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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Kai, Zhang Shangyi, Li Juanjuan, He Yunyun, Niu School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430081 China School of Information Engineering China University of Geosciences Beijing100083 China

ISBN: (纸本)9789811036132

Nucleic acid nanotechnology offers many methods to fold into a variety of complex and functional nanostructures. These selfassemble scaffolds are valuable in various applications, such as molecular programming, sensing, drug delivery and nanofabrication. However, existing algorithms are typically lacking on predicting pseudoknots structure fast and accurately. This paper proposes a novel genetic algorithm to predict nucleic acid secondary structure including pseudoknots. The length of continues base pairs stacking and free energy are considered to evaluate individuals. Furthermore, the performance of our algorithm is compared with RNAStructure using PseudoBase benchmark instances, and the results show that our algorithm outperforms on accuracy and efficiency. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Nucleic acids

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Applying K-means clustering and genetic algorithm for solving MTSP 11th

Applying K-means clustering and genetic algorithm for solvin...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Lu, Zhanqing Zhang, Kai He, Juanjuan Niu, Yunyun School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430081 China School of Information Engineering China University of Geosciences Beijing100083 China

ISBN: (纸本)9789811036132

In this paper, a new algorithm is designed to solve Multiple Traveling Salesman Problem (MTSP) that avoiding the path intersection among the traveling salesmen. There are three objectives in this problem including the shortest path of every salesman, the balance of each salesmans task and avoiding the crosses of each routes. We combine the K-means algorithm and genetic algorithm. K-means algorithm is designed to divide all points into several subsets and choose the start city for the genetic algorithm, and then using GA to process every subsets in parallel. This method not only achieve these multiple objectives, but also use much less time, since we have divided all the points into several parts and make them calculated at the same time. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Genetic algorithms

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An Improved Quantum information Hiding Protocol Based on Entanglement Swapping of χ-type Quantum States

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Communications in Theoretical Physics 2016年第6期65卷 705-710页

作者： Shu-Jiang Xu Xiu-Bo Chen Lian-Hai Wang Qing-Yan Ding Shu-Hui Zhang Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

In 2011, Qu et al. proposed a quantum information hiding protocol based on the entanglement swapping of χ-type quantum states. Because a χ-type state can be described by the 4-particle cat states which have good symmetry,the possible output results of the entanglement swapping between a given χ-type state and all of the 16 χ-type states are divided into 8 groups instead of 16 groups of different results when the global phase is not considered. So it is difficult to read out the secret messages since each result occurs twice in each line(column) of the secret messages encoding rule for the original protocol. In fact, a 3-bit instead of a 4-bit secret message can be encoded by performing two unitary transformations on 2 particles of a χ-type quantum state in the original protocol. To overcome this defect, we propose an improved quantum information hiding protocol based on the general term formulas of the entanglement swapping among χ-type states.

关键词： quantum communication quantum information hiding χ-type quantum state entanglement swapping

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A survey on image-based approaches of synthesizing objects 6

A survey on image-based approaches of synthesizing objects

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6th International Conference on Virtual Reality and Visualization, ICVRV 2016

作者： Ren, Haocheng Qiu, Hang He, Feifei Leng, Kaixuan School of Optoelectronic Information University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Key Sichuan Provincial Key Laboratory of Digital Media Chengdu China Big Data Research Center University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781509051885

Synthesizing virtual objects into real scenes is an important process in augmented reality. Illumination consistency is the key problem of expressing the photorealistic rendering result. Consequently, a wide range of solutions for illumination estimation are proposed, such as capturing real scenes and estimation from single image. Because of its convenience, image based methods have become a popular way to synthesize virtual objects gradually. This paper provides a survey on state-of-Art image-based methods for illumination estimation. As for a special case, outdoor illumination is discussed separately because of different assumptions and process. Through discussion of advantages and shortcoming of existing methods, main challenges and future work are explained at last. © 2016 IEEE.

关键词： Augmented reality

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Robust monocular visual odometry using optical flows for mobile robots

Robust monocular visual odometry using optical flows for mob...

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Chinese Control Conference (CCC)

作者： Haifeng Li Zunhe Hu Xinwei Chen College of Computer Science and Technology Civil Aviation University of China Tianjin Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou Department of Computer Science Minjiang University Fuzhou

A robust optical flow-based visual odometry method using a single onboard camera is proposed in this paper. To improve the quality of the noisy optical flows, a correction method across multiple frames is developed. Furthermore, the optical flows in the plane at infinity are detected and removed as these optical flows have very low signal to noise ratio for robot translation estimation. Finally, a RANSAC approach for robot ego-motion estimation is proposed. Physical experiments are carried out and the results show that the proposed method is able to estimate the camera trajectory robustly with reasonable accuracy.

关键词： Adaptive optics Image motion analysis computer vision Optical imaging Optical sensors Cameras Optical noise

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Consensus algorithms for biased labeling in crowdsourcing

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information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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ECNU at 2016 eHealth Task 1: Handover information extraction

ECNU at 2016 eHealth Task 1: Handover information extraction

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2016 Working Notes of Conference and Labs of the Evaluation Forum, CLEF 2016

作者： Song, Yang He, Yun Liu, Hongyu Wang, Yueyao Hu, Qinmin He, Liang Luo, Guihua Shanghai Key Laboratory of Multidimensional Information Processing China Department of Computer Science and Technology East China Normal University Shanghai200241 China Science and Technology Commission of Shanghai Municipality China Shanghai Digital Trade Co. Ltd Shanghai20024 China

The CLEF eHealth 2016 Task 1 is set to automatically assign pre-defined medical tag to each word in the patient case records. The dificulty of the task is that many classes have little training data. This paper presents our work on the 2016 CLEF eHealth Task 1. In particular, we propose an optimized Conditional Random Field algorithm to better fulfill the task. We also utilize the information extracted through association rules and MetaMap to boost the performance of our results. The evaluation results show our runs outperform the four oficial baselines in this dificulty task.

关键词： Random processes

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ECNU at 2016 eHealth Task 3: Patient-centred information retrieval

ECNU at 2016 eHealth Task 3: Patient-centred information ret...

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2016 Working Notes of Conference and Labs of the Evaluation Forum, CLEF 2016

The 2016 CLEF eHealth Task 3 aims to evaluation the efiectiveness of information retrieval systems when searching for health content on the web. The ClueWeb12 B13 data set is utilized in this task. This paper presents our work on the 2016 CLEF e-Health Task 3. We propose a Web-based query expansion method and a combination method to better understand and satisfy the task. In particular, we test our Web-based query expansion method in many medical data sets and achieve an outstanding performance.

关键词： Search engines

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A novel image tag completion method based on convolutional neural transformation

arXiv

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arXiv 2017年

作者： Geng, Yanyan Zhang, Guohui Li, Weizhi Gu, Yi Liang, Ru-Ze Liang, Gaoyuan Wang, Jingbin Wu, Yanbin Patil, Nitin Wang, Jing-Yan Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Huawei Technologies Co. Ltd. Shanghai China Suning Commerce R&D Center USA Inc. Palo AltoCA94304 United States Analytics & Research Travelers HartfordCT06183 United States King Abdullah University of Science and Technology Saudi Arabia Jiangsu University of Technology Jiangsu213001 China Information Technology Service Center Intermediate People’s Court of Linyi City Linyi China Hebei University of Economics and Business Shijiazhuang050061 China Savitribai Phule Pune University Pune Maharashtra411007 India

In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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Log-Demons with Driving Force for Large Deformation Image Registration

Log-Demons with Driving Force for Large Deformation Image Re...

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International Joint Conference on Neural Networks

作者： Le Zhang Ying Wen Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University

ISBN: (纸本)9781509006212

Capturing large diffeomorphic deformations is difficult for many non-rigid registration methods. In this paper, we propose Log-Demons with driving force for large deformation image registration. The driving force obtained by boundary points correspondence exerts influence on continuous optimization of Log-Demons to improve the motion direction of points. We utilize MROGH descriptor matching to obtain points correspondence as driving force, then the driving force is added to the optimization of Log-Demons. We integrate the driving force in an exponentially decreasing form with velocity field of Log-Demons to drive the points moving globally and to speed up the convergence. Experiments performed on synthetic images, real scene images and brain images demonstrate that the proposed method can not only capture large deformations but also preserve details and register images at a higher accuracy.

关键词： DRIVING FORCE Automobile driving Force image registration Correspondence motivation velocity field images

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