cross-user WHAR under annotation scarcity. Existing UDA models usually align samples across domains without differentiation, which ignores the difference among samples. In this paper, we propose an unsupervised domain adaptation model with sample weight learning (SWL-Adapt) for cross-user WHAR. SWL-Adapt calculates sample weights according to the classification loss and domain discrimination loss of each sample with a parameterized network. We introduce the meta-optimization based update rule to learn this network end-to-end, which is guided by meta-classification loss on the selected pseudo-labeled target samples. Therefore, this network can fit a weighting function according to the cross-user WHAR task at hand, which is superior to existing sample differentiation rules fixed for special scenarios. Extensive experiments on three public WHAR datasets demonstrate that SWL-Adapt achieves the state-of-the-art performance on the cross-user WHAR task, outperforming the best baseline by an average of 3.1% and 5.3% in accuracy and macro F1 score, respectively.

JBHI Journal 2022 Journal Article

A Graph Convolutional Multiple Instance Learning on a Hypersphere Manifold Approach for Diagnosing Chronic Obstructive Pulmonary Disease in CT Images

• Ling Chen
• Qixing Feng
• Xi Yin
• Xiangde Min
• Lei Shi
• Defu Yang
• Yen-Wei Chen
• Daoqiang Zhang

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic disease with high morbidity and mortality. The early diagnosis of COPD is vital for clinical treatment, which helps patients to have a better quality of life. Because COPD can be ascribed to chronic bronchitis and emphysema, lesions in a computed tomography (CT) image can present anywhere inside the lung with different types, shapes and sizes. Multiple instance learning (MIL) is an effective tool for solving COPD discrimination. In this study, a novel graph convolutional MIL with the adaptive additive margin loss (GCMIL-AAMS) approach is proposed to diagnose COPD by CT. Specifically, for those early stage patients, the selected instance-level features can be more discriminative if they were learned by our proposed graph convolution and pooling with self-attention mechanism. The AAMS loss can utilize the information of COPD severity on a hypersphere manifold by adaptively setting the angular margins to improve the performance, as the severity can be quantified as four grades by pulmonary function test. The results show that our proposed GCMIL-AAMS method provides superior discrimination and generalization abilities in COPD discrimination, with areas under a receiver operating characteristic curve (AUCs) of 0. 960 $\pm$ 0. 014 and 0. 862 $\pm$ 0. 010 in the test set and external testing set, respectively, in 5-fold stratified cross validation; moreover, it demonstrates that graph learning is applicable to MIL and suggests that MIL may be adaptable to graph learning.

NeurIPS Conference 2022 Conference Paper

Mask Matching Transformer for Few-Shot Segmentation

• Siyu Jiao
• Gengwei Zhang
• Shant Navasardyan
• Ling Chen
• Yao Zhao
• Yunchao Wei
• Humphrey Shi

In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results. However, to obtain satisfactory segments, such a paradigm needs to couple the learning of the matching operations with heavy segmentation modules, limiting the flexibility of design and increasing the learning complexity. To alleviate this issue, we propose Mask Matching Transformer (MM-Former), a new paradigm for the few-shot segmentation task. Specifically, MM-Former first uses a class-agnostic segmenter to decompose the query image into multiple segment proposals. Then, a simple matching mechanism is applied to merge the related segment proposals into the final mask guided by the support images. The advantages of our MM-Former are two-fold. First, the MM-Former follows the paradigm of 'decompose first and then blend', allowing our method to benefit from the advanced potential objects segmenter to produce high-quality mask proposals for query images. Second, the mission of prototypical features is relaxed to learn coefficients to fuse correct ones within a proposal pool, making the MM-Former be well generalized to complex scenarios or cases. We conduct extensive experiments on the popular COCO-$20^i$ and Pascal-$5^i$ benchmarks. Competitive results well demonstrate the effectiveness and the generalization ability of our MM-Former. Code is available at https: //github. com/Picsart-AI-Research/Mask-Matching-Transformer.

TCS Journal 2021 Journal Article

Online algorithms for BP functions maximization

• Zhicheng Liu
• Ling Chen
• Hong Chang
• Donglei Du
• Xiaoyan Zhang

NeurIPS Conference 2020 Conference Paper

Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

• Yunqiu Xu
• Meng Fang
• Ling Chen
• Yali Du
• Joey Tianyi Zhou
• Chengqi Zhang

We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.

IJCAI Conference 2020 Conference Paper

Discrete Embedding for Latent Networks

• Hong Yang
• Ling Chen
• Minglong Lei
• Lingfeng Niu
• Chuan Zhou
• Peng Zhang

Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e. g. , edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.

AAAI Conference 2020 Conference Paper

Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

• Weiqi Chen
• Ling Chen
• Yu Xie
• Wei Cao
• Yusong Gao
• Xiaojie Feng

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i. e. , the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the stateof-the-art results.

TIST Journal 2020 Journal Article

Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification

• Wenhe Liu
• Xiaojun Chang
• Ling Chen
• Dinh Phung
• Xiaoqin Zhang
• Yi Yang
• Alexander G. Hauptmann

The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.

IJCAI Conference 2020 Conference Paper

Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling

• Yaqiong Li
• Xuhui Fan
• Ling Chen
• Bin Li
• Zheng Yu
• Scott A. Sisson

The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent long-term temporal dependence modelling, which outperforms the one-order Markov descriptions in most of the dynamic probabilistic frameworks; (3) the computational cost scales to the number of positive links only. In addition, we develop a new inference strategy, which first upward-and-backward propagates latent counts and then downward-and-forward samples variables, to enable efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to dynamic relational data problems. The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance.

AAAI Conference 2019 Conference Paper

Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection

• Yanbin Liu
• Yan Yan
• Ling Chen
• Yahong Han
• Yi Yang

In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the stateof-the-art online learning methods in the online-batch setting.

IJCAI Conference 2019 Conference Paper

Low-Bit Quantization for Attributed Network Representation Learning

• Hong Yang
• Shirui Pan
• Ling Chen
• Chuan Zhou
• Peng Zhang

Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data redundancy or in binary coding spaces which incur significant loss of representation accuracy. To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy. Specifically, we formulate a new representation learning function based on matrix factorization that can jointly learn the low-bit node representations and the layer aggregation weights under the low-bit quantization constraint. Because the new learning function falls into the category of mixed integer optimization, we propose an efficient mixed-integer based alternating direction method of multipliers (ADMM) algorithm as the solution. Experiments on real-world node classification and link prediction tasks validate the promising results of the proposed LQANR model.

NeurIPS Conference 2019 Conference Paper

Scalable Deep Generative Relational Model with High-Order Node Dependence

• Xuhui Fan
• Bin Li
• Caoyuan Li
• Scott SIsson
• Ling Chen

In this work, we propose a probabilistic framework for relational data modelling and latent structure exploring. Given the possible feature information for the nodes in a network, our model builds up a deep architecture that can approximate to the possible nonlinear mappings between the nodes' feature information and latent representations. For each node, we incorporate all its neighborhoods' high-order structure information to generate latent representation, such that these latent representations are ``smooth'' in terms of the network. Since the latent representations are generated from Dirichlet distributions, we further develop a data augmentation trick to enable efficient Gibbs sampling for Ber-Poisson likelihood with Dirichlet random variables. Our model can be ready to apply to large sparse network as its computations cost scales to the number of positive links in the networks. The superior performance of our model is demonstrated through improved link prediction performance on a range of real-world datasets.

IJCAI Conference 2018 Conference Paper

Efficient Attributed Network Embedding via Recursive Randomized Hashing

• Wei Wu
• Bin Li
• Ling Chen
• Chengqi Zhang

Attributed network embedding aims to learn a low-dimensional representation for each node of a network, considering both attributes and structure information of the node. However, the learning based methods usually involve substantial cost in time, which makes them impractical without the help of a powerful workhorse. In this paper, we propose a simple yet effective algorithm, named NetHash, to solve this problem only with moderate computing capacity. NetHash employs the randomized hashing technique to encode shallow trees, each of which is rooted at a node of the network. The main idea is to efficiently encode both attributes and structure information of each node by recursively sketching the corresponding rooted tree from bottom (i. e. , the predefined highest-order neighboring nodes) to top (i. e. , the root node), and particularly, to preserve as much information closer to the root node as possible. Our extensive experimental results show that the proposed algorithm, which does not need learning, runs significantly faster than the state-of-the-art learning-based network embedding methods while achieving competitive or even better performance in accuracy.

AAAI Conference 2018 Conference Paper

Search Action Sequence Modeling With Long Short-Term Memory for Search Task Success Evaluation

• Alin Fan
• Ling Chen
• Gencai Chen

Search task success rate is a crucial metric based on the search experience of users to measure the performance of search systems. Modeling search action sequence would help to capture the latent search patterns of users in successful and unsuccessful search tasks. Existing approaches use aggregated features to describe the user behavior in search action sequences, which depend on heuristic hand-crafted feature design and ignore a lot of information inherent in the user behavior. In this paper, we employ Long Short-Term Memory (LSTM) that performs end-to-end fine-tuning during the training to learn search action sequence representation for search task success evaluation. Concretely, we normalize the search action sequences by introducing a dummy idle action, which guarantees that the time intervals between contiguous actions are fixed. Simultaneously, we propose a novel data augmentation strategy to increase the pattern variations on search action sequence data to improve the generalization ability of LSTM. We evaluate the proposed approach on open datasets with two different definitions of search task success. The experimental results show that the proposed approach achieves significant performance improvement compared with several excellent search task success evaluation approaches.

AAAI Conference 2018 Conference Paper

Semi-Supervised Bayesian Attribute Learning for Person Re-Identification

• Wenhe Liu
• Xiaojun Chang
• Ling Chen
• Yi Yang

Person re-identification (re-ID) tasks aim to identify the same person in multiple images captured from non-overlapping camera views. Most previous re-ID studies have attempted to solve this problem through either representation learning or metric learning, or by combining both techniques. Representation learning relies on the latent factors or attributes of the data. In most of these works, the dimensionality of the factors/attributes has to be manually determined for each new dataset. Thus, this approach is not robust. Metric learning optimizes a metric across the dataset to measure similarity according to distance. However, choosing the optimal method for computing these distances is data dependent, and learning the appropriate metric relies on a sufficient number of pair-wise labels. To overcome these limitations, we propose a novel algorithm for person re-ID, called semi-supervised Bayesian attribute learning. We introduce an Indian Buffet Process to identify the priors of the latent attributes. The dimensionality of attributes factors is then automatically determined by nonparametric Bayesian learning. Meanwhile, unlike traditional distance metric learning, we propose a reidentification probability distribution to describe how likely it is that a pair of images contains the same person. This technique relies solely on the latent attributes of both images. Moreover, pair-wise labels that are not known can be estimated from pair-wise labels that are known, making this a robust approach for semi-supervised learning. Extensive experiments demonstrate the superior performance of our algorithm over several state-of-the-art algorithms on small-scale datasets and comparable performance on large-scale re-ID datasets.

AAAI Conference 2018 Conference Paper

Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data

• Guansong Pang
• Longbing Cao
• Ling Chen
• Defu Lian
• Huan Liu

The large proportion of irrelevant or noisy features in reallife high-dimensional data presents a significant challenge to subspace/feature selection-based high-dimensional outlier detection (a. k. a. outlier scoring) methods. These methods often perform the two dependent tasks: relevant feature subset search and outlier scoring independently, consequently retaining features/subspaces irrelevant to the scoring method and downgrading the detection performance. This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue. SEMSE learns the sequential ensembles to mutually refine feature selection and outlier scoring by iterative sparse modeling with outlier scores as the pseudo target feature. CINFO instantiates SEMSE by using three successive recurrent components to build such sequential ensembles. Given outlier scores output by an existing outlier scoring method on a feature subset, CINFO first defines a Cantelli’s inequality-based outlier thresholding function to select outlier candidates with a false positive upper bound. It then performs lasso-based sparse regression by treating the outlier scores as the target feature and the original features as predictors on the outlier candidate set to obtain a feature subset that is tailored for the outlier scoring method. Our experiments show that two different outlier scoring methods enabled by CINFO (i) perform significantly better on 11 real-life high-dimensional data sets, and (ii) have much better resilience to noisy features, compared to their bare versions and three state-of-theart competitors. The source code of CINFO is available at https: //sites. google. com/site/gspangsite/sourcecode.

IJCAI Conference 2017 Conference Paper

Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection

• Guansong Pang
• Longbing Cao
• Ling Chen
• Huan Liu

This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i. e. , data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i. e. , abnormal behaviors are not independent - they bond together) in constructing a noise-resilient outlier scoring function to produce a reliable outlier ranking in each iteration. We show that HOUR (i) retains a 2-approximation outlier ranking to the optimal one; and (ii) significantly outperforms five state-of-the-art competitors on 15 real-world data sets with different noise levels in terms of AUC and/or P@n. The source code of HOUR is available at https: //sites. google. com/site/gspangsite/sourcecode.

TIST Journal 2017 Journal Article

ST-SAGE

• Weiqing Wang
• Hongzhi Yin
• Ling Chen
• Yizhou Sun
• Shazia Sadiq
• Xiaofang Zhou

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user’s home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd’s preferences over a well-designed spatial index structure called the spatial pyramid. To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.

IJCAI Conference 2016 Conference Paper

Outlier Detection in Complex Categorical Data by Modeling the Feature Value Couplings

• Guansong Pang
• Longbing Cao
• Ling Chen

This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features. Existing pattern-based outlier detection methods are ineffective in handling such complex scenarios, as they misfit such data. CBRW estimates outlier scores of feature values by modelling feature value level couplings, which carry intrinsic data characteristics, via biased random walks to handle this complex data. The outlier scores of feature values can either measure the outlierness of an object or facilitate the existing methods as a feature weighting and selection indicator. Substantial experiments show that CBRW can not only detect outliers in complex data significantly better than the state-of-the-art methods, but also greatly improve the performance of existing methods on data sets with many noisy features.

ICRA Conference 2014 Conference Paper

Single beacon based multi-robot cooperative localization using Moving Horizon Estimation

• Sen Wang 0002
• Ling Chen
• Dongbing Gu
• Huosheng Hu

This paper studies three-dimensional multi-robot Cooperative Localization (CL) problem. Most of existing CL strategies adopt Extended Kalman Filter (EKF) or Maximum a Posteriori (MAP). In this paper, a novel approach based on Moving Horizon Estimation (MHE) is proposed. The main contribution of this paper is twofold: 1) MHE is integrated with EKF for three-dimensional CL using single mobile beacon, which can bound localization error, impose various constraints on states and noises, and make use of previous range measurements for current estimation. 2) A sufficient condition on observability of multi-robot CL is derived by using Fisher Information Matrix. Simulation is conducted to verify that the proposed MHE based CL algorithm outperforms EKF based method in terms of localization accuracy, and two scenarios where our algorithm is superior to EKF are discussed.

IROS Conference 2013 Conference Paper

Single beacon based localization of AUVs using moving Horizon estimation

• Sen Wang 0002
• Ling Chen
• Huosheng Hu
• Dongbing Gu

This paper studies the underwater localization problem for a school of robotic fish, i. e. , a kind of Autonomous Underwater Vehicles with limited size, power and payload. These robotic fish cannot be equipped with traditional underwater localization sensors that are big and heavy. The proposed localization system is performed by using a single surface mobile beacon which provides range measurement to bound the localization error. The main contribution of this paper lies in twofold: 1) Observability of single beacon based localization is first analyzed in the context of nonlinear discrete time system, deriving a sufficient condition on observability. 2) Moving Horizon Estimation is then integrated with Extended Kalman Filters for three-dimensional localization using single beacon, which can reduce the computational complexity, impose various constraints and make use of previous range measurements for current estimation. Extensive numerical simulations are conducted to verify the observability and high localization accuracy of the proposed underwater localization method.

AAAI Conference 2013 Conference Paper

WordNet Based Multi-Way Concept Hierarchy Construction from Text Corpus

• Ding Tu
• Ling Chen
• Gencai Chen

In this paper, we propose an approach to build a multi-way concept hierarchy from a text corpus, which is based on WordNet and multi-way hierarchical clustering. In addition, a new evaluation metric is presented, and our approach is compared with 4 kinds of existing methods on the Amazon Customer Review data set.

AAAI Conference 2012 Conference Paper

Mining Context-Aware Significant Travel Sequences from Geotagged Social Media

• Abdul Majid
• Ling Chen
• Hamid Mirza
• Ibrar Hussain
• Gencai Chen

AIIM Journal 2011 Journal Article

Resolution of redundant semantic type assignments for organic chemicals in the UMLS

• C. Paul Morrey
• Ling Chen
• Michael Halper
• Yehoshua Perl