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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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    Research Progresses of Multi-modal Intelligent Robotic Manipulation
    ZHANG Qiuju, LYU Qing
    Journal of Frontiers of Computer Science and Technology    2023, 17 (4): 792-809.   DOI: 10.3778/j.issn.1673-9418.2212070
    The flexible production trend in manufacturing and the diversified expansion of applications in service industry have prompted fundamental changes in the application demands of robots. The uncertainty of tasks and environments imposes higher requirements on the intelligence of robotic manipulation. The use of multi-modal information to monitor robotic manipulation can effectively improve the intelligence and flexibility of robot. This paper provides an in-depth analysis of the role of multi-modal information in enhancing the intelligence of robotic manipulation from the perspective of multi-modal information fusion on the basis of the two key issues of manipulation cognition and manipulation control. Firstly, the concepts of intelligent robotic manipulation and multi-modal information are clarified, and the merits of applying multi-modal information are also introduced. Then, the commonly used perception models and control methods are deeply analyzed and the existing work is sorted out and introduced in a systematic way. According to different levels of perception goals, robotic manipulation perception is divided into object perception, constraint perception and state perception; according to different control methods, the most commonly used control fusion based on analysis model, imitation learning control and reinforcement learning control are introduced. Finally, the current technical challenges and potential development trends are also discussed.
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    Review of Real-Time Ray Tracing Technique Research
    YAN Run, HUANG Libo, GUO Hui, WANG Yongxin, ZHANG Xincheng, ZHANG Hongru
    Journal of Frontiers of Computer Science and Technology    2023, 17 (2): 263-278.   DOI: 10.3778/j.issn.1673-9418.2207067
    Ray tracing has been regarded as the next generation of mainstream image rendering technology for a long time because of the authenticity of its rendering effect, and it is a hot research point in the field of computer graphics. In recent years, academics and commercials have extensively researched real-time ray tracing. To promote the research of real-time ray tracing, this paper reviews, analyses, and summaries the related literature. Firstly, the concept, algorithms, and classification of acceleration structures are introduced. Three commercial graphics processing units (GPU) supporting ray tracing are introduced and the differences between them are compared. This paper summarizes the optimization of ray tracing from six aspects, ray packet, stackless traversal, ray reorder, wide BVH, denoising techniques, and real-time ray tracing combined with the artificial?neural network, and expounds on the advantages and disadvantages of the relevant specific methods. Based on the acceleration of the algorithms, the hardware acceleration method on GPU and the dedicated architectures are summarized. Finally, this paper makes a brief summary of the content, points out the difficulties that real-time ray tracing is still challenged, and looks forward to the future development direction. It can help researchers systematically understand real-time ray tracing status and provide follow-up research ideas.
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    Survey on Personality Recognition Based on Social Media Data
    LIN Hao, WANG Chundong, SUN Yongjie
    Journal of Frontiers of Computer Science and Technology    2023, 17 (5): 1002-1016.   DOI: 10.3778/j.issn.1673-9418.2212012
    Personality is a stable construction, which is associated with thoughts, emotions and behaviors of human. Any technology involved in understanding, analyzing and predicting human behaviors may benefit from personality recognition. Accurately recognizing the personality will contribute to the research of human-computer interaction, recommendation system, cyberspace security, etc. Social media provides high-quality data for personality recog-nition, while classic personality measurement methods such as self-report scales and projective tests can no longer match the social media data in the age of big data. Moreover, the current mainstream personality recognition methods based on machine learning still have lots of room for performance improvement. Therefore, this paper investigates the literature on personality recognition based on social media data, introduces the background knowledge of personality recognition and summarizes the research status according to the data types of recognition model input used, specifically based on social text data, social image data, social application data and multimodal data. Finally, this paper proposes seven future research directions of personality recognition based on social media data.
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    Survey of Fake News Detection with Multi-model Learning
    LIU Hualing, CHEN Shanghui, CAO Shijie, ZHU Jianliang, REN Qingqing
    Journal of Frontiers of Computer Science and Technology    2023, 17 (9): 2015-2029.   DOI: 10.3778/j.issn.1673-9418.2301064
    While social media brings convenience to people, it has also become a channel for the arbitrary spread of fake news. If not detected and stopped in time, it is easy to cause public panic and social unrest. Therefore, exploring accurate and efficient fake news detection technology has high theoretical value and practical significance. This paper provides a comprehensive overview of the related fake news detection techniques. Firstly, the relevant concepts of multi-modal fake news are sorted and summarized, and the trend of changes in single-modal and multi-modal news datasets is analyzed. Secondly, this paper introduces single-modal fake news detection techniques based on machine learning and deep learning, which have been widely used in the field of fake news detection. However, traditional single-modal techniques cannot fully explore the deep logic of fake news because fake news usually contains multiple data presentations. Thus, they are unable to effectively deal with the challenges brought by multi-modal fake news data. To address this issue, this paper summarizes and discusses advanced multi-modal fake news detection techniques from the perspectives of multi-stream and graph architectures, and explores their concepts and potential drawbacks. Finally, this paper analyzes the difficulties and bottlenecks in the current research on fake news detection and provides future research directions based on these analyses.
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    Review of Privacy-Preserving Research in Recommendation Systems
    FENG Han, YI Huawei, LI Xiaohui, LI Rui
    Journal of Frontiers of Computer Science and Technology    2023, 17 (8): 1814-1832.   DOI: 10.3778/j.issn.1673-9418.2211069
    The recommendation system needs to extract the historical data information of the relevant users on a large scale as the training set of the prediction model. The larger and more specific the amount of data provided by users is, the easier it is for the personal information to be inferred, which is easy to lead to the leakage of personal privacy, so that the user’s trust in the service provider is reduced and the relevant data are no longer provided for the system, resulting in the reduction of the system recommendation accuracy or even more difficult to complete the recommendation. Therefore, how to obtain user information for effective recommendation with high accuracy under the premise of protecting user privacy has become a research hotspot. This paper firstly summarizes the privacy-preserving technology, including differential privacy technology, homomorphic encryption technology, federated learning and secure multi-party computing technology, and compares these commonly used privacy-preserving tech-nologies. Then, from the perspective of balancing the relationship between privacy injection and recommendation accuracy, the privacy-preserving technologies adopted by the user side, the server side and the user-server side are introduced, and the research results of privacy-preserving in recommendation systems at home and abroad are systematically elaborated. Based on this, a summary, comparison, and analysis are conducted. Next, the experi-mental results of the recommendation algorithm based on differential privacy technology are compared, and the shortcomings of the corresponding technology are analyzed. Finally, the future development directions of the recommendation system based on privacy-preserving are prospected.
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    Research Progress and Prospect of Ring Signatures
    XIE Jia, LIU Shizhao, WANG Lu, GAO Juntao, WANG Baocang
    Journal of Frontiers of Computer Science and Technology    2023, 17 (5): 985-1001.   DOI: 10.3778/j.issn.1673-9418.2210022
    As a special group signature, ring signature has been widely used in anonymous voting, anonymous deposit and anonymous transaction because it can not only complete the signature without the cooperation of ring members, but also ensure the anonymity of the signer. Firstly, this paper takes time as the main line, divides the development of ring signatures into different stages, and divides ring signatures into threshold ring signatures, linkable ring signatures, ring signatures with revocable anonymity, and repudiable ring signatures according to the attributes in each stage. Through the analysis of the development process of ring signature, it can be seen that the research progress of ring signature in the field of threshold ring signature and linkable ring signature is prominent, and their application fields are also the most extensive. In the post-quantum era, cryptographic schemes based on traditional number theory problems such as large integer factorization and discrete logarithms are no longer secure, and lattice-based public key cryptography has become the best candidate for cryptographic standards in the post-quantum era because of its advantages such as quantum-immune, the reduction of the worst-case to average-case, and so on. Therefore, this paper focuses on the detailed analysis and efficiency comparison of existing lattice-based threshold ring signatures and lattice-based linkable ring signatures. The inherent anonymity of ring signatures makes them have unique advantages in the era of industrial blockchain, so this paper elaborates on several applications of ring signatures in blockchain. For example, the application of ring signature in anonymous voting, medical data sharing, and Internet of vehicles is summarized and analyzed. The application significance of ring signature in the fields of virtual currency, SIP cloud call protocol, and Ad Hoc network is briefly sorted out. Finally,  the research of ring signature technology in recent years is analyzed, and the current problems are summarized.
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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.
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    Overview of Visual Inertial Odometry Technology Based on Deep Learning
    WANG Wensen, HUANG Fengrong, WANG Xu, LIU Qinglin, YI Boheng
    Journal of Frontiers of Computer Science and Technology    2023, 17 (3): 549-560.   DOI: 10.3778/j.issn.1673-9418.2209014
    Visual inertial odometer can well realize the complementary advantages of vision and inertial sensors, and obtain high precision 6-DOF navigation and positioning, so it has a very wide range of applications. However, the errors of sensors themselves, the disturbance of abnormal visual environment, and the space-time calibration errors between multi-sensor will interfere with the navigation results, leading to the decline of navigation accuracy. In recent years, the deep learning method is developing rapidly. With its powerful data processing and prediction ability, it provides a new direction for the development of visual inertial odometer. This paper reviews the main development achievements of deep learning-based methods. First of all, according to the fusion mode, the research methods are summarized, which are divided into the method combining deep learning with traditional models and the end-to-end method based on deep learning. Then, according to the type of deep learning, visual inertial odometer can be divided into supervised learning and unsupervised/self-supervised learning methods, and the model structures of these methods are described respectively. Next, the optimization and evaluation methods of the system are summarized, and the performance of some of them is compared. Finally, this paper summarizes the key and difficult problems that need to be solved in this field, and looks forward to the future development.
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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.
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    Review of Research on Fatigue Driving Detection Based on Driver Facial Features
    YANG Yanyan, LI Leixiao, LIN Hao
    Journal of Frontiers of Computer Science and Technology    2023, 17 (6): 1249-1267.   DOI: 10.3778/j.issn.1673-9418.2208041
    Fatigue driving is one of the main factors that threaten the safety of drivers and traffic. Efficient and accurate fatigue driving detection method can effectively ensure the safety of drivers and their surrounding traffic, maintain traffic order, and reduce property losses and casualties. The fatigue driving detection method based on the driver’s physiological characteristics and vehicle driving information has many limitations, such as being unfriendly to the driver and having numerous influencing factors. Therefore, the fatigue driving detection method based on the driver’s facial features has become a research hotspot. Firstly, the facial feature performance of fatigue driving is described, and advantages, disadvantages and application scenarios of common public datasets in the field of fatigue driving are summarized. Secondly, the advantages and disadvantages of common face detection algorithms in the field of fatigue driving detection are analyzed and studied by using open datasets and comparative experiments. Then, this paper generalizes the process of detection methods based on driver facial features, whose methods and technologies used in the key steps are reviewed. Furthermore, fatigue discriminant parameters and methods of fatigue driving results prediction are summarized. Finally, this paper ends with a discussion of current challenges of fatigue driving detection methods based on driver facial features and looks forward to the future research.
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    High-Speed Tracking Algorithm Based on Siamese Network with Enhanced Features
    LI Hongjin, PENG Li
    Journal of Frontiers of Computer Science and Technology    2023, 17 (2): 396-408.   DOI: 10.3778/j.issn.1673-9418.2105091
    In recent years, real-time object tracking technology has played an important role in many complex vision systems. As a key component, tracking algorithms have high accuracy and meet real-time requirements. SiamFC algorithm has received considerable attention because it can better balance accuracy and speed. However, the SiamFC algorithm uses a shallow backbone network, and the extracted features are difficult to cope with the complex and challenging tracking scenarios, which makes the tracker easily drift. In order to simultaneously imp-rove the tracking accuracy and speed, a high-speed tracking algorithm based on lightweight Siamese network with enhanced features is proposed. Firstly, the improved lightweight network ShuffleNetV2 is applied as the backbone network to extract features, which greatly improves the tracking speed while reducing the amount of model parameters and calculations. Secondly, a dual attention module including channel attention and spatial attention is embedded at the ends of the template branch within Siamese network, aiming at adjusting the response weights of different channels and spatial positions. Thus, the features that are useful for tracking are highlighted. Finally, the hierarchical feature fusion strategy is adopted, and the deep semantic features and shallow structure features extracted by the network are used to represent the target from multiple angles. Experimental results show that the proposed algorithm has greater advantages in tracking accuracy and stronger robustness in difficult scenarios in comparison with some current outstanding tracking algorithms on OTB100 and VOT2018 datasets. At the same time, the algo-rithm speed can reach 110 FPS under NVIDIA GTX1070, which can better balance tracking accuracy and speed in comparison with SiamFC algorithm.
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    Multi-head Self-attention Neural Network for Detecting EEG Epilepsy
    TONG Hang, YANG Yan, JIANG Yongquan
    Journal of Frontiers of Computer Science and Technology    2023, 17 (2): 442-452.   DOI: 10.3778/j.issn.1673-9418.2104089
    Epilepsy is a life-threatening and challenging nervous system disease. There are still many challenges in the detection of epilepsy based on electroencephalogram (EEG). Because the EEG signal is unstable, different patients show different seizure patterns. In addition, EEG detection is time-consuming and laborious, which will not only bring heavy burden to medical staff, but also easily lead to false detection. Therefore, it is necessary to study an efficient automatic epilepsy detection technology across multiple patients. In this paper, an epileptic EEG detection method (convolutional attention bidirectional long short-term memory network, CABLNet) based on the multi-head self-attention mechanism neural network is proposed. Firstly, the convolution layer is used to capture short-term temporal patterns of EEG time series and local dependence among channels. Secondly, this paper uses the multi-head self-attention mechanism to capture the long-distance dependence and time dynamic correlation of the short-term time pattern feature vectors with temporal relationship. Thirdly, the context representation is sent into a bidirectional long short-term memory (BiLSTM) to extract the information in the front and back directions. Finally, logsoftmax function is used for training and classification. Using CHB-MIT scalp EEG database data, the sensitivity, specificity, accuracy and F1-score are 96.18%, 97.04%, 96.61% and 96.59% respectively. The results show that the proposed method is superior to the existing methods and significantly improved in epilepsy detection performance, which is of great significance to the auxiliary diagnosis of epilepsy.
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    Liver CT Image Segmentation Method Based on MSFA-Net
    SHEN Huaiyan, WU Yun
    Journal of Frontiers of Computer Science and Technology    2023, 17 (3): 646-656.   DOI: 10.3778/j.issn.1673-9418.2106020
    Automatic segmentation of the liver from the patient’s abdominal CT images is significant for the diagnosis of liver disease. Since the bottom-up feature fusion methods in U-Net ignore the importance of low-level features, the segmentation results are not satisfactory. In addition, the gray values for the liver and its adjacent organs are very similar, it is therefore difficult to distinguish some tiny detail features. To address the above problems, a liver segmentation network based on multi-scale semantic features fusion and attention mechanism (MSFA-Net) is proposed. Firstly, dilated residual convolution (DRC) is used to capture multi-scale features. Then, the MSFA module combines top-down and bottom-up multi-scale feature fusion methods with the attention mecha-nism to fully fuse multi-scale features and pay attention to tiny features. Finally, the feature maps are summed by deep supervise (DS) to improve the segmentation effect. The ablation study is performed on the MICCAI2017 LiTS and 3DIRCADb datasets. The dice per case (DC) and dice global (DG) scores of 0.961 and 0.965 are obtained on the LiTS dataset, with an improvement of 3.4% and 2.0% respectively compared with baseline network. Both DC and DG scores of 0.965 are obtained on the 3DIRCADb dataset, with an increase of 3.5% and 3.3% respectively compared with baseline network.
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    Survey on Inductive Learning for Knowledge Graph Completion
    LIANG Xinyu, SI Guannan, LI Jianxin, TIAN Pengxin, AN Zhaoliang, ZHOU Fengyu
    Journal of Frontiers of Computer Science and Technology    2023, 17 (11): 2580-2604.   DOI: 10.3778/j.issn.1673-9418.2303063
    Knowledge graph completion can make knowledge graph more complete. However, traditional knowledge graph completion methods assume that all test entities and relations appear in the training process. Due to the evolving nature of real world KG, once unseen entities or relations appear, the knowledge graph needs to be retrained. Inductive learning for knowledge graph completion aims to complete triples containing unseen entities or unseen relations without training the knowledge graph from scratch, so it has received much attention in recent years. Firstly, starting from the basic concept of knowledge graph, this paper divides knowledge graph completion into two categories: transductive and inductive. Secondly, from the theoretical perspective of inductive knowledge graph completion, it is divided into two categories: semi-inductive and fully-inductive, and the models are summarized from this perspective. Then, from the technical perspective of inductive knowledge graph completion, it is divided into two categories: based on structural information and based on additional information. The methods based on structural information are subdivided into three categories: based on inductive embedding, based on logical rules and based on meta learning, and the methods based on additional information are subdivided into two categories: based on text information and other information. The current methods are further subdivided, analyzed and compared. Finally, it forecasts the main research directions in the future.
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    Local and Global Feature Fusion Network Model for Aspect-Based Sentiment Analysis
    XIA Hongbin, LI Qiang, LIU Yuan
    Journal of Frontiers of Computer Science and Technology    2023, 17 (4): 902-911.   DOI: 10.3778/j.issn.1673-9418.2107069
    Aspect-based sentiment analysis aims to predict the sentiment polarity of a specific aspect in a sentence or document. Most of the recent research is to use attention mechanism and external semantic knowledge to model the global context. The sentiment polarity of an aspect often depends on the local context which is highly related to the aspect, but most models focus too much on the global context, which makes the parameter amount of the model generally larger, resulting in an increase in the amount of calculation. To this end, this paper proposes a lightweight network model based on multi-head attention mechanism, which is named local and global feature fusion network. Firstly, a bidirectional gated recurrent unit is used to encode the context. Secondly, context words that are less relevant to the aspect term are masked out according to the semantic relation distance with the aspect term, so as to obtain the local context representation. Finally, the local and global context are extracted respectively by multi-head Aspect-aware attention network, and the results of the two extraction are combined. In addition, the pre-trained BERT is also applied to the proposed model and better results are obtained. Experiments are conducted on three datasets: Twitter, Laptop, and Restaurant. Accuracy and F1 indicators are used for evaluation. Experimental results show that the proposed model achieves better results than other aspect-based sentiment classification algorithms with a small amount of parameters.
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    Review of Visual Question Answering Technology
    WANG Yu, SUN Haichun
    Journal of Frontiers of Computer Science and Technology    2023, 17 (7): 1487-1505.   DOI: 10.3778/j.issn.1673-9418.2303025
    Visual question answering (VQA) is a popular cross-modal task that combines natural language pro-cessing and computer vision techniques. The main objective of this task is to enable computers to intelligently recognize and retrieve visual content and provide accurate answers. VQA involves the integration of multiple technologies such as object recognition and detection, intelligent question answering, image attribute classification, and scene analysis. It can support a wide range of cutting-edge interactive AI tasks such as visual dialogue and visual navigation, and has broad application prospects and great value. Over the past few years, the development of computer vision, natural language processing, and cross-modal AI models has provided many new technologies and methods for achieving the task of visual question answering. This paper mainly summarizes the mainstream models and specialized datasets in the field of visual question answering between 2019 and 2022. Firstly, this paper provides a review and discussion of the mainstream technical methods used in the key steps of implementing the visual question answering task, based on the module framework. Next, it subdivides various types of models in this field according to the technical methods adopted by mainstream models, and briefly introduces their improvement focus and limitations. Then, it summarizes the commonly used datasets and evaluation metrics for visual question answering, and compares and discusses the performance of several typical models. Finally, this paper focuses on the key issues that need to be addressed in the current visual question answering field, and predicts and prospects the future application and technological development in this field.
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    Protein-HVGAE: Protein Encoding Method in Hyperbolic Space
    WANG Haobai, SHEN Xin, HUANG Weijian, CHEN Kejia
    Journal of Frontiers of Computer Science and Technology    2023, 17 (3): 701-708.   DOI: 10.3778/j.issn.1673-9418.2105041
    Protein function prediction, protein interaction prediction and complex identification in protein-protein interaction (PPI) networks are important tasks in the field of bioinformatics, which rely heavily on the protein expression. Since the PPI network is a scale-free network dominated by a small number of hub nodes, it is difficult for the embedding method in traditional Euclidean space to capture the hierarchical structure in the network, resulting in unsatisfactory protein embeddings. This paper proposes a protein auto-encoder in hyperbolic space, Protein-HVGAE (hyperbolic graph auto-encoder for protein interaction networks). This paper uses two hyperbolic graph convolutional networks as encoders, calculates the mean and variance of the hidden layer and captures the hierarchical structure of the PPI network in hyperbolic spaces with different curvatures to distinguish the low-dimensional representation of each node; it uses the Fermi-Dirac function as the decoder, and reconstructs the network through the inner product operation on the hyperbolic space. Experimental results in three PPI networks show that the performance of this model in two downstream tasks (i.e., PPI prediction and protein function prediction) is superior to the previous methods in Euclidean space (around 0.07 improvement of AUC in PPI prediction and 0.02 improvement of Macro-F1 in protein function prediction compared with VGAE model).
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