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23rd IEEE International Symposium on Quality of Service, IWQoS 201.

作者： Wu, Yao Peng, Hui Zhang, Xiaoying Chen, Hong Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781467371131

With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assumption that the server is untrusted and a trusted third party is introduced to protect the user location privacy when a user sends queries for the location service. In this paper, we reconsider this assumption and propose a Personalized and Flexible location privacy protection Model (PFM) based on user relationship strength. We conduct researches in the situation that the server is trusted while malicious users in the MSN can disguise as a friend to break location privacy. We present an entropic TF-IDF based approach to measure the bi-directional relationship strength and propose probability distribution based cloaking model to protect user location privacy. We thoroughly evaluate our methods based on Quality of Privacy (QoP) via real and synthetic data. © 201. IEEE.

关键词： Location based services

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I-mRMR: Incremental max-relevance, and min-redundancy feature selection 3rd

I-mRMR: Incremental max-relevance, and min-redundancy featur...

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3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 201.

作者： Xiu, Yeliang Zhao, Suyun Chen, Hong Li, Cuiping Information of School Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China

ISBN: (纸本)9783030260743

An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundancy feature selection (mRMR), which is used to handle streaming data or large-scale data. First, Incremental key Instance Set is proposed which composes of the non-distinguished instances by the historical selected features. Second, an incremental feature selection algorithm is designed in which the incremental key instance set, replacing of all the seen instances so far, is used in the process of adding representative features. Since the Incremental key Instance Set is far less than the whole instances, the incremental feature selection by using this key set avoids redundant computation and save computation time and space. Finally, the experimental results show that I-mRMR could significantly or even dramatically reduce the time of feature selection with an acceptable classification accuracy. © 201., Springer Nature Switzerland AG.

关键词： Redundancy

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knowledge Graph for China's Genealogy1

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IEEE Transactions on knowledge and {1. engineering 2023年第1期35卷 634-646页

作者： Wu, Xindong Jiang, Tingting Zhu, Yi Bu, Chenyang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei230009 China Hefei University of Technology Research Institute of Big Knowledge Anhui Hefei230009 China Mininglamp Technology Mininglamp Academy of Sciences Beijing100102 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230009 China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230009 China

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical {1., search for genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical {1. bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical {1. may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical {1.sets verify the feasibility and effectiveness of our proposed model. © 1.89-201. IEEE.

关键词： History

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Accuracy estimation of link-based similarity measures and its application

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Frontiers of Computer Science 2016年第1期10卷 113-123页

作者： Yinglong ZHANG Cuiping LI Chengwang XIE Hong CHEN School of Software East China Jiaotong University Nanchang 330045 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and Department of Computer Science Renmin University of China Beijing 100872 China Intelligent Optimization and Information Processing Laboratory East China Jiaotong University Nanchang 330013 China

Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank （PPR） and Sim- Rank （SR） have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank （P-Rank）, which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition： the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

关键词： personalized PageRank SimRank P-Rank up-per bound

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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows 8

Task Offloading Optimization Based on Position Prediction in...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zheng, Dawen Wang, Lusheng Kai, Caihong Peng, Min Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665450515

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay. © 2022 IEEE.

关键词： Edge computing

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Full-scale experimental research on fire fume refluence of sloped long and large curved tunnel

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Science China(Technological Sciences) 2011年第S1期54卷 89-94页

作者： ZHONG MaoHua 1.SHI CongLing 1.HE Li 1.SHI JieHong 1.& FU TaiRan 2 1.China Academy of Safety Science and Technology,Beijing 1.001.,China 2 key {1. for Thermal Science and Power engineering of ministry of education,Department of Thermal engineering,Tsinghua University,Beijing 1.0084,China 1. China Academy of Safety Science and Technology Beijing 100012 China 2. Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Thermal Engineering Tsinghua University Beijing 100084 China

A full-scale experimental research is conducted on the fire fume refluence of a sloped long and large curved tunnel in the underground plant of a hydraulic and hydropower engineering project under three different fire *** a result,the eddy effect and pattern of the fire fume flow in the tunnel in case of fire is discovered and the refluence of the fume layers in case of fire is *** analysis of the temperature measurement resulted in the course of the experiment,the main characteristics and performance of the fire fume refluence are discussed under the three different fire powers.

关键词： large and long tunnel fire fume refluence experiment

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Meta-path Enhanced knowledge Graph Convolutional Network for Recommender Systems 12

Meta-path Enhanced Knowledge Graph Convolutional Network for...

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1.th IEEE International Conference on Big knowledge, ICBK 2021

作者： Wang, Ru Wu, Meng Ji, Shengwei Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Ministry of Education School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665438582

knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in recommender systems, the Graph Convolutional Network (GCN) model is introduced to mine the relatedness between entities in a KG because of its efficiency in extracting spatial features on topological graphs. The knowledge Graph Convolutional Network (KGCN) model up-dates the embedding of a currently positioned entity by aggregating the information of adjacent entities selected randomly. Never-theless, it has two limititations: 1. the information of neighbors se-lected randomly cannot accurately represent the current entity in the KG;2) the model is hard to converge as graph features (i.e. The spatial relation features and semantic information features of en-tities in the KG) grow. To solve these limitations, in this paper, a meta-path (i.e., a sequence of artificially constructed relationships) is introduced into the selection of neighbors in the KGCN model to enhance the representation of each entity. Furthermore, two construction methods of the meta-path - constructing a meta-path based on the same relation (KGCN-SP) and the characteris-tics of KG (KGCN-MP) -are proposed. The experiments based on three real-world datasets demonstrate that the neighbor selection based on the meta-path is able to collect more accurate infor-mation from a KG and improve the recommendation performance effectively. © 2021.IEEE.

关键词： knowledge graph

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A highly efficient electric additive for enhancing photovoltaic performance of dye-sensitized solar cells

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Science China Chemistry 2010年第6期53卷 1352-1357页

作者： LAN Zhang,WU JiHuai,LIN JianMing & HUANG MiaoLiang engineering Research Center of Environment-Friendly Functional Materials,{1. of education key laboratory of Functional Materials for Fujian Higher education Institute of Materials Physical Chemistry,Huaqiao University,Quanzhou 362021.China 1. Engineering Research Center of Environment-Friendly Functional Materials Ministry of Education Key Laboratory of Functional Materials for Fujian Higher Education Institute of Materials Physical Chemistry Huaqiao University Quanzhou 362021 China

N-cetylpyridinium iodide (N-CPI) as a new electric additive for enhancing photovoltaic performance of the dye-sensitized solar cell (DSSC) was *** showed high efficiency for enhancing both the open-circuit voltage and the short-circuit current density of DSSC when the suitable amount of N-CPI as 0.02 M was added in liquid *** energy conversion effi- ciency of DSSC increased from 4.429% to 6.535%,with 47.55% ***,it is a highly efficient electric addi- tive for *** intrinsic reason is owing to the special molecular structure of N-CPI,which contains two different polarity *** a surfactant,N-CPI could form ordered arrangement in liquid electrolyte,which affects the diffusing ability and the redox reaction of I-/I3-,and further affects the photovoltaic performance of DSSC.

关键词： dye-sensitized solar cell electric additive photovoltaic performance N-cetylpyridinium iodide Dapp

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Experimental study on drying of typical MSW under incinerator-like conditions

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Science China(Technological Sciences) 2007年第5期50卷 636-643页

作者： ZHANG YanGuo1. CHEN MeiQian2, MENG AiHong1. LI QingHai1.& CHEN YunXuan2 1.key {1. for Thermal Science and Power engineering of ministry of education, Tsinghua University, Beijing 1.0084, China 2 School of Mechanical, Electronical and Control engineering, Beijing Jiaotong University, Beijing 1.0044, China 1. Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Tsinghua University Beijing 100084 China 2. School of Mechanical Electronical and Control Engineering Beijing Jiaotong University Beijing 100044 China

In this paper, a series of experiments were conducted to investigate the drying of typical municipal solid wastes (MSW) in a muffle incinerator. From the experimental drying characteristic curves, the effects of temperature on drying process were observed to be more obvious in the low temperature region than in the high tem- perature region in muffle incinerator. At a specified drying temperature, drying time was highly dependent upon the packed waste thickness, having an exponent rela- tion with the thickness. When the drying temperature increased or the thickness of potato bulks was diminished, the maximum drying rate reduced. Based on the first-order reaction, activation energy (Ea), and factor A of potato and cabbage were determined.

关键词： drying, MSW, potato blocks, cabbage stems, temperature, dying rate

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Joint user profiling with hierarchical attention networks

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Frontiers of Computer Science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

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