ACM Computing Surveys最新文献_第10页

A Survey on the Shared Address Space with Privatized Static Variables (SAS-PSV) Execution Model for Many-Core Era 多核时代私有静态变量共享地址空间（SAS-PSV）执行模型研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-04 DOI: 10.1145/3746169

Atsushi Hori, Kaiming Ouyang, Min Si, Pavan Balaji, Julien Jaeger, Marc Pérache, Sam` White, Evan Ramos, Laxmikant Kale, Kevin Pedretti, Ron Brightwell, Balazs Gerofi, Yutaka Ishikawa

{"title":"A Survey on the Shared Address Space with Privatized Static Variables (SAS-PSV) Execution Model for Many-Core Era","authors":"Atsushi Hori, Kaiming Ouyang, Min Si, Pavan Balaji, Julien Jaeger, Marc Pérache, Sam` White, Evan Ramos, Laxmikant Kale, Kevin Pedretti, Ron Brightwell, Balazs Gerofi, Yutaka Ishikawa","doi":"10.1145/3746169","DOIUrl":"https://doi.org/10.1145/3746169","url":null,"abstract":"Parallel applications often use MPI processes and OpenMP threads. Those parallel execution models, multi-process and multi-thread, were invented to increase efficiency on uniprocessor systems. In the multi-process approach, each process’s isolated address space may make communication expensive; in the multi-thread design, shared variables may cause access conflicts and stall executions. Processes or threads interact and exchange information more often as CPU cores increase, and traditional execution models may create bottlenecks. The paradigm shift from uniprocessor to many-core systems necessitates the development of new parallel execution models to address challenges posed by the two parallel models. When processes share an address space, what happens? If threads don’t share static variables? Sharing an address space and privatizing static variables reduces information exchange and shared static variable exclusion costs. This survey investigates Shared Address Space with Privatized Static Variables (SAS-PSV) , a new execution architecture that allows shared address space and static variable privatization. This notion is implemented by MPC, SMARTMAP, PVAS, PiP, and AMPI. Each has a different approach and execution. This paper analyzes these implementations’ concepts, details, and hidden defects. We also present SAS-PSV applications and issues that need to be solved.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"46 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-Shot Relation Extraction Based on Prompt Learning: A Taxonomy, Survey, Challenges and Future Directions 基于提示学习的少镜头关系提取：分类、综述、挑战和未来方向

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-04 DOI: 10.1145/3746281

Tingting Hang, Shuting Liu, Jun Feng, Hamza Djigal, Jun Huang

{"title":"Few-Shot Relation Extraction Based on Prompt Learning: A Taxonomy, Survey, Challenges and Future Directions","authors":"Tingting Hang, Shuting Liu, Jun Feng, Hamza Djigal, Jun Huang","doi":"10.1145/3746281","DOIUrl":"https://doi.org/10.1145/3746281","url":null,"abstract":"Relation extraction (RE) is critical in information extraction (IE) and knowledge graph construction. RE aims to identify the semantic relations between entities from natural language texts. Traditional RE models often rely on many manually annotated training samples, which are limited when data is scarce. Therefore, exploring how to perform relation extraction under few-shot conditions has become a research focus. Recently, prompt learning has attracted attention from researchers due to its ability to fully activate the potential of Pre-trained Language Models (PLMs), especially making significant progress in Few-Shot Relation Extraction (FSRE). This paper comprehensively reviews FSRE based on prompt learning. We first introduce the fundamental concepts of FSRE and prompt learning. Then, we systematically review recent research advances in FSRE with prompt learning, focusing on two perspectives: template construction and model fine-tuning strategies. Next, we summarize the benchmark datasets, evaluation metrics, and experimental results of representative works in FSRE. Afterward, we present practical applications of prompt-based FSRE in specialized domains. Finally, we discuss the critical challenges and future research directions of FSRE tasks based on prompt learning.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"85 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions 面向表格数据学习的图神经网络：分类与方向综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-01 DOI: 10.1145/3744918

Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen, Jay Chiehen Liao

{"title":"Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions","authors":"Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen, Jay Chiehen Liao","doi":"10.1145/3744918","DOIUrl":"https://doi.org/10.1145/3744918","url":null,"abstract":"This survey dives into Tabular Data Learning (TDL) using Graph Neural Networks (GNNs), a domain where deep learning-based approaches have increasingly shown superior performance in both classification and regression tasks compared to traditional methods. We highlight a critical gap in deep neural TDL: the underrepresentation of latent correlations among data instances and feature values. GNNs, with their innate capability to model intricate relationships and interactions between diverse elements of tabular data, have garnered significant interest and application across TDL domains. Our survey provides a systematic review of the methods involved in designing and implementing GNNs for TDL (GNN4TDL). It encompasses a detailed investigation into the foundational aspects and an overview of GNN-based TDL methods, offering insights into their evolving landscape. We present a comprehensive taxonomy focused on constructing graph structures and representation learning within GNN-based TDL methods. We also examine various training plans, emphasize the integration of auxiliary tasks to enhance the representation quality. A critical part of our discussion is dedicated to the practical applications across a spectrum of GNN4TDL scenarios, exhibiting their versatility and impact. Last, we discuss the limitations and future directions, aiming to spur advancements in GNN4TDL. This survey serves as a resource for researchers and practitioners, offering a thorough understanding of GNNs’ role in revolutionizing TDL and pointing towards future innovations in this promising area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"20 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy Preserving Identity Federation: A Literature Study 隐私保护身份联盟：文献研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-01 DOI: 10.1145/3745018

Anne Bumiller, Elisavet Kozyri, Håvard Dagenborg

{"title":"Privacy Preserving Identity Federation: A Literature Study","authors":"Anne Bumiller, Elisavet Kozyri, Håvard Dagenborg","doi":"10.1145/3745018","DOIUrl":"https://doi.org/10.1145/3745018","url":null,"abstract":"Within an Identity federation (IF) system, users gain access to multiple Service Providers (SPs) by submitting credentials issued by one or more Identity Providers (IdPs). Such Identity Federations (IFs) raise several privacy concerns: IdPs might track user activity, by recording the accessed services, and SPs might mismanage sensitive user attributes that comprise the submitted credentials. An extensive line of research on Privacy Preserving IF has been developed to expose and address these privacy concerns. This survey aims to systematize the privacy requirements and enhancement techniques that has been employed so far in this line of research. Specifically, we use Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) methodologies to organize research work from the last ten years and understand (i) the requirements that privacy-preserving IF is expected to satisfy, (ii) the degree at which these requirements have been formalized, (iii) the techniques employed to enforce these requirements, (iv) the means for providing enforcement assurance, and (v) the degree at which these techniques preserve fundamental authentication objectives and are aligned with existing IF standards. Based on this characterization of the literature, we draw conclusions about the rigorousness of the proposed approaches, their deployability into practice, and lessons learned for future research and practice in the field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"19 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital Twins in Security Operations: State of the Art and Future Perspectives 安全操作中的数字孪生：技术现状和未来展望

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-07-01 DOI: 10.1145/3746279

Philip Empl, David Koch, Marietheres Dietz, Günther Pernul

{"title":"Digital Twins in Security Operations: State of the Art and Future Perspectives","authors":"Philip Empl, David Koch, Marietheres Dietz, Günther Pernul","doi":"10.1145/3746279","DOIUrl":"https://doi.org/10.1145/3746279","url":null,"abstract":"In an era of rapid technological advancements, digital twins are gaining attention in industry and research. These virtual representations of real-world entities, enabled by the Internet of Things (IoT), offer advanced simulation and analysis capabilities. Their application spans various sectors, from smart manufacturing to healthcare, highlighting their versatility. However, the rise of digital technologies has also escalated cybersecurity concerns. Historical cyberattacks underscore the urgency for enhanced security operations. In this context, digital twins represent a novel approach to cybersecurity. Industry and academic research are increasingly exploring their potential to protect their assets. Despite growing interest and applications, more comprehensive research synthesis needs to be done, particularly in security operations based on digital twins. Our paper aims to fill this gap through a structured literature review aggregating knowledge from 201 publications. We focus on defining the digital twin in cybersecurity, exploring its applications, and outlining implementations and challenges. To maintain transparency, our data is documented and is publicly available. This survey serves as a crucial guide for academic and industry stakeholders, fostering digital twins in security operations.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"69 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding World or Predicting Future? A Comprehensive Survey of World Models 了解世界还是预测未来？世界模型的综合调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-27 DOI: 10.1145/3746449

Jingtao Ding, Yunke Zhang, Yu Shang, Yuheng Zhang, Zefang Zong, Jie Feng, Yuan Yuan, Hongyuan Su, Nian Li, Nicholas Sukiennik, Fengli Xu, Yong Li

{"title":"Understanding World or Predicting Future? A Comprehensive Survey of World Models","authors":"Jingtao Ding, Yunke Zhang, Yu Shang, Yuheng Zhang, Zefang Zong, Jie Feng, Yuan Yuan, Hongyuan Su, Nian Li, Nicholas Sukiennik, Fengli Xu, Yong Li","doi":"10.1145/3746449","DOIUrl":"https://doi.org/10.1145/3746449","url":null,"abstract":"The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models. Generally, world models are regarded as tools for either understanding the present state of the world or predicting its future dynamics. This review presents a systematic categorization of world models, emphasizing two primary functions: (1) constructing internal representations to understand the mechanisms of the world, and (2) predicting future states to simulate and guide decision-making. Initially, we examine the current progress in these two categories. We then explore the application of world models in key domains, including autonomous driving, robotics, and social simulacra, with a focus on how each domain utilizes these aspects. Finally, we outline key challenges and provide insights into potential future research directions. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/World-Model.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"11 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of AIOps in the Era of Large Language Models 大型语言模型时代的AIOps概述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-27 DOI: 10.1145/3746635

Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Aiwei Liu, Yong Yang, Zhonghai Wu, Xuming Hu, Philip Yu, Ying Li

{"title":"A Survey of AIOps in the Era of Large Language Models","authors":"Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Aiwei Liu, Yong Yang, Zhonghai Wu, Xuming Hu, Philip Yu, Ying Li","doi":"10.1145/3746635","DOIUrl":"https://doi.org/10.1145/3746635","url":null,"abstract":"As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered significant attention. However, a comprehensive understanding of the impact, potential, and limitations of LLMs in AIOps remains in its infancy. To address this gap, we conducted a detailed survey of LLM4AIOps, focusing on how LLMs can optimize processes and improve outcomes in this domain. We analyzed 183 research papers published between January 2020 and December 2024 to answer four key research questions (RQs). In RQ1, we examine the diverse failure data sources utilized, including advanced LLM-based processing techniques for legacy data and the incorporation of new data sources enabled by LLMs. RQ2 explores the evolution of AIOps tasks, highlighting the emergence of novel tasks and the publication trends across these tasks. RQ3 investigates the various LLM-based methods applied to address AIOps challenges. Finally, RQ4 reviews evaluation methodologies tailored to assess LLM-integrated AIOps approaches. Based on our findings, we discuss the state-of-the-art advancements and trends, identify gaps in existing research, and propose promising directions for future exploration.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Survey on Self-Supervised Learning for Recommendation 基于推荐的自监督学习研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-27 DOI: 10.1145/3746280

Xubin Ren, Wei Wei, Lianghao Xia, Chao Huang

{"title":"A Comprehensive Survey on Self-Supervised Learning for Recommendation","authors":"Xubin Ren, Wei Wei, Lianghao Xia, Chao Huang","doi":"10.1145/3746280","DOIUrl":"https://doi.org/10.1145/3746280","url":null,"abstract":"Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep learning techniques, such as RNNs, GNNs, and Transformer architectures, have significantly propelled the advancement of recommender systems by enhancing their comprehension of user behaviors and preferences. However, supervised learning methods encounter challenges in real-life scenarios due to data sparsity, resulting in limitations in their ability to learn representations effectively. To address this, self-supervised learning (SSL) techniques have emerged as a solution, leveraging inherent data structures to generate supervision signals without relying solely on labeled data. By leveraging unlabeled data and extracting meaningful representations, recommender systems utilizing SSL can make accurate predictions and recommendations even when confronted with data sparsity. In this paper, we provide a comprehensive review of self-supervised learning frameworks designed for recommender systems, encompassing a thorough analysis of over 170 papers. We conduct an exploration of nine distinct scenarios, enabling a comprehensive understanding of SSL-enhanced recommenders in different contexts. For each domain, we elaborate on different self-supervised learning paradigms, namely contrastive learning, generative learning, and adversarial learning, so as to present technical details of how SSL enhances recommender systems in various contexts. We consistently maintain the related open-source materials at https://github.com/HKUDS/Awesome-SSLRec-Papers.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"36 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study 基于少镜头学习的医学图像分割：综述与比较研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-27 DOI: 10.1145/3746224

Theekshana Dissanayake, Yasmeen George, Dwarikanath Mahapatra, Shridha Sridharan, Clinton Fookes, Zongyuan Ge

{"title":"Few-Shot Learning for Medical Image Segmentation: A Review and Comparative Study","authors":"Theekshana Dissanayake, Yasmeen George, Dwarikanath Mahapatra, Shridha Sridharan, Clinton Fookes, Zongyuan Ge","doi":"10.1145/3746224","DOIUrl":"https://doi.org/10.1145/3746224","url":null,"abstract":"Medical image segmentation plays a crucial role in assisting clinicians with diagnosing critical medical conditions. In deep learning, few-shot learning methods aim to replicate human learning by leveraging fewer examples for determining a prediction for a novel class. Researchers in the medical imaging community have also explored novel methods for few-shot medical image segmentation, leveraging meta-learning, foundation models and self-supervised learning (SSL). Acknowledging this growing interest, we review the literature on few-shot medical image segmentation from 2020 to early 2025, focusing on architectural modifications, loss-inspired learning strategies, and meta-learning frameworks. We further divide each category into fine-grained deep learning-oriented solutions, including self-supervised learning, contrastive learning, regularization, and foundation models providing in-depth discussions on architectural improvements and representation learning strategies. Additionally, we present preliminary results from several few-shot segmentation models across both medical and computer vision domains, evaluating their strengths and limitations for medical image applications. Finally, based on the limitations observed, advancements from the natural image domain, and empirical findings, we outline future research directions, providing specific insights into data-efficient learning, rapid adaptation of foundation models and generalization. The code is available here.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"7 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Certification in MOOCs: A Systematic Literature Review mooc认证预测：系统文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-06-20 DOI: 10.1145/3743671

Ahmed Alamri, Mohammad Alshehri, Laila Alrajhi, Alexandra Cristea

{"title":"Prediction of Certification in MOOCs: A Systematic Literature Review","authors":"Ahmed Alamri, Mohammad Alshehri, Laila Alrajhi, Alexandra Cristea","doi":"10.1145/3743671","DOIUrl":"https://doi.org/10.1145/3743671","url":null,"abstract":"Massive Open Online Courses (MOOCs) have been proliferating, offering free or low-cost content for learners. Nevertheless, the certification rate of both free and paid courses has been low (between 4.5% - 13% and 1% - 3%, respectively). Thus, this study aims to survey MOOCs certification predictive models, synthesise results for a comprehensive and deep understanding of this field and explore how these models contributed to addressing the very low certification level. We adopted the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) for transparently conducting the present review and reporting the results from the works reviewed. Additionally, this SLR highlights several trends and limitations within the present predictive models, including some methodological concerns: the extent to which the present models are generalisable, the excessive filtration of the experimental population, the incompatibility of some experiments with real-time scenarios (nonrealistic modelling), and the shallow reporting of model performances. We have also discussed the replicability of the present models and ongoing efforts towards building a state-of-the-art predictive model. Finally, we highlight future research opportunities in the field of MOOC certification prediction that either deal with the limitations of the present models or address unanswered questions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"24 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0