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arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

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Partial Label Learning via Conditional-Label-Aware Disambiguation

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Journal of Computer Science & Technology 2021年第3期36卷 590-605页

作者： Peng Ni Su-Yun Zhao Zhi-Gang Dai Hong Chen Cui-Ping Li 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

Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth *** paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing *** existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise *** experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.

关键词： disambiguation partial label learning similarity and dissimilarity weak supervision

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Efficient Model Store and Reuse in an OLML database System

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Journal of Computer Science & Technology 2021年第4期36卷 792-805页

作者： Jian-Wei Cui Wei Lu Xin Zhao Xiao-Yong Du Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China

Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network ***,for these neural network models,it is necessary to label a tremendous amount of training data,which is prohibitively expensive in *** this paper,we propose OnLine Machine Learning(OLML)database which stores trained models and reuses these models in a new training task to achieve a better training effect with a small amount of training *** efficient model reuse algorithm AdaReuse is developed in the OLML ***,AdaReuse firstly estimates the reuse potential of trained models from domain relatedness and model quality,through which a group of trained models with high reuse potential for the training task could be selected ***,multi selected models will be trained iteratively to encourage diverse models,with which a better training effect could be achieved by *** evaluate AdaReuse on two types of natural language processing(NLP)tasks,and the results show AdaReuse could improve the training effect significantly compared with models training from scratch when the training data is *** on AdaReuse,we implement an OLML database prototype system which could accept a training task as an SQL-like query and automatically generate a training plan by selecting and reusing trained *** studies are conducted to illustrate the OLML database could properly store the trained models,and reuse the trained models efficiently in new training tasks.

关键词： model selection model reuse OnLine Machine Learning(OLML)database

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Efficient protocols for heavy hitter identification with local differential privacy

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Frontiers of Computer Science 2022年第5期16卷 193-203页

作者： Dan ZHAO Suyun ZHAO Hong CHEN Ruixuan LIU Cuiping LI Wenjuan LIANG Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of ChinaBeijing 100872China School of Information Renmin University of ChinaBeijing 100872China

Local differential privacy(LDP),which is a technique that employs unbiased statistical estimations instead of real data,is usually adopted in data collection,as it can protect every user’s privacy and prevent the leakage of sensitive *** segment pairs method(SPM),multiple-channel method(MCM)and prefix extending method(PEM)are three known LDP protocols for heavy hitter identification as well as the frequency oracle(FO)problem with large ***,the low scalability of these three LDP algorithms often limits their ***,communication and computation strongly affect their ***,excessive grouping or sharing of privacy budgets makes the results *** address the abovementioned problems,this study proposes independent channel(IC)and mixed independent channel(MIC),which are efficient LDP protocols for FO with a large *** design a flexible method for splitting a large domain to reduce the number of ***,we employ the false positive rate with interaction to obtain an accurate *** experiments demonstrate that IC outperforms all the existing solutions under the same privacy guarantee while MIC performs well under a small privacy budget with the lowest communication cost.

关键词： local differential privacy frequency oracle heavy hitter

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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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Study on the Characteristics of Cross-Domain knowledge Diffusion from Science to Policy: Evidence from Overton data

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Proceedings of the Association for Information Science and Technology 2023年第1期60卷 368-378页

作者： Ren, Chao Yang, Menghui School of Information Resource Management Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China

The cross-domain knowledge diffusion from science to policy is a prevalent phenomenon that demands academic attention. To investigate the characteristics of cross-domain knowledge diffusion from science to policy, this study suggests using the citation of policies to scientific articles as a basis for quantifying the diffusion strength, breadth, and speed. The study reveals that the strength and breadth of cross-domain knowledge diffusion from scientific papers to policies conform to a power-law distribution, while the speed follows a logarithmic normal distribution. Moreover, the papers with the highest diffusion strength, breadth, and fastest diffusion speed are predominantly from world-renowned universities, scholars, and top journals. The papers with the highest diffusion strength and breadth are mostly from social sciences, especially economics, those with the fastest diffusion speed are mainly from medical and life sciences, followed by social sciences. The findings indicate that cross-domain knowledge diffusion from science to policy follows the Matthew effect, whereby individuals or institutions with high academic achievements are more likely to achieve successful cross-domain knowledge diffusion. Furthermore, papers in the field of economics tend to have the higher cross-domain knowledge diffusion strength and breadth, while those in medical and life sciences have the faster cross-domain knowledge diffusion speed. 86 Annual Meeting of the Association for Information Science & Technology | Oct. 27 – 31, 2023 | London, United Kingdom. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Diffusion

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Approximation for a scheduling problem with application in wireless networks

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Science China Mathematics 2010年第6期53卷 1643-1655页

作者： ZHU QingHua & LI DeYing key Laboratory of data engineering and knowledge engineering,MOE,School of Information,Renmin University of China,Beijing 100872,China 1. Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing 100872 China

A network of many sensors and a base station that are deployed over a region is *** sensor has a transmission range,an interference range and a carrier sensing range,which are r,αr and βr,*** this paper,we study the minimum latency conflict-aware many-to-one data aggregation scheduling problem:Given locations of sensors along with a base station,a subset of all sensors,and parameters r,α and β,to find a schedule in which the data of each sensor in the subset can be transmitted to the base station with no conflicts,such that the latency is *** designe an algorithm based on maximal independent sets,which has a latency bound of(a+19b)R + Δb-a + 5 time slots,where a and b are two constant integers relying on α and β,Δ is the maximum degree of network topology,and R is the trivial lower bound of *** Δ contributes to an additive factor instead of a multiplicative one,thus our algorithm is nearly a constant(a+19b)-ratio.

关键词： approximation algorithm conflict-aware many-to-one data aggregation latency wireless sensor network

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Multi-Cluster Feature Selection Based on Isometric Mapping

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 570-572页

作者： Yadi Wang Zefeng Zhang Yinghao Lin Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004 Institute of Data and Knowledge Engineering School of Computer and Information EngineeringHenan UniversityKaifeng 475004China

Dear editor,This letter presents an unsupervised feature selection method based on machine *** selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of d... 详细信息

关键词： problem letter dimensionality

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Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy

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Journal of Computer Science & Technology 2023年第6期38卷 1403-1422页

作者：赵丹赵素云陈红刘睿瑄李翠平张晓莹 Institute of Scientific and Technical Information of China Beijing 100038China Key Laboratory of Data Engineering and Knowledge Engineering(Ministry of Education) School of InformationRenmin University of ChinaBeijing 100872China

Local differential privacy(LDP)approaches to collecting sensitive information for frequent itemset mining(FIM)can reliably guarantee *** current approaches to FIM under LDP add"padding and sampling"steps to obtain frequent itemsets and their frequencies because each user transaction represents a set of *** current state-of-the-art approach,namely set-value itemset mining(SVSM),must balance variance and bias to achieve accurate ***,an unbiased FIM approach with lower variance is highly *** narrow this gap,we propose an Item-Level LDP frequency oracle approach,named the Integrated-with-Hadamard-Transform-Based Frequency Oracle(IHFO).For the first time,Hadamard encoding is introduced to a set of values to encode all items into a fixed vector,and perturbation can be subsequently applied to the *** FIM approach,called optimized united itemset mining(O-UISM),is pro-posed to combine the padding-and-sampling-based frequency oracle(PSFO)and the IHFO into a framework for acquiring accurate frequent itemsets with their ***,we theoretically and experimentally demonstrate that O-UISM significantly outperforms the extant approaches in finding frequent itemsets and estimating their frequencies under the same privacy guarantee.

关键词： local differential privacy frequent itemset mining frequency oracle

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Enhancing Storage Efficiency and Performance: A Survey of data Partitioning Techniques

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Journal of Computer Science & Technology 2024年第2期39卷 346-368页

作者：刘鹏举李翠平陈红 Distinguished Member CCF 1.School of InformationRenmin University of ChinaBeijing 100872China Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education Beijing 100872China

data partitioning techniques are pivotal for optimal data placement across storage devices,thereby enhancing resource utilization and overall system ***,the design of effective partition schemes faces multiple challenges,including considerations of the cluster environment,storage device characteristics,optimization objectives,and the balance between partition quality and computational ***,dynamic environments necessitate robust partition detection *** paper presents a comprehensive survey structured around partition deployment environments,outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are *** discuss partitioning features pertaining to database schema,table data,workload,and runtime *** then delve into the partition generation process,segmenting it into initialization and optimization stages.A comparative analysis of partition generation and update algorithms is provided,emphasizing their suitability for different scenarios and optimization ***,we illustrate the applications of partitioning in prevalent database products and suggest potential future research directions and *** survey aims to foster the implementation,deployment,and updating of high-quality partitions for specific system scenarios.

关键词： data partitioning survey partitioning feature partition generation partition update

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