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Quantitative assessment of the supply,demand and flows of ecosystem services in the Yangtze River Basin,China

Frontiers of Earth Science 2024年第2期18卷 422-445页

作者： dongjie GUAN Xiaofeng FAN Lilei ZHOU Kangwen zhu School of Smart City Institute Chongqing Jiaotong UniversityChongqing 400074China

Ecosystem service flow is essential for transporting,transforming,and maintaining ecosystem services and connecting supply and *** study attempted to quantitatively assess the supply and demand flows of ecosystem services in the Yangtze River Basin in 2000,2010,and 2020;assess the evolution of the spatial patterns of ecosystem service flow at the provincial,watershed and urban agglomeration scales;and design a zoning standard for ecosystem service *** results showed as follows.1)Between 2000 and 2020,the Yangtze River had a progressive drop in its freshwater supply,water conservation service and carbon sequestration service *** decline rates for these services were measured at 10.90%,11.11%,and 5.17%,*** climate regulation service flow exhibited a pattern of initial fall followed by a subsequent increase,characterized by a decline rate of 35.53%.2)The lowest was the ecosystem service flow in the lower reaches of the Yangtze River and the Yangtze River Delta urban *** supply service flow and water conservation service flow were the highest in the upper reaches of the Yangtze River and the Chengdu-Chongqing urban *** sequestration service flow and climate regulation service flow were the highest in the middle reaches of the Yangtze River Basin and the urban agglomeration in the middle reaches of the Yangtze River.3)From 2000 to 2020,the change ratios of the area proportion of the confluence,flow,and outflow areas in the Yangtze River Basin were 1.06,3.17,and 0.86,*** results of this research could offer decision support for regulating ecosystem services in the Yangtze River Basin,promoting sustainable regional development and achieving rational use of the basin resources.

关键词： Yangtze River Basin ecosystem services supply and demand service flow quantitative assessment spatial matching

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Dis-NDVW: Distributed Network Asset Detection and Vulnerability Warning Platform

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Computers, Materials & Continua 2023年第7期76卷 771-791页

作者： Leilei Li Yansong Wang dongjie zhu Xiaofang Li Haiwen Du Yixuan Lu Rongning Qu Russell Higgs Artificial Intelligence Academy Wuxi Vocational College of Science and TechnologyWuxi214068China School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China Department of Mathematics Harbin Institute of TechnologyWeihai264209China School of Astronautics Harbin Institute of TechnologyHarbin150001China School of Mathematics and Statistics University College DublinDublinDO4 V1W8Ireland

With the rapid development of Internet technology,the issues of network asset detection and vulnerability warning have become hot topics of concern in the ***,most existing detection tools operate in a single-node mode and cannot parallelly process large-scale tasks,which cannot meet the current needs of the *** address the above issues,this paper proposes a distributed network asset detection and vulnerability warning platform(Dis-NDVW)based on distributed systems and multiple detection ***,this paper proposes a distributed message sub-scription and publication system based on Zookeeper and Kafka,which endows Dis-NDVW with the ability to parallelly process large-scale ***,Dis-NDVW combines the RangeAssignor,RoundRobinAssignor,and StickyAssignor algorithms to achieve load balancing of task nodes in a distributed detection *** terms of a large-scale task processing strategy,this paper proposes a task partitioning method based on First-In-First-Out(FIFO)*** method realizes the parallel operation of task producers and task consumers by dividing pending tasks into different queues according to task *** ensure the data reliability of the task cluster,Dis-NDVW provides a redundant storage strategy for master-slave partition *** terms of distributed storage,Dis-NDVW utilizes a distributed elastic storage service based on ElasticSearch to achieve distributed storage and efficient retrieval of big *** verification shows that Dis-NDVW can better meet the basic requirements of ultra-large-scale detection tasks.

关键词： Distributed network security network asset detection vulnerability warning

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SNES: Social-Network-Oriented Public Opinion Monitoring Platform Based on ElasticSearch

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Computers, Materials & Continua 2019年第9期61卷 1271-1283页

作者： Chuiju You dongjie zhu Yundong Sun Anshan Ye Gangshan Wu Ning Cao Jinming Qiu Helen Min Zhou College of Information Engineering Sanming UniversitySanming365004China School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China College of Mathematics and Computer Science Xinyu UniversityXinyu338004China School of Information Engineering Jiangsu Polytechnic College of Agriculture and ForestryJurong212400China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

With the rapid development of social network,public opinion monitoring based on social networks is becoming more and more *** platforms have achieved some success in public opinion ***,these platforms cannot perform well in scalability,fault tolerance,and real-time *** this paper,we propose a novel social-network-oriented public opinion monitoring platform based on ElasticSearch(SNES).Firstly,SNES integrates the module of distributed crawler cluster,which provides real-time social media data ***,SNES integrates ElasticSearch which can store and retrieve massive unstructured data in near real ***,we design subscription module based on Apache Kafka to connect the modules of the platform together in the form of message push and consumption,improving message throughput and the ability of dynamic horizontal scaling.A great number of empirical experiments prove that the platform can adapt well to the social network with highly real-time data and has good performance in public opinion monitoring.

关键词： Social network public opinion monitoring elasticsearch scrapy-redis

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MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

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Computers, Materials & Continua 2019年第9期61卷 1105-1115页

作者： dongjie zhu Yuhua Wang Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China College of Information Engineering Sanming University365004SanmingChina Fujian Province University Key Lab for Industry Big Data Analysis and Application FujianChina College of Information Engineering Qingdao Binhai UniversityQingdaoChina Jiangsu Province Wireless Sensing System Application Engneering Technology Research and Development Centre China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media *** to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation *** this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a ***,we design their own feature extraction models for multiple heterogeneous data ***,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task ***,we design each task’s own unique presentation layer for discriminant output of ***,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each *** experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.

关键词： User profiles multi-source multi-task learning social network

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Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data

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Computers, Materials & Continua 2019年第7期61卷 227-241页

作者： Ning Cao Shengfang Li Keyong Shen Sheng Bin Gengxin Sun dongjie zhu Xiuli Han Guangsheng Cao Abraham Campbell College of Computer Information and Engineering Nanchang Institute of TechnologyNanchangChina College of Information Engineering Sanming UniversitySanmingChina School of Data Science and Software Engineering Qingdao UniversityQingdaoChina School of Computer Science and Technology Harbin Institute of TechnologyWeihaiChina Public Teaching Department Qingdao Technical CollegeQingdaoChina School of Computer Science University College DublinDublinIreland

Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human ***-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD *** this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in *** spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD *** based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows *** propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.

关键词： Origin-destination(OD)flows semantics analytics complex network big data analysis

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MINE:A Method of Multi-Interaction Heterogeneous Information Network Embedding

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Computers, Materials & Continua 2020年第6期63卷 1343-1356页

作者： dongjie zhu Yundong Sun Xiaofang Li Haiwen Du Rongning Qu Pingping Yu Xuefeng Piao Russell Higgs Ning Cao School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China Department of Mathematics Harbin Institute of TechnologyWeihai264209China School of Astronautics Harbin Institute of TechnologyHarbin150001China School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050018China School of Mathematics and Statistics University College DublinDublinD04 V1W8Ireland School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China

Interactivity is the most significant feature of network data,especially in social *** network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between *** tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction ***,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional ***,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.

关键词： Network embedding network representation learning interactive network data mining

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Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph

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Computer Systems Science & Engineering 2022年第9期42卷 1133-1146页

作者： Donglei Lu dongjie zhu Haiwen Du Yundong Sun Yansong Wang Xiaofang Li Rongning Qu Ning Cao Russell Higgs School of Artificial Intelligence Wuxi Vocational College of Science and TechnologyWuxi214028China School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China School of Astronautics Harbin Institute of TechnologyHarbin150001China Department of Mathematics Harbin Institute of TechnologyWeihai264209China School of Mathematical Sciences University College DublinDublinDublin4Ireland

The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to theuser based on the known historical interaction data of the target user. Furthermore,the combination of the recommended algorithm based on collaborative filtrationand other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model(CoFM) is one representative research. CoFM, a fusion recommendation modelcombining the collaborative filtering model FM and the graph embeddingmodel TransE, introduces the information of many entities and their relationsin the knowledge graph into the recommendation system as effective auxiliaryinformation. It can effectively improve the accuracy of recommendations andalleviate the problem of sparse user historical interaction data. Unfortunately,the graph-embedded model TransE used in the CoFM model cannot solve the1-N, N-1, and N-N problems well. To tackle this problem, a novel fusion recommendation model Joint Factorization Machines and TransH Model (JFMH) isproposed, which improves CoFM by replacing the TransE model with TransHmodel. A large number of experiments on two widely used benchmark data setsshow that compared with CoFM, JFMH has improved performance in terms ofitem recommendation and knowledge graph completion, and is more competitivethan multiple baseline methods.

关键词： Fusion recommendation system knowledge graph graph embedding

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Exploration on the Load Balancing Technique for Platform of Internet of Things

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Computer Systems Science & Engineering 2021年第9期38卷 339-350页

作者： Donglei Lu dongjie zhu Yundong Sun Haiwen Du Xiaofang Li Rongning Qu Yansong Wang Ning Cao Helen Min Zhou School of Artificial Intelligence Wuxi Vocational College of Science and TechnologyWuxi214028China School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China School of Astronautics Harbin Institute of TechnologyHarbin150001China Department of Mathematics Harbin Institute of TechnologyWeihai264209China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

In recent years,the Internet of Things technology has developed rapidly,and smart Internet of Things devices have also been widely popularized.A large amount of data is generated every *** we are in the era of big data in the Internet of *** rapid growth of massive data has brought great challenges to storage technology,which cannot be well coped with by traditional storage *** demand for massive data storage has given birth to cloud storage *** balancing technology plays an important role in improving the performance and resource utilization of cloud storage ***,it is of great practical significance to study how to improve the performance and resource utilization of cloud storage systems through load balancing *** the basis of studying the read strategy of Swift,this article proposes a reread strategy based on load balancing of storage resources to solve the problem of unbalanced read load between interruptions caused by random data copying in *** storage asynchronously tracks the I/O conversion to select the storage with the smallest load for asynchronous *** experimental results indicate that the proposed strategy can achieve a better load balancing state in terms of storage I/O utilization and CPU utilization than the random read strategy index of Swift.

关键词： The Internet of Things cloud storage Swift load balancing scheduling algorithm

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Massive Files Prefetching Model Based on LSTM Neural Network with Cache Transaction Strategy

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Computers, Materials & Continua 2020年第5期63卷 979-993页

作者： dongjie zhu Haiwen Du Yundong Sun Xiaofang Li Rongning Qu Hao Hu Shuangshuang Dong Helen Min Zhou Ning Cao School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China School of Science Harbin Institute of TechnologyWeihai264209China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand College of Mathematics and Computer Science Xinyu UniversityXinyu338004China College of Information Engineering Sanming UniversitySanming365004China School of Astronautics Harbin Institute of TechnologyHarbin150001China

In distributed storage systems,file access efficiency has an important impact on the real-time nature of information *** a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system ***,prefetching model needs to mine the degree of association between files to ensure the accuracy of *** the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance *** this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access ***,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational ***,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature ***,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be *** show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time.

关键词： Massive files prefetching model cache transaction distributed storage systems LSTM neural network

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DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3295-3307页

作者： Leilei Li Yansheng Fu dongjie zhu Xiaofang Li Yundong Sun Jianrui Ding Mingrui Wu Ning Cao Russell Higgs Artificial Intelligence Academy Wuxi Vocational College of Science and TechnologyWuxi214068China School of Computer Science and Technology Harbin Institute of TechnologyWeihai204209China Department of Mathematics Harbin Institute of TechnologyWeihai264209China College of Information Engineering Shandong Vocational and Technical University of International StudiesRizhao276826China School of Mathematics and Statistics University College DublinDublinD04 V1W8Ireland

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval *** and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge *** the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge *** representation learning method is sig-nificant to the application of multi-modal knowledge *** paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data ***,use distributed technology to classify and store the data in the knowledge graph to improve retrieval ***,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of ***,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image *** paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

关键词： Multi-modal retrieval distributed storage knowledge graph

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