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    Review of Application of Transfer Learning in Medical Image Field
    GAO Shuang, XU Qiaozhi
    Computer Engineering and Applications    2021, 57 (24): 39-50.   DOI: 10.3778/j.issn.1002-8331.2107-0300
    Abstract360)      PDF(pc) (896KB)(526)       Save

    Deep learning technology has developed rapidly and achieved significant results in the field of medical image treatment. However, due to the small number of medical image samples and difficult annotation, the effect of deep learning is far from reaching the expectation. In recent years, using transfer learning method to alleviate the problem of insufficient medical image samples and improve the effect of deep learning technology in the field of medical image has become one of the research hotspots. This paper first introduces the basic concepts, types, common strategies and models of transfer learning methods, then combs and summarizes the representative related research in the field of medical images according to the types of transfer learning methods, and finally summarizes and prospects the future development of this field.

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    Overview on Reinforcement Learning of Multi-agent Game
    WANG Jun, CAO Lei, CHEN Xiliang, LAI Jun, ZHANG Legui
    Computer Engineering and Applications    2021, 57 (21): 1-13.   DOI: 10.3778/j.issn.1002-8331.2104-0432
    Abstract271)      PDF(pc) (779KB)(429)       Save

    The use of deep reinforcement learning to solve single-agent tasks has made breakthrough progress. Since the complexity of multi-agent systems, common algorithms cannot solve the main difficulties. At the same time, due to the increase in the number of agents, taking the expected value of maximizing the cumulative return of a single agent as the learning goal often fails to converge and some special convergence points do not satisfy the rationality of the strategy. For practical problems that there is no optimal solution, the reinforcement learning algorithm is even more helpless. The introduction of game theory into reinforcement learning can solve the interrelationship of agents very well and explain the rationality of the strategy corresponding to the convergence point. More importantly, it can use the equilibrium solution to replace the optimal solution in order to obtain a relatively effective strategy. Therefore, this article investigates the reinforcement learning algorithms that have emerged in recent years from the perspective of game theory, summarizes the important and difficult points of current game reinforcement learning algorithms and gives several breakthrough directions that may solve the above-mentioned difficulties.

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    Research Progress of Transformer Based on Computer Vision
    LIU Wenting, LU Xinming
    Computer Engineering and Applications    2022, 58 (6): 1-16.   DOI: 10.3778/j.issn.1002-8331.2106-0442
    Abstract452)      PDF(pc) (1089KB)(401)       Save
    Transformer is a deep neural network based on the self-attention mechanism and parallel processing data. In recent years, Transformer-based models have emerged as an important area of research for computer vision tasks. Aiming at the current blanks in domestic review articles based on Transformer, this paper covers its application in computer vision. This paper reviews the basic principles of the Transformer model, mainly focuses on the application of seven visual tasks such as image classification, object detection and segmentation, and analyzes Transformer-based models with significant effects. Finally, this paper summarizes the challenges and future development trends of the Transformer model in computer vision.
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    YOLOv5 Helmet Wear Detection Method with Introduction of Attention Mechanism
    WANG Lingmin, DUAN Jun, XIN Liwei
    Computer Engineering and Applications    2022, 58 (9): 303-312.   DOI: 10.3778/j.issn.1002-8331.2112-0242
    Abstract357)      PDF(pc) (1381KB)(333)       Save
    For high-risk industries such as steel manufacturing, coal mining and construction industries, wearing helmets during construction is one of effective ways to avoid injuries. For the current helmet wearing detection model in a complex environment for small and dense targets, there are problems such as false detection and missed detection, an improved YOLOv5 target detection method is proposed to detect the helmet wearing. A coordinate attention mechanism(coordinate attention) is added to the backbone network of YOLOv5, which embeds location information into channel attention so that the network can pay attention on a larger area. The original feature pyramid module in the feature fusion module is replaced with a weighted bi-directional feature pyramid(BiFPN)network structure to achieve efficient bi-directional cross-scale connectivity and weighted feature fusion. The experimental results on the homemade helmet dataset show that the improved YOLOv5 model achieves an average accuracy of 95.9%, which is 5.1 percentage points higher than the YOLOv5 model, and meets the requirements for small and dense target detection in complex environments.
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    Review on Integration Analysis and Application of Multi-omics Data
    ZHONG Yating, LIN Yanmei, CHEN Dingjia, PENG Yuzhong, ZENG Yuanpeng
    Computer Engineering and Applications    2021, 57 (23): 1-17.   DOI: 10.3778/j.issn.1002-8331.2106-0341
    Abstract218)      PDF(pc) (806KB)(323)       Save

    With the continuous emergence and popularization of new omics sequencing technology, a large number of omics data have been produced, which is of great significance for people to further study and reveal the mysteries of life. Using multi-omics data to integrate and analyze life science problems can obtain more abundant and more comprehensive information related to life system, which has become a new direction for scientists to explore the mechanism of life. This paper introduces the research background and significance of multi-omics data integration analysis, summarizes the methods of data integration analysis of multiomics in recent years and the applied research in related fields, and finally discusses the current existing problems and future prospects of multi-omics data integration analysis methods.

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    Remote Sensing Military Target Detection Algorithm Based on Lightweight YOLOv3
    QIN Weiwei, SONG Tainian, LIU Jieyu, WANG Hongwei, LIANG Zhuo
    Computer Engineering and Applications    2021, 57 (21): 263-269.   DOI: 10.3778/j.issn.1002-8331.2106-0026
    Abstract157)      PDF(pc) (14418KB)(319)       Save

    In the process of intelligent missile penetration, detecting enemy anti-missile positions from massive remote sensing image data has great application value. Due to the limited computing power of the missile-borne deployment environment, this paper designs a remote sensing target detection algorithm that takes into account lightweight, detection accuracy and detection speed. A typical remote sensing military target data set is produced, and the data set is clustered and analyzed by the K-means algorithm. The MobileNetV2 network is used to replace the backbone network of the YOLOv3 algorithm to ensure the lightweight and detection speed of the network. A lightweight and efficient channel coordinated attention module and a target rotation invariance detection module suitable for remote sensing target characteristics are proposed, and they are embedded in the detection algorithm to improve the detection accuracy on the basis of network lightweight. Experimental results show that the accuracy rate of the algorithm in this paper reaches 97.8%, an increase of 6.7 percentage points, the recall rate reaches 95.7%, an increase of 3.9 percentage points, the average detection accuracy reaches 95.2%, an increase of 4.4 percentage points, and the detection speed reached 34.19 images per, and the network size is only 17.5?MB. The results show that the algorithm in this paper can meet the comprehensive requirements of intelligent missile penetration.

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    Improved Sparrow Search Algorithm Based on Multi-Strategy Mixing
    HUI Lichuan, CHEN Xuelian, MENG Sibo
    Computer Engineering and Applications    2022, 58 (16): 71-83.   DOI: 10.3778/j.issn.1002-8331.2202-0134
    Abstract77)      PDF(pc) (10927KB)(298)       Save
    Dedicated to tackling the shortcomings of the simple sparrow search algorithm(SSA) with inadequate search area, sluggish convergence speed and convenient to crumple into partial top of the line when dealing with complicated optimization problems, an improved sparrow search algorithm based on multi-strategy mixing(IMSSA) is proposed. The sparrow individual position is initialized by the usage of Sine chaotic map, which enriches the vary of the population and compensates for the uneven population distribution and inadequate search space. The diversity global optimal guidance strategy with inertia weight is adopted to promote the convergence speed and regulate the overall search and local exploitation ability of the algorithm. The double-sample learning strategy is used which enables the algorithm soar out of the local optimum and enhance the population’s search capability of the solution space. The algorithm is simulated via test functions, and the effectiveness of three improved strategies is verified, as well as Wilcoxon rank sum test and time complexity evaluation have been carried out. The effects point out that the overall performance of IMSSA is notably improved. Finally, the algorithm is used to optimize the parameters of support vector machine and establish the bearing fault diagnosis model which confirms the validity of the modified strategy.
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    Summary of Dynamic Gesture Recognition Based on Vision
    XIE Yinggang, WANG Quan
    Computer Engineering and Applications    2021, 57 (22): 68-77.   DOI: 10.3778/j.issn.1002-8331.2105-0314
    Abstract190)      PDF(pc) (598KB)(288)       Save

    Gestures have played a very important role in human communication since ancient times, and the visual dynamic gesture identification technology is to use new technologies such as computer vision and IOT(Internet of Things) perception, and 3D visual sensors, allowing the machine to understand human gestures, thus making humanity and machine more good communication, because of far-reaching research significance for human-machine interaction. The sensor techniques used in dynamic gesture identification are introduced, and the technical parameters of the related sensors are compared. By tracking the dynamic gesture recognition technology of vision at home and abroad, the processing process of dynamic gesture recognition is first stated:gesture detection and segmentation, gesture tracking, gesture classification. By comparing the methods involved in each process, it can be seen that deep learning has strong fault tolerance, robustness, high parallelism, anti-interference, etc., which has achieved great achievements above the traditional learning algorithm in the field of gesture identification. Finally, the challenges currently encountering and the future possible development of dynamic gesture identification are analyzed.

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    Computer Engineering and Applications    2021, 57 (24): 0-0.  
    Abstract207)      PDF(pc) (1168KB)(284)       Save
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    Computer Engineering and Applications    2022, 58 (9): 0-0.  
    Abstract70)      PDF(pc) (38025KB)(277)       Save
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    Weakly Supervised Fine-Grained Image Classification Based on Xception Network
    DING Wenqian, YU Pengfei, LI Haiyan, LU Xinwei
    Computer Engineering and Applications    2022, 58 (2): 235-243.   DOI: 10.3778/j.issn.1002-8331.2008-0402
    Abstract94)      PDF(pc) (3580KB)(263)       Save
    With the rapid development of deep learning, the classification of images research in the field of computer vision is not only limited to recognizing the categories of objects, but also needs more detailed classification based on the traditional image classification task. Based on the existing fine-grained image classification algorithm and model analysis, a model based on Xception model and WSDAN(weakly supervised data augmentation network) weak supervision data augment method of combination of deep learning network is applied to fine-grained image classification task. The method takes Xception network as the backbone network and feature extraction network, uses the improved WSDAN model for data augment, and feeds the augmented image back to the network as the input image to enhance the generalization ability of the network. Experiments on the commonly used fine-grained image data sets and NABirds data set show that the classification accuracy rate is 89.28%, 91.18%, 94.47%, 93.04% and 88.4%, respectively. The experimental results show that this method achieves better classification results compared with WSDAN(Pytorch) model and other mainstream fine-grained classification algorithms.
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    Algorithm for Portrait Segmentation Combined with MobileNetv2 and Attention Mechanism
    WANG Xin, WANG Meili, BIAN Dangwei
    Computer Engineering and Applications    2022, 58 (7): 220-228.   DOI: 10.3778/j.issn.1002-8331.2106-0334
    Abstract121)      PDF(pc) (1064KB)(261)       Save
    As for low precision and efficiency in portrait segmentation, an algorithm for portrait segmentation combined with MobileNetv2 and attention mechanism is proposed to achieve the portrait segmentation. With keeping the encoder-decoder of U-typed network , MobileNetv2 is used as the backbone of the network and streamline the upsampling process, it can reduce the parameters of the network. It is helpful for transfer and network training. The network with attention mechanism can learn portrait features more effectively, and the mixed loss is beneficial to the classification of difficult pixels of portrait edges. A portrait bust can be selected as the input of the model, and the corresponding image mask can be produced by the network. The proposed algorithm is tested on Human_Matting dataset and EG1800 dataset. The results show that the accuracy of the proposed algorithm is 98.3%(Matting) and 97.8%(EG1800), which is higher than PortraitNet(96.3%(Matting) and 95.8%(EG1800)) and DeepLabv3+(96.8%(Matting) and 96.4%(EG1800)). The algorithm can clearly separate the target person from the background. The proposed algorithm’s IOU can reach to 98.6%(Matting) and 98.2%(EG1800), which can be used in lightweight applications and provides a new research idea for portrait segmentation.
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    Overview of Image Quality Assessment Method Based on Deep Learning
    CAO Yudong, LIU Haiyan, JIA Xu, LI Xiaohui
    Computer Engineering and Applications    2021, 57 (23): 27-36.   DOI: 10.3778/j.issn.1002-8331.2106-0228
    Abstract180)      PDF(pc) (646KB)(256)       Save

    Image quality evaluation is a measurement of the visual quality of an image or video. The researches on image quality evaluation algorithms in the past 10 years are reviewed. First, the measurement indicators of image quality evaluation algorithm and image quality evaluation datasets are introduced. Then, the different classification of image quality evaluation methods are analyzed, and image quality evaluation algorithms with deep learning technology are focused on, basic model of which is deep convolutional network, deep generative adversarial network and transformer. The performance of algorithms with deep learning is often higher than that of traditional image quality assessment algorithms. Subsequently, the principle of image quality assessment with deep learning is described in detail. A specific no-reference image quality evaluation algorithm based on deep generative adversarial network is introduced, which improves the reliability of simulated reference images through enhanced confrontation learning. Deep learning technology requires massive data support. Data enhancement methods are elaborated to improve the performance of the model. Finally, the future research trend of digital image quality evaluation is summarized.

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    Overview on Video Abnormal Behavior Detection of GAN via Human Density
    SHEN Xulin, LI Chaobo, LI Hongjun
    Computer Engineering and Applications    2022, 58 (7): 21-30.   DOI: 10.3778/j.issn.1002-8331.2110-0364
    Abstract116)      PDF(pc) (1720KB)(255)       Save
    As an important branch of computer vision, video anomaly detection is a challenging task for intelligent video surveillance systems. It is generally referred to as automatic recognition of videos that contain abnormal targets, events or behaviors, which plays a vital role in ensuring public safety. Generative adversarial network(GAN) is anemerging unsupervised method, which can not only be used to generate images, its unique adversarial learning idea also shows good development potential in the field of anomaly detection. Firstly, the framework of the GAN is introduced. Secondly, according to the density of the scene and the object on which the action is taking place, the research status of video anomaly detection based on GAN is discussed from two aspects of individual behavior anomalies, group anomalies. These two types of abnormalities are further elaborated on the basic of reconstruction and prediction methods respectively. Thirdly, the common datasets for video anomaly detection are briefly introduced, finally, the future development is prospected.
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    Research Review of Space-Frequency Domain Image Enhancement Methods
    GUO Yongkun, ZHU Yanchen, LIU Liping, HUANG Qiang
    Computer Engineering and Applications    2022, 58 (11): 23-32.   DOI: 10.3778/j.issn.1002-8331.2112-0280
    Abstract56)      PDF(pc) (1360KB)(248)       Save
    For small sample image data sets, the image enhancement method is often used to expand the amount of data to increase the rationality of the experiment. The image enhancement algorithm can improve the overall and local contrast of the image, highlight the detailed information of the image, make the image more in line with the visual characteristics of human eyes and easy to be recognized by the machine. In order to deeply study the new ideas and new directions in the application field of image enhancement, starting from the basic principle of image enhancement algorithms, based on the basic principle of image enhancement algorithms, this paper summarizes two kinds of image enhancement algorithms widely used in spatial domain and frequency domain in recent years, including histogram equalization image enhancement algorithm, gray transformation image enhancement algorithm, spatial filter image enhancement algorithm and frequency domain filter image enhancement algorithm. And their basic concepts and related definitions are introduced in detail, and their advantages and disadvantages are briefly described. In addition, subjective and objective evaluation methods are used to compare and analyze the enhancement effects of these algorithms, and the advantages and disadvantages, applicable scenarios and complexity of each algorithm are compared and analyzed, so as to further study the hidden useful information of each image enhancement algorithm, and find out the image enhancement methods with stronger robustness and applicability. Experimental results show that different algorithms have their own characteristics, for different image effects, spatial image enhancement method is more suitable for enhancing contrast, and frequency domain image enhancement method is more suitable for highlighting details. A single method can not meet the needs of image processing, and the image enhancement algorithm combined with advantages is more meaningful. The in-depth study of these algorithms can bring new opportunities for researchers, expand new research directions, promote the high-level development of the whole image enhancement technology, and make image enhancement technology play an important role in many subject fields.
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    Mixed Strategy Improved Sparrow Search Algorithm
    ZHANG Weikang, LIU Sheng, REN Chunhui
    Computer Engineering and Applications    2021, 57 (24): 74-82.   DOI: 10.3778/j.issn.1002-8331.2101-0161
    Abstract156)      PDF(pc) (1182KB)(245)       Save

    Aiming at the shortcomings of the sparrow search algorithm in the iterations of population diversity reduction, easy to fall into local optimality and slow convergence speed, a Mixed Strategy improved Sparrow Search Algorithm(MSSSA) is proposed. Circle map is used to initialize the individual positions of sparrows to increase the diversity of the initial population. Combining the butterfly optimization algorithm the location update method of the discoverer is improved to enhance global exploration ability of the algorithm. The dimensional-by-dimensional mutation method is used to perturb the individual position and improve the algorithm’s ability to jump out of the local optimum. In the simulation experiment, it compares with 4 basic algorithms and 5 improved algorithms based on 10 benchmark functions and performs Wilcoxon rank sum test. The results show that the proposed algorithm has better convergence and solution accuracy, global optimization ability has been greatly improved.

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    Survey on Deep Learning Based Image Super-Resolution
    XIA Hao, LYU Hongfeng, LUO Jun, CAI Nian
    Computer Engineering and Applications    2021, 57 (24): 51-60.   DOI: 10.3778/j.issn.1002-8331.2105-0418
    Abstract167)      PDF(pc) (914KB)(243)       Save

    Image super-resolution reconstruction is the process of using low-resolution images to reconstruct the corresponding high-resolution images. At present, image super-resolution technology has been successfully applied in the fields of computer vision and image processing. In recent years, due to deep learning’s ability of self-learning from a large amount of data, it has been widely used in the field of image super-resolution. This article introduces the background of image super-resolution reconstruction, and summarizes the deep learning based image super-resolution model in detail, and then elaborates the image super-resolution technology in satellite remote sensing images, medical imaging, video surveillance, and industrial inspection tasks application. Finally, this article summarizes the current research status and future development directions of image super-resolution algorithms.

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    COVID-19 Medical Imaging Dataset and Research Progress
    LIU Rui, DING Hui, SHANG Yuanyuan, SHAO Zhuhong, LIU Tie
    Computer Engineering and Applications    2021, 57 (22): 15-27.   DOI: 10.3778/j.issn.1002-8331.2106-0118
    Abstract317)      PDF(pc) (1013KB)(241)       Save

    As imaging technology has been playing an important role in the diagnosis and evaluation of the new coronavirus(COVID-19), COVID-19 related datasets have been successively published. But few review articles discuss COVID-19 image processing, especially in datasets. To this end, the new coronary pneumonia datasets and deep learning models are sorted and analyzed, through COVID-19-related journal papers, reports, and related open-source dataset websites, which include Computer Tomography(CT) image and X-rays(CXR)image datasets. At the same time, the characteristics of the medical images presented by these datasets are analyzed. This paper focuses on collating and describing open-source datasets related to COVID-19 medical imaging. In addition, some important segmentation and classification models that perform well on the related datasets are analyzed and compared. Finally, this paper discusses the future development trend on lung imaging technology.

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    Review of Cognitive and Joint Anti-Interference Communication in Unmanned System
    WANG Guisheng, DONG Shufu, HUANG Guoce
    Computer Engineering and Applications    2022, 58 (8): 1-11.   DOI: 10.3778/j.issn.1002-8331.2109-0334
    Abstract244)      PDF(pc) (913KB)(237)       Save
    As the electromagnetic environment becomes more and more complex as well as the confrontation becomes more and more intense, it puts forward higher requirements for the reliability of information transmission of unmanned systems whereas the traditional cognitive communication mode is difficult to adapt to the independent and distributed development trend of broadband joint anti-interference in future. For the need of low anti-interference intercepted communications surrounded in unmanned systems, this paper analyzes the cognitive anti-interference technologies about interference detection and identification, transformation analysis and suppression in multiple domains and so on. The research status of common detection and estimation, classification and recognition are summarized. Then, typical interference types are modeled correspondingly, and transformation methods and processing problems are concluded. Furthermore, traditional interference suppression methods and new interference suppression methods are systematically summarized. Finally, the key problems of restricting the joint interference of broadband are addressed, such as the classification and recognition of unknown interference, the temporal elimination of multiple interference, the joint separation of distributed interference and the optimal control of collaborative interference, which highlight the important role of cognitive interference suppression technology in unmanned system communication.
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    Review of Knowledge Tracing Preprocessing Based on Deep Learning
    LIANG Kun, REN Yimeng, SHANG Yuhu, ZHANG Yiying, WANG Cong
    Computer Engineering and Applications    2021, 57 (21): 41-58.   DOI: 10.3778/j.issn.1002-8331.2106-0552
    Abstract193)      PDF(pc) (920KB)(234)       Save

    As education informatization keep deepening, knowledge tracing with the goal of predicting students’ knowledge status is becoming an important and challenging task in individualized education. As a time sequence task of educational data mining, knowledge tracing combines with the powerful feature extraction and modeling capabilities of deep learning models, and it has the unique advantage when dealing with sequential tasks. To this end, this article discusses knowledge tracing from the following four aspects. Firstly, the article briefly analyzes the characteristics and limitations of traditional knowledge tracing models. Then, through taking the development process of in-depth knowledge tracing as the main line, it summarizes knowledge tracing models based on recurrent neural networks, memory augmented neural networks, graph neural networks and their improved models; and categorizes and organizes the existing models in this field according to methods and strategies. Besides, this article sorts out the public data sets and model evaluation indicators which can be used by researchers, compares and analyzes the characteristics of different modeling methods. Finally, it discusses and prospects the future development direction of knowledge tracing based on deep learning, and lays the foundation for further in-depth knowledge tracing research.

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    IPv6 Addressing Algorithm Using Prefix Feature
    HUANG Ping, LIU Xinlin, SUN Fengjie
    Computer Engineering and Applications    2022, 58 (11): 107-116.   DOI: 10.3778/j.issn.1002-8331.2012-0529
    Abstract32)      PDF(pc) (1230KB)(231)       Save
    With the development of the Internet and the expansion of the application of IPv6, the IP addressing engine must meet the three characteristics of high bandwidth, low search latency, and large capacity. However, the existing methods cannot simultaneously meet the above requirements. Therefore, a new IPv6 addressing algorithm is proposed here, which uses the prefix feature to construct a data structure to meet future application requirements. According to the prefix length distribution and density, it clusters them into clusters with similar characteristics, and then encodes them in a hybrid dictionary tree. The resulting data structure with memory efficiency and scalability can be stored in a low-latency memory, and allows parallelization and pipelining of the traversal process in order to support high bandwidth on the hardware. Experimental results show that the proposed algorithm reduces the amount of memory required for each prefix by 87%. In addition, when implemented on the most advanced field programmable gate array, the architecture can support processing 588 million packets per second.
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    Intelligent Analysis of Text Information Disclosure of Listed Companies
    LYU Pin, WU Qinjuan, XU Jia
    Computer Engineering and Applications    2021, 57 (24): 1-13.   DOI: 10.3778/j.issn.1002-8331.2106-0270
    Abstract287)      PDF(pc) (724KB)(229)       Save

    The analysis of the text disclosure issued by listed companies is an important way for investors to understand the companies’ operating conditions and to make investment decisions. However, the method based on manual reading and analysis has low efficiency and high cost. The development of artificial intelligence technology provides an opportunity for intelligent analysis of companies’ text information, which can mine valuable information from massive enterprise text data, fulfill the advantages of data-driven, and greatly improve the analysis efficiency. Hence, it has become a research hotspot in recent years. The research work in recent ten years about the announcement of listed companies is summarized from three aspects:the event types of the text information disclosure, intelligent analysis method and application scenario. The current challenges in this field are also discussed, and possible future research directions according to the existing shortcomings are finally pointed out.

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    Multi-channel Attention Mechanism Text Classification Model Based on CNN and LSTM
    TENG Jinbao, KONG Weiwei, TIAN Qiaoxin, WANG Zhaoqian, LI Long
    Computer Engineering and Applications    2021, 57 (23): 154-162.   DOI: 10.3778/j.issn.1002-8331.2104-0212
    Abstract348)      PDF(pc) (844KB)(227)       Save

    Aiming at the problem that traditional Convolutional Neural Network(CNN) and Long Short-Term Memory (LSTM) can not reflect the importance of each word in the text when extracting features, this paper proposes a multi-channel text classification model based on CNN and LSTM. Firstly, CNN and LSTM are used to extract the local information and context features of the text; secondly, multi-channel attention mechanism is used to extract the attention score of the output information of CNN and LSTM; finally, the output information of multi-channel attention mechanism is fused to achieve the effective extraction of text features and focus attention on important words. Experimental results on three public datasets show that the proposed model is better than CNN, LSTM and their improved models, and can effectively improve the effect of text classification.

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    Survey of Blockchain Key Technologies and Existing Problems
    LIU Shuangyin, LEI Moyixi, WANG Lu, SUN Chuanheng, XU Longqin, CAO Liang, FENG Dachun, ZHENG Jianhua, LI Jingbin
    Computer Engineering and Applications    2022, 58 (3): 66-82.   DOI: 10.3778/j.issn.1002-8331.2107-0404
    Abstract108)      PDF(pc) (1090KB)(221)       Save
    Blockchain is a distributed database technology developed on the basis of digital encryption and comparison. The blockchain system has the characteristics of decentralized,non-tampering,high autonomy,distributed consensus,etc. It provides a solution to the problem of distributed consistency without third-party supervision. With the rapid development of blockchain technology,blockchain has become more popular in the application field of weak-trust platforms,but it also faces the challenges of its own system vulnerabilities and security attacks. This article starts with the research background of the blockchain and the development trend of vulnerabilities,summarizes and analyzes the key technical principles of the blockchain and its advantages and disadvantages,the technical vulnerabilities and security attacks that exist in the blockchain system,and summarizes and categorizes the types of technical vulnerabilities and vulnerability attacks. It points out that grammatical error,environmental configuration and graphical interface errors are the top three vulnerabilities in the blockchain system. Vulnerability attacks pose a greatest security threat to the blockchain system. It must be paid attention to and prevented in order to protect the future blockchain system. It also provides reference for technological improvement and development.
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    Survey of Research on Neural Network Verification and Testing Technology
    LI Duo, DONG Chaoqun, SI Pinchao, HE Man, LIU Qianchao
    Computer Engineering and Applications    2021, 57 (22): 53-67.   DOI: 10.3778/j.issn.1002-8331.2106-0342
    Abstract93)      PDF(pc) (811KB)(213)       Save

    Neural network technology has made remarkable achievements in the fields of image processing, text analysis and speech recognition. With the application of neural network technology to some security related fields, how to ensure the quality of these software applications is particularly important. Software based on neural network technology is essentially different from traditional software in development and programming. Traditional testing technology is difficult to be directly applied to this kind of software. It is necessary to study the verification and testing evaluation technology for neural network. To effectively evaluate and test neural networks, this paper reviews the research status of neural network verification and testing technology, summarizes and classifies the verification technology, testing technology based on coverage, testing technology based on adversarial sample, and fusing traditional testing technology. The basic idea and implementation of some key technologies are briefly introduced, and some testing frameworks and tools are listed. The challenges of neural network verification and testing are summarized, which can provide reference for researchers in this field.

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    Adaptive Enhancement Algorithm for Non-uniform Illumination Images
    TANG Zilin, LIU Xiang, ZHANG Xing
    Computer Engineering and Applications    2021, 57 (21): 216-223.   DOI: 10.3778/j.issn.1002-8331.2010-0368
    Abstract95)      PDF(pc) (9455KB)(212)       Save

    An adaptive image enhancement algorithm for non-illumination is proposed. According to the center surround method, using Gaussian kernel convoluted repeatedly to extract light of scene. Meanwhile, the size of the low-brightness area in scene is calculated. An adaptive Gamma correction function is constructed to take the ratio of light to the median brightness value in the low-brightness area as a parameter to correct the image. It is greater than 1 in high-light area, algorithm inhibits the brightness, and it is less than 1 in low-light area parameter, algorithm enhances the brightness. Top-Hat transformation is added to the result of Gamma correction. The Top-Hat transformation enhances the overall contrast of the image and the Gamma function preserves detail information. Visual perception and objective experimental indicators show that compared with the known algorithm, the proposed algorithm has a significant effect on non-uniform illumination images.

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    Small Object Detection Algorithm Based on Multiscale Receptive Field Fusion
    LI Chenghao, ZHANG Jing, HU Li, XIAO Xianpeng, ZHANG Hua
    Computer Engineering and Applications    2022, 58 (12): 177-182.   DOI: 10.3778/j.issn.1002-8331.2101-0009
    Abstract60)      PDF(pc) (1337KB)(212)       Save
    Aiming at the problem of low detection accuracy of general object detection algorithm in small target detection, a small object detection algorithm S-RetinaNet based on multi-scale receptive field fusion is proposed. The algorithm uses residual neural network (ResNet) to extract image features, uses recursive feature pyramid network(RFPN) to fuse features, and processes three outputs of RFPN by multiscale receptive field fusion(MRFF) to improve the ability of small target detection. Experimental results show that, compared with the original RetinaNet algorithm, the mean Average Precision(mAP) of S-RetinaNet algorithm on PASCAL VOC dataset and the average precision(AP) of MS COCO dataset are improved by 2.3 and 1.6 percentage points respectively, and the average precision small(APS) of small object detection accuracy is improved more significantly, increased by 2.7 percentage points.
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    Multi-Scale Transformer Lidar Point Cloud 3D Object Detection
    SUN Liujie, ZHAO Jin, WANG Wenju, ZHANG Yusen
    Computer Engineering and Applications    2022, 58 (8): 136-146.   DOI: 10.3778/j.issn.1002-8331.2109-0489
    Abstract104)      PDF(pc) (1383KB)(208)       Save
    Point cloud 3D object detection has low detection accuracy for small objects such as pedestrians and bicycles, which is easy to miss detection and false detection. A 3D object detection method MSPT-RCNN(multi-scale point transformer-RCNN) based on multi-scale point cloud transformer is proposed to improve the detection accuracy of point cloud 3D objects. The method consists of two stages, the first stage(RPN) and the second stage(RCNN). In RPN stage, point cloud features are extracted through multi-scale transformer network, which includes multi-scale neighborhood embedding module and jump connection offset attention module to obtain multi-scale neighborhood geometric information and different levels of global semantic information, and generate high-quality initial 3D bounding box. In the RCNN stage, the multi-scale neighborhood geometric information of point cloud in the bounding box is introduced to optimize the position, size, orientation and confidence of the bounding box. The experimental results show that this method(MSPT-RCNN) has high detection accuracy, especially for distant and small objects. MSPT-RCNN can effectively improve the accuracy of 3D object detection by effectively learning the multi-scale geometric information in point cloud data and extracting different levels of effective semantic information.
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    Epidermal Cell Image Recognition Research of Improved EfficientNet
    WANG Yiding, YAO Yi, LI Yaoli, CAI Shaoqing, YUAN Yuan
    Computer Engineering and Applications    2022, 58 (11): 200-208.   DOI: 10.3778/j.issn.1002-8331.2106-0186
    Abstract52)      PDF(pc) (1446KB)(207)       Save
    The amount of microscopic image data of traditional Chinese medicinal materials powder is small, and there are certain differences in features and shapes of different production areas and different collection environments. The traditional image classification methods have poor recognition results under cross-database condition. To solve the above problems, it proposes an improved method of deep convolutional neural network based on multi-channel fusion and SPP structure. First, it combines feature maps obtained by Canny edge detection and local binary pattern with original image to form a five-channel image and then it is sent to the network, in order to expand the data width of the network input; second, it embeds the improved SPP module in the EfficientNet network, in order to increase the depth of the network. The above methods can make the network pay more attention to the deep texture information of image, so that it is not affected by the collection environment, etc. , and solves the problem of cross-database recognition. The experimental results show that for two different batches of microscopic images of the epidermal cells of Chinese medicinal materials of 26 kinds, using library 1 as the training set and library 2 as the test set, the accuracy rate has increased by 2.7 percentage points to 81.5%, which proves the proposed research method has certain advantages for the task of classification of microscopic images of traditional Chinese medicinal materials under cross-database condition.
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    MTICA-AEO-SVR Model for Stock Price Forecasting
    DENG Jiali, ZHAO Fengqun, WANG Xiaoxia
    Computer Engineering and Applications    2022, 58 (8): 257-263.   DOI: 10.3778/j.issn.1002-8331.2108-0433
    Abstract81)      PDF(pc) (2491KB)(206)       Save
    In order to improve the stability and separation efficiency of traditional Fast ICA algorithm, a new nonlinear function based on Tukey M estimation is constructed in this paper, and then a MTICA algorithm is obtained. Furthermore, a novel MTICA-AEO-SVR model for stock price forecasting is established combining MTICA and SVR algorithms. Firstly, the original stock data is decomposed into independent components by MTICA algorithm for sorting and denoising, and then different SVR models are selected to predict the independent components and the stock price respectively. Artificial ecosystem optimization is introduced into the SVR algorithm to select parameters, as to improve the model prediction accuracy. The empirical results of the Shanghai B-share index show that MTICA-AEO-SVR model is more accurate and efficient than ICA-AEO-SVR model and ICA-SVR model in stock price prediction.
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    Lightweight Multi-scale Attention Fusion Algorithm for License Plate Detection
    ZHANG Shihao, YANG Xiujun, WU Linhuang, CHEN Pingping
    Computer Engineering and Applications    2021, 57 (22): 208-214.   DOI: 10.3778/j.issn.1002-8331.2012-0022
    Abstract103)      PDF(pc) (1467KB)(201)       Save

    License plate recognition technology plays an important role in traffic management, among which license plate detection has a significant impact on the subsequent recognition performance. The existing license plate detection system is easy to be interfered by external environment and has poor detection performance in natural scenes. In this paper, a license plate detection network model based on multi-scale attention fusion is proposed. The pyramid network feature map and CBAM(Convolutional Block Attention Module) attention structure are used to improve the detection accuracy of small targets. At the same time, this method can not only accurately detect and locate the license plate under natural scenes, but also accurately locate the four corners of the license plate, which is beneficial to the subsequent application of license plate recognition. In the experiment, the CCPD data set is amplified by data enhancement method, which effectively alleviates the influence of complex environment changes on license plate detection and enhances the robustness of the model. By training and testing the model, the average accuracy rate of 98.05% and recall rate of 98.71% are obtained, which are better than other license plate detection methods. Moreover, the frame rate reaches 64 frame/s and the real-time performance is high.

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    Summary of Intrusion Detection Models Based on Deep Learning
    ZHANG Hao, ZHANG Xiaoyu, ZHANG Zhenyou, LI Wei
    Computer Engineering and Applications    2022, 58 (6): 17-28.   DOI: 10.3778/j.issn.1002-8331.2107-0084
    Abstract208)      PDF(pc) (997KB)(192)       Save
    With the continuous in-depth development of deep learning technology, intrusion detection model based on deep learning has become a research hotspot in the field of network security. This paper summarizes the commonly used data preprocessing operations in network intrusion detection. The popular intrusion detection models based on deep learning, such as convolutional neural network, long short-term memory network, auto-encode and generative adversarial networks, are analyzed and compared. The data sets commonly used in the research of intrusion detection model based on deep learning are introduced. It points out the problems of the existing intrusion detection models based on deep learning in data set timeliness, real-time, universality, model training time and other aspects, and the possible research focus in the future.
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    Improved Leukocyte Detection Algorithm of YOLOv5
    WANG Jing, SUN Ziyun, GUO Ping, ZHANG Longmei
    Computer Engineering and Applications    2022, 58 (4): 134-142.   DOI: 10.3778/j.issn.1002-8331.2107-0332
    Abstract171)      PDF(pc) (1380KB)(191)       Save
    Aiming at the problems of low accuracy and poor effect caused by small white blood cell data samples, small difference between classes and small target size, this paper proposes a white blood cell detection algorithm YOLOv5-CHE based on improved YOLOv5. Firstly, coordinate attention mechanism is added to the convolutional layer of the backbone feature extraction network to improve the feature extraction capability of the algorithm. Secondly, the purpose of using four-scale feature detection and reacquiring anchor point frame is to increase the detection scale of shallow layer and improve the recognition accuracy of small targets. Finally, the purpose of changing the bounding box regression loss function is to improve the accuracy of check box detection. Experimental results show that the mean average precision(mAP), precision and recall of YOLOv5-CHE are improved by 3.8 percentage points, 1.8 percentage points and 1.5 percentage points in comparison with the benchmark YOLOv5 algorithm, respectively, which shows that the proposed algorithm is effective for leukocyte detection.
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    Field Weed Identification Method Based on Deep Connection Attention Mechanism
    SHU Yali, ZHANG Guowei, WANG Bo, XU Xiaokang
    Computer Engineering and Applications    2022, 58 (6): 271-277.   DOI: 10.3778/j.issn.1002-8331.2108-0077
    Abstract101)      PDF(pc) (1037KB)(174)       Save
    In order to achieve fast and accurate recognition of field weed images, a field weed recognition model based on deep connected attention mechanism residual network(DCECA-Resnet50-a) is proposed. Using the residual network as a benchmark, this paper improves the position of residual block downsampling, introduces the attention mechanism and connected attention mechanism modules to better extract the feature information in the images, combines the migration learning strategy to alleviate the overfitting phenomenon caused by small sample data sets, improves the generalization of the model and greatly reduces the training time of the model. The experimental results show that the improved model has the best overall performance and high recognition accuracy, with 96.31% accuracy for weeds and fewer model parameters, and achieves the accurate differentiation of four types of common weeds in pea fields, namely, silverleaf daisy, chaparral, matang and pigweed, which provides a corresponding reference for small sample data in the field of agricultural recognition.
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    Ranking Algorithms of Vital Nodes Based on Spring Model
    MENG Yuyu, WANG Xiao, YAN Guanghui, LUO Hao, YANG Bo, ZHANG Lei, WANG Qiong
    Computer Engineering and Applications    2022, 58 (7): 77-86.   DOI: 10.3778/j.issn.1002-8331.2103-0204
    Abstract82)      PDF(pc) (1393KB)(172)       Save
    Node ranking of vital nodes is an important problem in complex networks. When using the robustness and vulnerability of the network to evaluate the node ranking algorithms gravity model (GM) and local gravity model (LGM) based on the gravity model, once the nodes with large degrees have been removed from the network, the removal of neighbors with large gravitational values usually cannot largely affect the structure and function of the network, which shows that the algorithms still have some improvement in the ranking accuracy of vital nodes. Because of that, inspired by the spring model, further considering neighbors’ information and path information in the network, combined with the network diameter, spring model(SM) and local spring model(LSM), the node ranking algorithm and its local algorithm, are proposed. The results show that the SM algorithm and the LSM algorithm have higher accuracy for the ranking of vital nodes than other classical algorithms in synthetic networks and real networks. Especially, the SIR epidemic experiments on the Power network are conducted to furtherly verify the higher rationality and effectiveness of the SM algorithm than other algorithms.
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    Computer Engineering and Applications    2021, 57 (22): 0-0.  
    Abstract146)      PDF(pc) (647KB)(171)       Save
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    Research of Deep Learning-Based Semantic Segmentation for 3D Point Cloud
    WANG Tao, WANG Wenju, CAI Yu
    Computer Engineering and Applications    2021, 57 (23): 18-26.   DOI: 10.3778/j.issn.1002-8331.2107-0142
    Abstract185)      PDF(pc) (682KB)(171)       Save

    This paper summarizes the methods of deep learning-based semantic segmentation for 3D point cloud. The literature research method is used to describe deep learning-based semantic segmentation methods for 3D point cloud according to the representation of data. It discusses the current situation of domestic and foreign development in recent years, and analyzes the advantages and disadvantages of the current related methods, and prospects the future development trend. Deep learning plays an extremely important role in the research of semantic segmentation technology for point cloud, and promotes the manufacturing, packaging fields and etc to development in the direction of intelligence. According to the advantages and disadvantages of various methods, it is an important research direction to construct a framework model of semantic segmentation combined with 2D-3D for projection, voxel, multi-view and point cloud in the future.

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    Advances of Age Progression Based Cross-Age Face Recognition
    LI Junyao, LI Zhihui, XIE Lanchi, HOU Xinyu, YE Dong
    Computer Engineering and Applications    2021, 57 (24): 27-38.   DOI: 10.3778/j.issn.1002-8331.2107-0043
    Abstract147)      PDF(pc) (1319KB)(171)       Save

    Cross-age face recognition is one of the most difficult problems in face recognition at present. Face features will change with age, resulting in a decrease in recognition accuracy. The face recognition based on the generated aging images obtained from aging models is a popular solution for this problem. With the widespread application of computer technology and deep learning, the authenticity, aging effect, and algorithm efficiency of face aging have been significantly improved. This paper systematically reviews the current research status of cross-age face recognition based on aging models. The aging methods are sorted out in detail, the method evolution of aging models and the advantages and disadvantages of various methods are systematically introduced, and the existing model evaluation methods are summarized. In addition, the existing datasets that can be used for cross-age face recognition are introduced in detail, and a comparative analysis is made in terms of data volume, age span, age accuracy, and use of the data set. For the purpose of practical applications, the problems to be solved in cross-age face recognition based on the aging model are analyzed and discussed. Moreover, the future research directions are predicted and prospected.

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    Temporal and Spatial Fusion of Remote Sensing Images:A Review
    YANG Guangqi, LIU Hui, ZHONG Xiwu, CHEN Long, QIAN Yurong
    Computer Engineering and Applications    2022, 58 (10): 27-40.   DOI: 10.3778/j.issn.1002-8331.2111-0131
    Abstract86)      PDF(pc) (1548KB)(169)       Save
    Big data of remote sensing image with high temporal and spatial resolution plays an important role in remote sensing field. However, due to technique and budget constraints, a single satellite sensor cannot acquire remote sensing images with both high spatial resolution and high temporal resolution. Therefore, the temporal and spatial fusion technology of remote sensing image is regarded as one of the effective ways to solve the tradeoff between temporal resolution and spatial resolution. With the wide application of deep learning in various fields, deep learning technology has been proved to be a very effective method to solve image problems. According to the research results of scholars at home and abroad, the classical algorithm of remote sensing image spatiotemporal fusion is comprehensively summarized. Meanwhile, the research results of remote sensing image spatiotemporal fusion algorithm based on deep learning are analyzed, which are replicated on three datasets and the experimental results are analyzed, and the future of remote sensing image spatiotemporal fusion is prospected.
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    Aspect-Level Sentiment Analysis Model Incorporating Multi-layer Attention
    YUAN Xun, LIU Rong, LIU Ming
    Computer Engineering and Applications    2021, 57 (22): 147-152.   DOI: 10.3778/j.issn.1002-8331.2104-0019
    Abstract189)      PDF(pc) (699KB)(168)       Save

    Aspect sentiment analysis aims to analyze the sentiment polarity of a specific aspect in a given text. In order to solve the problem of insufficient introduction of aspect emotional attention in current research methods, this paper proposes an aspect level emotion classification model based on the fusion of BERT and Multi-Layer Attention(BMLA). Firstly, the model extracts the multi-layer aspect emotional attention information from the inner part of BERT, and designs the multi-layer aspect attention by fusing the encoded aspect information with the representation vector of the hidden layer of BERT, then cascades the multi-layer aspect attention with the encoded output text, finally enhances the long dependency relationship between sentences and aspect words. Experiments on the SemEval2014 Task4 and the AI Challenger 2018 datasets show that the proposed model is effective to enhance the weight of the target aspect and interact in context for aspect sentiment classification.

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