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

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

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

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

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

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

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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. Reference | Related Articles | Metrics

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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. Reference | Related Articles | Metrics

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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. Reference | Related Articles | Metrics

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Multivariate Time Series Density Clustering Algorithm Using Shapelet Space SHENG Jinchao, DU Mingjing, SUN Jiarui, LI Yurui Journal of Frontiers of Computer Science and Technology    2024, 18 (2): 387-402.   DOI: 10.3778/j.issn.1673-9418.2211099 Multivariate time series clustering has become an important research topic in the task of time series analysis. Compared with univariate time series, the research of multivariate time series is more complex and difficult. Although many clustering algorithms for multivariate time series have been proposed, these algorithms still have difficulties in solving the accuracy and interpretation at the same time. Firstly, most of the current work does not consider the length redundancy and variable correlation of multivariable time series, resulting in large errors in the final similarity matrix. Secondly, the data are commonly used in the clustering process with the division paradigm, when the numerical space presents a complex distribution, this idea does not perform well, and it does not have the explanatory power of each variable and space. To address the above problems, this paper proposes a multivariate time series adaptive weight density clustering algorithm using Shapelet (high information-rich continuous subsequence) space (MDCS). This algorithm firstly performs a Shapelet search for each variable, and obtains its own Shapelet space through an adaptive strategy. Then, it weights the numerical distribution generated by each variable to obtain a similarity matrix that is more consistent with the characteristics of data distribution. Finally, the data are finally allocated using the shared nearest neighbor density peak clustering algorithm with improved density calculation and secondary allocation. Experimental results on several real datasets demonstrate that MDCS has better clustering results compared with current state-of-the-art clustering algorithms, with an average increase of 0.344 and 0.09 in the normalized mutual information and Rand index, balancing performance and interpretability. Reference | Related Articles | Metrics

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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. Reference | Related Articles | Metrics

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Advances in Knowledge Graph Embedding Based on Graph Neural Networks YAN Zhaoyao, DING Cangfeng, MA Lerong, CAO Lu, YOU Hao Journal of Frontiers of Computer Science and Technology    2023, 17 (8): 1793-1813.   DOI: 10.3778/j.issn.1673-9418.2212063 As graph neural networks continue to develop, knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers. Compared with traditional methods, they can better handle the diversity and complexity of entities, and capture the multiple features and complex relationships of entities, thereby improving the representation ability and application value of knowledge graphs. This paper firstly outlines the development history of knowledge graphs and the basic concepts of knowledge graphs and graph neural networks. Secondly, it focuses on discussing the design ideas and algorithm frameworks of knowledge graph embedding based on graph convolution, graph neural networks, graph attention, and graph autoencoders. Then, it describes the performance of graph neural network knowledge graph embedding in tasks such as link prediction, entity alignment, knowledge graph reasoning, and knowledge graph completion, while supplementing some research on commonsense knowledge graphs with graph neural networks. Finally, this paper makes a comprehensive summary, and future research directions are outlined with respect to some challenges and issues in knowledge graph embedding. Reference | Related Articles | Metrics

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YOLOv8-VSC: Lightweight Algorithm for Strip Surface Defect Detection WANG Chunmei, LIU Huan Journal of Frontiers of Computer Science and Technology    2024, 18 (1): 151-160.   DOI: 10.3778/j.issn.1673-9418.2308060 Currently, in the field of strip steel surface defect detection, the generalized target detection algorithm is highly complex and computationally large, while terminal equipment responsible for the detection of some small and medium-sized enterprises usually does not have strong computational capabilities, and the computational resources are limited, which leads to difficulties in the deployment of detection algorithms. To solve this problem, this paper proposes a lightweight strip steel surface defect detection model YOLOv8-VSC based on the YOLOv8n target detec-tion framework, which uses a lightweight VanillaNet network as the backbone feature extraction network and reduces the complexity of the model by reducing the unnecessary branching structure. Meanwhile, the SPD module is introduced to speed up the inference of the model while reducing the number of network layers. To further improve the detection accuracy, a lightweight up-sampling operator, CARAFE, is used in the feature fusion network to improve the quality and richness of the features. Finally, extensive experiments on the NEU-DET dataset yield a model with parametric and computational quantities of 1.96×106 and 6.0 GFLOPs, which are only 65.1% and 74.1% of the baseline, and the mAP reaches 80.8%, which is an improvement of 1.8 percentage points from the baseline. In addition, experimental results on the aluminum surface defect dataset and the VOC2012 dataset show that the proposed algorithm has good robustness. Compared with advanced target detection algorithms, the proposed algorithm requires fewer computational resources while ensuring high detection accuracy. Reference | Related Articles | Metrics

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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. Reference | Related Articles | Metrics

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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

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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

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Deep Learning Compiler Load Balancing Optimization Method for Model Training WANG Li, GAO Kai, ZHAO Yaqian, LI Rengang, CAO Fang, GUO Zhenhua Journal of Frontiers of Computer Science and Technology    2024, 18 (1): 111-126.   DOI: 10.3778/j.issn.1673-9418.2209026 For computing-intensive artificial intelligence (AI) training tasks, the computational graph is more complex, and data loading, task division of the computational graph, and load balancing of task scheduling have become the key factors affecting the computing performance. This paper proposes three optimization methods to make the task scheduling of model training in deep learning compilers reach the load balance state. Firstly, the load balance between CPU and back-end computing devices is realized by automatically establishing an efficient pipeline for data loading and model training, which improves the overall energy efficiency of the system. Secondly, the layered optimization technology of computational graph is used to realize the load balance of computational graph when the back-end devices are scheduling. Finally, this paper improves the resource utilization of back-end devices by automatically establishing efficient pipeline between layers. Experimental results show that the proposed optimization method achieves the system load balancing in the process of automatically mapping the training tasks to underlying hardware devices. Compared with traditional deep learning frameworks and compilers such as TensorFlow, nGraph, etc., this paper achieves 2%~10% performance improvement in the training of different AI models, and the overall power consumption of the training system can be reduced by more than 10%. Reference | Related Articles | Metrics

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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

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Survey on Few-Shot Knowledge Graph Completion Technology PENG Yanfei, ZHANG Ruisi, WANG Ruihua, GUO Jialong Journal of Frontiers of Computer Science and Technology    2023, 17 (6): 1268-1284.   DOI: 10.3778/j.issn.1673-9418.2209069 Few-shot knowledge graph completion (FKGC) is a new research hotspot in the field of knowledge graph completion, which aims to complete knowledge graph with a few samples of data. This task is of great importance in practical application and the fields of knowledge graph. In order to further promote the development of the field of FKGC, this paper summarizes and analyzes the current methods. Firstly, this paper describes the concept of FKGC and related content. Secondly, three types of FKGC methods are summarized based on technical methods, including scale learning-based methods, meta learning-based methods, and other model-based methods. In addition, this paper analyzes and summarizes each method from the perspectives of model core, model ideas, advantages and disadvantages, etc. Then, the datasets and evaluation indexes of FKGC method are summarized, and the FKGC method is analyzed from two aspects of model characteristics and experimental results. Finally, starting from the practical problems, this paper summarizes the difficult problems of the current FKGC task, analyses the difficulties behind the problems, gives the corresponding solutions, and prospects several development directions that deserve attention in this field in the future. Reference | Related Articles | Metrics

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Named Entity Recognition Method of Large Language Model for Medical Question Answering System YANG Bo, SUN Xiaohu, DANG Jiayi, ZHAO Haiyan, JIN Zhi Journal of Frontiers of Computer Science and Technology    2023, 17 (10): 2389-2402.   DOI: 10.3778/j.issn.1673-9418.2307061 In medical question answering systems, entity recognition plays a major role. Entity recognition based on deep learning has received more and more attention. However, in the medical question answering system, due to the lack of annotated training data, deep learning methods cannot well identify discontinuous and nested entities in medical text. Therefore, a large language model-based entity recognition application method is proposed, and it is applied to the medical problem system. Firstly, the dataset related to medical question answering is processed into text that can be analyzed and processed by a large language model. Secondly, the output of the large language model is classified, and different classifications are processed accordingly. Then, the input text is used for intent recognition, and  finally the results of entity recognition and intent recognition are sent to the medical knowledge graph for query, and the answer to the medical question and answer is obtained. Experiments are performed on 3 typical datasets and compared with several typical correlation methods. The results show that the method proposed in this paper performs better. Reference | Related Articles | Metrics

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Low-Light Enhancement Method for Light Field Images by Fusing Multi-scale Features LI Mingyue, YAN Tao, JING Huahua, LIU Yuan Journal of Frontiers of Computer Science and Technology    2023, 17 (8): 1904-1916.   DOI: 10.3778/j.issn.1673-9418.2202064 Light field images (LFI) record rich 3D structural and textural details of target scenes, which have great advantages in a wide range of computer vision tasks. However, LFI captured under low-light conditions always suffer from low brightness and strong noise, which may seriously degrade the quality of LFI. In this paper, a low-light LFI enhancement method by fusing multi-scale light field (LF) structural features is proposed , which adopts digital single-lens reflex camera (DSLR) images to supervise the generated enlightened LFI. To explore and exploit light field structural features, angular and spatial Transformers are introduced to extract LF structural features from LFI at different scales, i.e., the complementary information between sub-views and local and long-range dependencies within each sub-view. A recurrent fusion module is proposed to preserve the long-term memory of features at diffe-rent scales by using a long-short-term memory network, while adaptively aggregating LF structural features in the entire feature space through local and global fusion layers. A 4D residual reconstruction module is designed to reconstruct target LFI sub-views from the aggregated features. In additional, a dataset of low-light LFI and normal-light DSLR image pairs is constructed to train the proposed network. Extensive experiment demonstrates that the proposed network can effectively improve the quality of low-light LFI, and it obviously outperforms other state-of-the-art methods. Reference | Related Articles | Metrics

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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

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Review of Computing Offloading Schemes for Multi-access Edge Computing ZHANG Bingjie, YANG Yanhong, CAO Shaozhong Journal of Frontiers of Computer Science and Technology    2023, 17 (9): 2030-2046.   DOI: 10.3778/j.issn.1673-9418.2301068 Under the background of the Internet of things, the development of massive and large-scale machine communication has brought about the explosive growth of data traffic. The traditional cloud computing model can no longer meet the needs of low delay and low energy consumption of terminal data processing. Multi-access edge computing (MEC) with distributed multi-nodes near the terminal side is becoming the best choice to solve this problem. Computational offloading is the key technology of MEC, the offloading performance is affected by many factors, and there is a large space for optimization. How to design a high-performance computational offloading scheme has become the main research goal of scholars at home and abroad. This paper reviews the research of computing offloading scheme for MEC, introduces the concept of MEC, sorts out the development and application of MEC, and the execution process of computing offloading, analyzes and compares the recent research methods of computing offloading. According to different improvements, a computing offloading scheme is summarized which takes the offloading system environment, offloading delay, energy consumption of mobile devices and multiple evaluation indexes as the optimization direction. The problems of resource allocation, universality and security in MEC-oriented computing offloading are presented, and the future research directions are forecasted based on these problems. Reference | Related Articles | Metrics

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Graph Neural Network Defense Combined with Contrastive Learning CHEN Na, HUANG Jincheng, LI Ping Journal of Frontiers of Computer Science and Technology    2023, 17 (8): 1949-1960.   DOI: 10.3778/j.issn.1673-9418.2204109 Although graph neural networks have achieved good performance in the field of graph representation learning, recent studies have shown that graph neural networks are more susceptible to adversarial attacks on graph structure, namely, by adding well-designed perturbations to the graph structure, the performance of graph neural network drops sharply. At present, although mainstream graph structure denoising methods can effectively resist graph structure adversarial attacks, due to the uncertainty of the degree of adversarial attack on the input graph, such methods are prone to more misidentifications when the input graph is not attacked or the attack intensity is small, which damages the prediction results of the graph neural network. To alleviate this problem, this paper proposes a graph neural network defense method combined with contrastive learning (CLD-GNN). Firstly, on the basis of feature similarity denoising, according to the characteristics of label inconsistency between edge endpoints after attack, the label propagation algorithm is used to obtain pseudo-labels of unlabeled nodes, and possible perturbed edges are removed according to the pseudo-label inconsistency between endpoints, resulting in the purification graph. Then, graph convolution is performed on the purification and input graph respectively. Finally, contrastive learning is applied to aligning the predicted label information on the two graphs and modifying the feature representation of the purification graph nodes. Defense experiments are conducted on 3 benchmark datasets and 2 attack scenarios for graph adversarial attacks. Experimental results show that CLD-GNN not only solves the problem of graph denoising methods and prediction effects, but also exhibits excellent defense ability. Reference | Related Articles | Metrics

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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

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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

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Using HLS to Develop FPGA Heterogeneous Acceleration System: Problems, Optimization Methods and Opportunities XU Cheng, GUO Jinyang, LI Chao, WANG Jing, WANG Taolei, ZHAO Jieru Journal of Frontiers of Computer Science and Technology    2023, 17 (8): 1729-1748.   DOI: 10.3778/j.issn.1673-9418.2210102 Currently, field programmable gate arrays (FPGAs) are favored by both academia and industry due to their programmability and excellent energy efficiency ratio. However, traditional FPGA development based on hardware description languages faces programming challenges. Hardware description languages, which are different from commonly used high-level languages, hinder software developers from utilizing FPGAs. High-level synthesis (HLS) enables developers to directly develop FPGA hardware from high-level languages such as C/C++, and is widely regarded as the preferred solution to this problem. In recent years, there have been many works in academia on HLS, dedicated to solving various problems in the HLS application process and improving the performance of systems developed through HLS. This paper lists feasible optimization directions from the perspective of heterogeneous system developers around the issue of developing FPGA heterogeneous systems using HLS. At the compilation optimization level, HLS tools can automatically generate high-performance RTL designs by inserting compilation guidance and designing efficient spatial exploration algorithms. At the memory access optimization level, HLS tools can set up buffers, split and replicate data to improve the overall system bandwidth. At the parallel optimization level, HLS tools can implement statement-level, task-level and board-level parallelism. Meanwhile, some technologies such as DSL, although they cannot directly improve the performance of heterogeneous acceleration systems, can further enhance the usability of HLS tools. Finally, this paper summarizes some challenges currently faced by HLS and prospects the future research on HLS. Reference | Related Articles | Metrics

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Research on Blockchain Interoperability and Cross-Chain Technology WANG Qun, LI Fujuan, NI Xueli, XIA Lingling, LIANG Guangjun, MA Zhuo Journal of Frontiers of Computer Science and Technology    2023, 17 (8): 1749-1775.   DOI: 10.3778/j.issn.1673-9418.2212081 Blockchain is a distributed ledger technology with multi-party consensus, traceability and tamper-proof, which provides a broad application prospect for constructing efficient, trusted and secure data sharing mechanism and optimizing business processes. However, when blockchain is in a stage of rapid development, how to realize cross-chain interaction of information and cross-chain transfer of value has become an urgent problem to be solved in the process of blockchain extension to depth. Firstly, based on the review of the existing research results, the concept of blockchain interoperability is proposed, and it is divided into five aspects: inter-chain interoperability, inter-layer interoperability, inter-fork interoperability, inter-slice interoperability and interoperability between on-chain and off-chain. Secondly, by sorting out the evolution and implementation of cross-chain operation of blockchain, and referring to the TCP/IP architecture, a cross-chain operation model is designed, and the main implementation steps are functionally described. Thirdly, in view of the current research status of cross-chain operation, four key technologies of cross-chain operation, including notary mechanism, side chain/relay, hash lock and distributed private key control, are selected for analysis. Then, combined with the technical characteristics and application scenarios, the application demonstration is highlighted, some typical cross-chain application projects are introduced, and the security of blockchain cross-chain operation is analyzed. Finally, the future development trend of blockchain interoperability and cross-chain technology is summarized and explored. Reference | Related Articles | Metrics

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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

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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

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