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Modern deep learning in bioinformatics

Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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Weighted partial order oriented three-way decisions under score-based common voting rules

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International Journal of Approximate Reasoning 2020年 123卷 41-54页

作者： Li, Lei Wu, Xindong Chen, Huanhuan Zhou, Chuan Liu, Guanfeng Jiang, Yongkang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei 230601 China Mininglamp Academy of Sciences Mininglamp Technologies Beijing 100084 China School of Computer Science and Technology University of Science and Technology of China Hefei 230026 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Computing Dept Macquarie University Sydney A1C 5S7 Australia

Recently, with the trisecting and acting models of human cognitive behaviors, three-way decisions are introduced to deal with uncertain decisions in many applications. To define and conduct the three-way decisions on the possible or necessary loser or co-loser and/or winner or co-winner, we first define the score-based common voting rules, where a score-based function is introduced to evaluate certain utility, including positional scoring rules, maximin, Bucklin, and so on. Then on one hand, it has been proved that under certain conditions three-way decisions on the possible loser or co-loser and/or winner or co-winner are NP-complete with positional scoring rules, maximin, and Bucklin, respectively. On the other hand, we have presented algorithms whose time complexity is polynomial to conduct three-way decisions on the necessary winner or co-winner and/or necessary loser or co-loser with positional scoring rules, maximin, and Bucklin in time O(nm²), O(nm³), and O(nm²), respectively, which has been validated experimentally. © 2020 Elsevier Inc.

关键词： Common voting rules Three-way decisions Weighted partial orders

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Pyramid Pooling Dense Convolutional Neural Network for Multi-focus Image Fusion

Pyramid Pooling Dense Convolutional Neural Network for Multi...

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IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Yi Li Xuanjing Shen Haipeng Chen College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

In recent years, image fusion becomes one of the important technique in information fusion fields. In this paper, we proposed a Deep Learning-based pyramid pooling dense convolutional neural network for multi-focus image fusion problem. The PDenseCNN we proposed can accurately detect the clear regions and generate a fused decision feature map, afterward, morphological operation and guided filter are used to optimize the initial map. Finally, it can obtain a clear fusion image based on weighted-sum strategy. The experimental results proved that the proposed method can obtain accurate decision maps and state-of-art fusion results on both visual effect and objective evaluation assessment.

关键词： Economic indicators

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CircRNAFisher: a systematic computational approach for de novo circular RNA identification

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中国药理学报（英文版） 2019年第1期40卷 55-63页

作者： Guo-yi Jia Duo-lin Wang Meng-zhu Xue Yu-wei Liu Yu-chen Pei Ying-qun Yang Jing-mei Xu Yan-chun Liang Peng Wang School of Life Sciences Shanghai University Shanghai 200444 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China School of Life Science and Technology Shanghai Tech University Shanghai 201210 China

Circular RNAs (circRNAs) are emerging species of mRNA splicing products with largely unknown *** several computational pipelines for circRNA identification have been developed,these methods strictly rely on uniquely mapped reads overlapping back-splice junctions (BSJs) and lack approaches to model the statistical significance of the identified ***,we reported a systematic computational approach to identify circRNAs by simultaneously utilizing BSJ overlapping reads and discordant BSJ spanning reads to identify ***,we developed a novel procedure to estimate the P-values of the identified circRNAs.A computational cross-validation and experimental validations demonstrated that our method performed favorably compared to existing circRNA detection *** created a standalone tool,CircRNAFisher,to implement the method,which might be valuable to computational and experimental scientists studying circRNAs.

关键词： circRNA RNA-Seq alternative splicing pipeline

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Poisoning attack against estimating from pairwise comparisons

arXiv

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arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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An Unmanned Aerial Vehicle Pose Estimation System Guided by Desired Shot

An Unmanned Aerial Vehicle Pose Estimation System Guided by ...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Xinglei Dou Qijian Ding Qiao Du Min Yang Yuzhou Huo Xin Wang College of Software Engineering Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728150307

ISBN: (纸本)9781728150314

Unmanned aerial vehicles (UAVs) are widely used in aerial photography nowadays for their strong maneuverability, good image quality and high cost performance, while they have limited battery capacity and difficulty in operation. It is difficult to reach the ideal pose and get the desired shot by manual manipulation in a short time. To solve these problems, an UAV pose estimation system guided by desired shot is constructed. Either a sequence of images or a video is taken as input. The user interacts with the system to express the desired shot. The 3D-reconstruction is done by Structure from Motion (SfM). Then the Perspective-n-Point (PnP) problem is solved to estimate the ideal pose of the UAV. The experimental results proves that the system is effective and valid.

关键词： Three-dimensional displays Feature extraction Unmanned aerial vehicles Cameras Pose estimation Software engineering Data mining

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A pseudo label based dataless naive bayes algorithm for text classification with seed words 27

A pseudo label based dataless naive bayes algorithm for text...

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27th International Conference on Computational Linguistics, COLING 2018

作者： Li, Ximing Yang, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781948087506

Traditional supervised text classifiers require a large number of manually labeled documents, which are often expensive to obtain. Recently, dataless text classification has attracted more attention, since it only requires very few seed words of categories that are much cheaper. In this paper, we develop a pseudo-label based dataless Naive Bayes (PL-DNB) classifier with seed words. We initialize pseudo-labels for each document using seed word occurrences, and employ the expectation maximization algorithm to train PL-DNB in a semi-supervised manner. The pseudo-labels are iteratively updated using a mixture of seed word occurrences and estimations of label posteriors. To avoid noisy pseudo-labels, we also consider the information of nearest neighboring documents in the pseudo-label update step, i.e., preserving local neighborhood structure of documents. We empirically show that PL-DNB outperforms traditional dataless text classification algorithms with seed words. Especially, PL-DNB performs well on the imbalanced dataset. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Iterative methods

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Parking Space Prediction Algorithm Based on Recurrent Neural Network and Ensemble Learning Algorithm

Parking Space Prediction Algorithm Based on Recurrent Neural...

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IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Kedi Lv Haipeng Chen Yingda Lv Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Center for Computer Fundamental Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

The essence of parking space prediction is a practical application of time series prediction problem. It provides effective auxiliary information for traffic management and personal travel. As a new demand, they face challenges in data acquisition, accuracy and robustness. The parking space prediction algorithm based on recurrent neural network (RNN) and ensemble learning algorithm is proposed in this paper. However, because the neural network needs large data training, the lack of parking space data in specific areas is a common challenge for researchers. In this paper, a new network model structure based on ensemble learning algorithm is proposed, which is called E-RNN. The ensemble learning algorithm model is used as the primary learner and the neural network as the secondary learner for the ensemble learning. By using the adaptability of the algorithm in ensemble learning to the collective data, the neural network can get better prediction effect under the training of small data sets. Finally, the parameters of the new model are optimized by using particle swarm optimization (PSO) algorithm, and a better model is obtained. The experimental results show that the algorithm has high accuracy and robustness for parking space prediction with a small data sets.

关键词：

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Multiple Auxiliary Information Based Deep Model for Collaborative Filtering

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Journal of Computer Science & Technology 2018年第4期33卷 668-681页

作者： Lin Yue Xiao-Xin Sun Wen-Zhu Gao Guo-Zhong Feng Bang-Zuo Zhang School of Information Science and Technology Northeast Normal University Changchun 130117 China Key Laboratory of Applied Statistics of Ministry of Education Northeast Normal University Changchun 130024 China School of Environment Northeast Normal University Changchun 130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

With the ever-growing dynamicity, complexity, technique is proposed and becomes one of the most effective and volume of information resources, the recommendation techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder （DAE）, called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.

关键词： semantic representation plot information denoising autoencoder collaborative filtering auxiliary information

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Game Theoretic Approaches in Vehicular Networks: A Survey

arXiv

引用

arXiv 2020年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Anil, Carie Cao, Dongpu College of Computer Science and Technology Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Engineering VIT-AP AmaravatiAndhra Pradesh522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

—In the era of the Internet of Things (IoT), vehicles and other intelligent components in Intelligent Transportation System (ITS) are connected, forming the Vehicular Networks (VNs) that provide efficient and secure traffic, ubiquitous access to information, and various applications. However, as the number of connected nodes keeps increasing, it is challenging to satisfy various and large amounts of service requests with different Quality of Service (QoS) and security requirements in the highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources so that either an individual or a group objectives can be achieved. Game theory, a theoretical framework designed for strategic interactions among rational decision-makers who faced with scarce resources, can be used to model and analyze individual or group behaviors of communication entities in VNs. This paper primarily surveys the recent advantages of GT used in solving various challenges in VNs. As VNs and GT have been extensively investigate34d, this survey starts with a brief introduction of the basic concept and classification of GT used in VNs. Then, a comprehensive review of applications of GT in VNs is presented, which primarily covers the aspects of QoS and security. Moreover, with the development of fifth-generation (5G) wireless communication, recent contributions of GT to diverse emerging technologies of 5G integrated into VNs are surveyed in this paper. Finally, several key research challenges and possible solutions for applying GT in VNs are outlined. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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