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Characterization of Codon Usage patterns and Evolutionary Relationships in Partitiviruses 19

Characterization of Codon Usage Patterns and Evolutionary Re...

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3rd international conference on Computational Biology and bioinformatics (ICCBB)

作者： Je, Mikyung Kim, Hayeon Cho, Myeongji Son, Hyeon S. Seoul Natl Univ SNU Bioinformat Inst Interdisciplinary Grad Program Bioinformat Coll Nat Sci 1 Gwanak Ro Seoul 08826 South Korea Kyungdong Univ Dept Biomed Lab Sci 815 Gyeonhwon Ro Wonju 24695 Gangwondo South Korea Seoul Natl Univ Lab Computat Biol & Bioinformat Grad Sch Publ Hlth 1 Gwanak Ro Seoul 08826 South Korea

ISBN: (纸本)9781450376815

This study investigated the overall sequence characteristics and codon usage patterns of Partitiviridae viruses and applied them to viruses that are part of the family Partitiviridae but are unassigned to a genus. In addition, phylogenetic analysis was performed to investigate the evolutionary relationships of viruses belonging to Partitiviridae in relation to their fungal hosts. The sequence data of the CDS region were downloaded from GenBank, including that of the conserved rdRP, capsid protein. The results showed that Botryotinia fuckeliana partitivirus 1, Beauveria bassiana partitivirus 1, Beauveria bassiana partitivirus 2, Penicillium marneffei partitivirus-1, and Aspergillus fumigatus partitivirus 2 had evolutionary relationships with Gammapartitivirus. Among them, Aspergillus fumigatus partitivirus 2 and Penicillium marneffei partitivirus-1 preferred U-ended codons similar to Gammapartitivirus in terms of codon usage patterns. In addition, Botryosphaeria dothidea virus 1 showed a different pattern from the viruses belonging to Partitiviridae in all the analysis results. This virus showed a strong bias towards the C-ended codons and the lowest ENC value among the analyzed viruses, forming a separate cluster in the phylogenetic tree. These results may help to understand the genetic diversity and evolutionary relationships of the members and tentative members of the family Partitiviridae.

关键词： Partitiviridae rdRP capsid protein codon usage pattern

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An Improved Sensor pattern Noise Estimation Method Based on Edge Guided Weighted Averaging 3rd

An Improved Sensor Pattern Noise Estimation Method Based on ...

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3rd international conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Zhang, Wen-Na Liu, Yun-Xia Zhou, Jin Yang, Yang Law, Ngai-Fong School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China School of Information Science and Engineering Shandong University Qingdao266237 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030624590

Sensor pattern Noise (SPN) has proven to be an effective fingerprint for source camera identification. However, its estimation accuracy is still greatly affected by image contents. In this work, considering the confidence difference in varying image regions, an image edge guided weighted averaging scheme for robust SPN estimation is proposed. Firstly, the edge and non-edge regions are estimated by a Laplacian operator-based detector, based on which different weights are assigned to. Then, the improved SPN estimation is obtained by weighted averaging of image residuals. Finally, an edge guided weighted normalized cross-correlation measurement is proposed as similarity metric in source camera identification (SCI) applications. The effectiveness of the proposed method is verified by SCI experiments conducted on six models from the Dresden data set. Comparison results on different denoising algorithms and varying patch sizes reveal that performance improvement is more prominent for small image patches, which is demanding in real forensic applications. © 2020, Springer Nature Switzerland AG.

关键词： Mathematical operators

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Learning bronchiole-sensitive airway segmentation cnns by feature recalibration and attention distillation 23rd

Learning bronchiole-sensitive airway segmentation cnns by fe...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China UdL INSA Lyon CREATIS CNRS UMR 5220 INSERM U1206 Lyon France

ISBN: (纸本)9783030597092

Training deep convolutional neural networks (CNNs) for airway segmentation is challenging due to the sparse supervisory signals caused by severe class imbalance between long, thin airways and background. In view of the intricate pattern of tree-like airways, the segmentation model should pay extra attention to the morphology and distribution characteristics of airways. We propose a CNNs-based airway segmentation method that enjoys superior sensitivity to tenuous peripheral bronchioles. We first present a feature recalibration module to make the best use of learned features. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce the airway-specific representation learning. High-resolution attention maps with fine airway details are passing down from late layers to previous layers iteratively to enrich context knowledge. Extensive experiments demonstrate considerable performance gain brought by the two proposed modules. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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Weakly Supervised Deep Learning for Breast Cancer Segmentation with Coarse Annotations 23rd

Weakly Supervised Deep Learning for Breast Cancer Segmentati...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zheng, Hao Zhuang, Zhiguo Qin, Yulei Gu, Yun Yang, Jie Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Department of Radiology Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (纸本)9783030597184

Cancer lesion segmentation plays a vital role in breast cancer diagnosis and treatment planning. As creating labels for large medical image datasets can be time-consuming, laborious and error prone, a framework is proposed in this paper by using coarse annotations generated from boundary scribbles for training deep convolutional neural networks. These coarse annotations include locations of lesions but are lack of accurate information about boundaries. To mitigate the negative impact of annotation errors, we propose an adaptive weighted constrained loss that can change the weight of the task-specific penalty term according to the learning process. To impose further supervision about the boundaries, uncertainty-based boundary maps are generated, which can provide better descriptions for the blurry boundaries. Validation on a dataset containing 154 MRI scans has shown an average Dice coefficient of 82.25 %, which is comparable to results from fine annotations, demonstrating the efficacy of the proposed approach. © 2020, Springer Nature Switzerland AG.

关键词： Diagnosis

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Local Binary pattern Histogram for Face recognition in Student Attendance System

Local Binary Pattern Histogram for Face Recognition in Stude...

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international conference of Computer and Informatics Engineering (IC2IE)

作者： Allan D Alexander Ratna Salkiawati Hendarman Lubis Fathur Rahman Herlawati Herlawati Rahmadya Trias Handayanto Universitas Bhayangkara Jakarta Raya Bekasi Bekasi Indonesia Universitas Islam 45 Bekasi Bekasi Indonesia

ISBN: (数字)9781728182476

ISBN: (纸本)9781728182483

Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy.

关键词： Face recognition Iris recognition Histograms Feature extraction Training Fingerprint recognition Informatics

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Comparison EMG pattern recognition Using Bayes and NN Methods

Comparison EMG Pattern Recognition Using Bayes and NN Method...

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Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), international conference on

作者： Daniel Sutopo Pamungkas Imanuel Simatupang Electronics Dept Mechatronics Dept Politeknik Negeri Batam Indonesia

This article presents a comparative study for recognition hand gesture between Na39;ve Bayes and Neural Network (NN) methods for electromyography signals (EMG). EMG signals are obtained from five gestures from a su...

ISBN: (数字)9781728174037

ISBN: (纸本)9781728174044

This article presents a comparative study for recognition hand gesture between Naïve Bayes and Neural Network (NN) methods for electromyography signals (EMG). EMG signals are obtained from five gestures from a subject. Myo Armband is used to receive the information from the upper hand of the user. The outputs of the systems are used to control a mobile robot. The results showed that the NN method has a higher percentage of success than the Naïve Bayes algorithm. However, the Naïve Bayes method is faster than the NN algorithm.

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2nd international Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st international Workshop on Data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd APWeb and WAIM Joint conference on Web and Big Data, APWeb-WAIM 2019

2nd International Workshop on Knowledge Graph Management and...

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2nd international Workshop on Knowledge Graph Management and Analysis, KGMA 2019, and 1st international Workshop on Data Science for Emerging Applications, DSEA 2019, held jointly with the 3rd APWeb and WAIM Joint conference on Web and Big Data, APWeb-WAIM 2019

ISBN: (纸本)9783030339814

The proceedings contain 9 papers. The special focus in this conference is on Knowledge Graph Management and Analysis. The topics include: Classification-based emoji recommendation for user social networks;leveraging context information for joint entity and relation linking;community detection in knowledge graph network with matrix factorization learning;a survey of relation extraction of knowledge graphs;pevr: Pose estimation for vehicle re-identification;the research of chinese ethnical face recognition based on deep learning;model of charging stations construction and electric vehicles development prediction.

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Evaluation of Neural Network Output Results Reliability in pattern recognition 3rd

Evaluation of Neural Network Output Results Reliability in P...

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3rd international Scientific conference on Intelligent Information Technologies for Industry (IITI)

作者： Marshakov, Daniil V. Galushka, Vasily V. Fathi, Vladimir A. Fathi, Denis V. Don State Tech Univ Rostov Na Donu 344000 Russia

ISBN: (纸本)9783030018184

Artificial neural networks (ANN) are well-known effective parallel systems which have successfully approved themselves in solving of complicated artificial intelligence problems. The practice of the widespread ANN application due to their high efficiency in solving non-formalized or hard-formalized problems associated with the need for ANN training on experimental material particularly in pattern recognition. In solving problems of pattern recognition the feedforward neural network is usually used due to the simplicity of algorithmic implementation, the availability of advanced training methods, the possibilities of multi-parallel computations. When neural network classifier implementing within decision support systems, it is necessary to assess the ANN results reliability based upon interpretation of the output signals to establish trust between users and the neural network algorithm. In this paper the ANN output results reliability evaluation method in terms of the degree of belonging of the recognized patterns to the originally specified classes is considered. The proposed method is based on the computation of Euclidean distance between the actual ANN output vector characterizing the class of the object recognition and a set of sample vectors defining a priori known classes at the training stage followed by its evaluation by the curve construction of the normal probability distribution law coinciding with the Gaussian function. A feature of this method is the construction of an individual probability distribution curve computed for each ANN output vector. An experimental research of the proposed method in MATLAB is presented on the example of solving the known Fisher's Iris Database classification for input data without noise and with noise. The obtained results confirm the adequacy of the proposed method which can be used both in independent neural network pattern recognition systems and within decision support complexes.

关键词： Artificial neural network pattern recognition Classification Reliability

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Computer Data Processing Mode in the era of Big Data: from pattern recognition to Intelligent Sensing

Computer Data Processing Mode in the era of Big Data: from P...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： Deng Huaiyong Ma Qing ChongQing Water resources and electric engineering college Chong qing China

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Computer data processing mode in the era of big data from pattern recognition to intelligent sensing is discussed in this paper. Intelligence refers to the interconnection between things and things and people through the Internet of Things and mobile Internet, combined with the general big data computing, communication, artificial intelligence and other technologies. Neighborhood dependence reflects the proportion of approximate samples to all samples under decision class in mixed information system, and it is also a measure to describe the degree of relationship between conditional attribute set and decision attribute, so it can be used as a heuristic function to reduce the attribute of mixed information system. This paper designed the model from the collection to processing, from pattern analysis to the intelligent sensing. The experimental results have proven that the proposed model can sense the data effectively.

关键词： Organizations Computational modeling Task analysis Sensors Data models Servers Big Data

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3rd international conference on Cognitive Computing, ICCC 2019, held as part of the Services conference Federation, SCF 2019

3rd International Conference on Cognitive Computing, ICCC 20...

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3rd international conference on Cognitive Computing, ICCC 2019, held as part of the Services conference Federation, SCF 2019

ISBN: (纸本)9783030234065

The proceedings contain 17 papers. The special focus in this conference is on Cognitive Computing. The topics include: Speech emotion recognition using multi-granularity feature fusion through auditory cognitive mechanism;learning contextual features with multi-head self-attention for fake news detection;reduce the warfighters’ cognitive burden;an ensemble method: Case-based reasoning and the inverse problems in investigating financial bubbles;the development trend of intelligent speech interaction;imagery signal-based deep learning method for prescreening major depressive disorder;visual cognitive mechanism guided video shot segmentation;A best detecting synchrony method in audio STROOP EEG based on wavelet coherence;a neural framework for joint prediction on intent identification and slot filling;conditional joint model for spoken dialogue system;small traffic sign detection and recognition in high-resolution images;a feature extraction algorithm for the detection of “Five-Distances” in hazardous chemical storage and stacking in night vision environment;structural-Based graph publishing under differential privacy;face detection using bionic cascaded framework;a neural rumor detection framework by incorporating uncertainty attention on social media texts.

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