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A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization

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Chinese Journal of Electronics 2017年第1期26卷 13-21页

作者： DING Weiping WANG Jiandong LI Yuehua CHENG Xueyun School of Computer Science and Technology Nantong University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Provincial Key Laboratory for Computer Information Processing Technology Soochow University College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization(ARC-CABEO) is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.

关键词： Attribute reduction for classification Bidirectional elitist optimization Coordinating architecture Cascaded co-evolutionary model

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Automatic Summarization Generation technology of Network Document Based on Knowledge Graph 1

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2nd EAI International Conference on Advanced Hybrid information processing, ADHIP 2018

作者： Wu, Yuezhong Chen, Rongrong Li, Changyun Chen, Shuhong Zou, Wenjun College of Artificial Intelligence Hunan University of Technology Zhuzhou412007 China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory Zhuzhou412007 China College of Business Hunan University of Technology Zhuzhou412007 China School of Computer Science and Educational Software Guangzhou University Guangzhou510006 China School of Computer and Communication Hunan Institute of Engineering Xiangtan411104 China

ISBN: (数字)9783030190866

ISBN: (纸本)9783030190859

The Internet has become one of the important channels for users to access to information and knowledge. It is crucial that how to acquire key content accurately and effectively in the events from huge amount of network information. This paper proposes an algorithm for automatic generation of network document summaries based on knowledge graph and TextRank algorithm which can solve the problem of information overload and resource trek effectively. We run the system in the field of big data application in packaging engineering. The experimental results show that the proposed method KG-TextRank extracts network document summaries more accurately, and automatically generates more readable and coherent natural language text. Therefore, it can help people access information and knowledge more effectively. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Knowledge graph

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Learning a Layout Transfer Network for Context Aware Object Detection

arXiv

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arXiv 2019年

作者： Wang, Tao He, Xuming Cai, Yuanzheng Xiao, Guobao Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Science and Technology ShanghaiTech University Shanghai China College of Mathematics and Computer Science Fuzhou University NetDragon Fuzhou China

We present a context aware object detection method based on a retrieve-and-transform scene layout model. Given an input image, our approach first retrieves a coarse scene layout from a codebook of typical layout templates. In order to handle large layout variations, we use a variant of the spatial transformer network [1] to transform and refine the retrieved layout, resulting in a set of interpretable and semantically meaningful feature maps of object locations and scales. The above steps are implemented as a Layout Transfer Network which we integrate into Faster RCNN [2] to allow for joint reasoning of object detection and scene layout estimation. Extensive experiments on three public datasets verified that our approach provides consistent performance improvements to the state-of-the-art object detection baselines on a variety of challenging tasks in the traffic surveillance and the autonomous driving domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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An Efficient Low Delay Task Scheduling Algorithm Based on Ant Colony System in Heterogeneous Environments

An Efficient Low Delay Task Scheduling Algorithm Based on An...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Pei Yang Jing Liu Cen Chen Yucheng Ding Chunhua Deng Zeng Zeng College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Institute for Infocomm Research Singapore Hunan *** Interactive Entertainment Media Co. Ltd. Changsha China

ISBN: (纸本)9781728176505

Low delay is an important issue in a variety of applications on heterogeneous computing platforms. A great number of algorithms including heuristics as well as metaheuristics have been designed to minimize the completion time (i.e., makespan) of applications and achieved good results. However, the demand of high performance never stops. In this paper, we propose a new and efficient Ant-Colony based Low Delay Scheduling (ACLDS) algorithm to minimize the makespan of applications which can form directed acyclic graphs and are executed on a heterogeneous system. Simulated results show that our proposed algorithm is highly efficient in reducing makespan in comparison with state-of-the-art scheduling algorithms. For instance, the reduction ratio can be up to 15.2174% for a Fast Fourier Transform (FFT) benchmark.

关键词： Directed acyclic graph Smoothing methods Scheduling algorithms Fast Fourier transforms High performance computing Heuristic algorithms Scheduling

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Service Deployment for Latency Sensitive Applications in Mobile Edge Computing

Service Deployment for Latency Sensitive Applications in Mob...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Jingya Zhou Jianxi Fan Jin Wang Juncheng Jia Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China School of Computer Science and Technology Soochow University Suzhou China

Mobile edge computing (MEC) emerges as a promising computing paradigm to meet the increasing demand for compute-intensive latency-sensitive applications. By offloading tasks to the edge MEC can achieve many benefits such as ultra-low latency and saving backbone bandwidth, etc. Many studies have concentrated on the high efficient task offloading. However, the existing studies assume that tasks can be executed on an arbitrary edge server, but in reality different application's tasks require varied services to provide the execution environment and an edge server can host a limited number of services. How to determine the set of services deployed on each server in a MEC system is challenging. To address this challenge, in this paper, we focus on the problem of service deployment, and formulate the problem as a multi-slot latency minimization by leveraging the Lyapunov optimization. An efficient online algorithm is designed to minimize the average latency under a tight budget, and the supremum on algorithm performance is also presented. Experimental results indicate that our algorithm can reduce average latency for end users, and guarantee a low expenditure for the edge service provider.

关键词： Servers Task analysis Edge computing Cloud computing Bandwidth Heuristic algorithms Optimization

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Review and prospect: Deep learning in nuclear magnetic resonance spectroscopy

arXiv

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arXiv 2020年

作者： Chen, Dicheng Wang, Zi Guo, Di Orekhov, Vladislav Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance State Key Laboratory of Physical Chemistry of Solid Surfaces Xiamen University P.O.Box 979 Xiamen361005 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Department of Chemistry and Molecular Biology University of Gothenburg Box 465 Gothenburg40530 Sweden

Since the concept of Deep Learning (DL) was formally proposed in 2006, it had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, etc. In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science. Copyright © 2020, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance

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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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Adaptive Hybrid Projective Models for Aerial Video Stitching

Adaptive Hybrid Projective Models for Aerial Video Stitching

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International Conference on Systems and Informatics (ICSAI)

作者： Runping Xi Tianyi Han Yaqing Zhang Qianqian Han School of Computer Science Northwestern Polytechnical University National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Xi'an China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

Aerial video stitching has been widely used in many fields such as large-scale videos surveillance, disaster monitoring and unmanned aerial vehicle (UAV) navigation. Ghosting and parallax are challenging problems when the video streams are captured by UAV. This paper presents a novel method based on hybrid projective model which combines local warp alignment and global warp projection for better registration accuracy. The geometric alignment model is used for global registration of images and the content-preserving alignment model is used for local refinement. Simultaneously, reference frame is updated adaptively according to the shift of scenery, which is used to eliminate the cumulative error appearing in long video streams. The proposed approach has been tested under multiple datasets in different scenarios, which shows good performance.

关键词： Streaming media Feature extraction Adaptation models Cameras Image fusion Distortion Unmanned aerial vehicles

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Otem&Utem: Over- and under-translation evaluation metric for NMT

arXiv

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arXiv 2018年

作者： Yang, Jing Zhang, Biao Qin, Yue Zhang, Xiangwen Lin, Qian Su, Jinsong Xiamen University Xiamen China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from over- and under-translation issues [Tu et al., 2016], of which studies have become research hotspots in NMT. At present, these studies mainly apply the dominant automatic evaluation metrics, such as BLEU, to evaluate the overall translation quality with respect to both adequacy and fluency. However, they are unable to accurately measure the ability of NMT systems in dealing with the above-mentioned issues. In this paper, we propose two quantitative metrics, the Otem and Utem, to automatically evaluate the system performance in terms of over- and under-translation respectively. Both metrics are based on the proportion of mismatched n-grams between gold reference and system translation. We evaluate both metrics by comparing their scores with human evaluations, where the values of Pearson Correlation Coefficient reveal their strong correlation. Moreover, in-depth analyses on various translation systems indicate some inconsistency between BLEU and our proposed metrics, highlighting the necessity and significance of our metrics. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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A Resource Matching Algorithm for Dynamic Multi-Homing Service Environment 2

A Resource Matching Algorithm for Dynamic Multi-Homing Servi...

引用

2nd IEEE Advanced information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Zhang, Hui Liu, Shu Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Wireless Communications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing210003 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

ISBN: (纸本)9781538618035

T43 paper investigates the matching problem between radio resources and multi-homing services in a dynamic heterogeneous wireless environment. A mobile terminal (MT) with multi-homing service simultaneously transmits data to multiple radio access networks using multiple air interfaces. We develop an energy efficient resource matching algorithm for statistically guaranteeing the transmission rate of each MT with the minimum power consumption. Firstly, aided with the resource matching conception and the dynamic characteristics of wireless channels, we propose a matching probability metric to construct resource matching model for maximizing energy efficiency of all MTs subject to statistical guarantee requirements, and simplify the dynamic channels characteristic based on matching theory. Secondly, we propose a two-level distributed matching algorithm (TLDMA), with inner minimum power consumption and outer maximum matching probability, which realizes joint optimal allocation of bandwidth, transmission power and rate. Simulation results show that TLDMA outperforms conventional algorithms in terms of wireless resources management. © 2018 IEEE.

关键词： Energy efficiency

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