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Review of Deep Learning Applied to Time Series Prediction LIANG Hongtao, LIU Shuo, DU Junwei, HU Qiang, YU Xu Journal of Frontiers of Computer Science and Technology    2023, 17 (6): 1285-1300.   DOI: 10.3778/j.issn.1673-9418.2211108 The time series is generally a set of random variables that are observed and collected at a certain frequency in the course of something??s development. The task of time series forecasting is to extract the core patterns from a large amount of data and to make accurate estimates of future data based on known factors. Due to the access of a large number of IoT data collection devices, the explosive growth of multidimensional data and the increasingly demanding requirements for prediction accuracy, it is difficult for classical parametric models and traditional machine learning algorithms to meet high efficiency and high accuracy requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Trans-former models have achieved fruitful results in time series forecasting tasks. To further promote the development of time series prediction technology, common characteristics of time series data, evaluation indexes of datasets and models are reviewed, and the characteristics, advantages and limitations of each prediction algorithm are experimentally compared and analyzed with time and algorithm architecture as the main research line. Several time series prediction methods based on Transformer model are highlighted and compared. Finally, according to the problems and challenges of deep learning applied to time series prediction tasks, this paper provides an outlook on the future research trends in this direction. Reference | Related Articles | Metrics

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Review on Named Entity Recognition LI Dongmei, LUO Sisi, ZHANG Xiaoping, XU Fu Journal of Frontiers of Computer Science and Technology    2022, 16 (9): 1954-1968.   DOI: 10.3778/j.issn.1673-9418.2112109 In the field of natural language processing, named entity recognition is the first key step of information extraction. Named entity recognition task aims to recognize named entities from a large number of unstructured texts and classify them into predefined types. Named entity recognition provides basic support for many natural language processing tasks such as relationship extraction, text summarization, machine translation, etc. This paper first introduces the definition of named entity recognition, research difficulties, particularity of Chinese named entity recognition, and summarizes the common Chinese and English public datasets and evaluation criteria in named entity recognition tasks. Then, according to the development history of named entity recognition, the existing named entity recognition methods are investigated, which are the early named entity recognition methods based on rules and dictionaries, the named entity recognition methods based on statistic and machine learning, and the named entity recognition methods based on deep learning. This paper summarizes the key ideas, advantages and disadvan-tages and representative models of each named entity recognition method, and summarizes the Chinese named entity recognition methods in each stage. In particular, the latest named entity recognition based on Transformer and based on prompt learning are reviewed, which are state-of-the-art in deep learning-based named entity recognition methods. Finally, the challenges and future research trends of named entity recognition are discussed. Table and Figures | Reference | Related Articles | Metrics

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Research on Question Answering System on Joint of Knowledge Graph and Large Language Models ZHANG Heyi, WANG Xin, HAN Lifan, LI Zhao, CHEN Zirui, CHEN Zhe Journal of Frontiers of Computer Science and Technology    2023, 17 (10): 2377-2388.   DOI: 10.3778/j.issn.1673-9418.2308070 The large language model (LLM), including ChatGPT, has shown outstanding performance in understanding and responding to human instructions, and has a profound impact on natural language question answering (Q&A). However, due to the lack of training in the vertical field, the performance of LLM in the vertical field is not ideal. In addition, due to its high hardware requirements, training and deploying LLM remains difficult. In order to address these challenges, this paper takes the application of traditional Chinese medicine formulas as an example, collects the domain related data and preprocesses the data. Based on LLM and knowledge graph, a vertical domain Q&A system is designed. The system has the following capabilities: (1) Information filtering. Filter out vertical domain related questions and input them into LLM to answer. (2) Professional Q&A. Generate answers with more professional knowledge based on LLM and self-built knowledge base. Compared with the fine-tuning method of introducing professional data, using this technology can deploy large vertical domain models without the need for retraining. (3) Extract conversion. By strengthening the information extraction ability of LLM and utilizing its generated natural language responses, structured knowledge is extracted and matched with a professional knowledge graph for professional verification. At the same time, structured knowledge can be transformed into readable natural language, achieving a deep integration of large models and knowledge graphs. Finally, the effect of the system is demonstrated and the performance of the system is verified from both subjective and objective perspectives through two experiments of subjective evaluation of experts and objective evaluation of multiple choice questions. Reference | Related Articles | Metrics

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Survey of Camouflaged Object Detection Based on Deep Learning SHI Caijuan, REN Bijuan, WANG Ziwen, YAN Jinwei, SHI Ze Journal of Frontiers of Computer Science and Technology    2022, 16 (12): 2734-2751.   DOI: 10.3778/j.issn.1673-9418.2206078 Camouflaged object detection (COD) based on deep learning is an emerging visual detection task, which aims to detect the camouflaged objects “perfectly” embedded in the surrounding environment. However, most exiting work primarily focuses on building different COD models with little summary work for the existing methods. Therefore, this paper summarizes the existing COD methods based on deep learning and discusses the future development of COD. Firstly, 23 existing COD models based on deep learning are introduced and analyzed according to five detection mechanisms: coarse-to-fine strategy, multi-task learning strategy, confidence-aware learning strategy, multi-source information fusion strategy and transformer-based strategy. The advantages and disadvantages of each strategy are analyzed in depth. And then, 4 widely used datasets and 4 evaluation metrics for COD are introduced. In addition, the performance of the existing COD models based on deep learning is compared on four datasets, including quantitative comparison, visual comparison, efficiency analysis, and the detection effects on camouflaged objects of different types. Furthermore, the practical applications of COD in medicine, industry, agriculture, military, art, etc. are mentioned. Finally, the deficiencies and challenges of existing methods in complex scenes, multi-scale objects, real-time performance, practical application requirements, and COD in other multimodalities are pointed out, and the potential directions of COD are discussed. Table and Figures | Reference | Related Articles | Metrics

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Review of Deep Learning Applied to Occluded Object Detection SUN Fangwei, LI Chengyang, XIE Yongqiang, LI Zhongbo, YANG Caidong, QI Jin Journal of Frontiers of Computer Science and Technology    2022, 16 (6): 1243-1259.   DOI: 10.3778/j.issn.1673-9418.2112035 Occluded object detection has long been a difficulty and hot topic in the field of computer vision. Based on convolutional neural network, the deep learning takes the object detection task as a classification and regression task to handle, and obtains remarkable achievements. The mask confuses the features of object when the object is occluded, making the deep convolutional neural network cannot handle it well and reducing the performance of detector in ideal scenes. Considering the universality of occlusion in reality, the effective detection of occluded object has important research value. In order to further promote the development of occluded object detection, this paper makes a comprehensive summary of occluded object detection algorithms, and makes a reasonable classification and analysis. First of all, based on a simple overview of object detection, this paper introduces the relevant theoretic background, research difficulties and datasets about occluded object detection. After, this paper focuses on the algo-rithms to improve the performance of occluded object detection from the aspects of object structure, loss function, non-maximum suppression and semantic partial. This paper compares the performance of different detection algo-rithms after summarizing the relationship and development of various algorithms. Finally, this paper points out the difficulties of occluded object detection and looks forward to its future development directions. Table and Figures | Reference | Related Articles | Metrics

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Survey of Research on Image Inpainting Methods LUO Haiyin, ZHENG Yuhui Journal of Frontiers of Computer Science and Technology    2022, 16 (10): 2193-2218.   DOI: 10.3778/j.issn.1673-9418.2204101 Image inpainting refers to restoring the pixels in damaged areas of an image to make them as consistent as possible with the original image. Image inpainting is not only crucial in the computer vision tasks, but also serves as an important cornerstone of other image processing tasks. However, there are few researches related to image inpainting. In order to better learn and promote the research of image inpainting tasks, the classic image inpainting algorithms and representative deep learning image inpainting methods in the past ten years are reviewed and analyzed. Firstly, the classical traditional image inpainting methods are briefly summarized, and divided into partial differential equation-based and sample-based image inpainting methods, and the limitations of traditional image methods are further analyzed. Deep learning image inpainting methods are divided into single image inpainting and pluralistic image inpainting according to the number of output images of the model, and different methods are analyzed and summarized in combination with application images, loss functions, types, advantages, and limitations. After that, the commonly used datasets and quantitative evaluation indicators of image inpainting methods are described in detail, and the quantitative data of image inpainting methods to inpaint damaged areas of different areas on different image datasets are given. According to the quantitative data, the performance of image inpainting methods based on deep learning is compared and analyzed. Finally, the limitations of existing image inpainting methods are summarized and analyzed, and new ideas and prospects for future key research directions are proposed. Table and Figures | Reference | Related Articles | Metrics

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Survey on Cross-Chain Protocols of Blockchain MENG Bo, WANG Yibing, ZHAO Can, WANG Dejun, MA Binhao Journal of Frontiers of Computer Science and Technology    2022, 16 (10): 2177-2192.   DOI: 10.3778/j.issn.1673-9418.2203032 With the development of blockchain technology, due to the different system architecture and application scenarios of blockchain platforms, it is difficult to realize the interconnection and intercommunication of data and assets on different blockchains, which affects the promotion and application of blockchain. The cross-chain tech-nology of blockchain is an important technical solution to realize the interconnection of blockchain and improve the interoperability and extensibility of blockchain. The blockchain cross-chain protocol is the specific design specifi-cations to realize the cross-chain interoperability between different blockchains through cross-chain technology, so it is of great significance to the realization of blockchain interoperability and the construction of blockchain cross-chain application. This paper systematically arranges and analyzes the latest researches on the integration and implemen-tation of blockchain cross-chain protocols, and places them in four hierarchies: Firstly, the current research status of blockchain cross-chain interoperability is explained from three aspects, Internet of blockchains, cross-chain techno-logy and blockchain interoperability. Secondly, the cross-chain protocols are divided into cross-chain communi-cation agreements, cross-chain asset transaction agreements and cross-chain smart contract call agreements, and the latest research is analyzed. Thirdly, the key design principles of cross-chain protocols are summarized, and the solutions for the problems of security, privacy and scalability of cross-chain protocol are provided. Finally, com-bined with the actual needs of blockchain cross-chain applications, the future research direction of blockchain cross-chain protocol is given. Table and Figures | Reference | Related Articles | Metrics

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Survey on Pseudo-Labeling Methods in Deep Semi-supervised Learning LIU Yafen, ZHENG Yifeng, JIANG Lingyi, LI Guohe, ZHANG Wenjie Journal of Frontiers of Computer Science and Technology    2022, 16 (6): 1279-1290.   DOI: 10.3778/j.issn.1673-9418.2111144 With the development of intelligent technology, deep learning has become a hot topic in machine learning. It is playing a more and more important role in various fields. Deep learning requires a lot of labeled data to imp-rove model performance. Therefore, researchers effectively combine semi-supervised learning with deep learning to solve the labeled data problem. It utilizes a small amount of labeled data and a large amount of unlabeled data to build the model simultaneously. It can help to expand the sample space. In view of its theoretical significance and practical application value, this paper focuses on the pseudo-labeling methods as the starting point. Firstly, deep semi-supervised learning is introduced and the advantage of pseudo-labeling methods is pointed out. Secondly, the pseudo-labeling methods are described from self-training and multi-view training and the existing model is comprehensively analyzed. And then, the label propagation method based on graph and pseudo-labeling is introduced. Furthermore, the existing pseudo-labeling methods are analyzed and compared. Finally, the problems and future research direction of pseudo-labeling methods are summarized from the utility of unlabeled data, noise data, rationality, and the combi-nation of pseudo-labeling methods. Table and Figures | Reference | Related Articles | Metrics

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Survey of Few-Shot Object Detection LIU Chunlei, CHEN Tian‘en, WANG Cong, JIANG Shuwen, CHEN Dong Journal of Frontiers of Computer Science and Technology    2023, 17 (1): 53-73.   DOI: 10.3778/j.issn.1673-9418.2206020 Object detection as a hot field in computer vision, usually requires a large number of labeled images for model training, which will cost a lot of manpower and material resources. At the same time, due to the inherent long-tailed distribution of data in the real world, the number of samples of most objects is relatively small, such as many uncommon diseases, etc., and it is difficult to obtain a large number of labeled images. In this regard, few-shot object detection only needs to provide a small amount of annotation information to detect objects of interest. This paper makes a detailed review of few-shot object detection methods. Firstly, the development of general target detection and its existing problems are reviewed, the concept of few-shot object detection is introduced, and other tasks related to few-shot object detection are differentiated and explained. Then, two classical paradigms based on transfer learning and meta-learning for existing few-shot object detection are introduced. According to the improvement strategies of different methods, few-shot object detection is divided into four types: attention mechanism, graph convolutional neural network, metric learning and data augmentation. The public datasets and evaluation metrics used in these methods are explained. Advantages, disadvantages, applicable scenarios of different methods, and performance on different datasets are compared and analyzed. Finally, the practical application fields and future research trends of few-shot object detection are discussed. Reference | Related Articles | Metrics

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Survey of Graph Neural Network in Recommendation System WU Jing, XIE Hui, JIANG Huowen Journal of Frontiers of Computer Science and Technology    2022, 16 (10): 2249-2263.   DOI: 10.3778/j.issn.1673-9418.2203004 Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well. Graph neural network (GNN) can extract and represent the features from edges and nodes data in the graphs and has inherent advantages in processing the graphs structure data, so it flourishes in recommendation system. This paper sorts out the main references of graph neural network in recommendation system in recent years, focuses on the two perspectives of method and problem, and systematically reviews graph neural network in recommendation system. Firstly, from the method level, five graph neural networks of the recommendation system are elaborated, including the graph convolutional network in the recommendation system, graph attention network in the recommendation system, graph autoencoder in the recommendation system, graph generation network in the recommendation system and graph spatial-temporal network in the recommendation system. Secondly, from the perspective of problem similarity, six major problem types are summarized: sequence recommendation, social recommendation, cross-domain recommendation, multi-behavior recommendation, bundle recommendation, and session-based recommen-dation. Finally, based on the analysis and summary of the existing methods, this paper points out the main difficu-lties in the current research on graph neural network in recommendation system, proposes the corresponding issues that can be investigated, and looks forward to the future research directions on this topic. Table and Figures | Reference | Related Articles | Metrics

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Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning YANG Caidong, LI Chengyang, LI Zhongbo, XIE Yongqiang, SUN Fangwei, QI Jin Journal of Frontiers of Computer Science and Technology    2022, 16 (9): 1990-2010.   DOI: 10.3778/j.issn.1673-9418.2202063 The essence of image super-resolution reconstruction technology is to break through the limitation of hardware conditions, and reconstruct a high-resolution image from a low-resolution image which contains less infor-mation through the image super-resolution reconstruction algorithms. With the development of the technology on deep learning, deep learning has been introduced into the image super-resolution reconstruction field. This paper summarizes the image super-resolution reconstruction algorithms based on deep learning, classifies, analyzes and compares the typical algorithms. Firstly, the model framework, upsampling method, nonlinear mapping learning module and loss function of single image super-resolution reconstruction method are introduced in detail. Secondly, the reference-based super-resolution reconstruction method is analyzed from two aspects: pixel alignment and Patch matching. Then, the benchmark datasets and image quality evaluation indices used for image super-resolution recon-struction algorithms are summarized, the characteristics and performance of the typical super-resolution recons-truction algorithms are compared and analyzed. Finally, the future research trend on the image super-resolution reconstruction algorithms based on deep learning is prospected. Table and Figures | Reference | Related Articles | Metrics

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Review of Super-Resolution Image Reconstruction Algorithms ZHONG Mengyuan, JIANG Lin Journal of Frontiers of Computer Science and Technology    2022, 16 (5): 972-990.   DOI: 10.3778/j.issn.1673-9418.2111126 In human visual perception system, high-resolution (HR) image is an important medium to clearly express its spatial structure, detailed features, edge texture and other information, and it has a very wide range of practical value in medicine, criminal investigation, satellite and other fields. Super-resolution image reconstruction (SRIR) is a key research task in the field of computer vision and image processing, which aims to reconstruct a high-resolution image with clear details from a given low-resolution (LR) image. In this paper, the concept and mathematical model of super-resolution image reconstruction are firstly described, and the image reconstruction methods are systematically classified into three kinds of super-resolution image reconstruction methods：based on interpolation, based on reconstruction, based on learning (before and after deep learning). Secondly, the typical, commonly used and latest algorithms among the three methods and their research are comprehensively reviewed and summarized, and the listed image reconstruction algorithms are combed from the aspects of network structure, learning mechanism, application scenarios, advantages and limitations. Then, the datasets and image quality evaluation indices used for super-resolution image reconstruction algorithms are summarized, and the characteristics and performance of various super-resolution image reconstruction algorithms based on deep learning are compared. Finally, the future research direction or angle of super-resolution image reconstruction is prospected from four aspects. Table and Figures | Reference | Related Articles | Metrics

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Survey of Deep Learning Based Multimodal Emotion Recognition ZHAO Xiaoming, YANG Yijiao, ZHANG Shiqing Journal of Frontiers of Computer Science and Technology    2022, 16 (7): 1479-1503.   DOI: 10.3778/j.issn.1673-9418.2112081 Multimodal emotion recognition aims to recognize human emotional states through different modalities related to human emotion expression such as audio, vision, text, etc. This topic is of great importance in the fields of human-computer interaction, a.pngicial intelligence, affective computing, etc., and has attracted much attention. In view of the great success of deep learning methods developed in recent years in various tasks, a variety of deep neural networks have been used to learn high-level emotional feature representations for multimodal emotion recog-nition. In order to systematically summarize the research advance of deep learning methods in the field of multi-modal emotion recognition, this paper aims to present comprehensive analysis and summarization on recent multi-modal emotion recognition literatures based on deep learning. First, the general framework of multimodal emotion recognition is given, and the commonly used multimodal emotional dataset is introduced. Then, the principle of representative deep learning techniques and its advance in recent years are briefly reviewed. Subsequently, this paper focuses on the advance of two key steps in multimodal emotion recognition: emotional feature extraction methods related to audio, vision, text, etc., including hand-crafted feature extraction and deep feature extraction; multi-modal information fusion strategies integrating different modalities. Finally, the challenges and opportunities in this field are analyzed, and the future development direction is pointed out. Table and Figures | Reference | Related Articles | Metrics

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Review of Graph Neural Networks Applied to Knowledge Graph Reasoning SUN Shuifa, LI Xiaolong, LI Weisheng, LEI Dajiang, LI Sihui, YANG Liu, WU Yirong Journal of Frontiers of Computer Science and Technology    2023, 17 (1): 27-52.   DOI: 10.3778/j.issn.1673-9418.2207060 As an important element of knowledge graph construction, knowledge reasoning (KR) has always been a hot topic of research. With the deepening of knowledge graph application research and the expanding of its scope, graph neural network (GNN) based KR methods have received extensive attention due to their capability of obtaining semantic information such as entities and relationships in knowledge graph, high interpretability, and strong reasoning ability. In this paper, firstly, basic knowledge and research status of knowledge graph and KR are summarized. The advantages and disadvantages of KR approaches based on logic rules, representation learning, neural network and graph neural network are briefly introduced. Secondly, the latest progress in KR based on GNN is comprehensively summarized. GNN-based KR methods are categorized into knowledge reasoning based on recurrent graph neural networks (RecGNN), convolutional graph neural networks (ConvGNN), graph auto-encoders (GAE) and spatial-temporal graph neural networks (STGNN). Various typical network models are introduced and compared. Thirdly, this paper introduces the application of KR based on graph neural network in health care, intelligent manufacturing, military, transportation, etc. Finally, the future research directions of GNN-based KR are proposed, and related research in various directions in this rapidly growing field is discussed. Reference | Related Articles | Metrics

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Review of Medical Image Segmentation Based on UNet XU Guangxian, FENG Chun, MA Fei Journal of Frontiers of Computer Science and Technology    2023, 17 (8): 1776-1792.   DOI: 10.3778/j.issn.1673-9418.2301044 As one of the most important semantic segmentation frameworks in convolutional neural networks (CNN), UNet is widely used in image processing tasks such as classification, segmentation, and target detection of medical images. In this paper, the structural principles of UNet are described, and a comprehensive review of UNet-based networks and variant models is presented. The model algorithms are fully investigated from several perspectives, and an attempt is made to establish an evolutionary pattern among the models. Firstly, the UNet variant models are categorized according to the seven medical imaging systems they are applied to, and the algorithms with similar core composition are compared and described. Secondly, the principles, strengths and weaknesses, and applicable scenarios of each model are analyzed. Thirdly, the main UNet variant networks are summarized in terms of structural principles, core composition, datasets, and evaluation metrics. Finally, the inherent shortcomings and solutions of the UNet network structure are objectively described in light of the latest advances in deep learning, providing directions for continued improvement in the future. At the same time, other technological evolutions and application scenarios that can be combined with UNet are detailed, and the future development trend of UNet-based variant networks is further envisaged. Reference | Related Articles | Metrics

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Survey of Research on Instance Segmentation Methods HUANG TAO, LI Hua, ZHOU Gui, LI Shaobo, WANG Yang Journal of Frontiers of Computer Science and Technology    2023, 17 (4): 810-825.   DOI: 10.3778/j.issn.1673-9418.2209051 In recent years, with the continuous improvement of computing level, the research of instance segment-ation methods based on deep learning has made great breakthroughs. Image instance segmentation can distinguish different instances of the same class in images, which is an important research direction in the field of computer vision with broad research prospects, and has achieved great actual application value in scene comprehension, medical image analysis, machine vision, augmented reality, image compression, video monitoring, etc. Recently, instance segmentation methods have been updated more and more frequently, but there is a little literature to comprehensively and systematically analyze the research background related to instance segmentation. This paper  provides a comprehensive and systematic analysis and summary of the image instance segmentation methods based on deep learning. Firstly, this paper introduces the currently used common public datasets and evaluation indexes in instance segmentation, and analyzes the challenges of current datasets. Secondly, this paper respectively combs and summarizes the instance segmentation algorithms in the characteristics of two-stage segmentation methods and single-stage segmentation methods, elaborates their central ideas and design thoughts, and summarizes the advantages and shortcomings of the two types of methods. Thirdly, this paper evaluates the segmentation accuracy and speed of the models on a public dataset. Finally, this paper summarizes the current difficulties and challenges of instance segmentation, presents the solution ideas for facing the challenges, and makes a prospect for future research directions. Reference | Related Articles | Metrics

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Survey of Question Answering Based on Knowledge Graph Reasoning SA Rina, LI Yanling, LIN Min Journal of Frontiers of Computer Science and Technology    2022, 16 (8): 1727-1741.   DOI: 10.3778/j.issn.1673-9418.2111033 Knowledge graph question answering (KGQA) is based on analysis and understanding of questions and knowledge graph (KG) to obtain the answers. However, due to the complexity of natural language questions and the incompleteness of KG, the accuracy of answers can not be improved effectively. The knowledge graph reasoning technology can infer the missing entities in the KG and the implied relations between entities. Therefore, its application in KGQA can further improve the accuracy of answer prediction. In recent years, with the development of KGQA datasets and flexible application of knowledge graph reasoning technology, the development of the KGQA is greatly promoted. In this paper, question answering based on knowledge graph reasoning is summarized from three aspects. Firstly, this paper gives a brief overview of question answering based on knowledge graph reasoning, and introduces its challenges and related datasets. Secondly, this paper introduces the application of knowledge graph reasoning in open domain question answering, commonsense question answering and temporary knowledge question answering, and analyzes the advantages and disadvantages of each method. The open domain question answering methods are further summarized as graph embedding methods, deep learning methods and logic methods. Finally, this paper summarizes the work and prospects the future research in view of the current problems of question answering based on knowledge graph reasoning. Table and Figures | Reference | Related Articles | Metrics

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Survey on 3D Reconstruction Methods Based on Visual Deep Learning LI Mingyang, CHEN Wei, WANG Shanshan, LI Jie, TIAN Zijian, ZHANG Fan Journal of Frontiers of Computer Science and Technology    2023, 17 (2): 279-302.   DOI: 10.3778/j.issn.1673-9418.2205054 In recent years, as one of the important tasks of computer vision, 3D reconstruction has received extensive attention. This paper focuses on the research progress of using deep learning to reconstruct the 3D shape of general objects in recent years. Taking the steps of 3D reconstruction by deep learning as the context, according to the data feature representation in the process of 3D reconstruction, it is divided into voxel, point cloud, surface mesh and implicit surface. Then, according to the number of inputting 2D images, it can be divided into single view 3D reconstruction and multi-view 3D reconstruction, which are subdivided according to the network architecture and the training mechanism they use. While the research progress of each category is discussed, the development prospects, advantages and disadvantages of each training method are analyzed. This paper studies the new hotspots in specific 3D reconstruction fields in recent years, such as 3D reconstruction of dynamic human bodies and 3D completion of incomplete geometric data, compares some key papers and summarizes the problems in these fields. Then this paper introduces the key application scenarios and parameters of 3D datasets at this stage. The development prospect of 3D reconstruction in specific application fields in the future is illustrated and analyzed, and the research direction of 3D reconstruction is prospected. Reference | Related Articles | Metrics

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Overview of Facial Deepfake Video Detection Methods ZHANG Lu, LU Tianliang, DU Yanhui Journal of Frontiers of Computer Science and Technology    2023, 17 (1): 1-26.   DOI: 10.3778/j.issn.1673-9418.2205035 The illegal use of deepfake technology will have a serious impact on social stability, personal reputation and even national security. Therefore, it is imperative to develop research on facial deepfake videos detection tech-nology, which is also a research hotspot in the field of computer vision in recent years. At present, the research is based on traditional face recognition and image classification technology, building a deep neural network to deter-mine a facial video is real or not, but there are still problems such as the low quality of dataset, the combine of multimodal features and the poor performance of model generalization. In order to further promote the development of deepfake video detection technology, a comprehensive summary of various current algorithms is carried out, and the existing algorithms are classified, analyzed and compared. Firstly, this paper mainly introduces the facial deepfake videos detection datasets. Secondly, taking feature selection as the starting point, this paper summarizes the main method of detecting deepfake videos in the past three years, classifies various detection technologies from the pers-pectives of spatial features, spatial-temporal fusion features and biological features, and introduces some new detec-tion methods based on watermarking and blockchain. Then, this paper introduces the new trends of facial deepfake video detection methods from the aspects of feature selection, transfer learning, model architecture and training ideas. Finally, the full text is summarized and the future technology development is prospected. Reference | Related Articles | Metrics

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Review of Chinese Named Entity Recognition Research WANG Yingjie, ZHANG Chengye, BAI Fengbo, WANG Zumin, JI Changqing Journal of Frontiers of Computer Science and Technology    2023, 17 (2): 324-341.   DOI: 10.3778/j.issn.1673-9418.2208028 With the rapid development of related technologies in the field of natural language processing, as an upstream task of natural language processing, improving the accuracy of named entity recognition is of great significance for subsequent text processing tasks. However, due to the differences between Chinese and English languages, it is difficult to transfer the research results of English named entity recognition into Chinese research effectively. Therefore, the key issues in the current research of Chinese named entity recognition are analyzed from the following four aspects: Firstly, the development of named entity recognition is taken as the main clue, the advantages and disadvantages, common methods and research results of each stage are comprehensively discussed. Secondly, the Chinese text preprocessing methods are summarized from the perspective of sequence annotation, evaluation index, Chinese word segmentation methods and datasets. Then, aiming at the Chinese character and word feature fusion method, the current research is summarized from the perspective of character fusion and word fusion, and the optimization direction of the current Chinese named entity recognition model is discussed. Finally, the practical applications of Chinese named entity recognition in various fields are analyzed. This paper discusses the current research on Chinese named entity recognition, aiming to help researchers understand the research direction and significance of this task more comprehensively, so as to provide a certain reference for proposing new methods and new improvements. Reference | Related Articles | Metrics

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XR-MSF-Unet: Automatic Segmentation Model for COVID-19 Lung CT Images XIE Juanying, ZHANG Kaiyun Journal of Frontiers of Computer Science and Technology    2022, 16 (8): 1850-1864.   DOI: 10.3778/j.issn.1673-9418.2203023 The COVID-19 epidemic has threatened the human being. The automatic and accurate segmentation for the infected area of the COVID-19 CT images can help doctors to make correct diagnosis and treatment in time. However, it is very challenging to achieve perfect segmentation due to the diffuse infections of the COVID-19 to the patient lungs and irregular shapes of the infected areas and very similar infected areas to other lung tissues. To tackle these challenges, the XR-MSF-Unet model is proposed in this paper for segmenting the COVID-19 lung CT images of patients. The XR (X ResNet) convolution module is proposed in this model to replace the two-layer convolution operations of U-Net, so as to extract more informative features for achieving good segmentation results by multiple branches of XR. The plug and play attention mechanism module MSF (multi-scale features fusion module) is proposed in XR-MSF-Unet to fuse multi-scale features from different scales of reception fields, global, local and spatial features of CT images, so as to strengthen the detail segmentation effect of the model. Extensive experiments on the public COVID-19 CT images demonstrate that the proposed XR module can strengthen the capability of the XR-MSF-Unet model to extract effective features, and the MSF module plus XR module can effectively improve the segmentation capability of the XR-MSF-Unet model for the infected areas of the COVID-19 lung CT images. The proposed XR-MSF-Unet model obtains good segmentation results. Its segmentation perfor-mance is superior to that of the original U-Net model by 3.21, 5.96, 1.22 and 4.83 percentage points in terms of Dice, IOU, F1-Score and Sensitivity, and it defeats other same type of models, realizing automatic segmentation to the COVID-19 lung CT images. Table and Figures | Reference | Related Articles | Metrics

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Research Review of Image Semantic Segmentation Method in High-Resolution Remote Sensing Image Interpretation MA Yan, Gulimila·Kezierbieke Journal of Frontiers of Computer Science and Technology    2023, 17 (7): 1526-1548.   DOI: 10.3778/j.issn.1673-9418.2211015 Rapid acquisition of remote sensing information has important research significance for the development of image semantic segmentation methods in remote sensing image interpretation applications. With more and more types of data recorded by satellite remote sensing images and more and more complex feature information, accurate and effective extraction of information in remote sensing images has become the key to interpret remote sensing images by image semantic segmentation methods. In order to explore the image semantic segmentation method for fast and efficient interpretation of remote sensing images, a large number of image semantic segmentation methods for remote sensing images are summarized. Firstly, the traditional image semantic segmentation methods are reviewed and divided into edge detection-based segmentation methods, region-based segmentation methods, threshold-based segmentation methods and segmentation methods combined with specific theories. At the same time, the limitations of traditional image semantic segmentation methods are analyzed. Secondly, the semantic segmentation methods based on deep learning are elaborated in detail, and the basic ideas and technical characteristics of each method are used as the classification criteria. They are divided into four categories: FCN-based methods, codec-based methods, dilated convolution-based methods and attention-based methods. The sub-methods contained in each type of method are summarized, and the advantages and disadvantages of these methods are compared and analyzed. Then, the common datasets and performance evaluation indexes of remote sensing image semantic segmentation are briefly introduced. Experimental results of classical network models on different datasets are given, and the performance of different models is evaluated. Finally, the challenges of image semantic segmentation methods in high-resolution remote sensing image interpretation are analyzed, and the future development trend is prospected. Reference | Related Articles | Metrics

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Review of Knowledge-Enhanced Pre-trained Language Models HAN Yi, QIAO Linbo, LI Dongsheng, LIAO Xiangke Journal of Frontiers of Computer Science and Technology    2022, 16 (7): 1439-1461.   DOI: 10.3778/j.issn.1673-9418.2108105 The knowledge-enhanced pre-trained language models attempt to use the structured knowledge stored in the knowledge graph to strengthen the pre-trained language models, so that they can learn not only the general semantic knowledge from the free text, but also the factual entity knowledge behind the text. In this way, the enhanced models can effectively solve downstream knowledge-driven tasks. Although this is a promising research direction, the current works are still in the exploratory stage, and there is no comprehensive summary and systematic arrangement. This paper aims to address the lack of comprehensive reviews of this direction. To this end, on the basis of summarizing and sorting out a large number of relevant works, this paper firstly explains the background information from three aspects: the reasons, the advantages, and the difficulties of introducing knowledge, summarizes the basic concepts involved in the knowledge-enhanced pre-trained language models. Then, it discusses three types of knowledge enhancement methods: using knowledge to expand input features, using knowledge to modify model architecture, and using knowledge to constrain training tasks. Finally, it counts the scores of various knowledge enhanced pre-trained language models on several evaluation tasks, analyzes the performance, the current challenges, and possible future directions of knowledge-enhanced pre-trained language models. Table and Figures | Reference | Related Articles | Metrics

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Survey on Video Object Tracking Algorithms LIU Yi, LI Mengmeng, ZHENG Qibin, QIN Wei, REN Xiaoguang Journal of Frontiers of Computer Science and Technology    2022, 16 (7): 1504-1515.   DOI: 10.3778/j.issn.1673-9418.2111105 Video object tracking is an important research content in the field of computer vision, mainly studying the tracking of objects with interest in video streams or image sequences. Video object tracking has been widely used in cameras and surveillance, driverless, precision guidance and other fields. Therefore, a comprehensive review on video object tracking algorithms is of great significance. Firstly, according to different sources of challenges, the challenges faced by video object tracking are classified into two aspects, the objects’ factors and the backgrounds’ factors, and summed up respectively. Secondly, the typical video object tracking algorithms in recent years are classified into correlation filtering video object tracking algorithms and deep learning video object tracking algorithms. And further the correlation filtering video object tracking algorithms are classified into three categories: kernel correlation filtering algorithms, scale adaptive correlation filtering algorithms and multi-feature fusion corre-lation filtering algorithms. The deep learning video object tracking algorithms are classified into two categories: video object tracking algorithms based on siamese network and based on convolutional neural network. This paper analyzes various algorithms from the aspects of research motivation, algorithm ideas, advantages and disadvantages. Then, the widely used datasets and evaluation indicators are introduced. Finally, this paper sums up the research and looks forward to the development trends of video object tracking in the future. Table and Figures | Reference | Related Articles | Metrics

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Survey of Causal Inference for Knowledge Graphs and Large Language Models LI Yuan, MA Xinyu, YANG Guoli, ZHAO Huiqun, SONG Wei Journal of Frontiers of Computer Science and Technology    2023, 17 (10): 2358-2376.   DOI: 10.3778/j.issn.1673-9418.2307065 In recent decades, causal inference has been a significant research topic in various fields, including statistics, computer science, education, public policy, and economics. Most causal inference methods focus on the analysis of sample observational data and text corpora. However, with the emergence of various knowledge graphs and large language models, causal inference tailored to knowledge graphs and large models has gradually become a research hotspot. In this paper, different causal inference methods are classified based on their orientation towards sample observational data, text data, knowledge graphs, and large language models. Within each classification, this paper provides a detailed analysis of classical research works, including their problem definitions, solution methods, contributions, and limitations. Additionally, this paper places particular emphasis on discussing recent advancements in the integration of causal inference methods with knowledge graphs and large language models. Various causal inference methods are analyzed and compared from the perspectives of efficiency and cost, and specific applications of knowledge graphs and large language models in causal inference tasks are summarized. Finally, future development directions of causal inference in combination with knowledge graphs and large models are prospected. Reference | Related Articles | Metrics

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Survey of Knowledge Graph Recommendation System Research ZHAO Yehui, LIU Lin, WANG Hailong, HAN Haiyan, PEI Dongmei Journal of Frontiers of Computer Science and Technology    2023, 17 (4): 771-791.   DOI: 10.3778/j.issn.1673-9418.2205052 Recommendation systems can obtain user preferences in massive data information to better achieve personalized recommendations, improve user physical examination and solve information overload on the Internet. However, it still suffers from cold start and data sparsity problems. A knowledge graph, as a structured knowledge base with a large number of entities and rich semantic relationships, can not only improve the accuracy of recommendation systems, but also provide the interpretability for recommendation items, thus enhancing users?? trust in recommendation systems, and providing new methods and ideas to solve a series of key problems in recommendation systems. This paper firstly studies and analyzes knowledge graph recommendation systems, classifies them into multi-domain knowledge graph recommendation systems and domain-specific knowledge graph recommendation systems based on the classification of application fields, and further classifies them according to the characteristics of these knowledge graph recommendation methods, and conducts quantitative and qualitative analyses for each type of methods. Secondly, this paper lists the datasets commonly used by knowledge graph recommendation systems in the application fields, and gives an overview of the size and characteristics of the datasets. Finally, this paper outlooks and summarizes the future research directions of knowledge graph recommendation systems. Reference | Related Articles | Metrics

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Survey on Backdoor Attacks and Countermeasures in Deep Neural Network QIAN Hanwei, SUN Weisong Journal of Frontiers of Computer Science and Technology    2023, 17 (5): 1038-1048.   DOI: 10.3778/j.issn.1673-9418.2210061 The neural network backdoor attack aims to implant a hidden backdoor into the deep neural network, so that the infected model behaves normally on benign test samples, but behaves abnormally on poisoned test samples with backdoor triggers. For example, all poisoned test samples will be predicted as the target label by the infected model. This paper provides a comprehensive review and the taxonomy for existing attack methods according to the attack objects, which can be categorized into four types, including data poisoning attacks, physical world attacks, model poisoning attacks, and others. This paper summarizes the existing backdoor defense technologies from the perspective of attack and defense confrontation, which include poisoned sample identifying, poisoned model identifying, poisoned test sample filtering, and others. This paper explains the principles of deep neural network backdoor defects from the perspectives of deep learning mathematical principles and visualization, and discusses the difficulties and future development directions of deep neural network backdoor attacks and countermeasures from the perspectives of software engineering and program analysis. It is hoped that this survey can help researchers understand the research progress of deep neural network backdoor attacks and countermeasures, and provide more inspiration for designing more robust deep neural networks. Reference | Related Articles | Metrics

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Survey of Few-Shot Image Classification Research AN Shengbiao, GUO Yuqi, BAI Yu, WANG Tengbo Journal of Frontiers of Computer Science and Technology    2023, 17 (3): 511-532.   DOI: 10.3778/j.issn.1673-9418.2210035 In recent years, artificial intelligence algorithms represented by deep learning have achieved success in many fields by relying on large-scale datasets and huge computing resources. Among them, the image classification technology in the field of computer vision develops vigorously, and many mature visual task classification models emerge. All these models need to use a large number of annotated samples for training. However, in actual scena-rios, due to many restrictions, the amount of data is scarce, and it is often difficult to obtain high-quality annotated samples of corresponding scale. Therefore, how to use a small number of samples for learning has gradually become a research hotspot. In view of the classification task system, this paper reviews the current work related to few-shot image classification. Few-shot learning mainly adopts deep learning methods such as meta-learning, metric learning and data enhancement. This paper summarizes the research progress and typical technical models of few-shot image classification from supervised, semi-supervised and unsupervised levels, as well as the performance of these model methods on several public datasets, and makes comparative analysis from the mechanism, advantages, limitations, etc. Finally, the technical difficulties and future trends of few-shot image classification are discussed. Reference | Related Articles | Metrics

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Survey on 3D Human Pose Estimation of Deep Learning WANG Shichen, HUANG Kai, CHEN Zhigang, ZHANG Wendong Journal of Frontiers of Computer Science and Technology    2023, 17 (1): 74-87.   DOI: 10.3778/j.issn.1673-9418.2205070 The purpose of 3D human pose estimation is to predict information such as the 3D coordinate position and angle of human joint points, and construct human representations (such as human bones) for further analysis of human posture. With the continuous advancement of deep learning methods, more and more high-performance 3D human pose estimation methods based on deep learning have been proposed. However, due to the human occlusion of the picture and the large demand for training scale, there are still challenges in 3D human pose estimation. The research purpose of this paper is to review a number of research papers in recent years, analyze and compare the reasoning process and core elements of these methods, and comprehensively elaborate the 3D human pose estimation methods based on deep learning in recent years. In addition, this paper also introduces the relevant data- sets and evaluation indicators, compares the experimental data of some models on the Human3.6M dataset, Campus dataset and Shelf dataset, and analyzes and compares the experimental results. Finally, according to the results of this survey, the difficulties and challenges faced by the current 3D human pose estimation are discussed, and the future development of 3D human pose estimation is discussed. Reference | Related Articles | Metrics

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Survey of Sign Language Recognition and Translation YAN Siyi, XUE Wanli, YUAN Tiantian Journal of Frontiers of Computer Science and Technology    2022, 16 (11): 2415-2429.   DOI: 10.3778/j.issn.1673-9418.2205003 Different from spoken languages, sign language is mainly composed of continuous gestures. Sign langu-age recognition and translation are important means of facilitating barrier-free communication between the hearing-impaired and the hearing person. The sign language recognition and translation research task is a typical multi-domain cross-study by processing and analyzing sign language videos and displaying the recognition results in text form. In recent years, sign language recognition and translation research based on deep learning has made great progress. In order to facilitate researchers to systematically and comprehensively understand the research tasks of sign language recognition and translation, the review work is carried out from the perspectives of sign language recognition and sign language translation. Firstly, the translation research work is classified and summarized and its characteristics are analyzed. Secondly, the common sign language recognition and translation research datasets of different countries are summarized and classified from the perspectives of isolated sign language words and continuous sign language sentences. Based on the difference in research tasks, the corresponding evaluation index system is introduced. Finally, the major challenges of current research on sign language recognition and translation are summarized from the aspects of effective information extraction of sign language visual features, multi-cue weight assignment, relationship between sign language and natural language grammar, and sign language dataset resources. Table and Figures | Reference | Related Articles | Metrics

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Multi-modal Public Opinion Analysis Based on Image and Text Fusion LIU Ying, WANG Zhe, FANG Jie, ZHU Tingge, LI Linna, LIU Jiming Journal of Frontiers of Computer Science and Technology    2022, 16 (6): 1260-1278.   DOI: 10.3778/j.issn.1673-9418.2110056 Due to the continuous popularization of the Internet and mobile phones, people have gradually entered a participatory network era. More and more people like to publish their opinions, comments and emotions through text and image on the Internet. Effective analysis of these text and image information can not only help companies better improve the quality of their products, but also provide guidance for government decision-making and social production and life. This paper summarizes the sentiment analysis of online public opinion based on multi-modal image and text fusion. Firstly, it summarizes the basic concepts of public opinion analysis. Secondly, it explains the process of single-modal text and visual sentiment analysis on social media. Thirdly, it summarizes the public opinion analysis algorithms based on image and text fusion, and divides the algorithms into feature layer fusion, decision layer fusion and linear regression model according to different fusion strategies. In addition, it summarizes the commonly used multi-modal sentiment analysis for social media dataset. Finally, the difficulties of online opinion analysis and future research directions are discussed. Table and Figures | Reference | Related Articles | Metrics

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Summary of Expression Recognition Technology HONG Huiqun, SHEN Guiping, HUANG Fenghua Journal of Frontiers of Computer Science and Technology    2022, 16 (8): 1764-1778.   DOI: 10.3778/j.issn.1673-9418.2110049 Facial expression is an important basis for judging human emotion and human-computer interaction. The development of traditional machine learning and deep learning has brought many opportunities and challenges to facial expression recognition and analysis. First, this paper analyzes the internal relationship and difference between expression recognition and emotion analysis, and points out that expression recognition focuses on identifying facial expression and emotion. Then, it summarizes the advantages and disadvantages of expression recognition tech-nology based on single mode dataset and traditional machine learning method, and introduces the expression recognition technology based on single mode dataset and deep learning method. Then, it points out that the expres-sion recognition technology based on single mode data has certain limitations, such as insufficient quantity and quality of datasets, generally low recognition accuracy, mostly staying in the laboratory research stage. Then, it introduces the expression recognition and inter-modal fusion methods based on multimodal datasets, and introduces the commonly used multimodal expression datasets. The expression recognition technology based on multimodal dataset and the fusion technology between modes are analyzed, including feature level fusion, decision level fusion and hybrid fusion. Finally, the expression recognition analysis technology is summarized and prospected: considering the problem of dataset, more high-quality expression datasets in natural environment can be constructed; multimodal data-sets can also be constructed combined with physiological signals such as posture and EEG; GAN network can be used to enhance data; pay attention to the extraction of micro-expression, and study multimodal fusion algorithm, etc. Table and Figures | Reference | Related Articles | Metrics

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Review on Multi-lable Classification LI Dongmei, YANG Yu, MENG Xianghao, ZHANG Xiaoping, SONG Chao, ZHAO Yufeng Journal of Frontiers of Computer Science and Technology    2023, 17 (11): 2529-2542.   DOI: 10.3778/j.issn.1673-9418.2303082 Multi-label classification refers to the classification problem where multiple labels may coexist in a single sample. It has been widely applied in fields such as text classification, image classification, music and video classification. Unlike traditional single-label classification problems, multi-label classification problems become more complex due to the possible correlation or dependence among labels. In recent years, with the rapid development of deep learning technology, many multi-label classification methods combined with deep learning have gradually become a research hotspot. Therefore, this paper summarizes the multi-label classification methods from the traditional and deep learning-based perspectives, and analyzes the key ideas, representative models, and advantages and disadvantages of each method. In traditional multi-label classification methods, problem transformation methods and algorithm adaptation methods are introduced. In deep learning-based multi-label classification methods, the latest multi-label classification methods based on Transformer are reviewed particularly, which have become one of the mainstream methods to solve multi-label classification problems. Additionally, various multi-label classification datasets from different domains are introduced, and 15 evaluation metrics for multi-label classification are briefly analyzed. Finally, future work is discussed from the perspectives of multi-modal data multi-label classification, prompt learning-based multi-label classification, and imbalanced data multi-label classification, in order to further promote the development and application of multi-label classification. Reference | Related Articles | Metrics

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Review of Deep Learning in Classification of Tongue Image WU Xin, XU Hong, LIN Zhuosheng, LI Shengke, LIU Huilin, FENG Yue Journal of Frontiers of Computer Science and Technology    2023, 17 (2): 303-323.   DOI: 10.3778/j.issn.1673-9418.2208052 With the rapid development of technology and the improvement of computing power, deep learning will be widely used in the field of tongue classification. The classification of tongue image is an important part of tongue diagnosis in traditional Chinese medicine (TCM). Traditional tongue diagnosis is dependent on understanding and judgment skills gained from personal experience under the guidance of basic theory, which leads to ambiguity and variability, affecting diagnostic reproducibility. In order to reduce the error of subjective judgment, many researchers have devoted themselves to realizing the objectification, quantification and automation of tongue diagnosis in TCM through deep learning. This paper analyzes and summarizes the research status of tongue image classification methods based on deep learning. In the study of tongue image classification, various deep learning methods are used as the research objects. The research objects are divided into categories based on early neural networks, convolutional neural networks, regional convolutional neural networks, transfer learning and other methods for summary analysis. TCM syndromes/diseases in tongue diagnosis and classification of physical constitution are discussed. A 5-fold cross-validation experiment is conducted with the public tongue diagnosis dataset on Kaggle. The dataset is a small sample of the dentate tongue, and the classification methods based on deep learning and transfer learning are evaluated. The research development of single-label and multi-label classification is discussed and prospected. Reference | Related Articles | Metrics

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Survey of Research on Smart Contract Vulnerability Detection LI Leixiao, ZHENG Yue, GAO Haoyu, XIONG Xiao, NIU Tieming, DU Jinze, GAO Jing Journal of Frontiers of Computer Science and Technology    2022, 16 (11): 2456-2470.   DOI: 10.3778/j.issn.1673-9418.2203024 As an important part of blockchain technology, smart contracts are widely used in various fields through decentralized applications written by smart contracts, providing important technical support for the development and application of blockchain. However, the development has brought security problems at the same time, and a large number of vulnerability attacks against smart contracts have made researchers pay more attention to the security vulnerabilities of smart contracts. How to quickly and accurately perform vulnerability detection has become an urgent problem to be solved. Firstly, through the analysis of common vulnerabilities such as reentrancy attack vulnerabilities, integer overflow and access control vulnerabilities, researchers can fully understand the common vulnerabilities. Secondly, by investigating the current status of vulnerability detection methods such as formal verification, symbolic execution, machine learning and their corresponding tools at home and abroad, analyzing and discussing the advantages and disadvantages of the tools, at the same time, replicating some tools for experiments, the performance of the vulnerability detection tools is demonstrated based on the detection speed, accuracy, and the number of vulnerabilities that support detection. Finally, suggestions for future research directions are given based on the analysis results of smart contract vulnerability detection tools. Table and Figures | Reference | Related Articles | Metrics

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Review of Multiple Traveling Salesman Model and Its Application ZHANG Shuohang, GUO Gaizhi Journal of Frontiers of Computer Science and Technology    2022, 16 (7): 1516-1528.   DOI: 10.3778/j.issn.1673-9418.2112006 As a generalization of the classical traveling salesman problem (TSP), the multiple traveling salesman problem (MTSP) is one of the well-known combinatorial optimization problems. However, as a classical NP hard problem, the problem scale and computational complexity of the multiple traveling salesman problem have very high requirements for the solution method. This paper focuses on the multiple traveling salesman problem. Firstly, several characteristics, objective functions, problem constraints and variants of MTSP model are subdivided. Secondly, it classifies and sorts out the specific methods of several common heuristic algorithms in solving MTSP, and compares the similarities and differences of optimization objectives and solutions under different algorithms, so as to understand the general methods of solving multiple traveling salesman problems among different algorithms more intuitively. With the continuous development of multiple traveling salesman problem, scholars are not satisfied with simply solving mathematical problems, and try to regard many practical problems that meet conditions as multiple traveling salesman problems. This paper summarizes the specific construction methods of MTSP model in the context of practical applications such as logistics distribution, wireless sensor network, emergency rescue and UAV collaborative task planning. From the perspective of application results, using MTSP model to solve practical problems can not only reduce enterprise and individual costs, improve revenue, but also promote the development of this field towards a more efficient and intelligent direction. This paper mainly studies the multiple traveling salesman model and its application, which fills the gap in this research field. Table and Figures | Reference | Related Articles | Metrics

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Research and Development of Medical Knowledge Graph Reasoning DONG Wenbo, SUN Shiliang, YIN Minzhi Journal of Frontiers of Computer Science and Technology    2022, 16 (6): 1193-1213.   DOI: 10.3778/j.issn.1673-9418.2111031 Knowledge graphs can effectively organize and represent knowledge, which have been applied to many advanced applications, for example, intelligent medicine. However, the medical knowledge graphs constructed manually or automatically are usually incomplete, which seriously limits their performance. Medical knowledge reasoning can complete medical knowledge graph and assist doctors in medical diagnosis. This paper first gives the basic concept and definition of medical knowledge reasoning, and then summarizes the key technologies of constructing medical knowledge graphs and the auxiliary diagnosis methods based on medical knowledge reasoning. Subsequently, this paper reviews the research development of medical knowledge reasoning, and classifies its reasoning methods into rule-based medical reasoning, representation learning-based medical reasoning and deep learning-based medical reasoning. For each category, representative algorithms and newly proposed algorithms are presented. The main feature of this survey is that it provides a comprehensive introduction for the recent development of knowledge graph reasoning on the basis of coherence with early methods. Finally, this paper prospects the development of medical knowledge reasoning based on the major challenges and key problems faced by medical knowledge reasoning, hoping to promote further research in this rapidly developing field. Table and Figures | Reference | Related Articles | Metrics

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High-Performance Implementation and Optimization of Cooley-Tukey FFT Algorithm GUO Jinxin, ZHANG Guangting, ZHANG Yunquan, CHEN Zehua, JIA Haipeng Journal of Frontiers of Computer Science and Technology    2022, 16 (6): 1304-1315.   DOI: 10.3778/j.issn.1673-9418.2011092 The fast Fourier transform (FFT) algorithm is considered as an important element of the processor’s basic software ecology, and it is widely applied in the field of engineering, science, physics and mathematics. Meanwhile, the requirements for the performance of FFT in these applications are also continuously rising. Therefore, it is of definite significance to study the high-performance implementation of FFT algorithm, especially the high-performance implementation of large radices of FFT in ARMv8 and X86-64, and to improve the calculation performance of FFT algorithm. In view of the architectural features of the ARMv8 and X86-64 computing platforms, this paper studies the high-performance implementation and optimization methods of the FFT algorithm. Through the application of butterfly network optimization, large radices network stages decrease, large radices butterfly computation optimization, SIMD (single instruction multiple data) assembly optimization, and register usage optimization methods, this paper effectively improves the performance of the FFT algorithm, considerably improves the calculation performance of the large radices of FFT, and solves the performance bottlenecks of insufficiency of register resources. Lastly, the summary of a set of Cooley-Tukey FFT algorithm high-performance implementation strategies and optimization solutions is made. The experimental results indicate that for the ARM and X86-64 processors, the FFT algorithm implemented can achieve a significant improvement in performance compared with ARMPL (ARM performance library), Intel MKL (math kernel library) and FFTW (fastest Fourier transform in the West) and can achieve a significant improvement in performance compared with small and medium radices. Table and Figures | Reference | Related Articles | Metrics

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Research and Application Progress of Chinese Medical Knowledge Graph FAN Yuanyuan, LI Zhongmin Journal of Frontiers of Computer Science and Technology    2022, 16 (10): 2219-2233.   DOI: 10.3778/j.issn.1673-9418.2112118 Knowledge graph is a large-scale semantic network that gives machine background knowledge. Using knowledge graph to organize heterogeneous medical information can effectively improve the utilization value of massive medical resources and promote the development of medical intelligence. This paper describes the research, construction and application status of knowledge graph in medical field from three dimensions: the key technology of knowledge graph, the construction of medical knowledge graph and the application of medical knowledge graph, and explores the topics worthy of research in the future. Firstly, the development of knowledge representation, knowledge extraction, knowledge fusion and knowledge inference are systematically summarized, their latest progress is discussed, and the technical difficulties in the construction of Chinese medical knowledge graph are analyzed. Secondly, the existing research on Chinese medical knowledge graph is illustrated from three perspectives of medical ontology, general practice knowledge graph and single disease medical knowledge graph. The research characteristics of Chinese medical knowledge graph are also analyzed. Finally, the application of medical know-ledge graph in semantic search, decision support and intelligent question answering are analyzed, and the new app-lication scenarios are discussed. In view of the challenges faced by Chinese medical knowledge graph, such as low standardization of terminology, lack of annotated corpus, insufficient technical research and limitations of applica-tion scenarios, the future research directions of Chinese medical knowledge graph are prospected. Table and Figures | Reference | Related Articles | Metrics

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Survey on Applications of Knowledge Graph Embedding in Recommendation Tasks TIAN Xuan, CHEN Hangxue Journal of Frontiers of Computer Science and Technology    2022, 16 (8): 1681-1705.   DOI: 10.3778/j.issn.1673-9418.2112070 Recommendation systems are designed to recommend personalized content to improve user experience. At present, the recommendation systems still face some challenges such as poor interpretability, cold start problem and serialized recommendation modeling. Recently, the knowledge graph (KG) containing a large amount of semantic and structural information has been widely used in a variety of different recommendation tasks to alleviate the above problems. This paper systematically reviews the innovative applications of knowledge graph embedding (KGE) in different recommendation tasks. It first summarizes three common recommendation tasks and four applying goals of knowledge graph embedding. Then, it generalizes four types of knowledge graph embedding methods according to specific technologies, including traditional embedding method, embedding propagation method, heterogeneous graph embedding method and graph neural network based method. It further elaborates on the applying characteristics and strategies of the above four methods in different recommendation tasks, and evaluates advantages and limitations of each method. Also, it conducts qualitative and quantitative analysis of the associations and differences of four methods from multiple aspects. Finally, it puts forward some views on the development trend of applying knowledge graph embedding for recommendation systems, and proposes several noteworthy development directions in the future from multiple perspectives. Table and Figures | Reference | Related Articles | Metrics

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