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学者姓名：邱启荣
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Abstract ：
针对属性值为三角模糊数形式的动态多属性决策问题,提出基于TOPSIS的动态三角模糊多属性决策方法.该方法不仅可以得到各备选方案的差异程度,还可分析各备选方案的增长程度,同时设置偏好参数以考虑决策者对于差异性和增长性的不同偏好,运用时间权重进行二次加权以解决不同时点排序结果不一致的问题.最后,通过算例分析及偏好参数敏感性分析说明决策时考虑决策者偏好的必要性和重要性,并通过与其他方法的比较分析进一步验证了文章方法的可行性和有效性.
Keyword ：
三角模糊数 三角模糊数 动态多属性决策 动态多属性决策 理想解法 理想解法
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| GB/T 7714 | 李美娟 , 易思成 , 邱启荣 et al. 基于TOPSIS的动态三角模糊多属性决策方法 [J]. | 系统科学与数学 , 2022 , 42 (03) : 614-625 . |
| MLA | 李美娟 et al. "基于TOPSIS的动态三角模糊多属性决策方法" . | 系统科学与数学 42 . 03 (2022) : 614-625 . |
| APA | 李美娟 , 易思成 , 邱启荣 , 林琦 . 基于TOPSIS的动态三角模糊多属性决策方法 . | 系统科学与数学 , 2022 , 42 (03) , 614-625 . |
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结合地方区域特色和相关学科优势，福建省从1990年起相继批准组建了一批省重点实验室。经过多年的发展，福建省重点实验室已成为福建省经济社会发展和科技创新能力提升的重要支撑力量。依据创新生态系统相关理论，省重点实验室作为区域内高校和科研院所进行科学研究特别是基础科学研究的重要载体，是区域创新生态系统的重要组成部分，其自身发展壮大与区域整体的创新生态系统的发展状况密不可分，同时其自身的发展情况也会影响区域创新生态系统的发展质量。应基于创新生态系统平衡发展、创新生态系统发展动力、创新生态系统协同共生、创新生态系统环境支撑的角度发展省重点实验室。
Keyword ：
创新生态系统 创新生态系统 福建省重点实验室 福建省重点实验室 科技创新能力 科技创新能力
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| GB/T 7714 | 邱启荣 . 创新生态系统理论视域下的福建省重点实验室发展及提升策略 [J]. | 福州大学学报(哲学社会科学版) , 2022 , 36 (01) : 51-57 . |
| MLA | 邱启荣 . "创新生态系统理论视域下的福建省重点实验室发展及提升策略" . | 福州大学学报(哲学社会科学版) 36 . 01 (2022) : 51-57 . |
| APA | 邱启荣 . 创新生态系统理论视域下的福建省重点实验室发展及提升策略 . | 福州大学学报(哲学社会科学版) , 2022 , 36 (01) , 51-57 . |
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Aiming to improve the commercialization efficiency of scientific innovative achievements, this paper utilizes the time series visualization method to construct the time series network of each subsystem. After that, the network similarity is calculated by the cosine similarity theorem. On this basis, a new multilayer network adjacency matrix is obtained. With the adoption of k-core technology, the critical nodes can be identified to study the transformation efficiency of the innovation value in the network. Finally, according to the provincial innovation value transformation data of China from 1998 to 2016, an empirical study was carried out to calculate and analyze the transformation efficiency of innovation achievements in 30 provinces. The results indicate that (1) the transformation efficiency of innovation value can be expressed by the structure of the time series network constructed by the input-output vectors; (2) the mapping relationship of the value transformation vectors could be reflected by the cosine similarity of the time series network, while the transformation efficiency of innovation value could be identified using the k-core; and (3) the transformation efficiency of innovation value in three coastal provinces is relatively higher, while that of the rest of the provinces is roughly the same among the 30 provinces.
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| GB/T 7714 | Weng, Wuyan , Li, Zi , Qiu, Qirong et al. Critical Nodes Identification of Scientific Achievement Commercialization Network under k-Core [J]. | COMPLEXITY , 2022 , 2022 . |
| MLA | Weng, Wuyan et al. "Critical Nodes Identification of Scientific Achievement Commercialization Network under k-Core" . | COMPLEXITY 2022 (2022) . |
| APA | Weng, Wuyan , Li, Zi , Qiu, Qirong , Cheng, Junheng . Critical Nodes Identification of Scientific Achievement Commercialization Network under k-Core . | COMPLEXITY , 2022 , 2022 . |
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Patent is an important embodiment of innovation. Before patent application, many people will check a patent application process on the Internet to understand the steps of a patent application. In fact, these people's search is also a means to understand whether innovative enterprises or individuals imply the importance of innovation. It has become a new crucial problem to obtain more information about time-series data. Research has found that the concept of VG can provide deeper information in time-series data so that it can understand the information of patent applications more comprehensively. After analyzing the data from 1 January 2011 to 31 December 2018, we find: i) there are very few peaks and valleys, and 80% of searches are in the normal range. ii) according to the central value of the ranking, it can be found that the peaks of the annual patent application search times time series occurred in December last year, after January, February of this year or after August-October, and iii) after clustering the time series data, we find that the attention of people shows noticeable segmentation effect.
Keyword ：
clustering clustering community detection community detection complex network complex network patent application patent application visibility graph visibility graph
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| GB/T 7714 | Mao, Shihao , Hu, Yuxia , Yuan, Xuesong et al. Analysis of Patent Application Attention: A Network Analysis Method [J]. | FRONTIERS IN PHYSICS , 2022 , 10 . |
| MLA | Mao, Shihao et al. "Analysis of Patent Application Attention: A Network Analysis Method" . | FRONTIERS IN PHYSICS 10 (2022) . |
| APA | Mao, Shihao , Hu, Yuxia , Yuan, Xuesong , Zhang, Mengyue , Qiu, Qirong , Wu, Peng . Analysis of Patent Application Attention: A Network Analysis Method . | FRONTIERS IN PHYSICS , 2022 , 10 . |
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经过三十多年的发展,国家自然科学基金已经成为国内支持基础科学研究的主要渠道,被科研人员公认为最能反映研究者竞争能力的研究基金.基于国家自然科学基金项目立项数据,分析“十二五”期间F大学人才引进的情况,进而提出进一步优化人才发展的路径,以期能为地方高校人才引进与培养提供一定的经验借鉴.
Keyword ：
F大学 F大学 人才引进 人才引进 国家自然科学基金 国家自然科学基金 高校 高校
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| GB/T 7714 | 邱启荣 . 基于国家自然科学基金视角的高校人才引进情况分析——以F大学为例 [J]. | 中国高校科技 , 2018 , (1) : 25-28 . |
| MLA | 邱启荣 . "基于国家自然科学基金视角的高校人才引进情况分析——以F大学为例" . | 中国高校科技 1 (2018) : 25-28 . |
| APA | 邱启荣 . 基于国家自然科学基金视角的高校人才引进情况分析——以F大学为例 . | 中国高校科技 , 2018 , (1) , 25-28 . |
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针对多数负载均衡算法都以虚拟机的CPU、内存等资源的利用率作为优化目标,而未考虑虚拟机上总任务工作时长不均衡导致任务总等待时长增加的问题,提出一种结合随机森林分类器的粒子群优化算法用于解决虚拟机的负载均衡问题.该算法不仅均衡了虚拟机的CPU利用率和内存利用率,也将虚拟机上总任务工作时长作为优化目标,以达到均衡虚拟机资源利用率,同时减少任务总等待时间的目的.仿真实验结果表明,该算法能有效解决虚拟机的负载均衡问题.
Keyword ：
云计算 云计算 粒子群优化 粒子群优化 虚拟机 虚拟机 负载均衡 负载均衡 随机森林分类器 随机森林分类器
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| GB/T 7714 | 江伟 , 刘漳辉 , 邱启荣 et al. 一种云环境下的虚拟机负载均衡算法 [J]. | 福州大学学报（自然科学版） , 2018 , 46 (4) : 451-457 . |
| MLA | 江伟 et al. "一种云环境下的虚拟机负载均衡算法" . | 福州大学学报（自然科学版） 46 . 4 (2018) : 451-457 . |
| APA | 江伟 , 刘漳辉 , 邱启荣 , 黄启成 . 一种云环境下的虚拟机负载均衡算法 . | 福州大学学报（自然科学版） , 2018 , 46 (4) , 451-457 . |
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Finding communities in networks is one of the challenging issues in complex network research. We have to deal with very large networks that contain billions of vertices, which makes community discovery a computationally intensive work. Moreover, communities usually overlap each other, which greatly increases the difficulty of identifying the boundaries of communities. In this paper, we propose a parallel multi-label propagation algorithm (PMLPA) that enhances traditional multi-label propagation algorithm (MLPA) in two ways. First, the critical steps of MLPA are parallelized based on the MapReduce model to get higher scalability. Second, new label updating strategy is used to automatically determine the most valuable labels of each vertex. Furthermore, we study the improvement of PMLPA through considering the influence of vertices and labels on label updating. In this way, the importance of each label can be described with higher precision. Experiments on artificial and real networks prove that the proposed algorithms can achieve both high discovering accuracy and high scalability.
Keyword ：
Community discovery Community discovery influence model influence model multi-label propagation multi-label propagation overlapping community overlapping community
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| GB/T 7714 | Qiu, Qirong , Guo, Wenzhong , Chen, Yuzhong et al. Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery [J]. | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS , 2017 , 26 (3) . |
| MLA | Qiu, Qirong et al. "Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery" . | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 26 . 3 (2017) . |
| APA | Qiu, Qirong , Guo, Wenzhong , Chen, Yuzhong , Guo, Kun , Li, Rongrong . Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery . | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS , 2017 , 26 (3) . |
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Community detection is one of the important methods for understanding the mechanism behind the function of social networks. The recently developed label propagation algorithm (LPA) has been gaining increasing attention because of its excellent characteristics, such as a succinct framework, linear time and space complexity, easy parallelization, etc. However, several limitations of the LPA algorithm, including random label initialization and greedy label updating, hinder its application to complex networks. A new parallel LPA is proposed in this study. First, grey relational analysis is integrated into the label updating process, which is based on vertex similarity. Second, parallel propagation steps are comprehensively studied to utilize parallel computation power efficiently. Third, randomness in label updating is significantly reduced via automatic label selection and label weight thresholding. Experiments conducted on artificial and real social networks demonstrate that the proposed algorithm is scalable and exhibits high clustering accuracy. (C) 2016 Elsevier B.V. All rights reserved.
Keyword ：
Community detection Community detection Label propagation Label propagation Parallel computation Parallel computation
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| GB/T 7714 | Zhang, Qishan , Qiu, Qirong , Guo, Wenzhong et al. A social community detection algorithm based on parallel grey label propagation [J]. | COMPUTER NETWORKS , 2016 , 107 : 133-143 . |
| MLA | Zhang, Qishan et al. "A social community detection algorithm based on parallel grey label propagation" . | COMPUTER NETWORKS 107 (2016) : 133-143 . |
| APA | Zhang, Qishan , Qiu, Qirong , Guo, Wenzhong , Guo, Kun , Xiong, Naixue . A social community detection algorithm based on parallel grey label propagation . | COMPUTER NETWORKS , 2016 , 107 , 133-143 . |
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Community structure is a very important characteristic of complex networks, detecting communities within networks has very important significance in several disciplines like computer science, physics, biology, etc. To some extent, Real-world networks exhibit overlapping community structure. To solve this problem, we devise a novel algorithm to identify overlapping communities in social networks with Grey Relational Analysis. This paper presents the edge vector which is a measure of relationships among nodes, and uses balanced closeness degree to describe edge similarity, computes edge clusters and finally obtains overlapping community structure. The effectiveness and the efficiency of the new algorithm are evaluated by experiments on both real-world and the computer-generated datasets.
Keyword ：
Clustering Clustering Grey Relational Analysis Grey Relational Analysis Overlapping Community Structure Overlapping Community Structure Social Networks Social Networks
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| GB/T 7714 | Wu, Ling , Zhang, Qishan , Guo, Kun et al. Identification of Overlapping Community Structure with Grey Relational Analysis in Social Networks [J]. | JOURNAL OF GREY SYSTEM , 2016 , 28 (1) : 98-108 . |
| MLA | Wu, Ling et al. "Identification of Overlapping Community Structure with Grey Relational Analysis in Social Networks" . | JOURNAL OF GREY SYSTEM 28 . 1 (2016) : 98-108 . |
| APA | Wu, Ling , Zhang, Qishan , Guo, Kun , Qiu Qirong . Identification of Overlapping Community Structure with Grey Relational Analysis in Social Networks . | JOURNAL OF GREY SYSTEM , 2016 , 28 (1) , 98-108 . |
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Discovering communities in large-scale social networks efficiently and accurately is one of the challenges in social network data mining. We propose a clustering algorithm to discover social network communities based on the propagation of local and global information. Three strategies, namely, localizing propagation of affinity messages, relaxing self-exemplar constraints, and hierarchical processing, are employed in the algorithm to achieve reasonable time and space complexities in social networks. The local and global information is represented by the k-path edge centrality incorporated in the similarity calculation. The standalone algorithm is extended to provide parallel implementations based on the MapReduce model to accelerate processing in large-scale networks. Two well-known parallel computation frameworks, Hadoop and Spark, are adopted to implement the parallel algorithm. Experiments performed on artificial and real social network datasets show that the proposed algorithms can achieve near-linear time and space complexities with comparative clustering accuracy. (C) 2015 Elsevier Inc. All rights reserved.
Keyword ：
Affinity propagation Affinity propagation Community discovery Community discovery MapReduce model MapReduce model Social network Social network
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| GB/T 7714 | Guo, Kun , Guo, Wenzhong , Chen, Yuzhong et al. Community discovery by propagating local and global information based on the MapReduce model [J]. | INFORMATION SCIENCES , 2015 , 323 : 73-93 . |
| MLA | Guo, Kun et al. "Community discovery by propagating local and global information based on the MapReduce model" . | INFORMATION SCIENCES 323 (2015) : 73-93 . |
| APA | Guo, Kun , Guo, Wenzhong , Chen, Yuzhong , Qiu, Qirong , Zhang, Qishan . Community discovery by propagating local and global information based on the MapReduce model . | INFORMATION SCIENCES , 2015 , 323 , 73-93 . |
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