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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Zhang, Qingyi Wang, Xingwei Huang, Min Lv, Jianhui School of Computer Science and Engineering Northeastern University Shenyang China College of Software Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries School of Information Science and Engineering Northeastern University Shenyang China Central Research Institute Network Technology Laboratory Huawei Technologies Co. Ltd Shenzhen China

ISBN: (纸本)9783319938141

information-Centric Networking (ICN) aims to distribute and retrieve the content by name. In this paper, we review and approve the feasible Ant Colony Optimization (ACO)-inspired ICN routing solutions, i.e., applying ACO to solve ICN routing problem. At first, some significant challenges with respect to ICN routing are analyzed, such as explosive increasing of Forwarding information Base (FIB), retrieval of closest content copy, uniform distribution of content and mobility support. Then, the solutions inspired by biology feature and behavior is reviewed. In addition, a general design thought of ACO-inspired solution is presented. Finally, the feasibility of ACO-inspired ICN routing solution is evaluated. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Ant colony optimization

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Recent Advances on Neural Headline Generation

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Journal of computer Science & technology 2017年第4期32卷 768-784页

作者： Ayana Shi-Qi Shen Yan-Kai Lin Cun-Chao Tu Yu Zhao Zhi-Yuan Liu Mao-Song Sun Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Intelligent Technology and Systems Tsinqhua University Beijinq 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100083 China Department of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot 010000 China Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University Xuzhou 221009 China

Recently, neural models have been proposed for headline generation by learning to map documents to headlines with recurrent neural network. In this work, we give a detailed introduction and comparison of existing work and recent improvements in neural headline generation, with particular attention on how encoders, decoders and neural model training strategies alter the overall performance of the headline generation system. Furthermore, we perform quantitative analysis of most existing neural headline generation systems and summarize several key factors that impact the performance of headline generation systems. Meanwhile, we carry on detailed error analysis to typical neural headline generation systems in order to gain more comprehension. Our results and conclusions are hoped to benefit future research studies.

关键词： neural network headline generation data analysis

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Optimizing Non-Decomposable Evaluation Metrics for NeuralMachine Translation

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Journal of computer Science & technology 2017年第4期32卷 796-804页

作者： Shi-Qi Shen Yang Liu Mao-Song Sun Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University Xuzhou 221009 China

While optimizing model parameters with respect to evaluation metrics has recently proven to benefit end to-end neural machine translation (NMT), the evaluation metrics used in the training are restricted to be defined at the sentence level to facilitate online learning algorithms. This is undesirable because the final evaluation metrics used in the testing phase are usually non-decomposable (i.e., they are defined at the corpus level and cannot be expressed as the sum of sentence-level metrics). To minimize the discrepancy between the training and the testing, we propose to extend the minimum risk training (MRT) algorithm to take non-decomposable corpus-level evaluation metrics into consideration while still keeping the advantages of online training. This can be done by calculating corpus-level evaluation metrics on a subset of training data at each step in online training. Experiments on Chinese-English and English-French translation show that our approach improves the correlation between training and testing and significantly outperforms the MRT algorithm using decomposable evaluation metrics.

关键词： neural machine translation training criterion non-decomposable evaluation metric

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Target vehicle detection and tracking in surveillance videos based on exponential forgetting and perceptual hash algorithm

Advances in Transportation Studies

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Advances in Transportation Studies 2018年第SPECIAL ISSUE期2卷 97-106页

作者： Fan, H.H. Zhu, H.J. School of Computer Engineering Jiangsu University of Technology Changzhou213001 China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Changzhou213001 China

In order to obtain the stable background of a traffic surveillance video, the application scenarios, computational complexity, and results of the Gaussian background model were analyzed. In the background modeling process, many artifacts were generated, making it difficult to reconstruct a highly stable background image. In order to solve this problem, we proposed a background model based on exponential forgetting algorithm. This method satisfies the background reconstruction of complex environment, the computation is small and the background result is pure. To further improve the accuracy of the test, shadows of the vehicle were detected to reduce the error caused on vehicle detection. The perceptual hash algorithm was used to track the target vehicle. The proposed algorithm is verified by different video data. The experimental results show that a good vehicle tracking effect is achieved in a variety of weather conditions. © 2018, Gioacchino Onorati Editore. All rights reserved.

关键词： Vehicles

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Correction:Adaptive optical quantitative phase imaging based on annular illumination Fourier ptychographic microscopy

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PhotoniX 2022年第1期3卷 73-73页

作者： Yefeng Shu Jiasong Sun Jiaming Lyu Yao Fan Ning Zhou Ran Ye Guoan Zheng Qian Chen Chao Zuo Smart Computational Imaging Laboratory(SCILab) School of Electronic and Optical EngineeringNanjing University of Science and Technolog210094 NanjingJiangsu ProvincePeople’s Republic of China Smart Computational Imaging Research Institute(SCIRI)of Nanjing University of Science and Technology 210019 NanjingJiangsu ProvincePeople’s Republic of China Jiangsu Key Laboratory of Spectral Imaging Intelligent Sense 210094 NanjingJiangsu ProvincePeople’s Republic of China Terahertz Technology Innovation Research Institute University of Shanghai for Science and Technology200093 ShanghaiPeople’s Republic of China School of Computer and Electronic Information Nanjing Normal University210023 NanjingJiangsu ProvincePeople’s Republic of China Department of Biomedical Engineering University of ConnecticutStorrsConnecticut 06269USA.

In the original publication of this article[1],the video in the additional file 2 was uploaded mistakenly due to a typesetting error,and needs to be updated with the correct *** original article[1]was updated.

关键词： illumination annular phase

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Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking

arXiv

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arXiv 2020年

作者： Liu, Li Chen, Da Shu, Minglei Li, Baosheng Shu, Huazhong Paques, Michel Cohen, Laurent D. Qilu University of Technology Shandong Academy of Sciences Shandong Artificial Intelligence Institute Jinan250014 China Shandong's Key Laboratory of Radiation Oncology Shandong Cancer Hospital Shandong Academy of Medical Sciences Jinan China Department of Radiation Oncology Shandong Cancer Hospital & Institute Shandong Academy of Medical Sciences Jinan China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China The Centre Hospitalier National dOphtalmologie des Quinze-Vingts Paris France University Paris Dauphine PSL Research University CNRS UMR 7534 CEREMADE Paris75016 France

Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Trees (mathematics)

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Multisource multisink optimal evacuation routing with dynamic network changes: A geometric algebra approach

Multisource multisink optimal evacuation routing with dynami...

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作者： Yu, Zhaoyuan Li, Dongshuang Zhu, Shuai Luo, Wen Hu, Yong Yuan, Lingwang Key Laboratory of Virtual Geographic Environment MOE Nanjing Normal University Nanjing210023 China Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing China Department of Computer Science and Technology Nanjing Normal University Nanjing210023 China

In this paper, we introduced geometric algebra to develop a new multisource multisink optimal evacuation route planning method. A dynamically updatable data structure and a matrix-based greedy searching algorithm were developed to support the dynamic evacuation route searching for multiple evacuees. Unlike most existing methods, which iteratively search the optimal path for each evacuee, our method can search all the possible evacuation routes synchronously. The dynamic updating of the network topography, weights, and constraints during the route searching is direct and flexible, thus can support the evacuation in dynamic cases. The method is demonstrated and tested by an evacuation case in the city of Changzhou, China. The simulation experiments suggest that the method can well support the dynamic route searching in large scales with dynamic weight changes. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Iterative methods

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Voltage-Controlled Spintronic Stochastic Neuron Based on a Magnetic Tunnel Junction

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Physical Review Applied 2019年第3期11卷 034015-034015页

作者： Jialin Cai Bin Fang Like Zhang Wenxing Lv Baoshun Zhang Tiejun Zhou Giovanni Finocchio Zhongming Zeng Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-Bionics CAS Suzhou Jiangsu 215123 People’s Republic of China School of Nano Technology and Nano Bionics University of Science and Technology of China Hefei Anhui 230026 People’s Republic of China College of Electronics and Information Hangzhou Dianzi University Hangzhou 310018 People’s Republic of China Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina 98166 Italy

Stochastic units based on magnetic tunnel junctions have shown a high energy-efficient pathway to perform neuromorphic computing. We propose a voltage-controlled spintronic stochastic device based on a magnetic tunnel junction by introducing perpendicular anisotropy into the free layer. We observe a stochastic behavior at low bias and demonstrate that this behavior can be used to mimic the artificial neurons of an artificial neural network to recognize the handwritten digits in the Mixed National Institute of Standards and technology (MNIST) database. Furthermore, the stochastic behavior can be modulated by a bias voltage owing to the voltage-controlled magnetic anisotropy effect. The voltage-controlled stochastic behavior is theoretically and experimentally studied, which indicates that it can be used to perform as an adaptive neuron. These results provide a way for building energy-efficient spintronic neuromorphic computing systems.

关键词： Magnetization switching Spintronics Artificial neural networks Magnetic tunnel junctions Ferromagnetic resonance Magnetization measurements Spiking neuron models

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Wireless Service Prediction Algorithm in Mobile Social Environment 2

Wireless Service Prediction Algorithm in Mobile Social Envir...

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

作者： Hui Zhang Min Wang Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications Provincial Key Laboratory for Computer Information Processing Technology Soochow University

ISBN: (纸本)9781509036202

First,according to characteristics of mobile social environment,by using optimization models based on similarity degree and interaction degree respectively,the optimal correlated users can be selected for analyzing two main factors of a target user's behaviors(***-term habits and shortterm influences);furthermore,an adaptive update strategy based on fuzzy theory is proposed to describe the importance of two factors in real time and quantitative ***,an improved Apriori theory is introduced to predict user service behaviors accurately;particularly,a new update mechanism for Apriori sample database is built to effectively integrate the samples of optimal correlated ***,simulation results verify the effectiveness of proposed algorithm.

关键词： mobile social environment user behavior prediction fuzzy theory adaptive update strategy improved Apriori theory

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Prior knowledge integration for neural machine translation using posterior regularization

arXiv

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arXiv 2018年

作者： Zhang, Jiacheng Liu, Yang Luan, Huanbo Xu, Jingfang Sun, Maosong State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing Jiangsu Collaborative Innovation Center for Language Competence Jiangsu Sogou Inc. Beijing

Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources as features in a log-linear model, which guides the learning process of the neural translation model. Experiments on Chinese-English translation show that our approach leads to significant improvements. 1. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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