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9th International Symposium on Intelligence Computation and Applications, ISICA 2017

作者： Wang, Hui Chen, Zhangxin Wang, Wenjun Wu, Zhijian Wu, Keliu Li, Wei School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Department of Chemical and Petroleum Engineering University of Calgary CalgaryABT2N 1N4 Canada School of Business Administration Nanchang Institute of Technology Nanchang330099 China State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China

ISBN: (纸本)9789811316470

In recent years, energy demand has increased rapidly in developing countries. Energy demand estimation (EDE) plays an important role for policy makers and related organizations. Generally, energy demand can be mathematically modelled by population, economic, and other indicators using various forms of equations. However, it is difficult to choose optimal or near-optimal weighting factors for these models. In this paper, an adaptive firefly algorithm (AFA) is proposed to improve the efficiency of energy demand estimation in Turkey. Two different estimation models, including linear and quadratic forms, are used. Historical data in Turkey from 1979 to 2005 is utilized for training and testing these models. Experimental results show that our approach achieves better relative estimation errors than two other existing algorithms, ant colony (ACO) and particle swarm optimization (PSO). © 2018, Springer Nature Singapore Pte Ltd.

关键词： Developing countries

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A duplicate data detection approach based on MapReduce and HDFS

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Recent Patents on computer science 2017年第2期10卷 140-148页

作者： Wei, Fang Xue-Zhi, Wen Yu, Zheng Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210066 China Department of Electrical & Computer Engineering University of Florida GainesvilleFL32611 United States

Background: With the surge in the volume of collected data, deduplication will undoubtedly become one of the problems faced by researchers. There is significant advantage for deduplication to reduce storage, network bandwidth, and system scalability of coarse-grained redundant data. Since the conventional methods of deleting duplicate data include hash comparison and binary differential incremental. They will lead to several bottlenecks for processing large scale data. And, the traditional Simhash similarity method has less consideration on the natural similarity of text in some specific fields and cannot run in parallel program with large scale text data processing efficiently. This paper examines several most important patents in the area of data detection. Then, this paper will focus on large scale of data deduplication based on MapReduce and HDFS. Methods: We propose a duplicate data detection approach based on MapReduce and HDFS, which uses the Simhash similarity computing algorithm and SSN algorithm, and explain our distributed duplicate detection workflow. The important technical advantages of the invention include generating a checksum for each processed record and comparing the generated checksum to detect duplicate record. It produces the fingerprints of short text with Simhash similarity algorithm. It clusters the fingerprint results using Shared Nearest Neighbor (SNN) algorithm. The whole parallel progress is implemented using MapReduce programming model. Results: From the experimental results, we conclude that our proposed approach obtains MapReduce job schedules with significantly less executing time, making it suitable for processing large scale datasets in real applications. The experimental results show the proposed approach has better performance and efficiency. Conclusion: In this patent, we propose a duplicate data detection approach based on MapReduce and HDFS, which uses the Simhash similarity computing algorithm and SSN algorithm. The results

关键词： MapReduce

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Notice of Removal: Short-term Wind Power Forecast for Wind Farm Base on Artificial Neural Network

Notice of Removal: Short-term Wind Power Forecast for Wind F...

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International computers, Signals and Systems Conference (ICOMSSC)

作者： Wu Feiyan Liao Xianwen Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University Wuzhou Technology Department of Computer Science The Beihai Campus of the Guilin University of Electronic Beihai China

Removed.

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Incident detection based on mobile crowd sensing for smart city

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Journal of computers (Taiwan) 2019年第1期30卷 96-104页

作者： Zhang, Peng Zhang, Zhenjiang Chao, Han-Chieh Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Software Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Computer Science and Information Engineering National Ilan University Yilan County260 Taiwan

In recent years, mobile devices have taken a significant role in improving the quality of people’s life. In order to enhance the usability of those devices, more and more sensors have been built in. Furthermore, the data storage and data processing capacity are becoming larger and larger. To capture more information about the city conditions and make full use of the computing resource, mobile crowd sensing has been proposed. Incident detection is an important component of the safety monitoring system, however, existing incident detection methods are mostly based on computer vision which is vulnerability to environmental impacts. This paper proposes an automatic incident detection model based on Mobile Crowd Sensing to deal with the problem above. The incident detection algorithm in the model is based on the human activity recognition and the crowd density. The simulation results show that this model is feasible and effective. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Environmental impact

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Insulation Simulation Analysis of Press-Pack IGBT

Insulation Simulation Analysis of Press-Pack IGBT

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China International Electrical and Energy Conference (CIEEC)

作者： Yuzhe Liu Chaoqun Jiao Zhibin Zhao Chengzong Pang Yuanliang Fan School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China Department of Electrical Engineering and Computer Science Wichita State University Wichita USA Fujian Provincial Enterprise Key Laboratory of High Reliable Electric Power Distribution Technology State Grid Fujian Electric Power Research Institute Fuzhou China

ISBN: (数字)9781728116754

ISBN: (纸本)9781728116761

In high voltage applications such as power grid and rail transportation, IGBT devices generally operate under very harsh conditions, which places high demands on device reliability. In this paper, the electric field distribution of silicon chip termination area and press-pack IGBT package structure under high voltage reverse bias experimental conditions is simulated by Fem software. The results indicate the potential electrical insulation failure areas in the package model and the corresponding optimization measures are proposed. To some extent, the problem of breakdown discharge of IGBT devices during operation is alleviated.

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Top-rank enhanced listwise optimization for statistical machine translation 21

Top-rank enhanced listwise optimization for statistical mach...

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21st Conference on Computational Natural Language Learning, CoNLL 2017

作者： Chen, Huadong Huang, Shujian Chiang, David Dai, Xinyu Chen, Jiajun State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Engineering University of Notre Dame India

ISBN: (纸本)9781945626548

Pairwise ranking methods are the basis of many widely used discriminative training approaches for structure prediction problems in natural language processing (NLP). Decomposing the problem of ranking hypotheses into pairwise comparisons enables simple and efficient solutions. However, neglecting the global ordering of the hypothesis list may hinder learning. We propose a listwise learning framework for structure prediction problems such as machine translation. Our framework directly models the entire translation list’s ordering to learn parameters which may better fit the given listwise samples. Furthermore, we propose top-rank enhanced loss functions, which are more sensitive to ranking errors at higher positions. Experiments on a large-scale Chinese-English translation task show that both our listwise learning framework and top-rank enhanced listwise losses lead to significant improvements in translation quality. © 2017 Association for Computational Linguistics.

关键词： Machine translation

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Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification

arXiv

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang He, Lifang Li, Bo Wang, Lihong Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 State Key Laboratory of Software Development Environment Beihang University Beijing100083 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Biostatistics and Epidemiology University of Pennsylvania PhiladelphiaPA19104 United States National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent medoids, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Web Services: Problems and Future Directions

SSRN

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SSRN 2018年

作者： Wang, Hongbing Huang, Joshua Zhexue Qu, Yuzhong Xie, Junyuan Department of Computer Science and Technology Nanjing University Nanjing210093 China National Key Laboratory for Novel Software Technology E-Business Technology Institute The University of Hong Kong Hong Kong Hong Kong

Recently, Web services have generated great interests in both vendors and researchers. Web services, based on existing Internet protocols and open standards, can provide a flexible solution to the problem of application integration. With the help of WSDL, SOAP, and UDDI, Web services are becoming popular in Web applications. However, the current Web services architectures are confronted with a few stubborn problems, for instance, security. In this paper, we shall give an overview of these problems. We believe that solving these problems will become crucial to success of Web services. In the end, we predict distinct advances in semantic Grid services. © 2018, The Authors. All rights reserved.

关键词： Semantics

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An Auto-Contouring Method for Kidney based on SVM

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Journal of Physics: Conference Series 2021年第1期1944卷

作者： Bo Li Jing Xue Ping Wang Saikun Gu Wei Chen Pengjiang Qian Yizhang Jiang School of Artificial Intelligence and Computer Science Jiangnan University 1800 Lihu Avenue Wuxi Jiangsu 214122 People's Republic of China Jiangsu Key Laboratory of Media Design and Software Technology 1800 Lihu Avenue Wuxi 214122 Jiangsu People's Republic of China Department of Nephrology the Affiliated Wuxi People's Hospital of Nanjing Medical University 299 Qingyang Rd Wuxi Jiangsu 214023 People's Republic of China

In adaptive radiotherapy planning, the contour map of the treatment target area is still the most difficult problem. Usually this work is done by a professional radiologist oncologist, which is very time-consuming and labor-intensive. In order to solve this problem, this paper proposes an automatic contour method based on support vector machine. Experiments were conducted on the lower abdomen MR data set of eight patients. In the experiment, the simulated data was only used to train the classifier, and the treatment day images were used to evaluate the performance of the classifier. DSI was used to compare the manual contour and automatic contour of the kidney. The experiment showed that, The automatic contour method based on support vector machine has better classification performance than most classification algorithms. Among the eight sets of results, the DSI value of the six sets of results is 1, and the smallest DSI value is also greater than 9.423.

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A secure and effcient mutual authentication and key agreement protocol with smart cards for wireless communications

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International Journal of Network Security 2018年第2期20卷 323-331页

作者： Guo, Cheng Chang, Chin-Chen Chang, Shih-Chang School of Software Technology Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China Department of Information Engineering and Computer Science Feng Chia University Taiwan Department of Computer Science and Information Engineering Asia University Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan

Mobile user authentication and key agreement for wireless networks is an important security priority. In recent years, several user authentication and key agreement protocols with smart cards for wireless communications have been proposed. In 2011, Xu et al. proposed an effcient mutual authentication and key agreement protocol with an anonymity property. Although the protocol of Xu et al. has many benefits, we find that it still suffers from several weaknesses which have been previously overlooked. In this paper, we propose a secure and effcient mutual authentication and key agreement protocol. Confidentiality of the session key and updating of the password effciently are presented as the main contributions of this paper. Finally, evaluations of our proposed protocol show that our protocol can withstand various known types of attacks, and also satisfies essential functionality requirements. Additionally, effciency analyses show that our protocol is simple and cost-effcient.

关键词： Authentication

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