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2023 World Wide Web Conference, WWW 2023

作者： Zhu, Zhihao Wu, Chenwang Fan, Rui Lian, Defu Chen, Enhong School of Data Science University of Science and Technology of China Hefei China School of Computer Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9781450394161

Recent studies have demonstrated the vulnerability of recommender systems to membership inference attacks, which determine whether a user's historical data was utilized for model training, posing serious privacy leakage issues. Existing works assumed that member and non-member users follow different recommendation modes, and then infer membership based on the difference vector between the user's historical behaviors and the recommendation list. The previous frameworks are invalid against inductive recommendations, such as sequential recommendations, since the disparities of difference vectors constructed by the recommendations between members and non-members become imperceptible. This motivates us to dig deeper into the target model. In addition, most MIA frameworks assume that they can obtain some in-distribution data from the same distribution of the target data, which is hard to gain in recommender system. To address these difficulties, we propose a Membership Inference Attack framework against sequential recommenders based on Model Extraction(ME-MIA). Specifically, we train a surrogate model to simulate the target model based on two universal loss functions. For a given behavior sequence, the loss functions ensure the recommended items and corresponding rank of the surrogate model are consistent with the target model's recommendation. Due to the special training mode of the surrogate model, it is hard to judge which user is its member(non-member). Therefore, we establish a shadow model and use shadow model's members(non-members) to train the attack model later. Next, we build a user feature generator to construct representative feature vectors from the shadow(surrogate) model. The crafting feature vectors are finally input into the attack model to identify users' membership. Furthermore, to tackle the high cost of obtaining in-distribution data, we develop two variants of ME-MIA, realizing data-efficient and even data-free MIA by fabricating authentic in-distrib

关键词： Recommender systems

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A dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment

arXiv

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arXiv 2024年

作者： Wang, Fei Liu, Haoyu Bi, Haoyang Shen, Xiangzhuang Zhu, Renyu Wu, Runze Lin, Minmin Lv, Tangjie Fan, Changjie Liu, Qi Huang, Zhenya Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China NetEase Inc Hangzhou China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China

For the purpose of efficient and cost-effective large-scale data labeling, crowdsourcing is increasingly being utilized. To guarantee the quality of data labeling, multiple annotations need to be collected for each data sample, and truth inference algorithms have been developed to accurately infer the true labels. Despite previous studies having released public datasets to evaluate the efficacy of truth inference algorithms, these have typically focused on a single type of crowdsourcing task and neglected the temporal information associated with workers’ annotation activities. These limitations significantly restrict the practical applicability of these algorithms, particularly in the context of long-term and online truth inference. In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform. This dataset comprises approximately two thousand workers, one million tasks, and six million annotations. The data was gathered over a period of approximately six months from various types of tasks, and the timestamps of each annotation were preserved. We analyze the characteristics of the dataset from multiple perspectives and evaluate the effectiveness of several representative truth inference algorithms on this dataset. We anticipate that this dataset will stimulate future research on tracking workers’ abilities over time in relation to different types of tasks, as well as enhancing online truth inference. Copyright © 2024, The Authors. All rights reserved.

关键词： Crowdsourcing

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Differentiable Optimized Product Quantization and Beyond 23

Differentiable Optimized Product Quantization and Beyond

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2023 World Wide Web Conference, WWW 2023

作者： Lu, Zepu Lian, Defu Zhang, Jin Zhang, Zaixi Feng, Chao Wang, Hao Chen, Enhong School of Computer Science University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9781450394161

Vector quantization techniques, such as Product Quantization (PQ), play a vital role in approximate nearest neighbor search (ANNs) and maximum inner product search (MIPS) owing to their remarkable search and storage efficiency. However, the indexes in vector quantization cannot be trained together with the inference models since data indexing is not differentiable. To this end, differentiable vector quantization approaches, such as DiffPQ and DeepPQ, have been recently proposed, but existing methods have two drawbacks. First, they do not impose any constraints on codebooks, such that the resultant codebooks lack diversity, leading to limited retrieval performance. Second, since data indexing resorts to operator, differentiability is usually achieved by either relaxation or Straight-Through Estimation (STE), which leads to biased gradient and slow convergence. To address these problems, we propose a Differentiable Optimized Product Quantization method (DOPQ) and beyond in this paper. Particularly, each data is projected into multiple orthogonal spaces, to generate multiple views of data. Thus, each codebook is learned with one view of data, guaranteeing the diversity of codebooks. Moreover, instead of simple differentiable relaxation, DOPQ optimizes the loss based on direct loss minimization, significantly reducing the gradient bias problem. Finally, DOPQ is evaluated with seven datasets of both recommendation and image search tasks. Extensive experimental results show that DOPQ outperforms state-of-the-art baselines by a large margin. © 2023 ACM.

关键词： Vector quantization

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Design of a Physical Simulation Teaching Aid System for Industrial Internet Talent Training

Design of a Physical Simulation Teaching Aid System for Indu...

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2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023

作者： Wang, Haocheng Xu, Jiping Wang, Zhaoyang Beijing Technology and Business University School of Artificial Intelligence Beijing100048 China Key Laboratory of Internet and Big Data in China's Light Industry Beijing100048 China

ISBN: (纸本)9798350329032

In recent years, the rapid development of digital economy, the accelerated transformation of industrial digitalization, and the rapid iteration of digital industrialization, the shortage of digital talents has restricted the development of China's digital economy, so the cultivation of industrial digital talents has become the top priority of China's digital construction tasks. To address the current situation of lack of teaching aids in China's digital talent training, this paper takes the rice supply chain as a typical object and designs a set of physical simulation teaching aids system for digital talent training, which includes model hardware, real-time detection and control system and remote data interaction platform based on Identity Resolution Platform for the Industrial Internet of Things. Based on this, the paper combines digital twin technology to build a digital twin teaching and research platform for the rice supply chain, including physical simulation teaching aids, digital twin system and a comprehensive practical training course, to help China's digital talent training strategy. © 2023 IEEE.

关键词： Supply chains

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THE BLESSING OF RANDOMNESS: SDE BEATS ODE IN GENERAL DIFFUSION-BASED IMAGE EDITING 12

THE BLESSING OF RANDOMNESS: SDE BEATS ODE IN GENERAL DIFFUSI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Nie, Shen Guo, Hanzhong Allan Lu, Cheng Zhou, Yuhao Zheng, Chenyu Li, Chongxuan Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

We present a unified probabilistic formulation for diffusion-based image editing, where a latent variable is edited in a task-specific manner and generally deviates from the corresponding marginal distribution induced by the original stochastic or ordinary differential equation (SDE or ODE). Instead, it defines a corresponding SDE or ODE for editing. In the formulation, we prove that the Kullback-Leibler divergence between the marginal distributions of the two SDEs gradually decreases while that for the ODEs remains as the time approaches zero, which shows the promise of SDE in image editing. Inspired by it, we provide the SDE counterparts for widely used ODE baselines in various tasks including inpainting and image-to-image translation, where SDE shows a consistent and substantial improvement. Moreover, we propose SDE-Drag - a simple yet effective method built upon the SDE formulation for point-based content dragging. We build a challenging benchmark (termed DragBench) with open-set natural, art, and AI-generated images for evaluation. A user study on DragBench indicates that SDE-Drag significantly outperforms our ODE baseline, existing diffusion-based methods, and the renowned DragGAN. Our results demonstrate the superiority and versatility of SDE in image editing and push the boundary of diffusion-based editing methods. See the project page https://***/SDE-Drag-demo/for the code and DragBench dataset. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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Large Language Model-based Human-Agent Collaboration for Complex Task Solving

Large Language Model-based Human-Agent Collaboration for Com...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Feng, Xueyang Chen, Zhi-Yuan Qin, Yujia Lin, Yankai Chen, Xu Liu, Zhiyuan Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798891761681

In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs. In this work, we introduce the problem of LLM-based human-agent collaboration for complex task-solving, exploring their synergistic potential. To tackle the problem, we propose a Reinforcement Learning-based HumanAgent Collaboration method, ReHAC, which trains a policy model designed to determine the most opportune stages for human intervention within the task-solving process. We conduct experiments under real and simulated human-agent collaboration scenarios. Experimental results demonstrate that the synergistic efforts of humans and LLM-based agents significantly improve performance in complex tasks, primarily through well-planned, limited human intervention. datasets and code are available at: https://***/XueyangFeng/ReHAC/. © 2024 Association for Computational Linguistics.

关键词： Autonomous agents

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Event Argument Extraction with Two-Dimensional Enhanced Fusion Mechanism

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Computer Engineering and Applications 2025年第10期61卷 111-119页

作者： Wang, Luxiang Chen, Yanping Huang, Hui Huang, Ruizhang Qin, Yongbin Engineering Research Center of Ministry of Education for Text Computing and Cognitive Intelligence School of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Addressing the lack of interaction between trigger and arguments, and the lack of interaction within channels in existing studies of event argument extraction, this paper proposes a two-dimensional enhanced fusion mechanism for event argument extraction (W2-ARG) that incorporates a 2D enhanced fusion method. It inserts identifiers on both sides of a trigger word in a sentence, which encodes information about the event type. In the learning process, it is effective to enhance the interaction between trigger and argument, and encode the trigger separately to highlight its information in the sentence. The event argument extraction is implemented as a two-dimensional representation of label prediction. It has the advantage to capture semantic interactions of words at different distances through dilated convolution. Subsequently, a channel attention module is applied to enhance the interactions within the channel to strengthen the information transfer within the channel. Finally, the Laplace operator is utilized to highlight the positional features of event arguments in the semantic space to improve the performance. The proposed model is experimented on the ACE05-EN and ERE-EN datasets. Experimental results show clear performance improvement of the proposed model compared with other event argument extraction methods. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： BERT channel attention event argument extraction Laplace operator sentence planarization representation

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Federated intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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FairLISA: fair user modeling with limited sensitive attributes information 23

FairLISA: fair user modeling with limited sensitive attribut...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zheng Zhang Qi Liu Hao Jiang Fei Wang Yan Zhuang Le Wu Weibo Gao Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Hefei University of Technology

User modeling techniques profile users' latent characteristics (e.g., preference) from their observed behaviors, and play a crucial role in decision-making. Unfortunately, traditional user models may unconsciously capture biases related to sensitive attributes (e.g., gender) from behavior data, even when this sensitive information is not explicitly provided. This can lead to unfair issues and discrimination against certain groups based on these sensitive attributes. Recent studies have been proposed to improve fairness by explicitly decorrelating user modeling results and sensitive attributes. However, most existing approaches assume that fully sensitive attribute labels are available in the training set, which is unrealistic due to collection limitations like privacy concerns, and hence bear the limitation of performance. In this paper, we focus on a practical situation with limited sensitive data and propose a novel FairLISA framework, which can efficiently utilize data with known and unknown sensitive attributes to facilitate fair model training. We first propose a novel theoretical perspective to build the relationship between data with both known and unknown sensitive attributes with the fairness objective. Then, based on this, we provide a general adversarial framework to effectively leverage the whole user data for fair user modeling. We conduct experiments on representative user modeling tasks including recommender system and cognitive diagnosis. The results demonstrate that our FairLISA can effectively improve fairness while retaining high accuracy in scenarios with different ratios of missing sensitive attributes.

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Design and Implementation of a Vision- and Grating-Sensor-Based Intelligent Unmanned Settlement System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2022年第2期3卷 254-264页

作者： Zhang, Hong-Bo Zhou, Yi-Zhong Dong, Li-Jia Lei, Qing Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361000 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition The Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

In this article, a new vision- and grating-sensor-based intelligent unmanned settlement (IUS) system is proposed for convenience stores to automatically recognize the shopping behavior of customers, record their identities, and generate invoices. First, we design a new IUS architecture, which includes a shelf module and exit module. To achieve automatic settlement for each customer, a shopping event detection method is proposed. In this method, a vision-based human pose estimation algorithm is used to detect a human form standing in front of a shelf. The hand actions of each customer are detected by a grating sensor, and an image recognition method based on a convolutional neural network (CNN) is applied to recognize the items in the hands of customers. To reduce the image annotation workload, we propose a semisupervised training method for the recognition network. Based on hand action detection and item recognition, a shopping event recognition method is designed for the system, and a facial image of the customer corresponding to each shopping behavior is captured. Finally, each detected shopping event is added to the invoice of the corresponding customer via a facial recognition method. To verify the effectiveness of the proposed IUS system, we have built a handheld item image dataset and a shopping event dataset for an unmanned convenience store. The experimental results show that the proposed system can accurately recognize shopping behaviors and generate invoices. © 2020 IEEE.

关键词： Sales

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