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Catalytic ozonation-biological coupled processes for the treatment of industrial wastewater containing refractory chlorinated nitroaromatic compounds

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2010年第3期11卷 177-189页

作者： Bing-zhi li Xiang-yang xu liang ZHU Department of Environmental Engineering College of Environmental and Resource Sciences Zhejiang University Hangzhou 310029 China

A treatability study of industrial wastewater containing chlorinated nitroaromatic compounds （CNACs） by a catalytic ozonation process （COP） with a modified Mn/Co ceramic catalyst and an aerobic sequencing batch reactor （SBR） was investigated. A preliminary attempt to treat the diluted wastewater with a single SBR resulted in ineffective removal of the color, ammonia, total organic carbon （TOC） and chemical oxygen demand （COD）. Next, COP was applied as a pretreatment in order to obtain a bio-compatible wastewater for SBR treatment in a second step. The effectiveness of the COP pretreatment was assessed by evaluating wastewater biodegradability enhancement （the ratio of biology oxygen demand after 5 d （BOD5） to COD）, as well as monitoring the evolution of TOC, carbon oxidation state （COS）, average oxidation state （AOS）, color, and major pollutant concentrations with reaction time. In the COP, the catalyst preserved its catalytic properties even after 70 reuse cycles, exhibiting good durability and stability. The performance of SBR to treat COP effluent was also examined. At an organic loading rate of 2.0 kg COD/（m^3.d）, with hydraulic retention time （HRT）=10 h and temperature （30±2） ℃, the average removal efficiencies of NH3-N, COD, BOD5, TOC, and color in a coupled COP/SBR process were about 80%, 95.8%, 93.8%, 97.6% and 99.3%, respectively, with average effluent concentrations of 10 mg/L, 128 mg/L, 27.5 mg/L, 25.0 mg/L, and 20 multiples, respectively, which were all consistent with the national standards for secondary discharge of industrial wastewater into a public sewerage system （GB 8978-1996）. The results indicated that the coupling of COP with a biological process was proved to be a technically and economically effective method for treating industrial wastewater containing recalcitrant CNACs.

关键词： Industrial wastewater Catalytic ozonation Sequencing batch reactor Chlorinated nitroaromatic compounds

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Discovering Functional Organized Keyword Recommendation Point of Interest Groups for Spatial

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Journal of Computer Science & Technology 2018年第4期33卷 697-710页

作者： Yan-Xia xu Wei Chen Jia-Jie xu zhi-xu li Guan-Feng liu Lei Zhao School of Computer Science and Technology Soochow University Suzhou 215006 China

A point of interest （POI） is a specific point location that someone may find useful. With the development of urban modernization, a large number of functional organized POI groups （FOPGs）, such as shopping malls, electronic malls, and snacks streets, are springing up in the city. They have a great influence on people＇s lives. We aim to discover functional organized POI groups for spatial keyword recommendation because FOPGs-based recommendation is superior to POIs-based recommendation in efficiency and flexibility. To discover FOPGs, we design clustering algorithms to obtain organized POI groups （OPGs） and utilize OPGs-LDA （Latent Dirichlet Allocation） model to reveal functions of OPGs for further recommendation. To the best of our knowledge, we are the first to study functional organized POI groups which have important applications in urban planning and social marketing.

关键词： functional organized point of interest (POI) group POI clustering OPG-LDA (organized point of interest group-latent Dirichlet allocation) model spatial keyword recommendation

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Scalable and Adaptive Joins for Tra jectory Data in Distributed Stream System

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Journal of Computer Science & Technology 2019年第4期34卷 747-761页

作者： Jun-Hua Fang Peng-Peng Zhao An liu zhi-xu li Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006ChinaNeusoft CorporationShenyang 110179China Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China

As a fundamental operation in LBS(location-based services),the trajectory similarity of moving objects has been extensively studied in recent ***,due to the increasing volume of moving object trajectories and the demand of interactive query performance,the trajectory similarity queries are now required to be processed on massive datasets in a real-time *** work has proposed distributed or parallel solutions to enable large-scale trajectory similarity ***,those techniques cannot be directly adapted to the real-time scenario as it is likely to generate poor balancing performance when workload variance occurs on the incoming trajectory *** this paper,we propose a new workload partitioning framework,ART(Adaptive Framework for Real-Time Trajectory Similarity),which introduces practical algorithms to support dynamic workload assignment for RTTS(real-time trajectory similarity).Our proposal includes a processing model tailored for the RTTS scenario,a load balancing framework to maximize throughput,and an adaptive data partition manner designed to cut off unnecessary network *** on this,our model can handle the large-scale trajectory similarity in an on-line scenario,which achieves scalability,effectiveness,and efficiency by a single *** studies on synthetic data and real-world stream applications validate the usefulness of our proposal and prove the huge advantage of our approach over state-of-the-art solutions in the literature.

关键词： real-time data processing distributed computing trajectory similarity load balancing

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An Efficient Framework for Multiple Subgraph Pattern Matching Models

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Journal of Computer Science & Technology 2019年第6期34卷 1185-1202页

作者： Jiu-Ru Gao Wei Chen Jia-Jie xu An liu zhi-xu li Hongzhi Yin Lei Zhao School of Computer Science and Technology Soochow UniversitySuzhou 215006China School of Information Technology and Electrical Engineering The University of QueenslandBrisbane 4072Australia

With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

关键词： subgraph pattern matching k-automorphism label generalization

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Privacy-Preserving Task Assignment in Spatial Crowdsourcing

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Journal of Computer Science & Technology 2017年第5期32卷 905-918页

作者： An liu zhi-xu li Guan-Feng liu Kai Zheng Min Zhang Qing li Xiangliang Zhang School of Computer Science and Technology Soochow University Suzhou 215006 China School of Computer Science and Technology Soochow University Suzhou 215006 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing 100872 China Department of Computer Science City University of Hong Kong Hong Kong China King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia

With the progress of mobile devices and wireless networks, spatial crowdsourcing （SC） is emerging as a promising approach for problem solving. In SC, spatial tasks are assigned to and performed by a set of human workers. To enable effective task assignment, however, both workers and task requesters are required to disclose their locations to untrusted SC systems. In this paper, we study the problem of assigning workers to tasks in a way that location privacy for both workers and task requesters is preserved. We first combine the Paillier cryptosystem with Yao＆#39;s garbled circuits to construct a secure protocol that assigns the nearest worker to a task. Considering that this protocol cannot scale to a large number of workers, we then make use of Geohash, a hierarchical spatial index to design a more efficient protocol that can securely find approximate nearest workers. We theoretically show that these two protocols are secure against semi-honest adversaries. Through extensive experiments on two real-world datasets, we demonstrate the efficiency and effectiveness of our protocols.

关键词： spatial crowdsourcing spatial task assignment location privacy mutual privacy protection

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Solvability conditions for algebra inverse eigenvalue problem over set of anti-Hermitian generalized anti-Hamiltonian matrices

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Journal of Central South University of Technology 2005年第Z1期12卷 294-297页

作者： ZHANG Zhong-zhi HAN xu-li School of Mathematics and Computational Science Central South University Changsha 410083 China Department of Mathematics Dongguan University of Technology Dongguan 523000 China School of Mathematics and Computational Science Central South University Changsha 410083 China

By using the characteristic properties of the anti-Hermitian generalized anti-Hamiltonian matrices, we prove some necessary and sufficient conditions of the solvability for algebra inverse eigenvalue problem of anti-Hermitian generalized anti-Hamiltonian matrices, and obtain a general expression of the solution to this problem. By using the properties of the orthogonal projection matrix, we also obtain the expression of the solution to optimal approximate problem of an n× n complex matrix under spectral restriction.

关键词： anti-Hermitian generalized anti-Hamiltonian matrix algebra inverse eigenvalue problem optimal approximation

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Crowd-Guided Entity Matching with Consolidated Textual Data

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Journal of Computer Science & Technology 2017年第5期32卷 858-876页

作者： zhi-xu li Qiang Yang An liu Guan-Feng liu Jia Zhu Jia-Jie xu Kai Zheng Min Zhang School of Computer Science and Technology Soochow University Suzhou 215006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China School of Computer Science and Technology Soochow University Suzhou 215006 China School of Computer South China Normal University Guangzhou 510631 China School of Computer Science and Technology Soochow University Suzhou 215006 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing 100872 China

Entity matching (EM) identifies records referring to the same entity within or across databases. Existing methods using structured attribute values (such as digital, date or short string values) may fail when the structured information is not enough to reflect the matching relationships between records. Nowadays more and more databases may have some unstructured textual attribute containing extra consolidated textual information (CText) of the record, but seldom work has been done on using the CText for EM. Conventional string similarity metrics such as edit distance or bag-of-words are unsuitable for measuring the similarities between CText since there are hundreds or thousands of words with each piece of CText, while existing topic models either cannot work well since there are no obvious gaps between topics in CText. In this paper, we propose a novel cooccurrence-based topic model to identify various sub-topics from each piece of CText, and then measure the similarity between CText on the multiple sub-topic dimensions. To avoid ignoring some hidden important sub-topics, we let the crowd help us decide weights of different sub-topics in doing EM. Our empirical study on two real-world datasets based on Amzon Mechanical Turk Crowdsourcing Platform shows that our method outperforms the state-of-the-art EM methods and Text Understanding models.

关键词： entity matching consolidated textual data crowdsourcing

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Enriching Context Information for Entity linking with Web Data

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Journal of Computer Science & Technology 2020年第4期35卷 724-738页

作者： Yi-Ting Wang Jie Shen zhi-xu li Qiang Yang An liu Peng-Peng Zhao Jia-Jie xu Lei Zhao xun-Jie Yang Institute of Artificial Intelligence School of Computer Science and TechnologySoochou University Suzhou 215006China iFLYTEK Research Suzhou 215000China State Key Laboratory of Cognitive Intelligence iFLYTEKHefei 230000China Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology Jeddah 23955Saudi Arabia Anhui Toycloud Technologg Co. Ltd.Hefei 230000Chira

Entity linking(EL)is the task of determining the identity of textual entity mentions given a predefined knowledge base(KB).Plenty of existing efforts have been made on this task using either"local"information(contextual information of the mention in the text),or"global"information(relations among candidate entities).However,either local or global information might be insufficient especially when the given text is *** get richer local and global information for entity linking,we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines(WSE).Based on the intuition above,two novel attempts are *** first one adds web-searched results into an embedding-based method to expand the mention's local information,where we try two different methods to help generate high-quality web contexts:one is to apply the attention mechanism and the other is to use the abstract extraction *** second one uses the web contexts to extend the global information,i.e.,finding and utilizing more extra relevant mentions from the web contexts with a graph-based ***,we combine the two models we propose to use both extended local and global information from the extra web *** empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the F1 score of entity linking.

关键词： entity linking web search engine (WSE) attention mechanism abstract extraction

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Research advances in the study of Pistacia chinensis Bunge, a superior tree species for biomass energy

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Forestry Studies in China 2007年第2期9卷 164-168页

作者： li Hong-lin Zhang zhi-xiang lin Shan-zhi li Xiao-xu College of Biological Sciences and Biotechnology Beijing Forestry University Beijing 100083 P. R. China

As a renewable energy, biomass energy has aroused wide attention and studies of this issue have become a hot topic throughout the world. Pistacia chinensis Bunge （Anacardiaceae） is a superior species for biomass energy with high oil content in seeds and wide geographic distribution. It is a dioeciously, deciduous arbor, flowering from March to April and bearing fruits from September to November. The classification, regional distribution and biological characteristics ofP chinensis are stated in this paper, then, research advances in the growth, breeding and physiology of this species are summarized. The problems in present studies are broached. Finally, a future direction for research is proposed.

关键词： Pistacia chinensis Bunge biomass energy of forest

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A Generative Model Approach for Geo-Social Group Recommendation

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Journal of Computer Science & Technology 2018年第4期33卷 727-738页

作者： Peng-Peng Zhao Hai-Feng Zhu Yanchi liu Zi-Ting Zhou zhi-xu li Jia-Jie xu Lei Zhao Victor S. Sheng School of Computer Science and Technology Soochow University Suzhou 215006 China Department of Management Science and Information Systems Rutgers University Piscataway NJ 08854 U.S.A. Department of Computer Science University of Central Arkansas Conway 72035 U.S.A.

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem： 1） the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2） users have different influences when in diffe- rent groups. In this paper, we propose a generative geo-social group recommendation model （GSGR） to recommend points of interest （POIs） for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.

关键词： group recommendation topic model social network

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