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arXiv 2020年

作者： Hua, Xuan Han, Jungang Zhao, Chen Tang, Haipeng He, Zhuo Tang, Jinshan Chen, Qing-Hui Tang, Shaojie Zhou, Weihua College of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing in Shaanxi Xi’an710121 China College of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China College of Computing Michigan Technological University HoughtonMI49931 United States School of Computing University of Southern Mississippi Long Beach39560 United States Deprtment of Kinesiology and Integrative Physiology Michigan Technological University HoughtonMI49931 United States School of Automation Xi’an University of Posts and Telecommunications Xi’an710121 China

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural networks (CNN) to aid the classification of ECG signals. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) for segmenting the original ECG data into segments in order to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments. The performance of the classification models for 5-class and 6-class are verified with ECG signals from 48 records of the MIT-BIH arrhythmia database. As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile, according to the recommended practice from the Association for Advancement of Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes (i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unclassifiable beats), the beat classification accuracy, the sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The experimental results show that the proposed method achieves better performance than the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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aNMM: Ranking short answer texts with attention-based neural matching model

arXiv

引用

arXiv 2018年

作者： Yang, Liu Ai, Qingyao Guo, Jiafeng Bruce Croft, W. Center for Intelligent Information Retrieval University of Massachusetts AmherstMA United States CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Impact of Strain Relaxation on 2D Ruddlesden–Popper Perovskite Solar Cells

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Angewandte Chemie 2022年第36期134卷

作者： Qian Cheng Boxin Wang Gaosheng Huang Yanxun Li Dr. Xing Li Dr. Jieyi Chen Shengli Yue Kang Li Dr. Hong Zhang Prof. Yuan Zhang Prof. Huiqiong Zhou CAS Key Laboratory of Nanosystem and Hierarchical Fabrication CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing 100191 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 China Contribution: Data curation (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) Contribution: Investigation (supporting) Supervision (supporting) Contribution: Investigation (supporting) Software (supporting) Contribution: Methodology (supporting) School of Chemistry Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing 100191 China Contribution: Investigation (supporting) Contribution: Investigation (supporting) Methodology (supporting) Contribution: Investigation (supporting) Writing - review & editing (supporting) Contribution: Funding acquisition (supporting) Supervision (supporting) Writing - review & editing (supporting) Contribution: Funding acquisition (lead) Project administration (lead) Writing - review & editing (lead)

Although the photovoltaic performance of perovskite solar cells (PSCs) has reached the commercial standards, the unsatisfactory stability limits their further application. Hydrophobic interface and encapsulation can block the damage of water and oxygen, while the instability induced by intrinsic residual strain remains inevitable. Here, the residual strain in a two-dimensional (2D) Ruddlesden–Popper (RP) perovskite film is investigated by X-ray diffraction and atomic force microscopy. It's found that the spacer cations contribute to the residual strain even though they are not in the inorganic cages. Benefited from strain relaxation, the film quality is improved, leading to suppressed recombination, promoted charge transport and enhanced efficiency. More significantly, the strain-released devices maintain 86 % of the initial efficiency after being kept in air with 85 % relative humidity (RH) for 1080 h, 82 % under maximum power point (MPP) tracking at 50 °C for 804 h and 86 % after continuous heating at 85 °C for 1080 h.

关键词： Perovskites Ruddlesden–Popper Solar Cells Stability Strain Relaxation

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Probabilistic Adaptive Genetic Algorithm for Pre-treatment Radiomics Features Predict Patient Survival

Probabilistic Adaptive Genetic Algorithm for Pre-treatment R...

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2018 International Conference on Computer, Communications and Mechatronics Engineering（CCME 2018）

作者： Xiao-ying PAN Ting ZHANG Qing-ping YANG Liu CHEN School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

In order to provide personalized treatment for patients with tongue carcinoma, a probabilistic adaptive genetic algorithm neural network(PAGA-BP) model is proposed in this paper. The PAGA-BP model ameliorates selection sorting operator, adaptive crossover operator and u-adaptive mutation operator to optimize the initial weight of BP neural network. By comparing with traditional GA-BP neural network and BP neural network survival prediction model, the results show that PAGA-BP prediction model has the highest approximation accuracy and better survival period prediction.

关键词： PGA-BP neural network Radiomics features Survival Prediction

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A Fatigue Driving Detection Method based on Deep Learning and Image Processing

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhong Wang Peibei Shi Chao Wu School of Computer Science and Technology Hefei Normal University No. 1688 Lianhua Road Hefei Anhui China. Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China No. 96 Jinzhai Road Hefei Anhui China. Network and Information Center University of Science and Technology of China No. 96 Jinzhai Road Hefei Anhui China

Driving fatigue is one of the important causes of traffic accidents. It is of great significance to study fatigue driving detection algorithms to improve human life and property safety. This paper proposes a fatigue driving detection method based on deep learning and image processing. First, the driver's face image is obtained in real time through the camera and the face image is detected using the MTCNN model. Next the image processing is performed on the face image, including three steps: grayscale processing, binarization processing, and human eye detection. Then we check the legitimacy of the human eye image and calculate the eye closure rate, and finally use the PERCLOS principle to analyze the fatigue state of the driver. The experimental results show that the proposed method has high detection rate and low false alarm rate, and has strong practicality.

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Learning a deep listwise context model for ranking refinement

arXiv

引用

arXiv 2018年

作者： Ai, Qingyao Bi, Keping Guo, Jiafeng Croft, W. Bruce College of Information and Computer Sciences University of Massachusetts Amherst AmherstMA United States CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Learning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data, which can achieve good performance on average but may be suboptimal for individual queries by ignoring the fact that relevant documents for different queries may have different distributions in the feature space. Inspired by the idea of pseudo relevance feedback where top ranked documents, which we refer as the local ranking context, can provide important information about the query's characteristics, we propose to use the inherent feature distributions of the top results to learn a Deep Listwise Context Model that helps us fine tune the initial ranked list. Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results. There are three merits with our model: (1) Our model can capture the local ranking context based on the complex interactions between top results using a deep neural network;(2) Our model can be built upon existing learning-to-rank methods by directly using their extracted feature vectors;(3) Our model is trained with an attention-based loss function, which is more effective and efficient than many existing listwise methods. Experimental results show that the proposed model can significantly improve the state-of-the-art learning to rank methods on benchmark retrieval corpora. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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On the relationship between energy complexity and other boolean function measures

arXiv

引用

arXiv 2018年

作者： Sun, Xiaoming Sun, Yuan Wu, Kewen Xia, Zhiyu CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineering and Computer Science Peking University China

In this work we investigate energy complexity, a Boolean function measure related to circuit complexity. Given a circuit C over the standard basis {∨2, ∧2, ¬}, the energy complexity of C, denoted by EC(C), is the maximum number of its activated inner gates over all inputs. The energy complexity of a Boolean function f, denoted by EC(f), is the minimum of EC(C) over all circuits C computing f. Recently, K. Dinesh et al. [3] gave EC(f) an upper bound in terms of the decision tree complexity, EC(f) = O(D(f)3). They also showed that EC(f) ≤ 3n−1, where n is the input size. For the lower bound, they show that EC(f) ≥ 13 psens(f), where psens(f) is the positive sensitivity. They asked whether EC(f) can be lower bounded by a polynomial of D(f). We improve both the upper and lower bounds in this paper. For upper bounds, We show that EC(f) ≤ min{ 12 D(f)2 + O(D(f)), n + 2 D(f) − 2}. For the lower bound, we answer Dinesh et al.'s question by proving that EC(f) = Ω(pD(f)). For non-degenerated functions, we also give another lower bound EC(f) = Ω(log n) where n is the input size. These two lower bounds are incomparable to each other. Besides, we examine the energy complexity of OR functions and ADDRESS functions, which implies the tightness of our two lower bounds respectively. In addition, the former one answers another open question in [3] asking for non-trivial lower bound for energy complexity of OR functions. Copyright © 2018, The Authors. All rights reserved.

关键词： Decision trees

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An Overview of General Theory of Security

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China Communications 2017年第7期14卷 1-10页

作者： Min Lei Yixian Yang Xinxin Niu Yu Yang Jie Hao Information Security Center Beijing University of Posts and Telecommunications Guizhou University Guizhou Provincial Key Laboratory of Public Big Data Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology

Cyber security lacks comprehensive theoretical guidance. General security theory, as a set of basic security theory concepts, is intended to guide cyber security and all the other security work. The general theory of security aims to unify the main branches of cyber security and establish a unified basic theory. This paper proposal an overview on the general theory of security, which is devoted to constructing a comprehensive model of network security. The hierarchical structure of the meridian-collateral tree is described. Shannon information theory is employed to build a cyberspace security model. Some central concepts of security, i.e., the attack and defense, are discussed and several general theorems on security are presented.

关键词： cyber security network attack and defense meridian-collateral theory

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Visual Tracking Algorithm Based on Color Name Histogram

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IOP Conference Series: Materials science and Engineering 2020年第1期790卷

作者： Chenchen Yue Wangsheng Yu Zhiqiang Hou Sugang Ma College of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China Information and Navigation Institute of Air Force Engineering University Xi'an 710077 China

Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.

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Bismuth nanosheets as a Q-switcher for a mid-infrared erbium-doped SrF_2 laser

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Photonics Research 2018年第8期6卷 I0016-I0016,763-767页

作者： JINGJING LIU HAO HUANG FENG ZHANG ZHEN ZHANG JIE LIU HAN ZHANG LIANGBI SU Shandong Provincial Key Laboratory of Optics and Photonic Devices School of Physics and ElectronicsShandong Normal University SZU-NUS Collaborative Innovation Centre for Optoelectronic Science & Technology and Key Laboratory of Optoelectronic Devices and Systems of the Ministry of Education and Guangdong Province College of Optoelectronic Engineering Shenzhen University CAS Key Laboratory of Transparent and Opto-Functional Inorganic Materials Synthetic Single Crystal Research Center Shanghai Institute of Ceramics Chinese Academy of Sciences Institute of Data Science and Technology Shandong Normal University State Key Laboratory of High Performance Ceramics and Superfine Microstructure Chinese Academy of Sciences Shanghai Institute of Ceramics

Bismuth nanosheets(Bi-NSs) were successfully prepared and employed as saturable absorbers to generate a diodepumped dual-wavelength Er^(3+):SrF_2 laser in the mid-infrared region. Q-switched pulses with a maximum output power of 0.226 W were obtained at an absorbed pump power of 1.97 W. A repetition rate of 56.20 kHz and a minimum pulse duration of 980 ns were achieved. To the best of our knowledge, we present the first application of Bi-NSs in a mid-infrared all-solid-state laser. The results prove that Bi-NSs may be applied as an optical modulator in mid-infrared photonic devices or as a mode-locker and Q-switcher.

关键词：激光器铋纳米片吸收体通讯技术

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