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    Comparative Study of Several New Swarm Intelligence Optimization Algorithms
    LI Yali, WANG Shuqin, CHEN Qianru, WANG Xiaogang
    Computer Engineering and Applications    2020, 56 (22): 1-12.   DOI: 10.3778/j.issn.1002-8331.2006-0291
    Abstract961)      PDF(pc) (972KB)(566)       Save

    With the development of computer technology, algorithm technology is constantly and alternately being updated. In recent years, swarm intelligence algorithm has become more and more popular and received extensive attention and research, and has made progress in such fields as machine learning, process control and engineering prediction. Swarm intelligence optimization algorithm is a biological heuristic method, which is widely used in solving optimization problems. The traditional swarm intelligence algorithm provides some new ideas for solving some practical problems, but it also exposes some shortcomings in some experiments. In recent years, many scholars have proposed many new types of intelligent optimization algorithms. This paper selects the more typical swarm intelligence algorithms at home and abroad in recent years, such as Bat Algorithm(BA), Grey Wolf Optimization Algorithm(GWO), Dragonfly Algorithm(DA), Whale Optimization Algorithm(WOA), Grasshopper Optimization Algorithm(GOA) and Sparrows Search Algorithm(SSA), and further compares the experimental performance of these algorithms and the development potential by 22 standard CEC test functions from the convergence speed and accuracy, stability and so on, and the refinement analysis is carried out to compare and analyze the relevant improvement methods. Finally, the characteristics of swarm intelligence optimization algorithm are summarized and its development potential is discussed.

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    Survey of Small Object Detection Algorithms Based on Deep Learning
    LIU Yang, ZHAN Yinwei
    Computer Engineering and Applications    2021, 57 (2): 37-48.   DOI: 10.3778/j.issn.1002-8331.2009-0047
    Abstract915)      PDF(pc) (959KB)(917)       Save

    With the development of artificial intelligence technology, deep learning technology has been widely used in face recognition, pedestrian detection, unmanned driving and other fields. As one of the most basic and challenging problems in machine vision, object detection has attracted extensive attention in recent years. Aiming at the problem of object detection, especially small object detection, this paper summarizes the common data sets and performance evaluation metrics, and compares the characteristics, advantages and difficulties of various common data sets. At the same time, this paper systematically summarizes the common object detection methods and the challenges faced by small object detection. In addition, combing the latest work based on deep learning, this paper introduces the multi-scale and super-resolution small object detection methods in the highlight and presents the lightweight strategy and the performance of some lightweight models based on the object detection. Finally, this paper summarizes the characteristics, advantages and limitations of various methods, and looks at the future development direction of small object detection method based on deep learning.

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    Survey of Network Traffic Forecast Based on Deep Learning
    KANG Mengxuan, SONG Junping, FAN Pengfei, GAO Bowen, ZHOU Xu, LI Zhuo
    Computer Engineering and Applications    2021, 57 (10): 1-9.   DOI: 10.3778/j.issn.1002-8331.2101-0402
    Abstract900)      PDF(pc) (711KB)(820)       Save

    Precisely predicting the trend of network traffic changes can help operators accurately predict network usage, correctly allocate and efficiently use network resources to meet the growing and diverse user needs. Taking the progress of deep learning algorithms in the field of network traffic prediction as a clue, this paper firstly elaborates the evaluation indicators of network traffic prediction and the current public network traffic data sets. Secondly, this paper specifically analyzes four deep learning methods commonly used in network traffic prediction:deep belief networks, convolutional neural network, recurrent neural network, and long short term memory network, and focuses on the integrated neural network models used in recent years for different problems. The characteristics and application scenarios of each model are summarized. Finally, the future development of network traffic forecast is prospected.

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    Improved Algorithm of RetinaFace for Natural Scene Mask Wear Detection
    NIU Zuodong, QIN Tao, LI Handong, CHEN Jinjun
    Computer Engineering and Applications    2020, 56 (12): 1-7.   DOI: 10.3778/j.issn.1002-8331.2002-0402
    Abstract786)      PDF(pc) (1216KB)(816)       Save

    The 2019-nCoV can be transmitted through airborne droplets, aerosols, and other carriers. Correctly wearing a mask in public places can effectively prevent the infection of the virus. A face mask wearing detection method in a natural scene is proposed. The RetinaFace algorithm is improved to add the task of face mask wearing detection by optimizing the loss function. An improved self-attention mechanism is introduced into the feature pyramid network to enhance the expressive ability of the feature map. A data set containing 3, 000 pictures is created and manually annotated for network training. The experimental results show that the algorithm can effectively detect the wearing of masks, and has achieved good detection results in natural scene videos.

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    Survey of Intelligent Question Answering Research Based on Knowledge Graph
    WANG Zhiyue, YU Qing, WANG Nan, WANG Yaoguo
    Computer Engineering and Applications    2020, 56 (23): 1-11.   DOI: 10.3778/j.issn.1002-8331.2004-0370
    Abstract785)      PDF(pc) (774KB)(1043)       Save

    The answer selection model based on knowledge graph has become one of the hottest directions at present. This paper introduces the implementation of answer selection model based on knowledge graph from four aspects of template method, semantic parsing, deep learning and knowledge graph embedding, sums up their advantages, disadvantages and unsolved problem. Combined with the development of artificial intelligence technology, this paper introduces intelligent question-answer system based on deep learning. This research is helpful for more researchers to devote themselves to the intelligent question-answer system and develops different kinds of intelligent question-answer system to improve the social intelligent information service.

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    Survey of Multimodal Deep Learning
    SUN Yingying, JIA Zhentang, ZHU Haoyu
    Computer Engineering and Applications    2020, 56 (21): 1-10.   DOI: 10.3778/j.issn.1002-8331.2002-0342
    Abstract757)      PDF(pc) (769KB)(731)       Save

    Modal refers to the way people receive information, including hearing, vision, smell, touch and other ways. Multimodal learning refers to learning better feature representation by using the complementarity between multimodes and eliminating the redundancy between them. The purpose of multimodal learning is to build a model that can deal with and correlate information from multiple modes. It is a dynamic multidisciplinary field, with increasing importance and great potential. At present, the popular research direction is multimodal learning among image, video, audio and text. This paper focuses on the application of multimodality in audio-visual speech recognition, image and text emotion analysis, collaborative annotation and other practical levels, as well as the application in the core level of matching and classification, alignment representation learning, and gives an explanation for the core issues of multimodal learning:matching and classification, alignment representation learning. Finally, the common data sets in multimodal learning are introduced, and the development trend of multimodal learning in the future is prospected.

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    Survey of Data Fusion Based on Deep Learning
    ZHANG Hong, CHENG Chuanqi, XU Zhigang, LI Jianhua
    Computer Engineering and Applications    2020, 56 (24): 1-11.   DOI: 10.3778/j.issn.1002-8331.2007-0475
    Abstract724)      PDF(pc) (683KB)(1045)       Save

    As data fusion is the key to maximize the value of big data, while deep learning is a technical tool for mining deep characteristic information of data, data fusion based on deep learning can fully tap the potential value of big data, thus expanding the exploration and understanding of the world to a new depth and breadth. And this paper learns the advantages of deep learning in data fusion by reviewing the literature related to data fusion based on deep learning in recent years. The common data-fusion methods are classified, the advantages and disadvantages of which are pointed out. Analysis is conducted on data fusion method based on deep learning from three perspectives, namely the data fusion method extracted based on features of deep learning, data fusion method based on deep learning fusion and data fusion method based on the whole process of deep learning, and corresponding comparisons and summaries are conducted as well. This paper summarizes the whole document, discusses the difficulties in the application of deep learning in data fusion and the problems which require further research in the future.

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    Research Progress of Multi-label Text Classification
    HAO Chao, QIU Hangping, SUN Yi, ZHANG Chaoran
    Computer Engineering and Applications    2021, 57 (10): 48-56.   DOI: 10.3778/j.issn.1002-8331.2101-0096
    Abstract722)      PDF(pc) (906KB)(688)       Save

    As a basic task in natural language processing, text classification has been studied in the 1950s. Now the single-label text classification algorithm has matured, but there is still a lot of improvement on multi-label text classification. Firstly, the basic concepts and basic processes of multi-label text classification are introduced, including data set acquisition, text preprocessing, model training and prediction results. Secondly, the methods of multi-label text classification are introduced. These methods are mainly divided into two categories:traditional machine learning methods and the methods based on deep learning. Traditional machine learning methods mainly include problem transformation methods and algorithm adaptation methods. The methods based on deep learning use various neural network models to handle multi-label text classification problems. According to the model structure, they are divided into multi-label text classification methods based on CNN structure, RNN structure and Transfomer structure. The data sets commonly used in multi-label text classification are summarized. Finally, the future development trend is summarized and analyzed.

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    Research Status and Future Analysis of Capsule Neural Network
    HE Wenliang, ZHU Minling
    Computer Engineering and Applications    2021, 57 (3): 33-43.   DOI: 10.3778/j.issn.1002-8331.2009-0209
    Abstract697)      PDF(pc) (890KB)(549)       Save

    Nowadays, the rapid development of artificial intelligence technology has promoted the great progress of society. As an important part of artificial intelligence, deep learning has a very broad application prospect. In recent years, more and more experts and scholars have begun to study the related technologies in the field of deep learning. Two typical directions are natural language processing and computer vision. Among them, the development of computer vision strongly leads the progress in the field of deep learning. The application of convolutional neural network and a new neural network model in deep learning?capsule network and its dynamic routing algorithm are introduced, and their advantages and disadvantages are compared. Then, the application of capsule network is reviewed, and the application fields and advantages of capsule network are described in terms of image and text. Finally, the paper summarizes and looks forward to the possible improvement direction of capsule network.

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    Review of Pre-training Models for Natural Language Processing
    YU Tongrui, JIN Ran, HAN Xiaozhen, LI Jiahui, YU Ting
    Computer Engineering and Applications    2020, 56 (23): 12-22.   DOI: 10.3778/j.issn.1002-8331.2006-0040
    Abstract602)      PDF(pc) (689KB)(597)       Save

    In recent years, deep learning technology has been advancing, pre-training technology for deep learning brings natural language processing into a new era. Pre-training model aims to how to make pre-trained model stay in good initial state and achieve better performances in subsequent downstream tasks. This paper firstly introduces pre-training technology and its development history. And then, this paper further classifies it into the following two types, namely probability-statistics-based traditional model and deep-learning-based new model, according to different features of pre-training models to conduct corresponding detailed introductions. Firstly, it briefly analyzes the characteristics and limitations of today’s pre-training models and highlights the existing deep-learning-based pre-training models. And based on their performances in downstream tasks, it gives necessary comparisons and assessments accordingly. Finally, it combs out a series of whole-new pre-training models with instructive significances,briefly describes corresponding feasible improvement mechanisms and the performance enhancements achieved in downstream tasks, summarizes the problems existing therein, as well as prospectes its development trend in near future.

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    Review of Application Prospect of Deep Reinforcement Learning in Intelligent Manufacturing
    KONG Songtao, LIU Chichi, SHI Yong, XIE Yi, WANG Kun
    Computer Engineering and Applications    2021, 57 (2): 49-59.   DOI: 10.3778/j.issn.1002-8331.2008-0431
    Abstract601)      PDF(pc) (982KB)(1030)       Save

    As the latest development of machine learning, deep reinforcement learning has been shown in many application fields. The algorithm research and application research of deep reinforcement learning have produced many classical algorithms and typical application fields. The application of deep reinforcement learning in industrial manufacturing can realize high level control in complex environment. First of all, the research on deep reinforcement learning is summarized, and the basic principles of deep reinforcement learning are introduced, including deep learning and reinforcement learning. Then, the paper introduces the theoretical methods of the application of deep reinforcement learning algorithm. On this basis, it classifies the algorithms of deep reinforcement learning, respectively introduces the reinforcement learning algorithm based on value function and the reinforcement learning algorithm based on strategy gradient, and lists the main development results of these two kinds of algorithms, as well as other related research results. Then, the typical applications of deep reinforcement learning in industrial manufacturing are classified and analyzed. Finally, the existing problems and future development direction of deep reinforcement learning are discussed.

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    Analysis on Application of Machine Learning in Stock Forecasting
    XU Haoran, XU Bo, XU Kewen
    Computer Engineering and Applications    2020, 56 (12): 19-24.   DOI: 10.3778/j.issn.1002-8331.2001-0353
    Abstract586)      PDF(pc) (892KB)(829)       Save

    It has always been regarded as the emphasis of research to reveal the operation law of stock market. In recent years, machine learning method has made good progress in stock forecasting, and it has shown unique advantages over traditional methods such as fundamental analysis and technical analysis. This paper focuses on collecting the key references in the field of stock prediction that uses machine learning methods in recent years, and analyzing as well as summarizing feature engineering, the application of machine learning algorithms and the main problems in stock prediction research. The characteristics and shortcomings of each algorithm in application are reviewed, and future development direction of this field is made a thorough analysis and forecasted from the aspects of transfer learning, feature engineering and deep learning model fusion.

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    Overview of Image Denoising Methods Based on Deep Learning
    LIU Di, JIA Jinlu, ZHAO Yuqing, QIAN Yurong
    Computer Engineering and Applications    2021, 57 (7): 1-13.   DOI: 10.3778/j.issn.1002-8331.2011-0341
    Abstract556)      PDF(pc) (1139KB)(523)       Save

    Image denoising is a kind of technology that uses the context information of image sequence to remove noise and restore clear image. It is one of the important research contents in the field of computer vision. With the development of machine learning, deep learning has been widely used in the field of image denoising, and has become an effective solution for image denoising. Firstly, the deep learning image denoising method is analyzed. Secondly, the idea of image denoising method is analyzed in detail according to the network structure, and the advantages and disadvantages are summarized. Then, through the experimental results on DND, PolyU and other data sets, the performance of deep learning based image denoising methods is compared and analyzed. Finally, the key issues of image denoising research are summarized, and the future development trend of the research of this field is discussed.

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    Review of Development and Application of Artificial Neural Network Models
    ZHANG Chi, GUO Yuan, LI Ming
    Computer Engineering and Applications    2021, 57 (11): 57-69.   DOI: 10.3778/j.issn.1002-8331.2102-0256
    Abstract537)      PDF(pc) (781KB)(665)       Save

    Artificial neural networks are increasingly closely related to other subject areas. People solve problems in various fields by exploring and improving the layer structure of artificial neural networks. Based on the analysis of artificial neural networks related literature, this paper summarizes the history of artificial neural network growth and presents relevant principles of artificial neural networks based on the development of neural networks, including multilayer perceptron, back-propagation algorithm, convolutional neural network and recurrent neural network, explains the classic convolutional neural network model in the development of the convolutional neural network and the widely used variant network structure in the recurrent neural network, reviews the application of each artificial neural network algorithm in related fields, summarizes the possible direction of development of the artificial neural network.

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    Progress on Deep Reinforcement Learning in Path Planning
    ZHANG Rongxia, WU Changxu, SUN Tongchao, ZHAO Zengshun
    Computer Engineering and Applications    2021, 57 (19): 44-56.   DOI: 10.3778/j.issn.1002-8331.2104-0369
    Abstract537)      PDF(pc) (1134KB)(386)       Save

    The purpose of path planning is to allow the robot to avoid obstacles and quickly plan the shortest path during the movement. Having analyzed the advantages and disadvantages of the reinforcement learning based path planning algorithm, the paper derives a typical deep reinforcement learning, Deep Q-learning Network(DQN) algorithm that can perform excellent path planning in a complex dynamic environment. Firstly, the basic principles and limitations of the DQN algorithm are analyzed in depth, and the advantages and disadvantages of various DQN variant algorithms are compared from four aspects:the training algorithm, the neural network structure, the learning mechanism and AC(Actor-Critic) framework. The paper puts forward the current challenges and problems to be solved in the path planning method based on deep reinforcement learning. The future development directions are proposed, which can provide reference for the development of intelligent path planning and autonomous driving.

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    Survey of Breast Cancer Histopathology Image Classification Based on Deep Learning
    LI Hua, YANG Jianeng, LIU Feng, NAN Fangzhe, QIAN Yurong
    Computer Engineering and Applications    2020, 56 (13): 1-11.   DOI: 10.3778/j.issn.1002-8331.2001-0220
    Abstract532)      PDF(pc) (919KB)(879)       Save

    Accurate and efficient histopathological image classification of breast cancer is one of the important contents of computer-aided diagnosis. With the development of machine learning technology, deep learning has gradually become an effective method to classify breast cancer histopathological images. Firstly, the classification methods of breast cancer histopathological image and the existing problems are analyzed. Secondly, four relevant deep learning models are introduced, and the classification methods of breast cancer histopathological image based on deep learning are combed, and the performance of the existing models is compared and analyzed through experiments. Finally, the key issues of histopathological image classification of breast cancer are summarized and the future research trends are discussed.

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    Review of Research on Scene Text Recognition Technology
    WANG Deqing, Wushouer·Silamu, XU Miaomiao
    Computer Engineering and Applications    2020, 56 (18): 1-15.   DOI: 10.3778/j.issn.1002-8331.2004-0333
    Abstract527)      PDF(pc) (1900KB)(534)       Save

    The text detection and recognition technology is introduced in this paper. Firstly, the research background, application fields and technical difficulties of the natural scene character recognition technology are introduced. Secondly, the preprocessing technology and process of scene text recognition are introduced. Then it introduces the universal deep learning detection network based on deep learning, deep learning text detection network based on Uyghur and Chinese and English, deep learning network based on scene character recognition, end to end scene character detection and recognition deep learning network, this paper summarizes the structure features, advantages, limitations, application scenarios and implementation costs of various networks, and then makes a comprehensive analysis. Finally, it introduces the open data sets, the development trend and possible research direction of scene character recognition technology are also discussed.

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    Network Unknown Attack Detection with Deep Learning
    DI Chong, LI Tong
    Computer Engineering and Applications    2020, 56 (22): 109-116.   DOI: 10.3778/j.issn.1002-8331.2003-0353
    Abstract510)      PDF(pc) (753KB)(331)       Save

    A deep learning-based method for network anomaly detection is proposed to discriminate unknown attacks for an intrusion detection system. A confidence-based neural network is adopted to adaptively distinguish the traffic?information of given behaviors and?that of unknown attacks.?The proposed model?is trained to?assign a higher?confidence value to a piece of?traffic?information?from a known behavior and?a lower?confidence value to that?from an?unknown attack. Moreover, an adaptive loss balance strategy and a learning automata-based dynamic regularization strategy are designed?to improve the performance of the model. The proposed model is evaluated in benchmark datasets UNSW-NB15 and CICIDS 2017. Compared with traditional models, the simulation results indicate that the proposed model can detect the unknown attack effectively while preserving an advantageous classification effect for traffic from known attacks.

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    Research Progress of Medical Image Registration Technology Based on Deep Learning
    GUO Yanfen, CUI Zhe, YANG Zhipeng, PENG Jing, HU Jinrong
    Computer Engineering and Applications    2021, 57 (15): 1-8.   DOI: 10.3778/j.issn.1002-8331.2101-0281
    Abstract501)      PDF(pc) (681KB)(465)       Save

    Medical image registration technology has a wide range of application values for lesion detection, clinical diagnosis, surgical planning, and efficacy evaluation. This paper systematically summarizes the registration algorithm based on deep learning, and analyzes the advantages and limitations of various methods from deep iteration, full supervision, weak supervision to unsupervised learning. In general, unsupervised learning has become the mainstream direction of medical image registration research, because it does not rely on golden standards and uses an end-to-end network to save time. Meanwhile, compared with other methods, unsupervised learning can achieve higher accuracy and spends shorter time. However, medical image registration methods based on unsupervised learning also face some research difficulties and challenges in terms of interpretability, cross-modal diversity, and repeatable scalability in the field of medical images, which points out the research direction for achieving more accurate medical image registration methods in the future.

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    Survey of Application of Convolutional Neural Network in Classification of Hyperspectral Images
    WAN Yaling, ZHONG Xiwu, LIU Hui, QIAN Yurong
    Computer Engineering and Applications    2021, 57 (4): 1-10.   DOI: 10.3778/j.issn.1002-8331.2010-0423
    Abstract487)      PDF(pc) (764KB)(488)       Save

    Hyperspectral Imagery(HSI) classification is an important task of hyperspectral image processing and application. With the development of deep learning, Convolutional Neural Network(CNN) has gradually become an effective solution to the classification problem of HSI. Firstly, the task of HSI classification is summarized, and the existing problems are analyzed. Secondly, CNN and its classification methods based on spectral features, spatial features and spatial-spectral features have been systematically sorted out, and the above classification methods are carried out experimental comparison. Finally, the key issues of HSI classification are summarized and future research directions are discussed.

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    Improved Adaptive Parameter DBSCAN Clustering Algorithm
    WANG Guang, LIN Guoyu
    Computer Engineering and Applications    2020, 56 (14): 45-51.   DOI: 10.3778/j.issn.1002-8331.1908-0501
    Abstract481)      PDF(pc) (1344KB)(318)       Save

    Aiming at the problem that traditional DBSCAN algorithm needs to input [Eps] and [MinPts] parameters manually, and improper parameter selection leads to low clustering accuracy, an improved adaptive parameter density clustering algorithm is proposed. Firstly, the kernel density estimation is used to determine the reasonable interval of [Eps] and [MinPts] parameters, and the cluster number is determined by analyzing the local density characteristics of the data. Then, the clustering is performed according to the parameter values within the reasonable interval. Finally, the contour coefficients satisfying the cluster number condition are calculated, and the parameter corresponding to the maximum contour coefficient is the optimal parameter. The comparison experiments on four classical datasets show that the algorithm can automatically select the optimal [Eps] and [MinPts] parameters, and the accuracy is improved by 6.1% on average.

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    Survey of Image Compression Algorithm Based on Deep Learning
    YU Heng, MEI Hongyan, XU Xiaoming, JIA Huiping
    Computer Engineering and Applications    2020, 56 (15): 15-23.   DOI: 10.3778/j.issn.1002-8331.2003-0294
    Abstract481)      PDF(pc) (923KB)(939)       Save

    With the continuous development of deep learning and the explosive growth of image data, how to use deep learning to obtain higher compression ratio and higher quality images has gradually become one of the hot research issues. Through the analysis of the related literatures in recent years, the image compression method based on the deep learning is summarized and analyzed according to the Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Generative Adversarial Network(GAN). This paper enumerates the typical examples, and the image compression algorithm based on depth study of the training data set, commonly used evaluation indexes are introduced, according to the deep learning advantages in the field of image compression for its future development trend are summarized and discussed.

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    Review of Typical Object Detection Algorithms for Deep Learning
    XU Degang, WANG Lu, LI Fan
    Computer Engineering and Applications    2021, 57 (8): 10-25.   DOI: 10.3778/j.issn.1002-8331.2012-0449
    Abstract463)      PDF(pc) (736KB)(436)       Save

    Object detection is an important research direction of computer vision, its purpose is to accurately identify the category and location of a specific target object in a given image. In recent years, the feature learning and transfer learning capabilities of deep convolutional neural networks have made significant progress in target detection algorithm feature extraction, image expression, classification and recognition. This paper introduces the research progress of target detection algorithm based on deep learning, the characteristics of common data sets and the key parameters of performance index evaluation, compares and analyzes the network structure and implementation mode of target detection algorithm formed by two-stage, single-stage and other improved algorithms. Finally, the application progress of the algorithm in the detection of human faces, salient targets, pedestrians, remote sensing images, medical images, and grain insects is described. Combined with the current problems and challenges, the future research directions are analyzed.

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    Topic Discovery and Evolution of Blockchain Literature
    WANG Jinli, FAN Yong, ZHANG Hui
    Computer Engineering and Applications    2020, 56 (20): 1-8.   DOI: 10.3778/j.issn.1002-8331.2005-0359
    Abstract462)      PDF(pc) (1600KB)(405)       Save

    Statistical and visual research on blockchain literature is helpful to discover the research hotspots, research fields and evolution trends of blockchain. This paper obtains 1, 216 important papers by searching the theme of “blockchain” keywords on CNKI. First, this paper makes a statistical analysis of the number of articles published, the institutions that issued the articles, co-occurrence of authors, and citations of important documents, etc. , to understand the current status of blockchain research. Second, this paper uses CiteSpace for visual analysis to show the research hotspots and evolution of blockchain in terms of keywords, time zone distribution, etc. Finally, the paper discusses the blockchain thought, core technology and application fields from three dimensions. Studies have shown that:blockchain literature has shown an explosive trend in recent years, involving a variety of disciplines;hot topics mainly focus on smart contracts, financial technology, digital currency, e-government and other fields;blockchain applications have been extending to multiple disciplines and fields, becoming the main supporting technology of the next generation of information revolution, and promoting the integration and development of emerging technologies.

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    Overview of Chinese Domain Named Entity Recognition
    JIAO Kainan, LI Xin, ZHU Rongchen
    Computer Engineering and Applications    2021, 57 (16): 1-15.   DOI: 10.3778/j.issn.1002-8331.2103-0127
    Abstract457)      PDF(pc) (928KB)(416)       Save

    Named Entity Recognition(NER), as a classic research topic in the field of natural language processing, is the basic technology of intelligent question answering, knowledge graph and other tasks. Domain Named Entity Recognition(DNER) is the domain-specific NER scheme. Drived by deep learning technology, Chinese DNER has made a breakthrough. Firstly, this paper summarizes the research framework of Chinese DNER, and reviews the existing research results from four aspects:the determination of domain data sources, the establishment of domain entity types and specifications, the annotation of domain data sets, and the evaluation metrics of Chinese DNER. Then, this paper summarizes the current common technology framework of Chinese DNER, introduces the pattern matching method based on dictionaries and rules, statistical machine learning method, deep learning method, multi-party fusion deep learning method, and focuses on the analysis of Chinese DNER method based on word vector representation and deep learning. Finally, the typical application scenarios of Chinese DNER are discussed, and the future development direction is prospected.

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    Review on User Privacy Inference and Protection in Social Networks
    PIAO Yangheran, CUI Xiaohui
    Computer Engineering and Applications    2020, 56 (19): 1-12.   DOI: 10.3778/j.issn.1002-8331.2005-0361
    Abstract456)      PDF(pc) (761KB)(401)       Save

    Nowadays, social network platforms such as Weibo and Twitter are widely used to communicate, create online communities, and conduct social activities. The content posted by users can be inferred from a large amount of privacy information, which has led to the rise of privacy inference technology for users in social networks. By using knowledge such as the user’s text content and online behaviors, inference attacks can be performed on users. Social relationship inference and attribute inference are two basic attacks on social network user privacy. The research on the mechanism and method of inference attack protection is also increasing, this paper classifies and summarizes the research and literature related to privacy inference and protection technology. Finally, it discusses and prospects the privacy inference and protection in social networks.

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    Survey on Research Status of Blind-Guiding Robots
    WU Zhaohan, RONG Xuewen, FAN Yong
    Computer Engineering and Applications    2020, 56 (14): 1-13.   DOI: 10.3778/j.issn.1002-8331.2001-0298
    Abstract447)      PDF(pc) (1778KB)(754)       Save

    Blind people are the vulnerable groups in human society, and the number of them is increasing year by year. Providing safe, reliable, intelligent and efficient travel guarantees for the blind is an important sign of social progress. The research results of white-cane-based, wearable, handheld, intelligent-terminal-based and mobile blind-guiding robots are introduced. The research status of the common key technology of environment detection, positioning and navigation, and human-computer interaction of blind-guiding robots is summarized. Finally, the future development trend of blind-guiding robots is forecasted based on the latest technologies such as cloud platform and mobile communication.

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    Review of Text Sentiment Analysis Methods
    WANG Ting, YANG Wenzhong
    Computer Engineering and Applications    2021, 57 (12): 11-24.   DOI: 10.3778/j.issn.1002-8331.2101-0022
    Abstract441)      PDF(pc) (906KB)(509)       Save

    Text sentiment analysis is an important branch of natural language processing, which is widely used in public opinion analysis and content recommendation. It is also a hot topic in recent years. According to different methods used, it is divided into sentiment analysis based on emotional dictionary, sentiment analysis based on traditional machine learning, and sentiment analysis based on deep learning. Through comparing these three methods, the research results are analyzed, and the paper summarizes the advantages and disadvantages of different methods, introduces the related data sets and evaluation index, and application scenario, analysis of emotional subtasks is simple summarized. The future research trend and application field of sentiment analysis problem are found. Certain help and guidance are provided for the researchers in the related areas.

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    Survey of Medical Image Segmentation Algorithm in Deep Learning
    PENG Jing, LUO Haoyu, ZHAO Gansen, LIN Chengchuang, YI Xusheng, CHEN Shaojie
    Computer Engineering and Applications    2021, 57 (3): 44-57.   DOI: 10.3778/j.issn.1002-8331.2010-0335
    Abstract436)      PDF(pc) (1397KB)(835)       Save

    Medical image segmentation is an important application area of computer vision in the medical image processing, its goal is to segment the target area from medical images and provide effective help for subsequent diagnosis and treatment of diseases. Since deep learning technology has made great progress in the image processing, medical image segmentation algorithm based on deep learning has gradually become the focus and hotspot of research in this field. This paper gives a description on the tasks and difficulties of medical image segmentation. Then, it details the deep learning-based medical image segmentation algorithm, classifies and summarizes the current representative methods. Moreover, this paper presents the frequently-used algorithm evaluation indicators and datasets in the field of medical image segmentation. The development of medical image segmentation technology is summarized and forecasted.

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    Survey of Object Detection Algorithms for Deep Convolutional Neural Networks
    HUANG Jian, ZHANG Gang
    Computer Engineering and Applications    2020, 56 (17): 12-23.   DOI: 10.3778/j.issn.1002-8331.2005-0021
    Abstract434)      PDF(pc) (1058KB)(594)       Save

    Object detection is one of the core tasks in computer vision, which is widely used in intelligent video monitoring, automatic monitoring, industrial detection and other fields. In recent years, with the rapid development of deep learning, the object detection algorithm based on deep convolutional neural network has gradually replaced the traditional object detection algorithm and become the mainstream algorithm in this field. Firstly, the common data sets and performance evaluation indexes of the object detection algorithm are introduced, and then the development of the convolutional neural network is introduced. Then, the two-stage object detection algorithm and the single-stage object detection algorithm are analyzed and compared. Finally, the future development of the object detection algorithm based on the deep convolutional neural network is prospected.

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    Influence of Identity Loss on CycleGAN
    LIU Huachao, ZHANG Junran, LIU Yunfei
    Computer Engineering and Applications    2020, 56 (22): 217-223.   DOI: 10.3778/j.issn.1002-8331.1910-0405
    Abstract431)      PDF(pc) (1862KB)(648)       Save

    In the field of image generation, traditional image style transfer requires conversion between two paired images. CycleGAN(Cycle Generative Adversarial Network) has achieved good results by virtue of its ability to generate images for unpaired images, has become a research hotspot in the field of image generation. However, the classical CycleGAN generator cannot accurately identify the specific transformation domain and irrelevant domain of the image. Thus, it has the shortcoming of the arbitrary transformation of image irrelevant domain features, and the distortion of the generated image. For the above questions, identity loss is introduced to constrain the feature recognition of generator, L1 loss is used to guarantee that the converted image corresponds to the pixel level of the original image, effectively improve the problem, and make the generated image clearer. Meanwhile, by adjusting the ratio of identity loss [μ], the identity loss under different [μ] values is further analyzed, and the change of the loss of  CycleGAN and its effect on the quality of the generated image are discuessed. Finally, the selection strategy of proportional superparameter [μ] value of identity loss is given.

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    Small Object Detection Technology: A Review
    LIANG Hong, WANG Qingwei, ZHANG Qian, LI Chuanxiu
    Computer Engineering and Applications    2021, 57 (1): 17-28.   DOI: 10.3778/j.issn.1002-8331.2009-0099
    Abstract426)      PDF(pc) (606KB)(417)       Save

    Object detection is a kind of detection technology which can find and judge the object in the image through computer vision. Different from large and medium object detection, small objects have inherent defects such as less semantic information and small coverage area, which lead to unsatisfactory detection effect of small objects. Therefore, how to improve the detection effect of small objects is still a big problem in the field of computer vision. It outlines the research results in the field of small object detection in recent years at home and abroad. Firstly, it analyzes the definition and detection difficulties of small object. Secondly, it classifies and summarizes the methods that can effectively improve the detection accuracy of small object, and introduces the application and advantages of various methods. Finally, it forecasts and prospects the development trend of small object detection in the future.

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    Survey of Few Shot Learning of Deep Neural Network
    ZHU Juntao, YAO Guangle, ZHANG Gexiang, LI Jun, YANG Qiang, WANG Sheng, YE Shaoze
    Computer Engineering and Applications    2021, 57 (7): 22-33.   DOI: 10.3778/j.issn.1002-8331.2012-0200
    Abstract423)      PDF(pc) (859KB)(421)       Save

    With the recent vigorous development of deep learning, Deep Neural Networks(DNN) have made exciting breakthrough in large-scale image classification and recognition tasks, but they still face huge challenges in solving few shot learning problems. Few Shot Learning(FSL) is defined as learning a model that can solve practical problems with a small number of supervised samples, which is of great significance in the field of deep learning. This prompts people to systematically combs the recent work of few shot learning of DNN, and divide the solution into four strategies according to the technology they used to solve the small sample learning problem:data augmentation, metric learning, external memory, parameter optimization. According to these strategies, it comprehensively reviews the existing few shot learning methods of DNN, and summarizes the performance of each strategy on relevant benchmarks. Finally, the limitations of the existing technology are emphasized and its future development direction is prospected to provide reference for future research work.

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    3D Path Planning Algorithm Based on Deep Reinforcement Learning
    HUANG Dongjin, JIANG Chenfeng, HAN Kaili
    Computer Engineering and Applications    2020, 56 (15): 30-36.   DOI: 10.3778/j.issn.1002-8331.2001-0347
    Abstract422)      PDF(pc) (1152KB)(899)       Save

    Reasonable path selection is a difficulty in the field of 3D path planning. The existing 3D path planning methods can not adapt to the unknown terrain, and the obstacle avoidance form is single. In order to solve these problems, a 3D path planning algorithm for agents based on LSTM-PPO is proposed. Virtual ray is designed to detect simulation environment, and the collected state space and action states are introduced into Long Short-Term Memory Networks(LSTM). Through the extra reward function and intrinsic curiosity module, the agent can learn to jump through low obstacles and avoid large obstacles. Using the PPO’s clipped surrogate objective to optimize the update range of planning strategy. The results show that the algorithm is feasible, more intelligent and more reasonable for path planning, and can adapt well to the unknown environment with many obstacles.

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    Literature Survey on Research and Application of Sine Cosine Algorithm
    YONG Longquan, LI Yanhai, JIA Wei
    Computer Engineering and Applications    2020, 56 (14): 26-34.   DOI: 10.3778/j.issn.1002-8331.2004-0015
    Abstract394)      PDF(pc) (1453KB)(497)       Save

    Sine Cosine Algorithm(SCA) is a novel stochastic optimization algorithm that uses the volatility and periodicity of sine and cosine functions to achieve optimization. In this paper, the basic principle of SCA is introduced, the main parameters affecting the performance of SCA is discussed, and search mechanism of SCA is analyzed. The improved strategies of SCA are summarized, and applications of SCA are listed, such as scheduling problem, controller optimization, power system optimization, data mining, image processing, target tracking and other applications. Finally, the further research directions of SCA are proposed according to characteristics of SCA and its present state.

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    Research Progress of Natural Language Processing Based on Deep Learning
    JIANG Yangyang, JIN Bo, ZHANG Baochang
    Computer Engineering and Applications    2021, 57 (22): 1-14.   DOI: 10.3778/j.issn.1002-8331.2106-0166
    Abstract393)      PDF(pc) (1781KB)(103)       Save

    This paper comprehensively analyzes the research of deep learning in the field of natural language processing through a combination of quantitative and qualitative methods. It uses CiteSpace and VOSviewer to draw a knowledge graph of countries, institutions, journal distribution, keywords co-occurrence, co-citation network clustering, and timeline view of deep learning in the field of natural language processing to clarify the research. Through mining important researches in the field, this paper summarizes the research trend, the main problems, development bottlenecks, and gives corresponding solutions and ideas. Finally, suggestions are given on how to track the research of deep learning in the field of natural language processing, and provides references for subsequent research and development in the field.

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    Review of Research on Generative Adversarial Networks and Its Application
    WEI Fuqiang, Gulanbaier Tuerhong, Mairidan Wushouer
    Computer Engineering and Applications    2021, 57 (19): 18-31.   DOI: 10.3778/j.issn.1002-8331.2104-0248
    Abstract386)      PDF(pc) (1078KB)(994)       Save

    The theoretical research and applications of generative adversarial networks have been continuously successful and have become one of the current hot spots of research in the field of deep learning. This paper provides a systematic review of the theory of generative adversarial networks and their applications in terms of types of models, evaluation criteria and theoretical research progress; analyzing the strengths and weaknesses of generative models with explicit and implicit density-based, respectively; summarizing the evaluation criteria of generative adversarial networks, interpreting the relationship between the criteria, and introduces the research progress of the generative adversarial network in image generation from the application level, that is, through the image conversion, image generation, image restoration, video generation, text generation and image super-resolution applications; analyzing the theoretical research progress of generative adversarial networks from the perspectives of interpretability, controllability, stability and model evaluation methods. Finally, the paper discusses the challenges of studying generative adversarial networks and looks forward to the possible future directions of development.

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    Review of Task Scheduling Methods in Cloud Computing Environment
    TIAN Zhuojing, HUANG Zhenchun, ZHANG Yinong
    Computer Engineering and Applications    2021, 57 (2): 1-11.   DOI: 10.3778/j.issn.1002-8331.2006-0259
    Abstract385)      PDF(pc) (702KB)(762)       Save

    With the rapid growth of application computing demand, heterogeneous computing resources continue to increase, task scheduling has become an important research problem in the field of cloud computing. Task scheduling is responsible for matching user tasks to appropriate virtual computing resources. The quality of the algorithm will directly affect the response time, makespan, energy consumption, cost, resource utilization and a series of performance indexes that are closely related to the economic interests of users and cloud service providers. This paper summarizes and discusses the research progress of task scheduling algorithm based on the characteristics of different cloud environments, aiming at independent task and scientific workflow. Firstly, it reviews the existing task scheduling types, scheduling mechanisms and their advantages and disadvantages. Secondly, task scheduling characteristics under single cloud environment, and inter-cloud environment such as hybrid cloud, multi-cloud and federated cloud are summarized, and schedule methods, optimization objectives, pros and cons of some typical relevant literatures are described. On this basis, the research status of task scheduling under various environments is discussed. Then, the scheduling optimization methods used in various environments are further sorted out to clarify their scope of use. Finally, it summarizes the whole paper and points out that it is necessary to pay more attention to the research of task scheduling in computing data intensive applications under inter-cloud environment.

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    Research on Application of Object Detection Algorithm in Traffic Scene
    XIAO Yuqing, YANG Huimin
    Computer Engineering and Applications    2021, 57 (6): 30-41.   DOI: 10.3778/j.issn.1002-8331.2011-0361
    Abstract380)      PDF(pc) (919KB)(315)       Save

    Object detection is an important research task in the field of computer vision. It is widely used in robotics, automatic vehicles, industrial detection and other fields. On the basis of deep learning theory, the development and research status of object detection algorithm are firstly systematically summarized and the characteristics, advantages, disadvantages and real-time performance of the two categories of algorithms are compared. Next to the three kinds of typical targets (non-motor vehicles, motor vehicles and pedestrians) as objects in the traffic scene, the research status and application of object detection algorithm for detecting and identifying objects are discussed and summarized respectively from six aspects in traffic scene:traditional detection method, object detection algorithm, object detection algorithm optimization, 3d object detection, multimodal object detection and re-identification. And the application of focus on the advantages, limitations and applicable scenario of various methods. Finally, the common object detection and traffic scene data sets and evaluation criteria are summarized, the performance of the two categories of algorithms is compared and analyzed, and the development trend of the application of object detection algorithm in traffic scenes is prospected, providing research ideas for intelligent traffic and automatic vehicles.

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    Survey of Interpretability Research on Deep Learning Models
    ZENG Chunyan, YAN Kang, WANG Zhifeng, YU Yan, JI Chunmei
    Computer Engineering and Applications    2021, 57 (8): 1-9.   DOI: 10.3778/j.issn.1002-8331.2012-0357
    Abstract379)      PDF(pc) (677KB)(582)       Save

    With the characteristics of data-driven learning, deep learning technology has made great achievements in the fields of natural language processing, image processing, and speech recognition. However, due to the deep learning model featured by deep networks, many parameters, high complexity and other characteristics, the decisions and intermediate processes made by the model are difficult for humans to understand. Therefore, exploring the interpretability of deep learning has become a new topic in the current artificial intelligence field. This review takes the interpretability of deep learning models as the research object and summarizes its progress. Firstly, the main interpretability methods are summarized and analyzed from four aspects:self-explanatory model, model-specific explanation, model-agnostic explanation, and causal interpretability. At the same time, it enumerates the application of interpretability related technologies, and finally discusses the existing problems of current interpretability research to promote the further development of the deep learning interpretability research framework.

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