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Jin Li

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43 papers
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43

JBHI Journal 2026 Journal Article

Adaptive Feature Selection With Hierarchical Learning for Drug-Target Interaction Prediction

  • Zhen Tian
  • Miao Jiang
  • Jin Li
  • Wenjie Zhang
  • Mingliang Xu

Accurate prediction of drug–target interactions (DTIs) is essential for drug discovery and repurposing. Although deep learning has driven substantial progress, critical limitations remain: a singular focus on intermolecular associations results in suboptimal representation learning, and the failure to leverage key features during interactions constrains further performance gains. Here, we propose ASHL-DTI, a novel framework that integrates hierarchical learning with adaptive feature selection to significantly boost both feature quality and model generalizability. Specifically, the hierarchical learning component captures multi-level intramolecular associations to learn more discriminative representations. Simultaneously, we incorporate an adaptive Top-k selection mechanism to retain the most predictive features, facilitating effective interaction between drugs and targets. Experimental results across multiple public benchmark datasets demonstrate that ASHL-DTI achieves superior performance compared with state-of-the-art approaches. Moreover, ASHL-DTI exhibits strong generalization ability in predicting novel drug–target pairs, underscoring its potential in drug discovery. The complete source code of ASHL-DTI is available at https://github.com/Miwkwh/ASHL-DTI.

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AAAI Conference 2026 Conference Paper

Discovering Decoupled Functional Modules in Large Language Models

  • Yanke Yu
  • Jin Li
  • Ying Sun
  • Ping Li
  • Zhefeng Wang
  • Yi Zheng

Understanding the internal functional organization of Large Language Models (LLMs) is crucial for improving their trustworthiness and performance. However, how LLMs organize different functions into modules remains highly unexplored. To bridge this gap, we formulate a function module discovery problem and propose an Unsupervised LLM Cross-layer MOdule Discovery (ULCMOD) framework that simultaneously disentangles the large set of neurons in the entire LLM into modules while discovering the topics of input samples related to these modules. Our framework introduces a novel objective function and an efficient Iterative Decoupling (IterD) algorithm. Extensive experiments show that our method discovers high-quality, disentangled modules that capture more meaningful semantic information and achieve superior performance in various downstream tasks. Moreover, our qualitative analysis reveals that the discovered modules show function comprehensiveness, function hierarchy, and clear function spatial arrangement within LLMs. Our work provides a novel tool for interpreting LLMs' function modules, filling a critical gap in LLMs' interpretability research.

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AAAI Conference 2026 Conference Paper

When Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Models

  • Keyu Wang
  • Jin Li
  • Shu Yang
  • Zhuoran Zhang
  • Di Wang

Large Language Models (LLMs) often exhibit sycophantic behavior, agreeing with user-stated opinions even when those contradict factual knowledge. While prior work has documented this tendency, the internal mechanisms that enable such behavior remain poorly understood. In this paper, we provide a mechanistic account of how sycophancy arises within LLMs. We first systematically study how user opinions induce sycophancy across different model families. We find that simple opinion statements reliably induce sycophancy, whereas user expertise framing has a negligible impact. Through logit-lens analysis and causal activation patching, we identify a two-stage emergence of sycophancy: (1) a late-layer output preference shift and (2) deeper representational divergence. We also verify that user authority fails to influence behavior because models do not encode it internally. In addition, we examine how grammatical perspective affects sycophantic behavior, finding that first-person prompts (“I believe...”) consistently induce higher sycophancy rates than third-person framings (“They believe...”) by creating stronger representational perturbations in deeper layers. These findings highlight that sycophancy is not a surface-level artifact but emerges from a structural override of learned knowledge in deeper layers, with implications for alignment and truthful AI systems.

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JBHI Journal 2025 Journal Article

Benchmarking Large Language Models in Evidence-Based Medicine

  • Jin Li
  • Yiyan Deng
  • Qi Sun
  • Junjie Zhu
  • Yu Tian
  • Jingsong Li
  • Tingting Zhu

Evidence-based medicine (EBM) represents a paradigm of providing patient care grounded in the most current and rigorously evaluated research. Recent advances in large language models (LLMs) offer a potential solution to transform EBM by automating labor-intensive tasks and thereby improving the efficiency of clinical decision-making. This study explores integrating LLMs into the key stages in EBM, evaluating their ability across evidence retrieval (PICO extraction, biomedical question answering), synthesis (summarizing randomized controlled trials), and dissemination (medical text simplification). We conducted a comparative analysis of seven LLMs, including both proprietary and open-source models, as well as those fine-tuned on medical corpora. Specifically, we benchmarked the performance of various LLMs on each EBM task under zero-shot settings as baselines, and employed prompting techniques, including in-context learning, chain-of-thought reasoning, and knowledge-guided prompting to enhance their capabilities. Our extensive experiments revealed the strengths of LLMs, such as remarkable understanding capabilities even in zero-shot settings, strong summarization skills, and effective knowledge transfer via prompting. Promoting strategies such as knowledge-guided prompting proved highly effective (e. g. , improving the performance of GPT-4 by 13. 10% over zero-shot in PICO extraction). However, the experiments also showed limitations, with LLM performance falling well below state-of-the-art baselines like PubMedBERT in handling named entity recognition tasks. Moreover, human evaluation revealed persisting challenges with factual inconsistencies and domain inaccuracies, underscoring the need for rigorous quality control before clinical application. This study provides insights into enhancing EBM using LLMs while highlighting critical areas for further research.

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NeurIPS Conference 2025 Conference Paper

DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion

  • Jin Li
  • Zezhong Ding
  • Xike Xie

Knowledge graphs (KGs) are vital for enabling knowledge reasoning across various domains. Recent KG reasoning methods that integrate both global and local information have achieved promising results. However, existing methods often suffer from score over-smoothing, which blurs the distinction between correct and incorrect answers and hinders reasoning effectiveness. To address this, we propose DuetGraph, a **coarse-to-fine** KG reasoning mechanism with **dual-pathway** global-local fusion. DuetGraph tackles over-smoothing by segregating—rather than stacking—the processing of local (via message passing) and global (via attention) information into two distinct pathways, preventing mutual interference and preserving representational discrimination. In addition, DuetGraph introduces a **coarse-to-fine** optimization, which partitions entities into high- and low-score subsets. This strategy narrows the candidate space and sharpens the score gap between the two subsets, which alleviates over-smoothing and enhances inference quality. Extensive experiments on various datasets demonstrate that DuetGraph achieves state-of-the-art (SOTA) performance, with up to an **8. 7\%** improvement in reasoning quality and a **1. 8$\times$** acceleration in training efficiency.

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NeurIPS Conference 2025 Conference Paper

Revealing Multimodal Causality with Large Language Models

  • Jin Li
  • Shoujin Wang
  • Qi Zhang
  • Feng Liu
  • Tongliang Liu
  • Longbing Cao
  • Shui Yu
  • Fang Chen

Uncovering cause-and-effect mechanisms from data is fundamental to scientific progress. While large language models (LLMs) show promise for enhancing causal discovery (CD) from unstructured data, their application to the increasingly prevalent multimodal setting remains a critical challenge. Even with the advent of multimodal LLMs (MLLMs), their efficacy in multimodal CD is hindered by two primary limitations: (1) difficulty in exploring intra- and inter-modal interactions for comprehensive causal variable identification; and (2) insufficiency to handle structural ambiguities with purely observational data. To address these challenges, we propose MLLM-CD, a novel framework for multimodal causal discovery from unstructured data. It consists of three key components: (1) a novel contrastive factor discovery module to identify genuine multimodal factors based on the interactions explored from contrastive sample pairs; (2) a statistical causal structure discovery module to infer causal relationships among discovered factors; and (3) an iterative multimodal counterfactual reasoning module to refine the discovery outcomes iteratively by incorporating the world knowledge and reasoning capabilities of MLLMs. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed MLLM-CD in revealing genuine factors and causal relationships among them from multimodal unstructured data. The implementation code and data are available at https: //github. com/JinLi-i/MLLM-CD.

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NeurIPS Conference 2024 Conference Paper

AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks

  • Jin Li
  • Ziqiang He
  • Anwei Luo
  • Jian-Fang Hu
  • Z. Jane Wang
  • Xiangui Kang

Imperceptible adversarial attacks aim to fool DNNs by adding imperceptible perturbation to the input data. Previous methods typically improve the imperceptibility of attacks by integrating common attack paradigms with specifically designed perception-based losses or the capabilities of generative models. In this paper, we propose Adversarial Attacks in Diffusion (AdvAD), a novel modeling framework distinct from existing attack paradigms. AdvAD innovatively conceptualizes attacking as a non-parametric diffusion process by theoretically exploring basic modeling approach rather than using the denoising or generation abilities of regular diffusion models requiring neural networks. At each step, much subtler yet effective adversarial guidance is crafted using only the attacked model without any additional network, which gradually leads the end of diffusion process from the original image to a desired imperceptible adversarial example. Grounded in a solid theoretical foundation of the proposed non-parametric diffusion process, AdvAD achieves high attack efficacy and imperceptibility with intrinsically lower overall perturbation strength. Additionally, an enhanced version AdvAD-X is proposed to evaluate the extreme of our novel framework under an ideal scenario. Extensive experiments demonstrate the effectiveness of the proposed AdvAD and AdvAD-X. Compared with state-of-the-art imperceptible attacks, AdvAD achieves an average of 99. 9% (+17. 3%) ASR with 1. 34 (-0. 97) $l_2$ distance, 49. 74 (+4. 76) PSNR and 0. 9971 (+0. 0043) SSIM against four prevalent DNNs with three different architectures on the ImageNet-compatible dataset. Code is available at https: //github. com/XianguiKang/AdvAD.

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AAAI Conference 2024 Conference Paper

Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-Smoothness in Deep GNNs

  • Jin Li
  • Qirong Zhang
  • Shuling Xu
  • Xinlong Chen
  • Longkun Guo
  • Yang-Geng Fu

Despite Graph neural networks' significant performance gain over many classic techniques in various graph-related downstream tasks, their successes are restricted in shallow models due to over-smoothness and the difficulties of optimizations among many other issues. In this paper, to alleviate the over-smoothing issue, we propose a soft graph normalization method to preserve the diversities of node embeddings and prevent indiscrimination due to possible over-closeness. Combined with residual connections, we analyze the reason why the method can effectively capture the knowledge in both input graph structures and node features even with deep networks. Additionally, inspired by Curriculum Learning that learns easy examples before the hard ones, we propose a novel label-smoothing-based learning framework to enhance the optimization of deep GNNs, which iteratively smooths labels in an auxiliary graph and constructs many gradual non-smooth tasks for extracting increasingly complex knowledge and gradually discriminating nodes from coarse to fine. The method arguably reduces the risk of overfitting and generalizes better results. Finally, extensive experiments are carried out to demonstrate the effectiveness and potential of the proposed model and learning framework through comparison with twelve existing baselines including the state-of-the-art methods on twelve real-world node classification benchmarks.

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NeurIPS Conference 2024 Conference Paper

Scene Graph Generation with Role-Playing Large Language Models

  • Guikun Chen
  • Jin Li
  • Wenguan Wang

Current approaches for open-vocabulary scene graph generation (OVSGG) use vision-language models such as CLIP and follow a standard zero-shot pipeline – computing similarity between the query image and the text embeddings for each category (i. e. , text classifiers). In this work, we argue that the text classifiers adopted by existing OVSGG methods, i. e. , category-/part-level prompts, are scene-agnostic as they remain unchanged across contexts. Using such fixed text classifiers not only struggles to model visual relations with high variance, but also falls short in adapting to distinct contexts. To plug these intrinsic shortcomings, we devise SDSGG, a scene-specific description based OVSGG framework where the weights of text classifiers are adaptively adjusted according to the visual content. In particular, to generate comprehensive and diverse descriptions oriented to the scene, an LLM is asked to play different roles (e. g. , biologist and engineer) to analyze and discuss the descriptive features of a given scene from different views. Unlike previous efforts simply treating the generated descriptions as mutually equivalent text classifiers, SDSGG is equipped with an advanced renormalization mechanism to adjust the influence of each text classifier based on its relevance to the presented scene (this is what the term “specific” means). Furthermore, to capture the complicated interplay between subjects and objects, we propose a new lightweight module called mutual visual adapter. It refines CLIP’s ability to recognize relations by learning an interaction-aware semantic space. Extensive experiments on prevalent benchmarks show that SDSGG significantly outperforms top-leading methods.

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NeurIPS Conference 2023 Conference Paper

AiluRus: A Scalable ViT Framework for Dense Prediction

  • Jin Li
  • Yaoming Wang
  • Xiaopeng Zhang
  • Bowen Shi
  • Dongsheng Jiang
  • Chenglin Li
  • Wenrui Dai
  • Hongkai Xiong

Vision transformers (ViTs) have emerged as a prevalent architecture for vision tasks owing to their impressive performance. However, their complexity dramatically increases when handling long token sequences, particularly for dense prediction tasks that require high-resolution input. Notably, dense prediction tasks, such as semantic segmentation or object detection, emphasize more on the contours or shapes of objects, while the texture inside objects is less informative. Motivated by this observation, we propose to apply adaptive resolution for different regions in the image according to their importance. Specifically, at the intermediate layer of the ViT, we select anchors from the token sequence using the proposed spatial-aware density-based clustering algorithm. Tokens that are adjacent to anchors are merged to form low-resolution regions, while others are preserved independently as high-resolution. This strategy could significantly reduce the number of tokens, and the following layers only handle the reduced token sequence for acceleration. At the output end, the resolution of the feature map is recovered by unfolding merged tokens for task prediction. Consequently, we can considerably accelerate ViTs for dense prediction tasks. The proposed method is evaluated across three different datasets and demonstrates promising performance. For instance, "Segmenter ViT-L" can be accelerated by 48\% FPS without fine-tuning, while maintaining the performance. Moreover, our method can also be applied to accelerate fine-tuning. Experiments indicate that we can save 52\% training time while accelerating 2. 46$\times$ FPS with only a 0. 09\% performance drop.

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JBHI Journal 2023 Journal Article

Automatic Representative Frame Selection and Intrathoracic Lymph Node Diagnosis With Endobronchial Ultrasound Elastography Videos

  • Mingxing Xu
  • Junxiang Chen
  • Jin Li
  • Xinxin Zhi
  • Wenrui Dai
  • Jiayuan Sun
  • Hongkai Xiong

Endobronchial ultrasound (EBUS) elastography videos have shown great potential to supplement intrathoracic lymph node diagnosis. However, it is laborious and subjective for the specialists to select the representative frames from the tedious videos and make a diagnosis, and there lacks a framework for automatic representative frame selection and diagnosis. To this end, we propose a novel deep learning framework that achieves reliable diagnosis by explicitly selecting sparse representative frames and guaranteeing the invariance of diagnostic results to the permutations of video frames. Specifically, we develop a differentiable sparse graph attention mechanism that jointly considers frame-level features and the interactions across frames to select sparse representative frames and exclude disturbed frames. Furthermore, instead of adopting deep learning-based frame-level features, we introduce the normalized color histogram that considers the domain knowledge of EBUS elastography images and achieves superior performance. To our best knowledge, the proposed framework is the first to simultaneously achieve automatic representative frame selection and diagnosis with EBUS elastography videos. Experimental results demonstrate that it achieves an average accuracy of 81. 29% and area under the receiver operating characteristic curve (AUC) of 0. 8749 on the collected dataset of 727 EBUS elastography videos, which is comparable to the performance of the expert-based clinical methods based on manually-selected representative frames.

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JBHI Journal 2022 Journal Article

3DMol-Net: Learn 3D Molecular Representation Using Adaptive Graph Convolutional Network Based on Rotation Invariance

  • Chunyan Li
  • Wei Wei
  • Jin Li
  • Junfeng Yao
  • Xiangxiang Zeng
  • Zhihan Lv

Studying the deep learning-based molecular representation has great significance on predicting molecular property, promoted the development of drug screening and new drug discovery, and improving human well-being for avoiding illnesses. It is essential to learn the characterization of drug for various downstream tasks, such as molecular property prediction. In particular, the 3D structure features of molecules play an important role in biochemical function and activity prediction. The 3D characteristics of molecules largely determine the properties of the drug and the binding characteristics of the target. However, most current methods merely rely on 1D or 2D properties while ignoring the 3D topological structure, thereby degrading the performance of molecular inferring. In this paper, we propose 3DMol-Net to enhance the molecular representation, considering both the topology and rotation invariance (RI) of the 3D molecular structure. Specifically, we construct a molecular graph with soft relations related to the spatial arrangement of the 3D coordinates to learn 3D topology of arbitrary graph structure and employ an adaptive graph convolutional network to predict molecular properties and biochemical activities. Comparing with current graph-based methods, 3DMol-Net demonstrates superior performance in terms of both regression and classification tasks. Further verification of RI and visualization also show better robustness and representation capacity of our model.

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IJCAI Conference 2022 Conference Paper

Learning from Students: Online Contrastive Distillation Network for General Continual Learning

  • Jin Li
  • Zhong Ji
  • Gang Wang
  • Qiang Wang
  • Feng Gao

The goal of General Continual Learning (GCL) is to preserve learned knowledge and learn new knowledge with constant memory from an infinite data stream where task boundaries are blurry. Distilling the model's response of reserved samples between the old and the new models is an effective way to achieve promise performance on GCL. However, it accumulates the inherent old model's response bias and is not robust to model changes. To this end, we propose an Online Contrastive Distillation Network (OCD-Net) to tackle these problems, which explores the merit of the student model in each time step to guide the training process of the student model. Concretely, the teacher model is devised to help the student model to consolidate the learned knowledge, which is trained online via integrating the model weights of the student model to accumulate the new knowledge. Moreover, our OCD-Net incorporates both relation and adaptive response to help the student model alleviate the catastrophic forgetting, which is also beneficial for the teacher model preserves the learned knowledge. Extensive experiments on six benchmark datasets demonstrate that our proposed OCD-Net significantly outperforms state-of-the-art approaches in 3. 26%~8. 71% with various buffer sizes. Our code is available at https: //github. com/lijincm/OCD-Net.

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IJCAI Conference 2021 Conference Paper

Hiding Numerical Vectors in Local Private and Shuffled Messages

  • Shaowei Wang
  • Jin Li
  • Yuqiu Qian
  • Jiachun Du
  • Wenqing Lin
  • Wei Yang

Numerical vector aggregation has numerous applications in privacy-sensitive scenarios, such as distributed gradient estimation in federated learning, and statistical analysis on key-value data. Within the framework of local differential privacy, this work gives tight minimax error bounds of O(d s/(n epsilon^2)), where d is the dimension of the numerical vector and s is the number of non-zero entries. An attainable mechanism is then designed to improve from existing approaches suffering error rate of O(d^2/(n epsilon^2)) or O(d s^2/(n epsilon^2)). To break the error barrier in the local privacy, this work further consider privacy amplification in the shuffle model with anonymous channels, and shows the mechanism satisfies centralized (14 ln(2/delta) (s e^epsilon+2s-1)/(n-1))^0. 5, delta)-differential privacy, which is domain independent and thus scales to federated learning of large models. We experimentally validate and compare it with existing approaches, and demonstrate its significant error reduction.

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AAAI Conference 2020 Conference Paper

Graph Attention Based Proposal 3D ConvNets for Action Detection

  • Jin Li
  • Xianglong Liu
  • Zhuofan Zong
  • Wanru Zhao
  • Mingyuan Zhang
  • Jingkuan Song

The recent advances in 3D Convolutional Neural Networks (3D CNNs) have shown promising performance for untrimmed video action detection, employing the popular detection framework that heavily relies on the temporal action proposal generations as the input of the action detector and localization regressor. In practice the proposals usually contain strong intra and inter relations among them, mainly stemming from the temporal and spatial variations in the video actions. However, most of existing 3D CNNs ignore the relations and thus suffer from the redundant proposals degenerating the detection performance and efficiency. To address this problem, we propose graph attention based proposal 3D ConvNets (AGCN-P-3DCNNs) for video action detection. Specifically, our proposed graph attention is composed of intra attention based GCN and inter attention based GCN. We use intra attention to learn the intra long-range dependencies inside each action proposal and update node matrix of Intra Attention based GCN, and use inter attention to learn the inter dependencies between different action proposals as adjacency matrix of Inter Attention based GCN. Afterwards, we fuse intra and inter attention to model intra long-range dependencies and inter dependencies simultaneously. Another contribution is that we propose a simple and effective framewise classifier, which enhances the feature presentation capabilities of backbone model. Experiments on two proposal 3D ConvNets based models (P-C3D and P-ResNet) and two popular action detection benchmarks (THUMOS 2014, ActivityNet v1. 3) demonstrate the state-of-the-art performance achieved by our method. Particularly, P-C3D embedded with our module achieves average mAP 3. 7% improvement on THUMOS 2014 dataset compared to original model.

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IJCAI Conference 2020 Conference Paper

Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising

  • Xiaotian Hao
  • Junqi Jin
  • Jianye Hao
  • Jin Li
  • Weixun Wang
  • Yi Ma
  • Zhenzhe Zheng
  • Han Li

Bipartite b-matching is fundamental in algorithm design, and has been widely applied into diverse applications, such as economic markets, labor markets, etc. These practical problems usually exhibit two distinct features: large-scale and dynamic, which requires the matching algorithm to be repeatedly executed at regular intervals. However, existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. To address this issue, based on a key observation that the matching instances vary not too much, we propose NeuSearcher which leverage the knowledge learned from previously instances to solve new problem instances. Specifically, we design a multichannel graph neural network to predict the threshold of the matched edges, by which the search region could be significantly reduced. We further propose a parallel heuristic search algorithm to iteratively improve the solution quality until convergence. Experiments on both open and industrial datasets demonstrate that NeuSearcher can speed up 2 to 3 times while achieving exactly the same matching solution compared with the state-of-the-art approximation approaches.

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AAAI Conference 2019 Conference Paper

Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

  • Wenjing Fu
  • Zhaohui Peng
  • Senzhang Wang
  • Yang Xu
  • Jin Li

As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods.Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

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TIST Journal 2019 Journal Article

Secure Deduplication System with Active Key Update and Its Application in IoT

  • Jin Li
  • Tong Li
  • Zheli Liu
  • Xiaofeng Chen

The rich cloud services in the Internet of Things create certain needs for edge computing, in which devices should be able to handle storage tasks securely, reliably, and efficiently. When processing the storage requests from edge devices, each cloud server is supposed to eliminate duplicate copies of repeating data to reduce the amount of storage space and save on bandwidth. To protect data confidentiality while supporting deduplication, some convergent-encryption-based techniques have been proposed to encrypt the data before uploading. However, all these works cannot meet two requirements while preventing brute-force attacks: (i) power-constrained edge nodes should update encryption keys efficiently when an edge node is abandoned; and (ii) the access privacy of edge nodes should be guaranteed. In this article, we propose a novel encryption scheme for secure chunk-level deduplication. Based on this scheme, we present two constructions of the secure deduplication system that support an efficient key update protocol. The key update protocol does not involve any edge node in computational tasks, so that the deduplication system can adopt an active key update strategy. Moreover, one of our constructions, which is called advance construction, can provide access privacy assurances for edge nodes. The security analysis is given in terms of the proposed threat model. The experimental analysis demonstrates that the proposed deduplication system is practical.

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AAAI Conference 2018 Conference Paper

Robust Formulation for PCA: Avoiding Mean Calculation With L 2,p -norm Maximization

  • Shuangli Liao
  • Jin Li
  • Yang Liu
  • Quanxue Gao
  • Xinbo Gao

Most existing robust principal component analysis (PCA) involve mean estimation for extracting low-dimensional representation. However, they do not get the optimal mean for real data, which include outliers, under the different robust distances metric learning, such as 1-norm and 2, 1-norm. This affects the robustness of algorithms. Motivated by the fact that the variance of data can be characterized by the variation between each pair of data, we propose a novel robust formulation for PCA. It avoids computing the mean of data in the criterion function. Our method employs 2, p norm as the distance metric to measure the variation in the criterion function and aims to seek the projection matrix that maximizes the sum of variation between each pair of the projected data. Both theoretical analysis and experimental results demonstrate that our methods are efficient and superior to most existing robust methods for data reconstruction.

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IJCAI Conference 2018 Conference Paper

Zero Shot Learning via Low-rank Embedded Semantic AutoEncoder

  • Yang Liu
  • Quanxue Gao
  • Jin Li
  • Jungong Han
  • Ling Shao

Zero-shot learning (ZSL) has been widely researched and get successful in machine learning. Most existing ZSL methods aim to accurately recognize objects of unseen classes by learning a shared mapping from the feature space to a semantic space. However, such methods did not investigate in-depth whether the mapping can precisely reconstruct the original visual feature. Motivated by the fact that the data have low intrinsic dimensionality e. g. low-dimensional subspace. In this paper, we formulate a novel framework named Low-rank Embedded Semantic AutoEncoder (LESAE) to jointly seek a low-rank mapping to link visual features with their semantic representations. Taking the encoder-decoder paradigm, the encoder part aims to learn a low-rank mapping from the visual feature to the semantic space, while decoder part manages to reconstruct the original data with the learned mapping. In addition, a non-greedy iterative algorithm is adopted to solve our model. Extensive experiments on six benchmark datasets demonstrate its superiority over several state-of-the-art algorithms.

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AAAI Conference 2015 Conference Paper

Prajna: Towards Recognizing Whatever You Want from Images without Image Labeling

  • Xian-Sheng Hua
  • Jin Li

With the advances in distributed computation, machine learning and deep neural networks, we enter into an era that it is possible to build a real world image recognition system. There are three essential components to build a real-world image recognition system: 1) creating representative features, 2) designing powerful learning approaches, and 3) identifying massive training data. While extensive researches have been done on the first two aspects, much less attention has been paid on the third. In this paper, we present an end-to-end Web knowledge discovery system, Prajna. Starting from an arbitrary set of entities as inputs, Prajna automatically crawls images from multiple sources, identifies images that have reliably labeled, trains models and build a recognition system that is capable of recognizing any new images of the entity set. Due to the high cost of manual data labeling, leveraging the massive yet noisy data on the Internet is a natural idea, but the practical engineering aspect is highly challenging. Prajna focuses on separating reliable training data from extensive noisy data, which is a key to the capability of extending an image recognition system to support arbitrary entities. In this paper, we will analyze the intrinsic characteristics of Internet image data, and find ways to mine accurate and informative information from those data to build a training set, which is then used to train image recognition models. Prajna is capable of automatically building an image recognition system for those entities as long as we can collect sufficient number of images of the entities on the Web.

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ICRA Conference 2011 Conference Paper

Design optimization and experimental study of acoustic transducer in Near Field Acoustic Levitation

  • Jin Li
  • Pinkuan Liu
  • Han Ding 0001
  • Wenwu Cao

Acoustic transducers with large radiation surface are commonly used in non-contact levitation and transportation systems. Traditional modeling can not predict its dynamic performance precisely. There is not enough documented information on design and optimization. A coupled 3D model has been built in this work. Modal and harmonic analysis has been performed to investigate mechanical and electrical behaviors. Identical experimental conditions were simulated by finite element method modeling to investigate size effect and optimize the transducer. Experiment has been set up to validate the model. The good agreement between the simulated and experimental results shows that the model in design procedure provides an optimal tool to construct an acoustic transducer used in Near Field Acoustic Levitation.

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