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Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Survey on Popularity Based Recommendation LEI Qinlan, TIAN Xuan Journal of Frontiers of Computer Science and Technology    2024, 18 (5): 1109-1134.   DOI: 10.3778/j.issn.1673-9418.2309016 Currently, popularity based recommendation has become a research hotspot. The use of popularity considerably improves the recommendation effects, while the Matthew effect caused by popularity bias has also garnered extensive attention among researchers. Some researchers consider combining both aspects to produce hybrid popularity based recommendation. Adopting the concept of popularity, a unified representation of popularity, popularity bias, and hybrid popularity is provided in this paper. Firstly, the background of popularity in the field of recommendation is introduced. Then, based on different perspectives, a comprehensive survey on popularity-enhanced recommendation methods, popularity debias recommendation methods, and hybrid popularity based recommendation methods is provided. Each type of method is further subdivided in specific subtasks of modeling or concrete strategies. The representative models of each method are introduced and analyzed, and their advantages and limitations are evaluated. The mechanisms and applicable scenarios of each method are also summarized in detail. Furthermore, the commonly used datasets, performance evaluation indicators and baseline are introduced. A comparative analysis of the representative methods performance is also listed. Finally, some opinions on the trends of popularity based recommendation are presented. An outlook on the technical difficulties and hotspots for future development from multiple perspectives is analyzed and predicted. Reference | Related Articles | Metrics Abstract （112） PDF （117） Select Survey of Research on SMOTE Type Algorithms WANG Xiaoxia, LI Leixiao, LIN Hao Journal of Frontiers of Computer Science and Technology    2024, 18 (5): 1135-1159.   DOI: 10.3778/j.issn.1673-9418.2309079 Synthetic minority oversampling technique (SMOTE) has become one of the mainstream methods for dealing with unbalanced data due to its ability to effectively deal with minority samples, and many SMOTE improvement algorithms have been proposed, but very little research existing considers popular algorithmic-level improvement methods. Therefore a more comprehensive analysis of existing SMOTE class algorithms is provided. Firstly, the basic principles of the SMOTE method are elaborated in detail, and then the SMOTE class algorithms are systematically analyzed mainly from the two levels of data level and algorithmic level, and the new ideas of the hybrid improvement of data level and algorithmic level are introduced. Data-level improvement is to balance the data distribution by deleting or adding data through different operations during preprocessing; algorithmic-level improvement will not change the data distribution, and mainly strengthens the focus on minority samples by modifying or creating algorithms. Comparison between these two kinds of methods shows that, data-level methods are less restricted in their application, and algorithmic-level improvements generally have higher algorithmic robustness. In order to provide more comprehensive basic research material on SMOTE class algorithms, this paper finally lists the commonly used datasets, evaluation metrics, and gives ideas of research in the future to better cope with unbalanced data problem. Reference | Related Articles | Metrics Abstract （247） PDF （148） Select Survey on Natural Scene Text Recognition Methods of Deep Learning ZENG Fanzhi, FENG Wenjie, ZHOU Yan Journal of Frontiers of Computer Science and Technology    2024, 18 (5): 1160-1181.   DOI: 10.3778/j.issn.1673-9418.2306024 Natural scene text recognition holds significant value in both academic research and practical applications, making it one of the research hotspots in the field of computer vision. However, the recognition process faces challenges such as diverse text styles and complex background environments, leading to unsatisfactory efficiency and accuracy. Traditional text recognition methods based on manually designed features have limited representation capabilities, which are insufficient for effectively handling complex tasks in natural scene text recognition. In recent years, significant progress has been made in natural scene text recognition by adopting deep learning methods. This paper systematically reviews the recent research work in this area. Firstly, the natural scene text recognition methods are categorized into segmentation-based and non-segmentation-based approaches based on character segmentation required or not. The non-segmentation-based methods are further subdivided according to their technical implementation characteristics, and the working principles of the most representative methods in each category are described. Next, commonly used datasets and evaluation metrics are introduced, and the performance of various methods is compared on these datasets. The advantages and limitations of different approaches are discussed from multiple perspectives. Finally, the shortcomings and challenges are given, and the future development trends are also put forward. Reference | Related Articles | Metrics Abstract （213） PDF （205） Select Survey of Transformer-Based Single Image Dehazing Methods ZHANG Kaili, WANG Anzhi, XIONG Yawei, LIU Yun Journal of Frontiers of Computer Science and Technology    2024, 18 (5): 1182-1196.   DOI: 10.3778/j.issn.1673-9418.2307103 As a fundamental computer vision task, image dehazing aims to preprocess degraded images by restoring color contrast and texture information to improve visibility and image quality, thereby the clear images can be recovered for subsequent high-level visual tasks, such as object detection, tracking, and object segmentation. In recent years, neural network-based dehazing methods have achieved notable success, with a growing number of Transformer-based dehazing approaches being proposed. Up to now, there is a lack of comprehensive review that thoroughly analyzes Transformer-based image dehazing algorithms. To fill this gap, this paper comprehensively sorts out Transformer-based daytime, nighttime and remote sensing image dehazing algorithms, which not only covers the fundamental principles of various types of dehazing algorithms, but also explores the applicability and performance of these algorithms in different scenarios. In addition, the commonly used datasets and evaluation metrics in image dehazing tasks are introduced. On this basis, analysis of the performance of existing representative dehazing algorithms is carried out from both quantitative and qualitative perspectives, and the performance of typical dehazing algorithms in terms of dehazing effect, operation speed, resource consumption is compared. Finally, the application scenarios of image dehazing technology are summarized, and the challenges and future development directions in the field of image dehazing are analyzed and prospected. Reference | Related Articles | Metrics Abstract （228） PDF （227） Select Survey on Solving Cold Start Problem in Recommendation Systems MAO Qian, XIE Weicheng, QIAO Yitian, HUANG Xiaolong, DONG Gang Journal of Frontiers of Computer Science and Technology    2024, 18 (5): 1197-1210.   DOI: 10.3778/j.issn.1673-9418.2308044 Recommender systems provide important functions in areas such as dealing with data overload, providing personalized consulting services, and assisting clients in investment decisions. However, the cold start problem in recommender systems has always been in urgent need of solution and optimization. Based on this, this paper classifies the traditional methods and cutting-edge methods to solve the cold start problem, and expounds the research progress and excellent methods in recent years. Firstly, three traditional solutions to the cold start problem are summarized: recommendation based on content filtering, recommendation based on collaborative filtering, and hybrid recommendation. Secondly, the current cutting-edge recommendation algorithms to solve the cold start problem are summarized, and they are classified into the data-driven strategy and the method-driven strategy. The method-driven strategy is divided into algorithms based on meta-learning, algorithms based on context information and session str-ategy, algorithms based on random walk, algorithms based on heterogeneous graph information and attribute graph, and algorithms based on adversarial mechanism. According to the type of cold start problem, the algorithms are divided into two categories: new users and new items. Then, according to the particularity of the recommendation field, the cold start problem of the recommendation in the multimedia information field and the online e-commerce platform field is expounded. Finally, the possible research directions to solve the cold start problem in the future are summarized. Reference | Related Articles | Metrics Abstract （158） PDF （150） Select Review of Research on 3D Reconstruction of Dynamic Scenes SUN Shuifa, TANG Yongheng, WANG Ben, DONG Fangmin, LI Xiaolong, CAI Jiacheng, WU Yirong Journal of Frontiers of Computer Science and Technology    2024, 18 (4): 831-860.   DOI: 10.3778/j.issn.1673-9418.2305016 As static scene 3D reconstruction algorithms become more mature, dynamic scene 3D reconstruction has become a hot and challenging research topic in recent years. Existing static scene 3D reconstruction algorithms have good reconstruction results for stationary objects. However, when objects in the scene undergo deformation or relative motion, their reconstruction results are not ideal. Therefore, developing research on 3D reconstruction of dynamic scenes is essential. This paper first introduces the related concepts and basic knowledge of 3D reconstruction, as well as the research classification and current status of static and dynamic scene 3D reconstruction. Then, the latest research progress on dynamic scene 3D reconstruction is comprehensively summarized, and the reconstruction algorithms are classified into dynamic 3D reconstruction based on RGB data sources and dynamic 3D reconstruction based on RGB-D data sources. RGB data sources can be further divided into template based dynamic 3D reconstruction, non rigid motion recovery structure based dynamic 3D reconstruction, and learning based dynamic 3D reconstruction under RGB data sources. The RGB-D data source mainly summarizes dynamic 3D reconstruction based on learning, with typical examples introduced and compared. The applications of dynamic scene 3D reconstruction in medical, intelligent manufacturing, virtual reality and augmented reality, and transportation fields are also discussed. Finally, future research directions for dynamic scene 3D reconstruction are proposed, and an outlook on the research progress in this rapidly developing field is presented. Reference | Related Articles | Metrics Abstract （422） PDF （425） Select Survey on Deep Learning in Oriented Object Detection in Remote Sensing Images LAN Xin, WU Song, FU Boyi, QIN Xiaolin Journal of Frontiers of Computer Science and Technology    2024, 18 (4): 861-877.   DOI: 10.3778/j.issn.1673-9418.2308031 The objects in remote sensing images have the characteristics of arbitrary direction and dense arrangement, and thus objects can be located and separated more precisely by using inclined bounding boxes in object detection task. Nowadays, oriented object detection in remote sensing images has been widely applied in both civil and military defense fields, which shows great significance in the research and application, and it has gradually become a research hotspot. This paper provides a systematic summary of oriented object detection methods in remote sensing images. Firstly, three widely-used representations of inclined bounding boxes are summarized. Then, the main challenges faced in supervised learning are elaborated from four aspects: feature misalignment, boundary discontinuity, inconsistency between metric and loss and oriented object location. Next, according to the motivations and improved strategies of different methods, the main ideas and advantages and disadvantages of each algorithm are analyzed in detail, and the overall framework of oriented object detection in remote sensing images is summarized. Furthermore, the commonly used oriented object detection datasets in remote sensing field are introduced. Experimental results of classical methods on different datasets are given, and the performance of different methods is evaluated. Finally, according to the challenges of deep learning applied to oriented object detection in remote sensing images tasks, the future research trend in this direction is prospected. Reference | Related Articles | Metrics Abstract （309） PDF （388） Select Review of Research on Rolling Bearing Health Intelligent Monitoring and Fault Diagnosis Mechanism WANG Jing, XU Zhiwei, LIU Wenjing, WANG Yongsheng, LIU Limin Journal of Frontiers of Computer Science and Technology    2024, 18 (4): 878-898.   DOI: 10.3778/j.issn.1673-9418.2307005 As one of the most critical and failure-prone parts of the mechanical systems of industrial equipment, bearings are subjected to high loads for long periods of time. When they fail or wear irreversibly, they may cause accidents or even huge economic losses. Therefore, effective health monitoring and fault diagnosis are of great significance to ensure safe and stable operation of industrial equipment. In order to further promote the development of bearing health monitoring and fault diagnosis technology, the current existing models and methods are analyzed and summarized, and the existing technologies are divided and compared. Starting from the distribution of vibration signal data used, firstly, the relevant methods under uniform data distribution are sorted out, the classification, analysis and summary of the current research status are carried out mainly according to signal-based analysis and data-driven-based, and the shortcomings and defects of the fault detection methods in this case are outlined. Secondly, considering the problem of uneven data acquisition under actual working conditions, the detection methods for dealing with such cases are summarized, and different processing techniques for this problem in existing research are classified into data processing methods, feature extraction methods, and model improvement methods according to their different focuses, and the existing problems are analyzed and summarized. Finally, the challenges and future development directions of bearing fault detection in existing industrial equipment are summarized and prospected. Reference | Related Articles | Metrics Abstract （485） PDF （245） Select Deep Learning-Based Infrared and Visible Image Fusion: A Survey WANG Enlong, LI Jiawei, LEI Jia, ZHOU Shihua Journal of Frontiers of Computer Science and Technology    2024, 18 (4): 899-915.   DOI: 10.3778/j.issn.1673-9418.2306061 How to preserve the complementary information in multiple images to represent the scene in one image is a challenging topic. Based on this topic, various image fusion methods have been proposed. As an important branch of image fusion, infrared and visible image fusion (IVIF) has a wide range of applications in segmentation, target detection and military reconnaissance fields. In recent years, deep learning has led the development direction of image fusion. Researchers have explored the field of IVIF using deep learning. Relevant experimental work has proven that applying deep learning to achieving IVIF has significant advantages compared with traditional methods. This paper provides a detailed analysis on the advanced algorithms for IVIF based on deep learning. Firstly, this paper reports on the current research status from the aspects of network architecture, method innovation, and limitations. Secondly, this paper introduces the commonly used datasets in IVIF methods and provides the definition of commonly used evaluation metrics in quantitative experiments. Qualitative and quantitative evaluation experiments of fusion and segmentation and fusion efficiency analysis experiments are conducted on some representative methods mentioned in the paper to comprehensively evaluate the performance of the methods. Finally, this paper provides conclusions and prospects for possible future research directions in the field. Reference | Related Articles | Metrics Abstract （481） PDF （455） Select Survey of 3D Model Recognition Based on Deep Learning ZHOU Yan, LI Wenjun, DANG Zhaolong, ZENG Fanzhi, YE Dewang Journal of Frontiers of Computer Science and Technology    2024, 18 (4): 916-929.   DOI: 10.3778/j.issn.1673-9418.2309010 With the rapid advancement of three-dimensional visual perception devices such as 3D scanners and LiDAR, the field of 3D model recognition is gradually gaining the attention of a growing number of researchers. This domain encompasses two core tasks: 3D model classification and retrieval. Since deep learning has already achieved significant success in two-dimensional visual tasks, its introduction into the realm of three-dimensional visual perception not only breaks free from the constraints of traditional methods but also makes notable strides in areas such as autonomous driving and intelligent robotics. However, the application of deep learning techniques to 3D model recognition tasks still faces several challenges. In light of this, there is a need for a comprehensive review of the application of deep learning in 3D model recognition. This review begins by discussing commonly used evaluation metrics and public datasets, providing relevant information and sources for each dataset. Subsequently, it delves into representative methods from various angles, including point clouds, views, voxels, and multimodal fusion. It also summarizes recent research development in the field. Through performance comparison on these datasets, the strengths and limitations of each method are analyzed. Finally, based on the merits and demerits of these approaches, the review outlines the challenges currently faced by 3D model recognition tasks and provides an outlook on future trends in this field. Reference | Related Articles | Metrics Abstract （92） PDF （127） Select Review of Application of Generative Adversarial Networks in Image Restoration GONG Ying, XU Wentao, ZHAO Ce, WANG Binjun Journal of Frontiers of Computer Science and Technology    2024, 18 (3): 553-573.   DOI: 10.3778/j.issn.1673-9418.2307073 With the rapid development of generative adversarial networks, many image restoration problems that are difficult to solve based on traditional methods have gained new research approaches. With its powerful generation ability, generative adversarial networks can restore intact images from damaged images, so they are widely used in image restoration. In order to summarize the relevant theories and research on the problem of using generative adversarial networks to repair damaged images in recent years, based on the categories of damaged images and their adapted repair methods, the applications of image restoration are divided into three main aspects: image inpainting, image deblurring, and image denoising. For each aspect, the applications are further subdivided through technical principles, application objects and other dimensions. For the field of image inpainting, different image completion methods based on generative adversarial networks are discussed from the perspectives of using conditional guidance and latent coding. For the field of image deblurring, the essential differences between motion blurred images and static blurred images and their repair methods are explained. For the field of image denoising, personalized denoising methods for different categories of images are summarized. For each type of applications, the characteristics of the specific GAN models employed are summarized. Finally, the advantages and disadvantages of GAN applied to image restoration are summarized, and the future application scenarios are prospected. Reference | Related Articles | Metrics Abstract （358） PDF （385） Select Survey of Entity Relationship Extraction Methods in Knowledge Graphs ZHANG Xishuo, LIU Lin, WANG Hailong, SU Guibin, LIU Jing Journal of Frontiers of Computer Science and Technology    2024, 18 (3): 574-596.   DOI: 10.3778/j.issn.1673-9418.2305019 Entity-relationship extraction has gained more and more attention from researchers as a basis for knowledge graph construction. Entity-relationship extraction can automatically and accurately obtain knowledge from a large amount of data, and represent and store it in a structured form. Therefore, the correctness of entity-relationship extraction directly affects the accuracy of knowledge graph construction and the effect of subsequent knowledge graph application. However, for different research hotspots such as complex structure, open domain, multi-language, multi-modal, small sample data, and joint extraction of entity-relationships, the existing entity-relationship extraction methods still have some limitations. Based on the current research hotspots of entity-relationship extraction, this paper tries to categorize entity-relationship extraction into six aspects: complex structure, open domain, multilingual, multimodal, small-sample data, and joint entity-relationship extraction, and categorizes each aspect according to the specific problems and lists out some solutions. Not only the current problems and solutions of each category are systematically sorted out, but the research results of each category are summarized, and the advantages and disadvantages of each method are analyzed in detail from the dimensions of quantitative analysis and qualitative analysis. Finally, the problems to be solved in the current hot areas are summarized, and the future development trend of entity-relationship extraction methods in the knowledge graph is also prospected. Reference | Related Articles | Metrics Abstract （208） PDF （153） Select Overview of Cross-Chain Identity Authentication Based on DID BAI Yirui, TIAN Ning, LEI Hong, LIU Xuefeng, LU Xiang, ZHOU Yong Journal of Frontiers of Computer Science and Technology    2024, 18 (3): 597-611.   DOI: 10.3778/j.issn.1673-9418.2304003 With the emergence of concepts such as metaverse and Web3.0, blockchain plays a very important role in many fields. Cross-chain technology is an important technical means to achieve inter-chain interconnection and value transfer. At this stage, traditional cross-chain technologies such as notary and sidechain have trust issues. At the same time, in the field of cross-chain identity authentication, there are problems that the identities of each chain are not unified and users do not have control over their own identities. Firstly, it systematically summarizes the development process and technical solutions of digital identity and cross-chain technology, and analyzes and compares four digital identity models and nine mainstream cross-chain projects. Secondly, by analyzing the main research results of cross-chain identity authentication in recent years, a general model of cross-chain identity authentication is designed, and the shortcomings of existing solutions are summarized. Then, it focuses on the cross-chain identity authentication implementation scheme based on DID, and analyzes the technical characteristics, advantages and disadvantages of different solutions. On this basis, three DID-based cross-chain identity authentication models are summarized, the main implementation steps are functionally described, and their advantages, limitations and efficiency are analyzed. Finally, in view of the shortcomings of the current DID-based cross-chain identity authentication model, its development difficulties are discussed and five possible future research directions are given. Reference | Related Articles | Metrics Abstract （334） PDF （370） Select Research Progress in Application of Deep Learning in Animal Behavior Analysis SHEN Tong, WANG Shuo, LI Meng, QIN Lunming Journal of Frontiers of Computer Science and Technology    2024, 18 (3): 612-626.   DOI: 10.3778/j.issn.1673-9418.2306033 In recent years, animal behavior analysis has become one of the most important methods in the fields of neuroscience and artificial intelligence. Taking advantage of the powerful deep-learning-based image analysis technology, researchers have developed state-of-the-art automatic animal behavior analysis methods with complex functions. Compared with traditional methods of animal behavior analysis, special labeling is not required in these methods, animal pose can be efficiently estimated and tracked. These methods like in a natural environment, which hold the potential for complex animal behavior experiments. Therefore, the application of deep learning in animal behavior analysis is reviewed. Firstly, this paper analyzes the tasks and current status of animal behavior analysis. Then, it highlights and compares existing deep learning-based animal behavior analysis tools. According to the dimension of experimental analysis, the deep learning-based animal behavior analysis tools are divided into two-dimensional animal behavior analysis tools and three-dimensional animal behavior analysis tools, and the functions, performance and scope of application of tools are discussed. Furthermore, the existing animal datasets and evaluation metrics are introduced, and the algorithm mechanism used in the existing animal behavior analysis tool is summarized from the advantages, limitations and applicable scenarios. Finally, the deep learning-based animal behavior analysis tools are prospected from the aspects of dataset, experimental paradigm and low latency. Reference | Related Articles | Metrics Abstract （339） PDF （405） Select Review of Application of Convolutional Neural Network in Auxiliary Diagnosis of Colorectal Polyps KAO Wentao, LI Ming, MA Jingang Journal of Frontiers of Computer Science and Technology    2024, 18 (3): 627-645.   DOI: 10.3778/j.issn.1673-9418.2310062 Colorectal cancer is a malignant tumor that mainly occurs in the tissues of the colon and rectum, and its early detection and treatment are of great significance. The early detection and prevention of colorectal cancer mainly involves visual examination of the patient??s intestines to screen for colorectal polyps, but manual examination has the disadvantage of high misdiagnosis rate. The auxiliary diagnostic system based on convolutional neural networks (CNN) has shown the most advanced performance in the diagnosis of colorectal polyps, and is currently a research hotspot in the field of computer-aided diagnosis. Based on important literature published in recent years, a systematic review of the application of convolutional neural networks in the auxiliary diagnosis of colorectal polyps is conducted. Firstly, the commonly used datasets in the field of colorectal polyp diagnosis are introduced, including image and video datasets. Secondly, the application of CNN in colorectal polyp detection, segmentation, and classification is systematically elaborated. The main improvement ideas, advantages and disadvantages, and performance of each algorithm are analyzed in depth, aiming to provide researchers with a more systematic reference, and summarize the interpretability of deep learning models. Finally, a summary of various algorithms for assisting the diagnosis of colorectal polyps based on CNN is provided, and future research directions are prospected. Reference | Related Articles | Metrics Abstract （112） PDF （179） Select Survey of Research on Construction Method of Industry Internet Security Knowledge Graph CHANG Yu, WANG Gang, ZHU Peng, KONG Lingfei, HE Jingheng Journal of Frontiers of Computer Science and Technology    2024, 18 (2): 279-300.   DOI: 10.3778/j.issn.1673-9418.2304081 The industry Internet security knowledge graph plays an important role in enriching the semantic relationships of security concepts, improving the quality of the security knowledge base, and enhancing the ability to visualize and analyze the security situation. It has become the key to recognize, trace and protect against the attacks targeting new energy industry control systems. However, compared with the construction of the general domain knowledge graph, there are still many problems in each stage of the construction of the industry Internet security knowledge graph, which affect its practical application effect. This paper introduces the concept and significance of the industry Internet security knowledge graph and its difference from the general knowledge graph, summarizes the related work and role of the ontology construction of industry Internet security knowledge graph. Under the background of industry Internet security, it focuses on the related work of the three important components of knowledge graph construction, respectively named entity recognition, relationship extraction and reference resolution. For each component, it detailedly reports on the development history and research status of this component in the domain, and deeply analyses the domain challenges in this component, such as non-continuous entity recognition, candidate word extraction, the lack of domain-quality datasets and so on. It predicts the future research directions of this component, provides reference and enlightenment to further improve the quality and usefulness of industry Internet security knowledge graph, so as to deal with emerging threats and attacks more effectively. Reference | Related Articles | Metrics Abstract （255） PDF （321） Select Application Progress of Deep Learning in Imaging Examination of Breast Cancer WANG Yifan, LIU Jing, MA Jingang, SHAO Runhua, CHEN Tianzhen, LI Ming Journal of Frontiers of Computer Science and Technology    2024, 18 (2): 301-319.   DOI: 10.3778/j.issn.1673-9418.2309033 Breast cancer is the most common malignant tumor in women and its early detection is decisive. Breast imaging plays an important role in early detection of breast cancer as well as monitoring and evaluation during treatment, but manual detection of medical images is usually time-consuming and labor-intensive. Recently, deep learning algorithms have made significant progress in early breast cancer diagnosis. By combing the relevant literature in recent years, a systematic review of the application of deep learning techniques in breast cancer diagnosis with different imaging modalities is conducted, aiming to provide a reference for in-depth research on deep learning-based breast cancer diagnosis. Firstly, four breast cancer imaging modalities, namely mammography, ultrasonography, magnetic resonance imaging and positron emission tomography, are outlined and briefly compared, and the public datasets corresponding to multiple imaging modalities are listed. Focusing on the different tasks (lesion detection, segmentation and classification) of deep learning architectures based on the above four different imaging modalities, a systematic review of the algorithms is conducted, and the performance of each algorithm, improvement ideas, and their advantages and disadvantages are compared and analyzed. Finally, the problems of the existing techniques are analyzed and the future development direction is prospected with respect to the limitations of the current work. Reference | Related Articles | Metrics Abstract （207） PDF （215） Select Survey on Visual Transformer for Image Classification PENG Bin, BAI Jing, LI Wenjing, ZHENG Hu, MA Xiangyu Journal of Frontiers of Computer Science and Technology    2024, 18 (2): 320-344.   DOI: 10.3778/j.issn.1673-9418.2310092 Transformer is a deep learning model based on the self-attention mechanism, showing tremendous potential in computer vision. In image classification tasks, the key challenge lies in efficiently and accurately capturing both local and global features of input images. Traditional approaches rely on convolutional neural networks to extract local features at the lower layers, expanding the receptive field through stacked convolutional layers to obtain global features. However, this strategy aggregates information over relatively short distances, making it difficult to model long-term dependencies. In contrast, the self-attention mechanism of Transformer directly compares features across all spatial positions, capturing long-range dependencies at both local and global levels and exhibiting stronger global modeling capabilities. Therefore, a thorough exploration of the challenges faced by Transformer in image classification tasks is crucial. Taking Vision Transformer as an example, this paper provides a detailed overview of the core principles and architecture of Transformer. It then focuses on image classification tasks, summarizing key issues and recent advancements in visual Transformer research related to performance enhancement, computational costs, and training optimization. Furthermore, applications of Transformer in specific domains such as medical imagery, remote sensing, and agricultural images are summarized, highlighting its versatility and generality. Finally, a comprehensive analysis of the research progress in visual Transformer for image classification is presented, offering insights into future directions for the development of visual Transformer. Reference | Related Articles | Metrics Abstract （399） PDF （322） Select Review of Attention Mechanisms in Image Processing QI Xuanhao, ZHI Min Journal of Frontiers of Computer Science and Technology    2024, 18 (2): 345-362.   DOI: 10.3778/j.issn.1673-9418.2305057 Attention mechanism in image processing has become one of the popular and important techniques in the field of deep learning, and is widely used in various deep learning models in image processing because of its excellent plug-and-play convenience. By weighting the input features, the attention mechanism focuses the model’s attention on the most important regions to improve the accuracy and performance of image processing tasks. Firstly, this paper divides the development process of attention mechanism into four stages, and on this basis, reviews and summarizes the research status and progress of four aspects: channel attention, spatial attention, channel and spatial mixed attention, and self-attention. Secondly, this paper provides a detailed discussion on the core idea, key structure and specific implementation of attention mechanism, and further summarizes the advantages and disadvantages of used models. Finally, by comparing the current mainstream attention mechanisms and analyzing the results, this paper discusses the problems of attention mechanisms in the image processing field at this stage, and provides an outlook on the future development of attention mechanisms in image processing, so as to provide references for further research. Reference | Related Articles | Metrics Abstract （501） PDF （375） Select Survey of Multi-task Recommendation Algorithms WEN Minwei, MEI Hongyan, YUAN Fengyuan, ZHANG Xiaoyu, ZHANG Xing Journal of Frontiers of Computer Science and Technology    2024, 18 (2): 363-377.   DOI: 10.3778/j.issn.1673-9418.2303014 Single-task recommendation algorithms have problems such as sparse data, cold start and unstable recommendation effect. Multi-task recommendation algorithms can jointly model multiple types of user behaviour data and additional information, to better explore the user’s interests and needs in order to improve the recommendation effect and user satisfaction, which provides a new way of thinking to solve a series of problems existing in single-task recommendation algorithms. Firstly, the development background and trend of multi-task recommendation algorithms are sorted out. Secondly, the implementation steps of the multi-task recommendation algorithm and the construction principle are introduced, and the advantages of multi-task learning with data enhancement, feature identification, feature complementation and regularization effect are elaborated. Then, the application of multi-task learning methods in recommendation algorithms with different sharing models is introduced, and the advantages and disadvantages of some classical models and the relationship between tasks are summarized. Then, the commonly used   datasets and evaluation metrics for multi-task recommendation algorithms are introduced, and the differences and connections with other recommendation algorithms in terms of dataset evaluation metrics are elaborated. Finally, it is pointed out that multi-task learning has shortcomings such as negative migration, parameter optimization conflicts, poor interpretability, etc., and an outlook is given to the combination of multi-task recommendation algorithms with reinforcement learning, convex function optimization methods, and heterogeneous information networks. Reference | Related Articles | Metrics Abstract （277） PDF （342） Select Overview of Android Intelligent Terminal Automation Testing Technology CAO Jie, HUANG Han, LEI Fengqiang, LIU Fangqing Journal of Frontiers of Computer Science and Technology    2024, 18 (1): 1-23.   DOI: 10.3778/j.issn.1673-9418.2304032 With the development of new generation mobile communication technology and chips, the number of    intelligent mobile terminal users is increasing. In order to quickly seize the market, developers have shortened the development cycle of intelligent terminals, which raises higher requirements for the reliability and stability of application systems. Automation testing technology is an important means to ensure the high reliability and strong stability of these intelligent terminals. This paper discusses the black box testing technology and white box testing technology of Android system respectively, combined with the architectural characteristics and component features of mainstream intelligent terminals. In terms of black box testing, this paper compares and analyzes the latest UI testing and fuzz testing technology and tool usage, and evaluates their effects in ensuring the reliability and stability of application systems. In terms of white box testing, this paper summarizes the technology of automatically generating test cases, dynamic and static taint analysis technology, third-party library detection technology, and permission detection technology. Finally, with the emergence of emerging technologies such as AI models, more and more intelligent terminal devices are starting to carry various deep learning models. The opacity of these models makes the internal decision-making process difficult to explain and understand, so the black box testing is increasingly important in evaluating model reliability and stability. Automation testing is undergoing a transformation from traditional rule-based testing to more intelligent machine learning-driven testing. In the future, it is necessary to introduce emerging technologies such as AI models into existing intelligent terminal testing practices, which has become a necessary trend to solve this problem. Reference | Related Articles | Metrics Abstract （188） PDF （205） Select Word Embedding Methods in Natural Language Processing: a Review ZENG Jun, WANG Ziwei, YU Yang, WEN Junhao, GAO Min Journal of Frontiers of Computer Science and Technology    2024, 18 (1): 24-43.   DOI: 10.3778/j.issn.1673-9418.2303056 Word embedding, as the first step in natural language processing (NLP) tasks, aims to transform input natural language text into numerical vectors, known as word vectors or distributed representations, which artificial intelligence models can process. Word vectors, the foundation of NLP tasks, are a prerequisite for accomplishing various NLP downstream tasks. However, most existing review literature on word embedding methods focuses on the technical routes of different word embedding methods, neglecting comprehensive analysis of the tokenization methods and the complete evolutionary trends of word embedding. This paper takes the introduction of the word2vec model and the Transformer model as pivotal points. From the perspective of whether generated word vectors can dynamically change their implicit semantic information to adapt to the overall semantics of input sentences, this paper categorizes word embedding methods into static and dynamic approaches and extensively discusses this classification. Simultaneously, it compares and analyzes tokenization methods in word embedding, including whole and sub-word segmentation. This paper also provides a detailed enumeration of the evolution of language models used to train word vectors, progressing from probability language models to neural probability language models and the current deep contextual language models. Additionally, this paper summarizes and explores the training strategies employed in pre-training language models. Finally, this paper concludes with a summary of methods for evaluating word vector quality, an analysis of the current state of word embedding methods, and a prospective outlook on their development. Reference | Related Articles | Metrics Abstract （529） PDF （382） Select Review of Bare Footprint Recognition WANG Kun, GUO Wei, WANG Zunyan, HAN Wenqiang Journal of Frontiers of Computer Science and Technology    2024, 18 (1): 44-57.   DOI: 10.3778/j.issn.1673-9418.2307048 Bare footprint recognition technology is a branch of image recognition technology, which plays an important role in criminal investigation, medical treatment and security fields, and is expected to become a new means of personal identification. However, this technology has not yet formed a relatively unified framework, nor has it established a standardized procedure. In order to provide guidance for future researchers, it is necessary to standardize the recognition process of different bare footprint images and summarize the relevant research of bare footprint recognition technology. Firstly, the background and significance of bare footprint recognition research are expounded. Then, the development history of this technology is reviewed, and the bare footprint images are divided into four categories according to different acquisition methods: ink stamped bare footprint images, plantar scanning images, footprint images acquired by optical footprint acquisition equipment and foot pressure images acquired by footprint pressure acquisition system. It is pointed out that the latter two images are the hot spots of bare footprint recognition research at present. Then, the research status of bare footprint recognition technology is analyzed from three aspects: dataset, image preprocessing and recognition methods. Among them, the recognition methods are divided into traditional methods and deep learning-based methods, and the latter is further divided into network structure innovation methods and loss function optimization methods. The evaluation indices of identification methods are given, and various methods are compared from many aspects. Finally, the problems faced by this technology are pointed out, and its future development direction is prospected. Reference | Related Articles | Metrics Abstract （75） PDF （65） Select Survey of Research on Knowledge-Driven Dialogue Generation Models XU Biqi, MA Zhiqiang, ZHOU Yutong, JIA Wenchao, LIU Jia, LYU Kai Journal of Frontiers of Computer Science and Technology    2024, 18 (1): 58-74.   DOI: 10.3778/j.issn.1673-9418.2304022 Knowledge-driven dialogue generation models aim to enhance dialogue generation models by using different forms of knowledge, so that dialogue generation models can not only learn semantic interactions from dialogue data, but also deeply understand user input, background knowledge and dialogue context to generate more reasonable, diverse, informative and anthropomorphic responses, and thus promote the development of dialogue systems. Currently, the related work is still in the early stages of exploration, and there is a lack of comprehensive reviews and systematic summaries of existing results. This paper provides a comprehensive review of the research on knowledge-driven dialogue generation models. Firstly, in response to the existing research results, it sorts out and introduces the current knowledge-driven dialogue generation tasks and the main problems encountered, and provides detailed task definitions and problem definitions. Secondly, it organizes and introduces the datasets required for the modeling of knowledge-driven dialogue generation models. Then, it focuses on the improvement, research status, evaluation indicators involved, and performance of each model in the process of knowledge-driven dialogue generation research, including knowledge acquisition, knowledge representation, knowledge selection, and knowledge integration-related studies. Finally, the future development directions of knowledge-based dialogue generation models are prospected. Reference | Related Articles | Metrics Abstract （216） PDF （263） Select Survey on Progress of Blockchain Interoperability Technology CHEN Xianyi, WANG Kang, DING Sizhe, FU Zhangjie Journal of Frontiers of Computer Science and Technology    2024, 18 (1): 75-92.   DOI: 10.3778/j.issn.1673-9418.2303119 Blockchain technology leads the development of a new generation of data storage technology due to its excellent features such as decentralization and high transparency. It has been widely used in social governance, finance and insurance, digital copyright and other fields in recent years. However, as a self-contained system based on standards and protocols, data communication between different blockchains cannot be realized directly, which makes it difficult to meet the data sharing needs of cross-domain applications, and blockchain interoperability technology comes into being. At present, there are few project examples of this technology analyzed. Based on the latest research of blockchain interoperability, this paper systematically summarizes the representative solutions of interoperability technology recently and prospects the future development directions. Firstly, the blockchain interoperability methods are classified into two types of homogeneous expansion and heterogeneous interoperability, according to the structural differences of each blockchain in communication. Secondly, this paper introduces two types of representative methods according to the technical development and blockchain hierarchy, sorts out the implementation principles and technical details of each method, and summarizes their advantages and disadvantages. Thirdly, this paper introduces the progress of currently implemented blockchain interoperability projects, focusing on their implementation ideas, operation modes and status quo, and their performance is compared and analyzed in terms of throughput, security and other indicators. Finally, this paper foresees the development trend of blockchain interoperability technology in various directions. Reference | Related Articles | Metrics Abstract （176） PDF （171） Select Review of Community Detection in Complex Brain Networks WEN Xuyun, NIE Ziyu, CAO Qumei, ZHANG Daoqiang Journal of Frontiers of Computer Science and Technology    2023, 17 (12): 2795-2807.   DOI: 10.3778/j.issn.1673-9418.2209007 The brain network community detection algorithm has become a highly regarded topic in recent years within the fields of neuroscience and network science, widely employed to unveil patterns of structural and functional connectivity in the brain. Due to the complexity of the brain networks and the need to handle multiple subjects and various task scenarios, it significantly increases the difficulty of community detection in this field. This paper focuses on functional magnetic resonance imaging (fMRI) technology and comprehensively reviews the advancements in research regarding algorithms for detecting communities within brain functional networks. Firstly, the basic process, task categories, and method types of brain network community detection algorithms are described. Next, various brain network community detection algorithms are classified in different task scenarios, including separate communities, overlapping communities, hierarchical communities, and dynamic community detection algorithms. A detailed analysis of the advantages and disadvantages of different methods is provided, along with their applicable scopes. Finally, the future directions of brain network community detection algorithms are discussed, including the problem of community detection in multi-subject networks, robustness issues in brain network community detection, and studies on brain network community detection algorithms for multimodal imaging data. This paper can serve as a methodological guide for future research on brain network community structures. Reference | Related Articles | Metrics Abstract （225） PDF （319） Select Review of 2D Animation Restoration in Visual Domain Based on Deep Learning LI Yuhang, XIE Liangbin, DONG Chao Journal of Frontiers of Computer Science and Technology    2023, 17 (12): 2808-2826.   DOI: 10.3778/j.issn.1673-9418.2303078 Traditional 2D animation is a distinct visual style with a production process and image characteristics that differ significantly from real-life scenes. It usually requires drawing pictures frame by frame and saving them as bitmaps. During the storage, transmission, and playback process, 2D animation may encounter problems such as picture quality degradation, insufficient resolution, and discontinuous timing. With the development of deep learning technology, it has been widely used in the field of animation restoration. This paper provides a comprehensive summary of 2D animation restoration based on deep learning. Firstly, exploring existing animation datasets can help identify the available data support and the bottleneck in establishing animation datasets. Secondly, investigating and testing deep learning-based algorithms for animation image quality restoration and animation interpolation can help identify key points and challenges in animation restoration. Additionally, introducing methods designed to ensure consistency between animation frames can provide insights for future animation video restoration. Analyzing the effectiveness of existing image quality assessment (IQA) methods for animation images can help identify practical IQA methods to guide restoration results. Finally, based on the above analysis, this paper clarifies the challenges in animation restoration tasks and presents future development directions of deep learning in animation restoration field. Reference | Related Articles | Metrics Abstract （199） PDF （290） Select Survey of Image Adversarial Example Defense Techniques LIU Ruiqi, LI Hu, WANG Dongxia, ZHAO Chongyang, LI Boyu Journal of Frontiers of Computer Science and Technology    2023, 17 (12): 2827-2839.   DOI: 10.3778/j.issn.1673-9418.2303080 The rapid and extensive growth of artificial intelligence introduces new security challenges. The generation and defense of adversarial examples for deep neural networks is one of the hot spots. Deep neural networks are  most widely used in the field of images and most easily cheated by image adversarial examples. The research on the defense techniques for image adversarial examples is an important tool to improve the security of AI applications. There is no standard explanation for the existence of image adversarial examples, but it can be observed and understood from different dimensions, which can provide insights for proposing targeted defense approaches. This paper sorts out and analyzes current mainstream hypotheses of the reason for the existence of adversarial examples, such as the blind spot hypothesis, linear hypothesis, decision boundary hypothesis, and feature hypothesis, and the correlations between various hypotheses and typical adversarial example generation methods. Based on this, this paper summarizes the image adversarial example defense techniques in two dimensions, model-based and data-based, and compares and analyzes the adaptation scenarios, advantages and disadvantages of different technical methods. Most of the existing image adversarial example defense techniques are aimed at defending against specific adversarial example generation methods, and there is no universal defense theory and method yet. In the real application, it needs to consider the specific application scenarios, potential security risks and other factors, optimize and combine the configuration in the existing defense methods. Future researchers can deepen their technical research in terms of generalized defense theory, evaluation of defense effectiveness, and systematic protection strategies. Reference | Related Articles | Metrics Abstract （249） PDF （194） Select Survey of Research on Personalized News Recommendation Approaches MENG Xiangfu, HUO Hongjin, ZHANG Xiaoyan, WANG Wanchun, ZHU Jinxia Journal of Frontiers of Computer Science and Technology    2023, 17 (12): 2840-2860.   DOI: 10.3778/j.issn.1673-9418.2303026 Personalized news recommendation is an important technology to help users obtain the news information they are interested in and alleviate information overload. In recent years, with the development of information technology and society, personalized news recommendation has been increasingly widely studied, and has achieved remarkable success in improving the news reading experience of users. This paper aims to systematically summarize personalized news recommendation methods based on deep learning. Firstly, it introduces personalized news recommendation methods and analyzes their characteristics and influencing factors. Then, the overall framework of personalized news recommendation is given, and the personalized news recommendation methods based on deep learning are analyzed and summarized. On this basis, it focuses on personalized news recommendation methods based on graph structure learning, including user-news interaction graph, knowledge graph and social relationship graph. Finally, it analyzes the challenges of the current personalized news recommendation, discusses how to solve the problems of data sparsity, model interpretability, diversity of recommendation results and news privacy protection in personalized news recommendation system, and puts forward more specific and operable research ideas in the future research direction. Reference | Related Articles | Metrics Abstract （233） PDF （156） Select Survey of Automatic Labeling Methods for Topic Models HE Dongbin, TAO Sha, ZHU Yanhong, REN Yanzhao, CHU Yunxia Journal of Frontiers of Computer Science and Technology    2023, 17 (12): 2861-2879.   DOI: 10.3778/j.issn.1673-9418.2303083 Topic models are often used in modeling unstructured corpora and discrete data to extract the latent topic. As topics are generally expressed in the form of word lists, it is usually difficult for users to understand the meanings of topics, especially when users lack knowledge in the subject area. Although manually labeling topics can generate more explanatory and easily understandable topic labels, the cost is too high for the method to be feasible. Therefore, research on automatic labeling of topic discovered provides solutions to the problem. Firstly, the currently most popular technique, latent Dirichlet allocation (LDA), is elaborated and analyzed. According to the three different representations of topic labels, based on phrases, abstracts, and pictures, the topic labeling methods are classified into three types. Then, centered on improving the interpretability of topics, with different types of generated topic labels utilized, the relevant research in recent years is sorted out, analyzed, and summarized. The applicable scenarios and usability of different labels are also discussed. Meanwhile, methods are further categorized according to their different characteristics. The focus is placed on the quantitative and qualitative analysis of the abstract topic labels generated through lexical-based, submodular optimization, and graph-based methods. The differences between separate methods with respect to the learning types, technologies used, and data sources are then compared. Finally, the existing problems and trend of development of research on automatic topic labeling are discussed. Based on deep learning, integrating with sentiment analysis, and continuously expanding the applicable scenarios of topic labeling, will be the directions of future development. Reference | Related Articles | Metrics Abstract （76） PDF （104） Select 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 Abstract （922） PDF （834） Select Review of Application of Neural Networks in Epileptic Seizure Prediction HUANG Honghong, ZHANG Feng, LYU Liangfu, SI Xiaopeng Journal of Frontiers of Computer Science and Technology    2023, 17 (11): 2543-2556.   DOI: 10.3778/j.issn.1673-9418.2302001 Epilepsy, a central nervous system disease caused by abnormal discharge of brain neurons, has a significant impact on patients’ normal life. Early prediction of epileptic seizures and timely preventive measures can effectively improve the quality of life of patients. With the development of data science and big data technology, neural networks are increasingly being applied in the field of epilepsy prediction and have shown great potential for application. This paper provides a review of the application and deficiencies of neural networks in the field of epilepsy prediction, discussing the construction process of epilepsy prediction models in the following order: data- sets, data preprocessing, feature extraction, and neural networks. After introducing the characteristics of EEG signals, common types of datasets, common data preprocessing methods, and common feature extraction methods, especially manual feature extraction methods, this paper focuses on analyzing and summarizing the principles and applications of multi-layer artificial neural networks and spiking neural networks in the field of epilepsy prediction. The disadvantages of neural networks are systematically analyzed, and further application of neural networks in the field of epilepsy prediction is prospected. Reference | Related Articles | Metrics Abstract （285） PDF （424） Select Survey of Application of Deep Learning in Finger Vein Recognition LI Jie, QU Zhong Journal of Frontiers of Computer Science and Technology    2023, 17 (11): 2557-2579.   DOI: 10.3778/j.issn.1673-9418.2303099 Finger vein recognition technology has become a research hotspot in the new generation of biometrics because of its advantages of non-contact, high security and living body detection. With the development of deep learning, finger vein recognition technology based on deep neural network has made remarkable achievements. This paper firstly introduces the common public datasets in the field of finger vein recognition, and then classifies the applications of deep learning methods in finger vein recognition in recent years according to different neural network learning tasks, and analyzes the technical characteristics and application scenarios of each type. This paper also introduces the design techniques of deep learning in finger vein recognition from the aspects of lightweight network, data augmentation, attention mechanism and so on, and then expounds the common loss function in the model from two aspects of classifying loss and measuring learning loss. Finally, the evaluation indices of finger vein recognition system are introduced and the results of some researches on accuracy and equal error rate are summarized. In addition, the challenges and potential development directions of finger vein recognition are also presented. Reference | Related Articles | Metrics Abstract （262） PDF （352） Select 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. Reference | Related Articles | Metrics Abstract （488） PDF （571） Select Survey on Image Panoptic Segmentation Based on Deep Learning BI Yangyang, ZHENG Yuanfan, SHI Caijuan, ZHANG Kun, LIU Jian Journal of Frontiers of Computer Science and Technology    2023, 17 (11): 2605-2619.   DOI: 10.3778/j.issn.1673-9418.2304063 With the continuous development of deep learning and image segmentation, image panoptic segmentation has become a research hotspot in the field of computer vision, and many image panoptic segmentation methods have been proposed. This paper summarizes the research methods of image panoptic segmentation based on deep learning. Firstly, the research status of image panoptic segmentation at home and abroad is introduced, and the existing image panoptic segmentation methods are classified according to different optimization tasks in the network architecture, mainly including image panoptic segmentation optimized by feature extraction, image panoptic segmentation optimized by sub-task segmentation, image panoptic segmentation optimized by sub-task fusion, and other image panoptic segmentation. Secondly, 5 commonly used datasets, i.e. MS COCO, PASCAL VOC, Cityscapes, ADE20K and Mapillary Vistas,  and 2 evaluation criteria, i.e. panoptic quality (PQ) and parsing covering (PC) in image panoptic segmentation are briefly introduced. And then, performance comparison of typical image panoptic segmentation methods has been conducted on different datasets. Thirdly, the application of image panoptic segmentation in medicine, autonomous driving, drones, agriculture, animal husbandry, military and other fields are listed. Finally, the deficiencies and challenges of existing methods in complex scene applications, real-time performance, and conflicts are pointed out, and the potential directions of image panoptic segmentation are discussed, including image panoptic segmentation based on a simple unified framework, real-time high-quality image panoptic segmentation, and image panoptic segmentation in complex application scenarios. Reference | Related Articles | Metrics Abstract （207） PDF （113） Select Survey of Linear Algebra Solvers for Exascale Computing HE Lianhua, XU Shun, JIN Zhong Journal of Frontiers of Computer Science and Technology    2023, 17 (10): 2265-2277.   DOI: 10.3778/j.issn.1673-9418.2303076 The application of scientific engineering computing based on exascale computing not only offers oppor-tunities but also creates challenges for the development of numerical linear algebra algorithms. Firstly, the charac-teristics of exascale computing are analyzed, including: parallel programming for large-scale heterogeneous parallel architecture has become the mainstream approach; reducing the extremely high energy costs associated with running large-scale applications is a major concern; multi-precision heterogeneous computing hardware has triggered further research of mixed precision computing. Secondly, the optimization work of mainstream dense and sparse linear algebra solvers for high-performance computing architectures is reviewed, and the characteristics and advantages of each solver are compared. Then, the main technology progress of linear algebra solvers is summarized, mainly including: isolating heterogeneous computing modules and designing a new unified programming framework to achieve performance portability of software algorithms; improving the performance level of numerical computing and data storage using mixed precision methods while ensuring the overall requirements of scientific engineering computing applications; combined with hardware multi-level cache and network communication characteristics, advanced parallel computing algorithms are developed to avoid or reduce inefficient large-scale data communication. Finally, this paper provides an outlook on the future research trends in this direction. Reference | Related Articles | Metrics Abstract （181） PDF （224） Select Research Progress of Graph Neural Network in Knowledge Graph Construction and Application XU Xinran, WANG Tengyu, LU Cai Journal of Frontiers of Computer Science and Technology    2023, 17 (10): 2278-2299.   DOI: 10.3778/j.issn.1673-9418.2302059 As an effective representation of knowledge, knowledge graph network can be used to represent rich factual information between different categories and become an effective knowledge management tool. It has achieved great results in the application and research of knowledge engineering and artificial intelligence. Know-ledge graph is usually expressed as a complex network structure. Its unstructured characteristics make the applica-tion of graph neural network to the analysis and research of knowledge graph become a research hotspot in academia. The purpose of this paper is to provide extensive research on knowledge graph construction technology based on graph neural network to solve two types of knowledge graph construction tasks, including knowledge extraction (entity, relationship and attribute extraction) and knowledge merging and processing (link prediction, entity alignment and knowledge reasoning, etc.). Through these tasks, the structure of knowledge graph can be further improved and new knowledge and reasoning relationships can be discovered. This paper also studies the advanced graph neural network method for knowledge graph related applications, such as recommendation system, question answering system and computer vision. Finally, the future research directions of knowledge graph application based on graph neural network are proposed. Reference | Related Articles | Metrics Abstract （398） PDF （504） Select Anti-fraud Research Advances on Digital Credit Payment LIU Hualing, CAO Shijie, XU Junyi, CHEN Shanghui Journal of Frontiers of Computer Science and Technology    2023, 17 (10): 2300-2324.   DOI: 10.3778/j.issn.1673-9418.2211087 The development of digital technology has accelerated the transformation of financial online payment methods, bringing convenience to payment but also increasing the hidden dangers of fraudulent transactions. Anti-fraud research is particularly essential to protect users’ property and prevent financial crises. With the advancement of data governance and sharing technology, digital payment transaction data present new characteristics of massive, multi-source and heterogeneous. Integrating data intelligence technology based on big data and artificial intelligence into anti-fraud research has important theoretical research significance. The digital credit payment model formed by the full combination of credit card payment and digital payment has the most mature data accumulation and theoretical basis at present, providing the most ideal data resources and theoretical support for the research of anti-fraud models. Starting from the concept, this paper firstly introduces the definition, research difficulties, and data framework of the digital credit anti-fraud research problem in combination with the actual business scenarios in China. Secondly, based on the modeling strategy, the frontier progress of digital credit transaction anti-fraud research is reviewed from two aspects of data balance and model optimization. This paper focuses on the theoretical basis, applicable scenarios, and latest achievements of various machine learning algorithms and deep learning algorithms in anti-fraud research, and based on the above content, a comprehensive evaluation is made. Finally, combined with the research status and from the perspective of demand, this paper summarizes the three major hotspots including the generalization and interpretability of anti-fraud research, and the sensitivity to new fraudulent transaction models, and concludes with an outlook on future research directions. Reference | Related Articles | Metrics Abstract （146） PDF （192） Select Survey of Task Offloading Technology in Cloud-Edge Resource Collaboration TIAN Yumeng, LIU Zhibo, ZHANG Kai, LI Zhongbo, XIE Yongqiang Journal of Frontiers of Computer Science and Technology    2023, 17 (10): 2325-2342.   DOI: 10.3778/j.issn.1673-9418.2303042 With the development of wireless communication technology and the Internet of things, the Internet of everything is becoming a reality and the number of connected devices is growing exponentially. Traditional cloud computing has powerful computation, storage, and network resources, but uncontrollable service delays will occur in the face of surges in data traffic. Edge computing places resources closer to the terminal, but the storage capacity of edge devices is small, and the processors equipped with them usually have weak computing power, so edge computing has low latency but limited resources. By combining the advantages of both cloud computing and edge computing, cloud-edge collaboration can effectively improve resource service capacity and service quality. It has a broad development prospect. Resource collaboration is an important aspect of cloud-edge collaborative service capabilities. Task offloading technology is one of the key technologies of cloud-edge resource collaboration. To promote the future development of this field and inspire researchers, the task offloading technology in cloud-edge resource collaboration is analyzed. Firstly, this paper sorts out the development history of cloud-edge collaboration and introduces the concept connotation of cloud-edge resource collaboration and task offloading, as well as the application scenarios of cloud-edge resource collaboration. Then, this paper summarizes the development of this technology at home and abroad from three aspects: uninstall objects, uninstall granularity, and service quality evaluation indicators. Finally, this paper proposes future development directions of task offloading technology in cloud-edge resource collaboration. Reference | Related Articles | Metrics Abstract （194） PDF （149） Select Survey on Blockchain-Based Cross-Domain Authentication for Internet of Things Terminals HUO Wei, ZHANG Qionglu, OU Wei, HAN Wenbao Journal of Frontiers of Computer Science and Technology    2023, 17 (9): 1995-2014.   DOI: 10.3778/j.issn.1673-9418.2211004 Internet of things (IoT) devices are widely distributed, numerous and complex, which are involved in multiple management domains. They are often in uncontrollable environments and are more vulnerable to attacks than traditional Internet terminals, the security management and protection of IoT terminals face greater risks and challenges. As “the first line of defense” for the security protection of IoT devices, authentication plays an irreplaceable and important role in the development of IoT security. The blockchain technology has the characteristics and advantages of decentralization, distribution, immutability and traceability. And thus, it can effectively solve the single-point trust failure of trusted third parties and satisfy the principle of least authorization for multi-domain heterogeneity in cross-domain authentication for IoT terminals. Using the blockchain technology is an important trend in the future development of the IoT device cross-domain authentication. This paper categorizes and summarizes the main research achievements of IoT cross-domain authentication based on blockchain technology in recent years according to three categories: integrating traditional identity authentication mechanisms such as PKI and IBS/IBC, adopting inter-blockchain technology, and other cross-domain authentication technologies based on blockchain. Then this paper analyzes the technical characteristics, advantages and disadvantages of each different scheme. On this basis, the current problems and issues in the field of cross-domain authentication of IoT devices are summarized, and the future research directions and development suggestions for the cross-domain authentication of IoT terminals are given, so as to achieve a general and overall grasp of the research progress and development trend of IoT device cross-domain authentication schemes based on blockchain technology. Reference | Related Articles | Metrics Abstract （485） PDF （538） page Page 1 of 3 Total 82 records First page Prev page Next page Last page