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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaowei Wang Jungang Han Ben Li Xiaoying Pan Hui Xu School of Computer Science Xi’an University of Posts and Telecommunications Xi’an China School of Computer Science Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligence Processing Xi’an China Life Singularity (Beijing) Technology Co. LTD Beijing Beijing China

ISBN: (纸本)9781665429825

Classifying diseases in electronic medical records into corresponding ICD codes requires not only a large amount of medical knowledge but also a large number of coders, which is time-consuming and labor-consuming. Therefore, automatic coding is of great significance. This paper aims to build a deep learning model for automatic ICD-10 coding from a batch of Chinese electronic medical records. The data enhancement, convolutional neural network, attention mechanism, and the gating residual network proposed by the author were used to code ICD code corresponding to the distribution of medical record information by supervised learning. The benchmark model and ablation model were tested on a data set of Chinese electronic medical records. The effectiveness of the proposed modules, such as feature aggregation, multi-head attention mechanism, dilated convolution, and gating residuals, was verified. In the automatic ICD coding task for 104 diseases, the accuracy of the proposed method is 91.71%, and the F1-Score is 92.11%. 1

关键词： Biological system modeling Supervised learning Neural networks Logic gates Encoding data models Electronic medical records

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Fully Synthetic Videos and Random Background-Pasting Method for Flame Segmentation

SSRN

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SSRN 2022年

作者： Jia, Yang Zhang, Xinmeng Kuang, Yaxi Chen, Yanping Wang, Gang Zhang, Qixing School of Computer Science Xi'an University of Posts and Telecommunications 710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications 710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications 710121 China School of Information Science and Technology University of Science and Technology of China 230026 China XNIF262 710061 China State Key Laboratory of Fire Science University of Science and Technology of China 230026 China

Video-based flame detection (VFD) aims to recognize fire events by the image features. Flame segmentation is an essential task in VFD, which provides suspected regions for feature analysis and object recognition. The development of deep learning has promoted VFD performances to a higher level. However, due to the rarity of recorded fire events, it is challenging to get tremendous positive flame samples for model training. Existing deep learning-based VFD methods are limited to the lack of flame samples under different scenarios. In this paper, we propose an assumption that we can train a segmentation model with virtual flame images and design experiments to prove it. We take many virtual flame videos to extend existing flame datasets, which provide adequate flame samples for deep learning-based VFD methods. We also take a random background-pasting method to distribute the flame images among different scenarios. The proposed method trains a flame segmentation model with zero real flame image. Moreover, we perform segmentation testing with real flame images, which the model has never touched, to see if the model trained with ‘fake’ images can segment real objects. We trained four segmentation models based on FCN, U-Net, Deeplabv3, and Mask-RCNN with synthetic flame video frames, and obtained the highest mPA of 0.783 and mIoU of 0.515. The experimental results on FIRE-SMOKE-dataSET and Fire-Detection-Image-dataset demonstrate that the ‘fake’ flame samples generated by the proposed random background-pasting method can improve the performances of existing state-of-the-art flame segmentation methods obviously on cross-dataset evaluation settings. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Multi-Source Heterogeneous Core data Acquisition Method in Edge Computing Nodes

Multi-Source Heterogeneous Core Data Acquisition Method in E...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Hong Xia Mingdao Zhao Yanping Chen Zhongmin Wang Zhong Yu Jingwei Yang School of Computer Science Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China School of Computer Science Xi’an University of Posts and Telecommunications Xi’an China School of Communications and Information Engineering Xi’an University of Posts and Telecommunication Xi’an China Department of Computer Science California State University Sacramento United State

As the volume of data grows exponentially, big data brings an unprecedented burden to the current computing infrastructure. How to deal with big data efficiently and concisely and reduce the burden of computing infrastructure has always been a big challenge. Therefore, this paper proposes a high-quality core data extraction method in edge computing nodes. Firstly, heterogeneous data are fused into a unified model, the data characteristics of the original data are retained. Then, a Lanzcos-based incremental tensor decomposition method is proposed to extracted the high quality core tensor dynamically. Finally, the model algorithm is verified using real data. The experimental results show that the approximate tensor reconstructed from the tensor containing 15% of the core data can guarantee 90% accuracy. At the same time, IncLHOSVD is significantly better than non-incremental HOSVD in execution time in guaranteeing the accuracy of approximate equal error.

关键词： Edge computing Internet of Things data mining Matrix decomposition Computational modeling data models

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Deep learning for depression recognition with audiovisual cues: A review

arXiv

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arXiv 2021年

作者： He, Lang Niu, Mingyue Tiwari, Prayag Marttinen, Pekka Su, Rui Jiang, Jiewei Guo, Chenguang Wang, Hongyu Ding, Songtao Wang, Zhongmin Pan, Xiaoying Dang, Wei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Beijing100190 China Beijing100049 China Department of Computer Science Aalto University Espoo Finland School of Foreign Languages Northwest University Shaanxi Xi'an China School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an China Shaanxi Mental Health Center Shaanxi Xi'an710061 China

With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. Promisingly, physiological and psychological studies have indicated some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection. To sort out and summarize these works, this review introduces the databases and describes objective markers for automatic depression estimation (ADE). Furthermore, we review the deep learning methods for automatic depression detection to extract the representation of depression from audio and video. Finally, this paper discusses challenges and promising directions related to automatic diagnosing of depression using deep learning technologies. © 2021, CC BY.

关键词： Diagnosis

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Research on the Application of Cryptography on the Blockchain

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Journal of Physics: Conference Series 2019年第3期1168卷

作者： Sheping Zhai Yuanyuan Yang Jing Li Cheng Qiu Jiangming Zhao School of Computer Science & Technology Xi'an University of Posts & Telecommunications Xi'an Shaanxi 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'anShaanxi 710121 China

Blockchain is an innovative application model that integrates distributed data storage, peer-to-peer transmission, consensus mechanisms, digital encryption technology and other computer technologies. It is decentralized, secure, and Information disclosure. In the blockchain, digital encryption technology has a core position. The security of user information and transaction data is a necessary condition for the promotion of blockchain. The development of cryptography technology promotes and restricts the further development of blockchain. This paper outlines the infrastructure of blockchain, including the data layer, network layer, consensus layer, contract layer and application layer. The principles of encryption technology is introduced briefly, such as hash function, asymmetric cryptosystem, digital signature. The application of cryptography in all levels of blockchain is analyzed, including data layer, network layer, consensus layer, etc. It shows that cryptography runs through the whole blockchain system. The existing security problems of blockchain is analyzed, and the future research direction is expected.

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Design of Convolutional Neural network Based on FPGA

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Journal of Physics: Conference Series 2019年第6期1168卷

作者： Sheping Zhai Cheng Qiu Yuanyuan Yang Jing Li Yiming Cui School of Computer Science & Technology Xi'an University of Posts & Telecommunications Xi'an Shaanxi 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'anShaanxi 710121 China

Recently, FPGAs have been widely used in the implementation of hardware accelerators for CNN, especially on mobile and embedded devices. This paper proposes an FPGA-based CNN accelerator. The highly reusable accelerator function is designed to construct the optimized convolutional neural network and memory optimization with a lower hardware resource consumption. The results show the advantage of performance and power when compared with Core i5 CPU and GTX 960 GPU.

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A Multi-Scale Two-Branch Fusion network for Simultaneous Segmentation in Electronic Laryngoscope Images

SSRN

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SSRN 2023年

作者： Chen, Hao Hao, Mayang Wang, Chenwu Wang, Pei School of Computer Science and Technology Xi’an University of Posts and Telecommunications 563 South Changan Road Yanta District Shaanxi Xi’an710061 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications 563 South Changan Road Yanta District Shaanxi Xi’an710061 China School of Modern Post Xi’an University of Posts and Telecommunications 563 South Changan Road Yanta District Shaanxi Xi’an710061 China School of Computer Science Northwestern Polytechnical University 127 West Youyi Road Beilin District Shaanxi Xi’an710072 China

Three issues reduced the performance of networks for handling the organs and lesions’ simultaneous segmentation in electronic laryngoscopy images. Firstly, the moving endoscope will cause noticeable variations of the shape and angle in lesions and organs. Secondly, the lesions, mainly the polyps, and the major organs differ considerably in size. Moreover, the boundaries between the lesions or organs and their backgrounds are usually indistinguishable since their color and texture are very close to the mucosal tissues. To improve the simultaneous segmentation accuracy, we propose a multi-scale two-branch fusion network (MsFusionNet), which adopted an asymmetric two-branch structure to fuse the fine-grained feature maps extracted by the convolution neural network with the global context feature maps extracted by the Vision Transformer at different scales. In addition, a Multi-scale Dark Part Feature Enhancement module (MsDFE) was designed to enhance the non-salient details of organs before the feature fusion in the two-branch network. To evaluate the universality and effectiveness of the proposed method, we used a mixed dataset collected from three institutions, including 2425 electronic laryngoscope images with major organs in the pharynx and larynx. The results show the proposed method performs better than nine existing segmentation networks in dealing with the experiment dataset, which has good potential for clinical practice. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Research on key Technologies of Student Electronic Depository System Based on Blockchain

引用

Journal of Physics: Conference Series 2019年第3期1168卷

作者： Sheping Zhai Jing Li Cheng Qiu Yuanyuan Yang Weixing Zuo School of Computer Science & Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an 710121 China

Aiming at the problem that a large number of students' electronic deposit data is easy to forge and destroy, a student electronic deposit application architecture based on blockchain is proposed. There are some features on blockchain, such as decentralized, the non-tamperable of data, the consensus mechanism and data encryption features, and the electronic depository network. All these features provide a secure and credible storage environment, ensures the traceable of the student's electronic deposit information, the trust of the entire network, and the security of the data. At the same time, an improved PBFT consensus algorithm is proposed, which eliminates the certificate elimination phase to reduce network resource consumption, and constructs a decentralized electronic depository platform in combination with the blockchain to realize the authenticity, integrity and reliability of the electronic certificate data. The further development and application of blockchain technology is prospected, hoping to provide inspiration and reference for future related research.

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Video Smoke Detection Based on Motion analysis and Fused Temporal-Spatial Model

SSRN

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SSRN 2022年

作者： Jia, Yang Mao, Zixu Zhang, Xinmeng Chen, Weiguang Wang, Ben Wang, Gang Zhang, Qixing School of Computer Science Xi'an University of Posts and Telecommunications Xi'An710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications Xi'An710121 China XNIC262 Xi'An710121 China School of Information Science and Technology University of Science and Technology of China Hefei230026 China State Key Laboratory of Fire Science University of Science and Technology of China Hefei230026 China

Researchers have proposed various video smoke detection methods based on deep learning. However, most methods are based on two-dimensional image analysis, which hasn't effectively used the motion information of smoke. Therefore, an improved two-stream CNN based smoke detection method is proposed. Firstly, we use sparse sampling to extract sampled frames and capture the suspected area of the sampled frame with background modeling. Then we extract the dense optical flow of consecutive frames of the sampled frames and merge them into a stacked optical flow sequence. A sampled frame and a stacked optical flow sequence are fed into the Two-Stream CNN to obtain video-level recognition results. The performance of 97.32% accuracy, and 4.07% false alarm rate can be achieved, and the effectiveness of the method has been proved through comparative experiments. Combined with the explicitly expressed motion information, we can get a better detection performance. © 2022, The Authors. All rights reserved.

关键词： Motion analysis

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Novel Color Orthogonal Moments-Based Image Representation and Recognition

Research Square

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Research Square 2020年

作者： He, Bing Liu, Jun Yang, Tengfei Xiao, Bin Peng, Yanguo School of Physics and Electrical Engineering Weinan Normal University Weinan714000 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts & Telecommunications Xi’an710121 China School of Computer Science and Technology Weinan Normal University Weinan714000 China School of Cyberspace Security Xi’an University of Posts & Telecommunications Xi’an710121 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Xidian University Xi’an710071 China

Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates;therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images. © 2020, CC BY.

关键词： Image reconstruction

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