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Xin Du

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24 papers

2 author rows

JBHI Journal 2026 Journal Article

FDEPCA: A Novel Adaptive Nonlinear Feature Extraction Method via Fruit Fly Olfactory Neural Network for IoMT Anomaly Detection

Yihan Chen
Zhixia Zeng
Xinhong Lin
Xin Du
Imad Rida
Ruliang Xiao

With the rapid development of 5G communication technology, the data in the Internet of Medical Things (IoMT) application systems exhibits complex characteristics such as large volume, high dimensionality, nonlinearity, and diversity, which significantly affect the efficiency and detection performance of anomaly detection tasks. How to efficiently extract nonlinear features from high-dimensional data in the context of the IoMT while minimizing information distortion in data objects are challenging problems in recent academic research. A novel adaptive nonlinear feature extraction method via fruit fly olfactory neural network (Fly dimension expansion projection and remain main components by PCA, FDEPCA) is proposed, where 1) the data are mean-centered; 2) a binary sparse random projection matrix is used for dimension expansion projection; and 3) PCA is used to extract principal component information. The proposed method overcomes the problems of present nonlinear feature extraction in the face of high-dimensional outliers where the intrinsic geometric structure of the data is severely distorted and computationally expensive. The dataset after nonlinear feature extraction by the FDEPCA algorithm is applied to specific anomaly detection models, using ROC curves and AUC as evaluation metrics for classification performance. Extensive comparison experiments are conducted on eight publicly available datasets, and experimental results show that compared with the popular nonlinear feature extraction algorithms, the FDEPCA algorithm has better classification performance and projection time advantage. When applied to proximity-based, probability-based, and ensemble-based different anomaly detection models respectively, the FDEPCA algorithm exhibits strong applicability in different types of anomaly detection classifiers.

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IJCAI Conference 2025 Conference Paper

Backdoor Attack on Vertical Federated Graph Neural Network Learning

Jirui Yang
Peng Chen
Zhihui Lu
Jianping Zeng
Qiang Duan
Xin Du
Ruijun Deng

Federated Graph Neural Network (FedGNN) integrate federated learning (FL) with graph neural networks (GNNs) to enable privacy-preserving training on distributed graph data. Vertical Federated Graph Neural Network (VFGNN), a key branch of FedGNN, handles scenarios where data features and labels are distributed among participants. Despite the robust privacy-preserving design of VFGNN, we have found that it still faces the risk of backdoor attacks, even in situations where labels are inaccessible. This paper proposes BVG, a novel backdoor attack method that leverages multi-hop triggers and backdoor retention, requiring only four target-class nodes to execute effective attacks. Experimental results demonstrate that BVG achieves nearly 100% attack success rates across three commonly used datasets and three GNN models, with minimal impact on the main task accuracy. We also evaluated various defense methods, and the BVG method maintained high attack effectiveness even under existing defenses. This finding highlights the need for advanced defense mechanisms to counter sophisticated backdoor attacks in practical VFGNN applications.

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NeurIPS Conference 2025 Conference Paper

In the field of AI-driven human-GUI interaction automation, while rapid advances in multimodal large language models and reinforcement fine-tuning techniques have yielded remarkable progress, a fundamental challenge persists: their interaction logic significantly deviates from natural human-GUI communication patterns. To address this gap, we propose Blink–Think–Link (BTL), a brain-inspired framework for human-GUI interaction that mimics the human cognitive process between users and graphical interfaces. The system decomposes interactions into three biologically plausible phases: (1) \textbf{Blink} - rapid detection and attention to relevant screen areas, analogous to saccadic eye movements; (2) \textbf{Think} - higher-level reasoning and decision-making, mirroring cognitive planning; and (3) \textbf{Link} - generation of executable commands for precise motor control, emulating human action selection mechanisms. Additionally, we introduce two key technical innovations for BTL framework: (1) Blink Data Generation - an automated annotation pipeline specifically optimized for blink data, and (2) {BTL Reward – the first rule-based reward mechanism that enables reinforcement learning driven by both process and outcome. } Building upon this framework, we develop a GUI agent model named BTL-UI, which demonstrates competitive performance across both static GUI understanding and dynamic interaction tasks in comprehensive benchmarks. These results provide conclusive empirical validation of the framework's efficacy in developing advanced GUI agents.

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NeurIPS Conference 2025 Conference Paper

Correlation Dimension of Autoregressive Large Language Models

Xin Du
Kumiko Tanaka-Ishii

Large language models (LLMs) have achieved remarkable progress in natural language generation, yet they continue to display puzzling behaviors—such as repetition and incoherence—even when exhibiting low perplexity. This highlights a key limitation of conventional evaluation metrics, which emphasize local prediction accuracy while overlooking long-range structural complexity. We introduce correlation dimension, a fractal-geometric measure of self-similarity, to quantify the epistemological complexity of text as perceived by a language model. This measure captures the hierarchical recurrence structure of language, bridging local and global properties in a unified framework. Through extensive experiments, we show that correlation dimension (1) reveals three distinct phases during pretraining, (2) reflects context-dependent complexity, (3) indicates a model's tendency toward hallucination, and (4) reliably detects multiple forms of degeneration in generated text. The method is computationally efficient, robust to model quantization (down to 4-bit precision), broadly applicable across autoregressive architectures (e. g. , Transformer and Mamba), and provides fresh insight into the generative dynamics of LLMs.

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IJCAI Conference 2025 Conference Paper

Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks

Di Yu
Changze Lv
Xin Du
Linshan Jiang
Qing Yin
Wentao Tong
Xiaoqing Zheng
Shuiguang Deng

On-device sequential recommendation (SR) systems are designed to make local inferences using real-time features, thereby alleviating the communication burden on server-based recommenders when handling concurrent requests from millions of users. However, the resource constraints of edge devices, including limited memory and computational capacity, pose significant challenges to deploying efficient SR models. Inspired by the energy-efficient and sparse computing properties of deep Spiking Neural Networks (SNNs), we propose a cost-effective on-device SR model named SSR, which encodes dense embedding representations into sparse spike-wise representations and integrates novel spiking filter modules to extract temporal patterns and critical features from item sequences, optimizing computational and memory efficiency without sacrificing recommendation accuracy. Extensive experiments on real-world datasets demonstrate the superiority of SSR. Compared to other SR baselines, SSR achieves comparable recommendation performance while reducing energy consumption by an average of 59. 43%. In addition, SSR significantly lowers memory usage, making it particularly well-suited for deployment on resource-constrained edge devices.

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EAAI Journal 2025 Journal Article

Coupled flows as guidance for model-based policy optimization

Shengrong Gong
Yi Wang
Xin Du
Yuya Sun
Lifan Zhou
Shan Zhong

Model-based reinforcement learning (MBRL) offers high sample efficiency but suffers from cumulative multi-step prediction errors that degrade long-term performance. To address this, we propose a coupled flows-guided policy optimization framework, where two coupled flows quantify and minimize the discrepancy between the true and learned state–action distributions. By reducing this divergence, the loss functions serve as both a discriminator, selecting more accurate rollouts for policy learning, and a reward signal, refining the dynamics model to mitigate multi-step errors. Theoretical analysis establishes a bound on the expected return discrepancy. Empirical evaluations demonstrate that our method achieves higher cumulative rewards than the representative model-based approaches across diverse control tasks. This highlights its applicability in data-scarce domains such as robotics, recommendation systems, and autonomous driving.

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IJCAI Conference 2025 Conference Paper

ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks

Di Yu
Changze Lv
Xin Du
Linshan Jiang
Wentao Tong
Zhenyu Liao
Xiaoqing Zheng
Shuiguang Deng

Most edge-cloud collaboration frameworks rely on the substantial computational and storage capabilities of cloud-based artificial neural networks (ANNs). However, this reliance results in significant communication overhead between edge devices and the cloud, as well as high computational energy consumption, especially when applied to resource-constrained edge devices. To address these challenges, we propose ECC-SNN, a novel edge-cloud collaboration framework that incorporates energy-efficient spiking neural networks (SNNs) to offload more computational workload from the cloud to the edge, thereby improving cost-effectiveness and reducing reliance on the cloud. ECC-SNN employs a joint training approach that integrates ANN and SNN models, enabling edge devices to leverage knowledge from cloud models for enhanced performance while reducing energy consumption and processing latency. Furthermore, ECC-SNN features an on-device incremental learning algorithm that enables edge models to continuously adapt to dynamic environments, reducing the communication overhead and resource consumption associated with frequent cloud update requests. Extensive experimental results on four datasets demonstrate that ECC-SNN improves accuracy by 4. 15%, reduces average energy consumption by 79. 4%, and lowers average processing latency by 39. 1%.

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IJCAI Conference 2025 Conference Paper

Exploiting Label Skewness for Spiking Neural Networks in Federated Learning

Di Yu
Xin Du
Linshan Jiang
Huijing Zhang
Shuiguang Deng

The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive data collected by these devices. To safeguard data privacy, federated learning (FL) facilitates collaborative SNN-based model training by leveraging data distributed across edge devices without transmitting local data to a central server. However, existing FL approaches encounter challenges in handling label-skewed data across devices, inducing drift in the local SNN model and consequently impairing the performance of the global SNN model. To tackle these problems, we propose a novel framework called FedLEC, which incorporates intra-client label weight calibration to balance the learning intensity across local labels and inter-client knowledge distillation to mitigate local SNN model bias caused by label absence. Extensive experiments with three different structured SNNs across five datasets (i. e. , three non-neuromorphic and two neuromorphic datasets) demonstrate the efficiency of FedLEC. Compared to seven state-of-the-art FL algorithms, FedLEC achieves an average accuracy improvement of approximately 11. 59% for the global SNN model under various label skew distribution settings.

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AAAI Conference 2025 Conference Paper

Information-Theoretic Generative Clustering of Documents

Xin Du
Kumiko Tanaka-Ishii

We present *generative clustering* (GC) for clustering a set of documents, X, by using texts Y generated by large language models (LLMs) instead of by clustering the original documents X. Because LLMs provide probability distributions, the similarity between two documents can be rigorously defined in an information-theoretic manner by the KL divergence. We also propose a natural, novel clustering algorithm by using importance sampling. We show that GC outperforms any previous clustering method, often by a large margin. Furthermore, we show an application to generative document retrieval in which documents are indexed via hierarchical clustering and our method improves the retrieval accuracy.

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IS Journal 2025 Journal Article

SNNL: A Programming Language for SNN Development

Qinghui Xing
Zirun Li
Ying Li
Schahram Dustdar
Xin Du
Gang Pan
Shuiguang Deng

Spiking Neural Networks (SNNs) are gaining attention for biological plausibility and energy efficiency. Advances in neuromorphic systems—integrating hardware and software tools—accelerate SNN implementation. Yet, deploying SNNs on such platforms remains challenging due to model complexity and system heterogeneity, requiring flexible frameworks. Existing tools (e. g. , PyNN, Brian2) show limited expressiveness for neuromorphic applications or poor cross-platform support. This paper proposes SNNL, a flexible domain-specific language for SNN development and deployment on neuromorphic hardware. SNNL decouples neuronal dynamics modeling from network topology specification: equation-based representations handle diverse neuron/synapse models, while hierarchical constructs define complex connectivity patterns. We present a Darwin3-targeted compiler with efficient code generation. Evaluations confirm SNNL achieves precise neuronal dynamic descriptions and flexible network configurations. This work bridges algorithm-hardware gaps in neuromorphic computing by enhancing programmability. Experimental results have demonstrated the feasibility of SNNL in developing SNNs for neuromorphic systems.

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IJCAI Conference 2025 Conference Paper

Universal Backdoor Defense via Label Consistency in Vertical Federated Learning

Peng Chen
Haolong Xiang
Xin Du
Xiaolong Xu
Xuhao Jiang
Zhihui Lu
Jirui Yang
Qiang Duan

Backdoor attacks in vertical federated learning (VFL) are particularly concerning as they can covertly compromise VFL decision-making, posing a severe threat to critical applications of VFL. Existing defense mechanisms typically involve either label obfuscation during training or model pruning during inference. However, the inherent limitations on the defender's access to the global model and complete training data in VFL environments fundamentally constrain the effectiveness of these conventional methods. To address these limitations, we propose the Universal Backdoor Defense (UBD) framework. UBD leverages Label Consistent Clustering (LCC) to synthesize plausible latent triggers associated with the backdoor class. This synthesized information is then utilized for mitigating backdoor threats through Linear Probing (LP), guided by a constraint on Batch Normalization (BN) statistics. Positioned within a unified VFL backdoor defense paradigm, UBD offers a generalized framework for both detection and mitigation that critically does not necessitate access to the entire model or dataset. Extensive experiments across multiple datasets rigorously demonstrate the efficacy of the UBD framework, achieving state-of-the-art performance against diverse backdoor attack types in VFL, including both dirty-label and clean-label variants.

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ICML Conference 2024 Conference Paper

Bottleneck-Minimal Indexing for Generative Document Retrieval

Xin Du
Lixin Xiu
Kumiko Tanaka-Ishii

We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document $x \in \mathcal{X}$ is indexed by $t \in \mathcal{T}$, and a neural autoregressive model is trained to map queries $\mathcal{Q}$ to $\mathcal{T}$. GDR can be considered to involve information transmission from documents $\mathcal{X}$ to queries $\mathcal{Q}$, with the requirement to transmit more bits via the indexes $\mathcal{T}$. By applying Shannon’s rate-distortion theory, the optimality of indexing can be analyzed in terms of the mutual information, and the design of the indexes $\mathcal{T}$ can then be regarded as a bottleneck in GDR. After reformulating GDR from this perspective, we empirically quantify the bottleneck underlying GDR. Finally, using the NQ320K and MARCO datasets, we evaluate our proposed bottleneck-minimal indexing method in comparison with various previous indexing methods, and we show that it outperforms those methods.

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IJCAI Conference 2024 Conference Paper

EC-SNN: Splitting Deep Spiking Neural Networks for Edge Devices

Di Yu
Xin Du
Linshan Jiang
Wentao Tong
Shuiguang Deng

Deep Spiking Neural Networks (SNNs), as an advanced form of SNNs characterized by their multi-layered structure, have recently achieved significant breakthroughs in performance across various domains. The biological plausibility and energy efficiency of SNNs naturally align with the requisites of edge computing (EC) scenarios, thereby prompting increased interest among researchers to explore the migration of these deep SNN models onto edge devices such as sensors and smartphones. However, the progress of migration work has been notably challenging due to the influence of the substantial increase in model parameters and the demanding computational requirements in practical applications. In this work, we propose a deep SNN splitting framework named EC-SNN to run the intricate SNN models on edge devices. We first partition the full SNN models into smaller sub-models to allocate their model parameters on multiple edge devices. Then, we provide a channel-wise pruning method to reduce the size of each sub-model, thereby further reducing the computational load. We design extensive experiments on six datasets (i. e. , four non-neuromorphic and two neuromorphic datasets) to substantiate that our approach can significantly diminish the inference execution latency on edge devices and reduce the overall energy consumption per deployed device with an average reduction of 60. 7% and 27. 7% respectively while keeping the effectiveness of the accuracy.

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EAAI Journal 2024 Journal Article

SCGRFuse: An infrared and visible image fusion network based on spatial/channel attention mechanism and gradient aggregation residual dense blocks

Yong Wang
Jianfei Pu
Duoqian Miao
L. Zhang
Lulu Zhang
Xin Du

The goal of image fusion is to retain the strengths of different images in the fused result. However, existing fusion algorithms are often complex in design and overlook the influence of attention mechanisms on deep features. To address these issues, we propose an image fusion network based on spatial/channel attention mechanisms and gradient-aggregated residual dense blocks(SCGRFuse). Firstly, we design a novel gradient-aggregated residual dense block (GRXDB) that combines the advantages of ResNeXt and DenseNet, which integrating the Sobel and Laplacian operators to preserve both strong and weak texture features. Then, we introduce spatial and channel attention mechanisms to refine the channel and spatial information of feature maps, enhancing their information capturing capability. Additionally, we leverage a pooling fusion block to merge the refined spatial and channel feature maps, yielding high-quality fusion features. Compared to the existing state-of-the-art methods, experimental results on the MSRS, RoadScene and TNO datasets demonstrate the outstanding fusion performance of our proposed approach. In addition, in the task-driven experiments, SCGRFuse achieved an mIoU accuracy of 71. 37%.

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IJCAI Conference 2022 Conference Paper

DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework

Chao Zhang
Zhijian Li
Xin Du
Hui Qian

The recently developed Particle-based Variational Inference (ParVI) methods drive the empirical distribution of a set of fixed-weight particles towards a given target distribution by iteratively updating particles' positions. However, the fixed weight restriction greatly confines the empirical distribution's approximation ability, especially when the particle number is limited. In this paper, we propose to dynamically adjust particles' weights according to a Fisher-Rao reaction flow. We develop a general Dynamic-weight Particle-based Variational Inference (DPVI) framework according to a novel continuous composite flow, which evolves the positions and weights of particles simultaneously. We show that the mean-field limit of our composite flow is actually a Wasserstein-Fisher-Rao gradient flow of the associated dissimilarity functional. By using different finite-particle approximations in our general framework, we derive several efficient DPVI algorithms. The empirical results demonstrate the superiority of our derived DPVI algorithms over their fixed-weight counterparts.

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NeurIPS Conference 2022 Conference Paper

FIRE: Semantic Field of Words Represented as Non-Linear Functions

Xin Du
Kumiko Tanaka-Ishii

State-of-the-art word embeddings presume a linear vector space, but this approach does not easily incorporate the nonlinearity that is necessary to represent polysemy. We thus propose a novel semantic FIeld REepresentation, called FIRE, which is a $D$-dimensional field in which every word is represented as a set of its locations and a nonlinear function covering the field. The strength of a word's relation to another word at a certain location is measured as the function value at that location. With FIRE, compositionality is represented via functional additivity, whereas polysemy is represented via the set of points and the function's multimodality. By implementing FIRE for English and comparing it with previous representation methods via word and sentence similarity tasks, we show that FIRE produces comparable or even better results. In an evaluation of polysemy to predict the number of word senses, FIRE greatly outperformed BERT and Word2vec, providing evidence of how FIRE represents polysemy. The code is available at https: //github. com/kduxin/firelang.

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NeurIPS Conference 2022 Conference Paper

Risk-Driven Design of Perception Systems

Anthony Corso
Sydney Katz
Craig Innes
Xin Du
Subramanian Ramamoorthy
Mykel J Kochenderfer

Modern autonomous systems rely on perception modules to process complex sensor measurements into state estimates. These estimates are then passed to a controller, which uses them to make safety-critical decisions. It is therefore important that we design perception systems to minimize errors that reduce the overall safety of the system. We develop a risk-driven approach to designing perception systems that accounts for the effect of perceptual errors on the performance of the fully-integrated, closed-loop system. We formulate a risk function to quantify the effect of a given perceptual error on overall safety, and show how we can use it to design safer perception systems by including a risk-dependent term in the loss function and generating training data in risk-sensitive regions. We evaluate our techniques on a realistic vision-based aircraft detect and avoid application and show that risk-driven design reduces collision risk by 37% over a baseline system.

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YNIMG Journal 2022 Journal Article

The role of low-frequency oscillations in three-dimensional perception with depth cues in virtual reality

Zhili Tang
Xiaoyu Liu
Hongqiang Huo
Min Tang
Tao Liu
Zhixin Wu
Xiaofeng Qiao
Duo Chen

Currently, vision-related neuroscience studies are undergoing a trend from simplified image stimuli toward more naturalistic stimuli. Virtual reality (VR), as an emerging technology for visual immersion, provides more depth cues for three-dimensional (3D) presentation than two-dimensional (2D) image. It is still unclear whether the depth cues used to create 3D visual perception modulate specific cortical activation. Here, we constructed two visual stimuli presented by stereoscopic vision in VR and graphical projection with 2D image, respectively, and used electroencephalography to examine neural oscillations and their functional connectivity during 3D perception. We find that neural oscillations are specific to delta and theta bands in stereoscopic vision and the functional connectivity in the two bands increase in cortical areas related to visual pathways. These findings indicate that low-frequency oscillations play an important role in 3D perception with depth cues.

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IS Journal 2021 Journal Article

GSMNet: Global Semantic Memory Network for Aspect-Level Sentiment Classification

Zhiyue Liu
Jiahai Wang
Xin Du
Yanghui Rao
Xiaojun Quan

Aspect-level sentiment classification determines the sentiment polarity of a targeted aspect. To solve this task, attention-based neural networks are typically adopted to explore the interaction between the aspect and its context in a single sentence. However, such approaches ignore the rich semantic information that can be obtained from other sentences. This article shows that the contexts of aspects with similar meanings should be considered global semantic information that can be incorporated as domain knowledge. Then, a novel global semantic memory network (GSMNet) is proposed to share the global semantic information of various aspects and generate a domain-specific representation. With the help of domain knowledge, crucial words can be focused on more precisely. Moreover, instead of employing the concatenating operation for vectors before classification, GSMNet adopts a fine-grained information fusion layer to capture the importance of representations for efficiently extracting the valid parts of each dimension. The experimental results demonstrate the effectiveness of our model.

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IJCAI Conference 2021 Conference Paper

SHPOS: A Theoretical Guaranteed Accelerated Particle Optimization Sampling Method

Zhijian Li
Chao Zhang
Hui Qian
Xin Du
Lingwei Peng

Recently, the Stochastic Particle Optimization Sampling (SPOS) method is proposed to solve the particle-collapsing pitfall of deterministic Particle Variational Inference methods by ultilizing the stochastic Overdamped Langevin dynamics to enhance exploration. In this paper, we propose an accelerated particle optimization sampling method called Stochastic Hamiltonian Particle Optimization Sampling (SHPOS). Compared to the first-order dynamics used in SPOS, SHPOS adopts an augmented second-order dynamics, which involves an extra momentum term to achieve acceleration. We establish a non-asymptotic convergence analysis for SHPOS, and show that it enjoys a faster convergence rate than SPOS. Besides, we also propose a variance-reduced stochastic gradient variant of SHPOS for tasks with large-scale datasets and complex models. Experiments on both synthetic and real data validate our theory and demonstrate the superiority of SHPOS over the state-of-the-art.

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ECAI Conference 2020 Conference Paper

A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage

Siyuan Chen 0005
Jiahai Wang
Xin Du
Yanqing Hu

User identity linkage across social networks is an essential problem for cross-network data mining. Since network structure, profile and content information describe different aspects of users, it is critical to learn effective user representations that integrate heterogeneous information. This paper proposes a novel framework with INformation FUsion and Neighborhood Enhancement (INFUNE) for user identity linkage. The information fusion component adopts a group of encoders and decoders to fuse heterogeneous information and generate discriminative node embeddings for preliminary matching. Then, these embeddings are fed to the neighborhood enhancement component, a novel graph neural network, to produce adaptive neighborhood embeddings that reflect the overlapping degree of neighborhoods of varying candidate user pairs. The importance of node embeddings and neighborhood embeddings are weighted for final prediction. The proposed method is evaluated on real-world social network data. The experimental results show that INFUNE significantly outperforms existing state-of-the-art methods.

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AAAI Conference 2020 Conference Paper

Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data

Xin Du
Yulong Pei
Wouter Duivesteijn
Mykola Pechenizkiy

While recent advances in machine learning put many focuses on fairness of algorithmic decision making, topics about fairness of representation, especially fairness of network representation, are still underexplored. Network representation learning learns a function mapping nodes to low-dimensional vectors. Structural properties, e. g. communities and roles, are preserved in the latent embedding space. In this paper, we argue that latent structural heterogeneity in the observational data could bias the classical network representation model. The unknown heterogeneous distribution across subgroups raises new challenges for fairness in machine learning. Pre-deﬁned groups with sensitive attributes cannot properly tackle the potential unfairness of network representation. We propose a method which can automatically discover subgroups which are unfairly treated by the network representation model. The fairness measure we propose can evaluate complex targets with multi-degree interactions. We conduct randomly controlled experiments on synthetic datasets and verify our methods on real-world datasets. Both quantitative and quantitative results show that our method is effective to recover the fairness of network representations. Our research draws insight on how structural heterogeneity across subgroups restricted by attributes would affect the fairness of network representation learning.

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YNICL Journal 2016 Journal Article

Effects of outcome on the covariance between risk level and brain activity in adolescents with internet gaming disorder

Xin Qi
Yongxin Yang
Shouping Dai
Peihong Gao
Xin Du
Yang Zhang
Guijin Du
Xiaodong Li

Individuals with internet gaming disorder (IGD) often have impaired risky decision-making abilities, and IGD-related functional changes have been observed during neuroimaging studies of decision-making tasks. However, it is still unclear how feedback (outcomes of decision-making) affects the subsequent risky decision-making in individuals with IGD. In this study, twenty-four adolescents with IGD and 24 healthy controls (HCs) were recruited and underwent functional magnetic resonance imaging while performing the balloon analog risk task (BART) to evaluate the effects of prior outcomes on brain activity during subsequent risky decision-making in adolescents with IGD. The covariance between risk level and activation of the bilateral ventral medial prefrontal cortex, left inferior frontal cortex, right ventral striatum (VS), left hippocampus/parahippocampus, right inferior occipital gyrus/fusiform gyrus and right inferior temporal gyrus demonstrated interaction effects of group by outcome (P <0. 05, AlphaSim correction). The regions with interactive effects were defined as ROI, and ROI-based intergroup comparisons showed that the covariance between risk level and brain activation was significantly greater in adolescents with IGD compared with HCs after a negative outcome occurred (P <0. 05). Our results indicated that negative outcomes affected the covariance between risk level and activation of the brain regions related to value estimation (prefrontal cortex), anticipation of rewards (VS), and emotional-related learning (hippocampus/parahippocampus), which may be one of the underlying neural mechanisms of disadvantageous risky decision-making in adolescents with IGD.

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ICRA Conference 2011 Conference Paper

A novel rectification framework for coaxial omni-directional stereo

Jie Lei
Xin Du
Jilin Liu

Epipolar rectification greatly simplifies the stereo matching which is important for vision based robotics navigation. In this paper we propose a novel rectification algorithm for coaxial omni-directional stereo system, i. e. systems aligned on the same axis of symmetry. The essential matrix is derived and computed for an arbitrary omni-directional stereo configuration based on Taylor model firstly. Using the one-to-one corresponding relationship between epipolar curve and its tangential space, we rectify the system to the direction of the line connecting two cameras' centers. This step removes the translation misalignment. After that, the corresponding points are constraint to the radial line. Image-resample based on the essential matrix compensates the rotation misalignment and makes corresponding points lie on the same scan column. The proposed algorithm is only performed on image plane so that the computation time and error caused by multi-projections in 3D space are decreased. Finally, experimental results on simulation and real data illustrate the effectiveness and accuracy of our algorithm.

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