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Improving Parameter Estimation and Defensive Ability of Latent Dirichlet Allocation Model Training Under Rényi Differential Privacy

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Journal of Computer Science & technology 2022年第6期37卷 1382-1397页

作者： Tao Huang Su-Yun Zhao Hong Chen Yi-Xuan Liu Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China) Ministry of Education Beijing 100087China School of Information Renmin University of ChinaBeijing 100087China

Latent Dirichlet allocation(LDA)is a topic model widely used for discovering hidden semantics in massive text *** Gibbs sampling(CGS),as a widely-used algorithm for learning the parameters of LDA,has the risk of privacy ***,word count statistics and updates of latent topics in CGS,which are essential for parameter estimation,could be employed by adversaries to conduct effective membership inference attacks(MIAs).Till now,there are two kinds of methods exploited in CGS to defend against MIAs:adding noise to word count statistics and utilizing inherent *** two kinds of methods have their respective *** sampled from the Laplacian distribution sometimes produces negative word count statistics,which render terrible parameter estimation in *** inherent privacy could only provide weak guaranteed privacy when defending against *** is promising to propose an effective framework to obtain accurate parameter estimations with guaranteed differential *** key issue of obtaining accurate parameter estimations when introducing differential privacy in CGS is making good use of the privacy budget such that a precise noise scale is *** is the first time that R′enyi differential privacy(RDP)has been introduced into CGS and we propose RDP-LDA,an effective framework for analyzing the privacy loss of any differentially private ***-LDA could be used to derive a tighter upper bound of privacy loss than the overestimated results of existing differentially private CGS obtained byε-*** RDP-LDA,we propose a novel truncated-Gaussian mechanism that keeps word count statistics *** we propose distribution perturbation which could provide more rigorous guaranteed privacy than utilizing inherent *** validate that our proposed methods produce more accurate parameter estimation under the JS-divergence metric and obtain lower precision and recall when defending against MIAs.

关键词： latent Dirichlet allocation parameter estimation membership inference attack Rényi differential privacy

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Rating Text Classification with Weighted Negative Supervision on Classifier Layer

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Chinese Journal of Electronics 2023年第6期32卷 1304-1318页

作者： ZHANG Jun QIU Longlong SHEN Fanfan HE Yueshun TAN Hai HE Yanxiang Radiological Geosciences Big Data Engineering Laboratory of Jiangxi Province East China University of Technology School of Information Engineering East China University of Technology School of Computer Science Nanjing Audit University Computer School Wuhan University

Bidirectional encoder representations from transformers(BERT) gives full play to the advantages of the attention mechanism, improves the performance of sentence representation, and provides a better choice for various natural language understanding(NLU)tasks. Many methods using BERT as the pre-trained model achieve state-of-the-art performance in almost various text classification scenarios. Among them, the multitask learning framework combining the negative supervision and the pre-trained model solves the issue of the model performance degradation that occurs as the semantic similarity of texts conflicts with the classification standards. The current model does not consider the degree of difference between labels, which leads to insufficient difference information learned by the model, and affects classification performance, especially in the rating classification tasks. On the basis of the multi-task learning model, this paper fully considers the degree of difference between labels, which is expressed by using weights to solve the above problems. We supervise negative samples on the classifier layer instead of the encoder layer, so that the classifier layer can also learn the difference information between the labels. Experimental results show that our model can not only performs well in 2-class and multi-class rating text classification tasks, but also performs well in different languages.

关键词： Training Text categorization Semantics Bidirectional control Transformers Multitasking Encoding

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Hierarchical All-Pairs SimRank Calculation 28th

Hierarchical All-Pairs SimRank Calculation

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Liangfu Li, Cuiping Zhang, Xue Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education School of Information Renmin University of China Beijing China

ISBN: (纸本)9783031306747

All-pairs SimRank calculation is a classic SimRank problem. However, all-pairs algorithms suffer from efficiency issues and accuracy issues. In this paper, we convert the non-linear simrank calculation into a new simple closed formulation of linear system. And we come up with a sequence of novel algorithms to efficiently solve the linear system with accuracy guarantees. To reduce the memory consumption and improve the computational efficiency, we build a hierarchical framework to calculate the all-pairs SimRank scores, which includes locally coarse calculation and globally refine calculation. We first solve the local linear systems generated from the subgraphs, then we refine the SimRank scores on the full graph from the residuals of the local structures. We also show that our algorithms outperform the state-of-the-art all-pairs SimRank computation algorithms on real graphs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational efficiency

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Trajectory optimization for UAV-enabled relaying with reinforcement learning

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Digital Communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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Video emotional description with fact reinforcement and emotion awaking

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Journal of Ambient Intelligence and Humanized Computing 2024年第6期15卷 2839-2852页

作者： Tang, Pengjie Rao, Hong Zhang, Ai Tan, Yunlan Electronics and Information Engineering College Jinggangshan University 28 Xueyuan Road Jiangxi Ji’an343009 China School of Software Nanchang University 235 Nanjing East Road Jiangxi Nanchang330096 China Key Laboratory of Jiangxi Electronic Data Control and Forensics Jinggangshan University Ji’an China

Video description aims to translate the visual content in a video with appropriate natural language. Most of current works only focus on the description of factual content, paying insufficient attention to the emotions in the video. And the sentences always lack flexibility and vividness. In this work, a fact enhancement and emotion awakening based model is proposed to describe the video, making the sentence more attractive and colorful. The strategy of deep incremental leaning is employed to build a multi-layer sequential network firstly, and multi-stage training method is used to sufficiently optimize the model. Secondly, the modules of fact inspiration, fact reinforcement and emotion awakening are constructed layer by layer to discovery more facts and embed emotions naturally. The three modules are cumulatively trained to sufficiently mine the factual and emotional information. Two public datasets including EmVidCap-S and EmVidCap are employed to evaluate the proposed model. The experimental results show that the performance of the proposed model is superior to not only the baseline models, but also the other popular methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Reinforcement

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Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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Tsinghua Science and technology 2024年第2期29卷 305-324页

作者： Yiqiao Cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain Technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of Computer Sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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An Intelligent Design Method Based on Case-based Reasoning and Reinforcement Learning

An Intelligent Design Method Based on Case-based Reasoning a...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Huang, Yu Wang, Ru Wei, Zhuqin Wang, Guoxin School of Machanical Engineering Beijing Institute of Technology Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing China

ISBN: (纸本)9798350323153

Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case. © 2023 IEEE.

关键词： case reconstruction case-based reasoning modular design rapid design reinforcement learning

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Ecological environment quality assessment of coal mining cities based on GEE platform: A case study of Shuozhou, China

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International Journal of Coal Science & technology 2024年第5期11卷 162-176页

作者： Linghua Duo Junqi Wang Yongping Zhong Chengqing Jiang Yaoyao Chen Xiaofei Guo Jiangxi Key Laboratory of Watershed Ecological Process and Information East China University of TechnologyNanchang 30013China School of Surveying and Geoinformation Engineering East China University of TechnologyNanchang 330013China Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake of Ministry of Natural Resources East China University of TechnologyNanchang 330013China

Shuozhou is a typical coal mining city,and the Pingshuo Antaibao open-pit coal mine in its area is one of the largest open-pit coal mines in *** mining of coal resources is an important part of ensuring national energy security,and at the same time,it inevitably has a certain impact on the ecology,such as coal dust generated by open-pit mining will affect air quality,soil,water and *** is of great significance to explore the temporal and spatial variation of ecological environment quality in coal mining cities for ecological protection and sustainable social and economic *** on the Google Earth Engine(GEE)platform,this paper combines the index-based coal dust index(ICDI)and Remote Sensing Ecological Index(RSEI)models to construct an improved RSEI(IRSEI)that can reflect coal mining *** paper explores the spatial-temporal evolution characteristics and spatial correlation of ecological environment quality in Shuozhou from 2000 to *** results showed that the average value of IRSEI in Shuozhou was between 0.262 and 0.418,and the overall change showed an upward *** growth areas of ecological environment quality are mainly located in the eastern and southwestern areas with good vegetation growth,and these regions have vigorously implemented the Northern Shelter Forest Project,afforestation and greening projects,implemented the forest resource management and protection responsibility system,promoted the construction of ecological civilization,and significantly improved the ecological *** the declining areas are mainly located in the central and southern regions where mining activities and human activities are more *** IRSEI in the study area showed a significant spatial positive correlation,and the agglomeration types of the spatial pattern were mainly high-high and low-low agglomeration types,with the high-high agglomeration types mainly distributed in the eastern and southwestern regions,and the low-

关键词： Coal mining city Index-based coal dust index(ICDI) Remote sensing ecological assessment Google earth engine(GEE)

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Adaptive Occlusion Face Recognition Feature Fusion Network Based on Precise Guidance Mask Extraction

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Journal of Shanghai Jiaotong University (Science) 2025年 1-12页

作者： Ye, Jihua Zhang, Lei Wang, Chao Tu, Qixuan Zheng, Rehua Huang, Huiyuan Jiang, Aiwen School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal University Nanchang330022 China State International Science & Technology Cooperation Base of Networked Supporting Software Jiangxi Normal University Nanchang330022 China

The uncertainty in the position and size of occluding objects greatly affects the extraction of identity features in facial recognition, which is a challenge that existing methods fail to effectively address. To tackle this issue, this paper introduces a novel feature fusion network for occluded facial recognition that adaptively handles arbitrary occlusions and accurately extracts occlusion information masks. The network effectively mitigates the impact of occlusions on facial identity features through a feature fusion module. The proposed method employs an occlusion classification module and an occlusion classification loss to precisely guide the extraction of accurate occlusion masks. Subsequently, an occlusion-aware hybrid attention module is introduced, which focuses on the relationship between facial features and occlusion masks across channel and spatial dimensions. This fusion strategy enables the extraction of facial identity features that are resilient to occlusions. Experimental results indicate that the proposed method holds a significant advantage in handling facial recognition tasks with arbitrary occlusions. On the LFW and MF1 synthetic occlusion datasets, the average accuracy rates reached 93.19% and 72.57%, respectively, showing improvements of 1.7 and 2.39 percent points over mainstream methods. © Shanghai Jiao Tong University 2025.

关键词： A feature fusion occluded facial recognition occlusion mask occlusion perception TP391.41

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PCQPR: Proactive Conversational Question Planning with Reflection

PCQPR: Proactive Conversational Question Planning with Refle...

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

作者： Guo, Shasha Liao, Lizi Zhang, Jing Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore

ISBN: (纸本)9798891761643

Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment. However, traditional CQG, focusing primarily on the immediate context, lacks the conversational foresight necessary to guide conversations toward specified conclusions. This limitation significantly restricts their ability to achieve conclusion-oriented conversational outcomes. In this work, we redefine the CQG task as Conclusion-driven Conversational Question Generation (CCQG) by focusing on proactivity, not merely reacting to the unfolding conversation but actively steering it towards a conclusion-oriented question-answer pair. To address this, we propose a novel approach, called Proactive Conversational Question Planning with self-Refining (PCQPR). Concretely, by integrating a planning algorithm inspired by Monte Carlo Tree Search (MCTS) with the analytical capabilities of large language models (LLMs), PCQPR predicts future conversation turns and continuously refines its questioning strategies. This iterative self-refining mechanism ensures the generation of contextually relevant questions strategically devised to reach a specified outcome. Our extensive evaluations demonstrate that PCQPR significantly surpasses existing CQG methods, marking a paradigm shift towards conclusion-oriented conversational question-answering systems. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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