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MLXR: multi-layer obstacle-avoiding X-architecture Steiner tree construction for VLSI routing

Science China(information Sciences) 2017年第1期60卷 211-213页

作者： Xing HUANG Wenzhong GUO Genggeng LIU Guolong CHEN College of Mathematics and Computer Sciences Fuzhou University Key Laboratory of Network Computing and Intelligent Information Processing (Fujian Province) Fuzhou University

Dear editor,Rectilinear Steiner minimal tree(RSMT)has been widely used in several modern very large scale integration(VLSI)circuit design *** of its importance,it has been fully studied in the past *** addition,the density of modern VLSI chips has increased *** today’s VLSI designs,there are increasingly more obstacles,such as IP blocks and pre-routed nets,these

关键词： VLSI MLXR multi-layer obstacle-avoiding X-architecture Steiner tree construction for VLSI routing tree

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Design and Realization of MATLAB-based Face Recognition System

Design and Realization of MATLAB-based Face Recognition Syst...

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1st International Conference on Advanced Algorithms and Control Engineering, ICAACE 2018

作者： Liu, Jianming College of Mathematics and Computer Science Quanzhou Normal University Fujian Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou Normal University Fujian Quanzhou362000 China

Through MATLAB, the paper makes a comparison between Principal Component Analysis (PCA)face recognition algorithm and Adaboost recognition algorithm and selects the algorithm with higher recognition rates to develop an auto face recognition system. The paper explicates primary techniques the system adopts and its specific realization process. By downloading face database online, the paper conducts an all-round test to the system, the result of which proves that this face recognition system is completely practical and feasible. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous information. All rights reserved.

关键词： Semantics

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Integrating MOOCs with Traditional Classroom Instruction to Improve Teaching Effectiveness

Integrating MOOCs with Traditional Classroom Instruction to ...

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2017 2nd International Seminar on Education Innovation and Economic Management(SEIEM 2017)

作者： Xiaodong Wu Yuzhu Zeng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

Massive Open Online Course(MOOC) is a new teaching mode appeared in recent years and its emergence has constituted some challenge to the traditional teaching. However, MOOCs teaching mode has both advantages and disadvantages. In this paper, both the merits and the demerits of MOOC education are analyzed. We point out that the MOOC education mode and the traditional face-to-face classroom instruction should be combined together so as to improve the teaching effectiveness in modern education. Finally, how to blend the MOOC mode and the traditional classroom teaching and further utilize the network platform to improve the teaching efficiency in several learning phases are examined.

关键词： MOOCs face-to-face teaching traditional teaching network teaching platform

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Web server security evaluation method based on multi-source data

Web server security evaluation method based on multi-source ...

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International Conference on Cloud computing, Big Data and Blockchain (ICCBB)

作者： Kedong Wu Xiaoling Gao Yanhua Liu Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian Fujian Fuzhou Fujian China

ISBN: (纸本)9781728112787

Traditional web security assessments are evaluated using a single data source, and the results of the calculations from different data sources are different. Based on multi-source data, this paper uses Analytic Hierarchy process to construct an evaluation model, calculates the weight of each level of indicators in the web security evaluation model, analyzes and processes the data, calculates the host security threat assessment values at various levels, and visualizes the evaluation results through ECharts+WebGL technology.

关键词： Security Servers IP networks XML information processing Data models Safety

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ANDROIDOFF:Offloading android application based on cost estimation

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Journal of Systems and Software 2019年 158卷 110418-000页

作者： Chen, Xing Chen, Jiaqing Liu, Bichun Ma, Yun Zhang, Ying Zhong, Hao College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China School of Software Tsinghua University Beijing100084 China National Engineering Research Center of Software Engineering Peking University China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Computation offloading is a promising way of improving the performance and reducing the battery power consumption, since it moves some time-consuming computation activities to nearby servers. Although various approaches have been proposed to support computation offloading, we argue that there is still sufficient space for improvements, since existing approaches cannot accurately estimate the execution costs. As a result, we find that their offloading plans are less optimized. To handle the problem, in this paper, given an Android application, we propose a novel approach, called ANDROIDOFF, that supports offloading at the granularity of objects. Supporting such capability is challenging due to the two reasons: (1) through dynamic execution, it is feasible to collect the execution costs of only partial methods, and (2) it is difficult to accurately estimate the execution costs of the remaining methods. To overcome the challenges, given an Android application, ANDROIDOFF first combines static and dynamic analysis to predict the execution costs of all its methods. After all the costs are estimated, ANDROIDOFF synthesizes an offloading plan, in which determines the offloading details. We evaluate ANDROIDOFF on a real-world application, with two mobile devices. Our results show that, compared with other approaches, ANDROIDOFF saves the response time by 8%–49% and reduces the energy consumption by 12%–49% on average for computation-intensive applications. © 2019 Elsevier Inc.

关键词： computation offloading

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A Data Placement Strategy for Scientific Workflow in Hybrid Cloud

A Data Placement Strategy for Scientific Workflow in Hybrid ...

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IEEE International Conference on Cloud computing, CLOUD

作者： Zhanghui Liu Tao Xiang Bing Lin Xinshu Ye Haijiang Wang Ying Zhang Xing Chen Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China College of Physics and Energy Fujian Normal University College of Mathematics and Computer Science Fuzhou University

In cloud computing environments, data centers can provide high-performance computing resources and distributed storage space. Scientific workflows often need to be implemented across multiple data centers, where copious amounts of application data are stored. Moving data across geographically distributed data centers leads to intolerable delays and hinders the efficient execution of scientific workflows, which are large-scale data-intensive. Reasonable data placement can reduce data scheduling between the data centers effectively. In this paper, an adaptive discrete particle swarm optimization (PSO) algorithm based on genetic algorithm has been proposed to decrease the number of data transmissions across data centers. The algorithm overcame the premature convergence defect of PSO by introducing the mutation and crossover of genetic algorithm. Moreover, it effectively improved the diversity in the process of population evolution. Compared with the previous work, the simulation results showed that the proposed strategy greatly reduced the volume of data transfer while reducing the number of data movement across data centers.

关键词： Data centers Task analysis Cloud computing Genetic algorithms Data communication Layout Particle swarm optimization

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A Dynamic Community Detection Algorithm Based on Parallel Incremental Related Vertices 2

A Dynamic Community Detection Algorithm Based on Parallel In...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Guohui Li Kun Guo YuZhong Chen Ling Wu Danhong Zhu College of Mathematics and Computer Sciences Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing(Fuzhou University) Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education

ISBN: (纸本)9781509036202

One of the traditional ways for detecting dynamic communities is to find the communities at each interval through the static community detection ***,it usually leads to high computation *** this paper,a novel algorithm based on the Map Reduce model and the label propagation progress with the strategy of incremental related vertices is proposed,which is called PLPIRV(Parallel Label Propagation and Incremental Related Vertices).Based on the communities found at the previous interval,the new algorithm adjusts the communities the incremental related vertices belong *** clustering of the whole network can be avoided by incrementally analyzing the variation of the networks,so that the time cost can be greatly *** on artificial and real datasets show that the proposed algorithm performs well on dynamic community detection.

关键词： dynamic community detection parallel computation incremental algorithm label propagation

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Semantic keyword searchable proxy re-encryption for postquantum secure cloud storage

Semantic keyword searchable proxy re-encryption for postquan...

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作者： Yang, Yang Zheng, Xianghan Chang, Victor Tang, Chunming College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Province University Key Laboratory of Information Security of Network Systems Fuzhou University Fuzhou350108 China Key Laboratory of Information Security School of Mathematics and Information Science Guangzhou University Guangzhou510006 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China IBSS Xian Jiaotong-Liverpool University Suzhou215123 United Kingdom

With the advent of cloud computing, more and more consumers prefer to use the cloud services with the pay-as-you-consume mode. The cloud storage brings about great convenience to users, who store data in cloud and access to it using the smart devices anytime and anywhere. Consumers' information should be encrypted to guarantee the data privacy. Flexible searching on ciphertext is a critical challenge to be solved for effective data utilization. In this paper, we propose a novel semantic keyword searchable proxy re-encryption scheme for secure cloud storage. A highlight of this work is that the scheme is quantum attack resistant, while most of the available searchable encryption schemes are not. It supports not only exact keyword search but also synonym keyword search. Moreover, the data owner is capable to delegate his search right to another user using the proxy re-encryption mechanism. In the generation process of re-encryption key, the delegator and delegatee do not need to be interactive with each other. The scheme is also collusion resistant. Under the learning with errors hardness problem, this scheme is proved secure in standard model. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Semantics

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Framework for adaptive computation offloading in IoT applications 17

Framework for adaptive computation offloading in IoT applica...

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Chen, Shihong Liu, Bichun Chen, Xing Zhang, Ying Huang, Gang College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Institute of Software School of Electronics Engineering and Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China

ISBN: (纸本)1595930361

The internet of things (IoT) attracts great interest in many application domains concerned with monitoring and control of physical phenomena. IoT applications try to provide more and more functionality and then they inevitably become so complex as to make the limits of devices worse, which may lead to poor performance of applications. Computation offloading is a promising way to improve the performance of an IoT application by executing some parts of the application on remote devices or servers. However, supporting such capability is not easy for application developers due to (1) adaptability: IoT applications often face changes of runtime environments so that the adaptation on offloading is needed. (2) effectiveness: when the device context changes, it needs to dynamically decide the deployment plan of computation tasks, and the reduced execution time must be greater than the network delay and extra overheads caused by offloading. This paper proposes a framework which supports IoT applications with adaptive computation offloading capability. First, a design pattern is proposed to enable an application to be computation offloaded on-demand. Second, an estimation model is presented to automatically decide the deployment plan for offloading. Third, a framework is implemented to support the design pattern and the estimation model. A thorough zxevaluation on the real-world application is proposed, and the results show that our approach can help reduce execution time by over 45% in most scenarios.

关键词： computation offloading

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