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Method for pests detecting in stored grain based on spectral residual saliency edge detection

Grain & Oil Science and technology 2019年第2期2卷 33-38页

作者： Yao Qin Yanli Wu Qifu Wang Suping Yu College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China Key Laboratory of Grain Information Processing and Control Henan University of TechnologyMinistry of EducationZhengzhou 450001China Henan Academy of Science Applied Physics Institute Co.LtdZhengzhou 450001China Luoyang Institute of Science and Technology Department of Computer and Information EngineeringLuoyang 471000China

Pests detecting is an important research subject in grain storage *** the past decades,many edge detection methods have been applied to the edge detection of stored grain *** some of them can realize the stored grain pests detecting,precision and robustness are not good *** residual(SR)saliency edge detection defines the logarithmic spectrumof image as novelty part of the image *** remaining spectrumis converted to the airspace to obtain edge detection *** algorithm is completely based on frequency domain *** not only can effectively simplify the target detection algorithm,but also can improve the effectiveness of target *** experimental results show that the edge results of stored grain pests detected by SR method are effective and stable.

关键词： Stored grain pests Saliency detection Spectral residual (SR) Edge detection

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Knowledge-aware Few-shot Learning Framework for Biomedical Event Trigger Identification

Knowledge-aware Few-shot Learning Framework for Biomedical E...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shujuan Yin Weizhong Zhao Xingpeng Jiang Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan Hubei PR China School of Computer Central China Normal University Wuhan Hubei PR China National Language Resources Monitoring & Research Center for Network Media Central China Normal University Wuhan Hubei PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical event extraction aims to detect fine-grained interactions between biomedical entities in biomedical texts, and has become a research hotspot for researchers in the biomedical field. As the first step in biomedical event extraction, biomedical event trigger identification plays an important role in the whole process. Although researchers have proposed many methods, the performance of existing methods is not desirable due to the reliance on a large number of labeled training samples and the need for expert knowledge in the biomedical field. To treat this issue, we model the biomedical event trigger identification as a few-shot learning problem. Specifically, we utilize a knowledge-aware attention layer to obtain a rich informative representation for entities, and improve the derived prototypes accordingly by prototypical network. In addition, the module of relation network is introduced to train a more reasonable distance function for trigger type prediction. The results demonstrate the effectiveness of the proposed framework according to F1-score.

关键词： Training Task analysis Biological system modeling Testing Prototypes Feature extraction Annotations

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An Effective Framework for Document-level Chemical-induced Disease Relation Extraction via Fine-grained Interaction between Contexts

An Effective Framework for Document-level Chemical-induced D...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinyong Zhang Weizhong Zhao Jincai Yang Xingpeng Jiang Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan Hubei PR China School of Computer Central China Normal University Wuhan Hubei PR China National Language Resources Monitoring & Research Center for Network Media Central China Normal University Wuhan Hubei PR China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In recent years, Chemical-induced Disease (CID) relations are the most searched topics by PubMed users worldwide, reflecting its extensive applications in biomedical research and public health field. However, for CID relation extraction, prior methods fail to make full use of the interaction between local and global contexts in biomedical document. To better capture the complex relationships among contexts, we propose an effective framework for document-level CID relation extraction. Specifically, the stacked Hypergraph Aggregation Neural Network (HANN) layers are applied to model effectively the interaction between local and global contexts. Moreover, by constructing CID Relation Heterogeneous Graph, we can capture the different granularities of information and learn better contextualized representations for CID relation extraction. Extensive experiments on a commonly used dataset demonstrate the effectiveness of the proposed method.

关键词： Chemicals Diseases Data mining Context modeling Biomedical optical imaging Biological system modeling Drugs

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A bidirectional collaborative filtering recommender system based on EM algorithm 1st

A bidirectional collaborative filtering recommender system b...

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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Mao, Yu Zhang, Fuquan Xu, Lin Zhang, Dakui Yang, Hui School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Software Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Innovative Information Industry Research Institute Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783319707297

Collaborative filtering is a promising recommendation technique for predicting the preferences of users in recommender systems. The date coming from recommender system is not only big but also sparse. It motivates the need for a more intelligent approach to obtain the tastes of users. In this paper we present a novel bidirectional collaborative filtering method based on EM algorithm. We combine user rating with item rating to build a new rating matrix and calculate a steady result by iterating this strategy. The empirical evaluation demonstrates that our technique have encouraging result contrast to the traditional approaches. © Springer International Publishing AG 2018.

关键词： Collaborative filtering

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Relative influence maximization in competitive social networks

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Science China(information Sciences) 2017年第10期60卷 252-254页

作者： Dingda YANG Xiangwen LIAO Huawei SHEN Xueqi CHENG Guolong CHEN College of Physics and Information Engineering Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Institute of Computing Technology Chinese Academy of Sciences

In many realistic scenarios,such as political election and viral marketing,two opposite opinions,i.e.,positive opinion and negative opinion,spread simultaneously in the same social networks[1,2].Consequently,to achiev... 详细信息

关键词： net RIM Relative influence maximization in competitive social networks Figure

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Tongue image segmentation via color decomposition and thresholding

Tongue image segmentation via color decomposition and thresh...

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作者： Li, Zuoyong Yu, Zhaochai Liu, Weixia Xu, Yong Zhang, Daoqiang Cheng, Yong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China The Fujian College's Research Base of Humanities and Social Science for Internet Innovation Research Center Minjiang University Fuzhou350121 China Straits Institute Minjiang University Fuzhou350121 China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen518055 China Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Communication Engineering Nanjing Institute of Technology Nanjing211167 China

Tongue image segmentation is a crucial step in developing an automatic tongue diagnosis system. After exploring characteristics of image thresholding in different color spaces, we propose a simple and effective tongue image segmentation method. Specifically, the proposed method first extracts an initial tongue body region by performing image thresholding on the transformed hue component in HSI (Hue, Saturation, and Intensity) color space. Then, one of two image thresholding results on the red component of an original tongue image is adaptively selected to find the gap region between the tongue body root and the upper lip. Finally, the initial tongue body region is refined by removing fake object regions such as the upper lip to obtain final tongue image segmentation result. Experimental results on a tongue image dataset containing 200 subjects demonstrate the effectiveness of the proposed method. © 2018 John Wiley & Sons, Ltd.

关键词： Image segmentation

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Modeling semantic relationship in multi-turn conversations with hierarchical latent variables

arXiv

引用

arXiv 2019年

作者： Shen, Lei Feng, Yang Zhan, Haolan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Multi-turn conversations consist of complex semantic structures, and it is still a challenge to generate coherent and diverse responses given previous utterances. It's practical that a conversation takes place under a background, meanwhile, the query and response are usually most related and they are consistent in topic but also different in content. However, little work focuses on such hierarchical relationship among utterances. To address this problem, we propose a Conversational Semantic Relationship RNN (CSRR) model to construct the dependency explicitly. The model contains latent variables in three hierarchies. The discourse-level one captures the global background, the pair-level one stands for the common topic information between query and response, and the utterance-level ones try to represent differences in content. Experimental results show that our model significantly improves the quality of responses in terms of fluency, coherence and diversity compared to baseline methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Multi-adapter RGBT tracking

arXiv

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arXiv 2019年

作者： Li, Chenglong Lu, Andong Zheng, Aihua Tu, Zhengzheng Tang, Jin School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230601 China

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Toward the Completely Independent Spanning Trees Problem on BCube 9

Toward the Completely Independent Spanning Trees Problem on ...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Ting Pan Baolei Cheng Jianxi Fan Cheng-Kuan Lin Dongfang Zhou School of Computer Science and Technology Soochow University Provincial Key Laboratory for Computer Information Processing Technology Soochow University

ISBN: (纸本)9781509038237;9781509038220

Completely independent spanning trees（CISTs） are important construct which can be used in data center networks for multi-node broadcasting, one-to-all broadcasting, reliable broadcasting, and secure message distribution, etc. As a recently proposed server-centric data center network, BCube has many good properties. By focusing on the connections between servers, we define the logic graph of BCube as LBCube. We study the construction of CISTs with small diameter on BCubek and show that there are n/2 [（n-1）/2 if n is odd] CISTs with the diameter 2 k + 3 on L-BCubek by presenting a recursive algorithm.

关键词： BCube completely independent spanning trees diameter data center network vertex-disjoint

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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