ACM Transactions on Intelligent Systems and Technology最新文献_第10页

A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification 图像分类中深度神经网络模型量化研究综述

IF 5 4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-14 DOI: 10.1145/3623402

Babak Rokh, Ali Azarpeyvand, Alireza Khanteymoori

{"title":"A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification","authors":"Babak Rokh, Ali Azarpeyvand, Alireza Khanteymoori","doi":"10.1145/3623402","DOIUrl":"https://doi.org/10.1145/3623402","url":null,"abstract":"Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy consumption. As a result, deploying DNNs on devices with constrained hardware resources poses significant challenges. To overcome this, various compression techniques have been widely employed to optimize DNN accelerators. A promising approach is quantization, in which the full-precision values are stored in low bit-width precision. Quantization not only reduces memory requirements but also replaces high-cost operations with low-cost ones. DNN quantization offers flexibility and efficiency in hardware design, making it a widely adopted technique in various methods. Since quantization has been extensively utilized in previous works, there is a need for an integrated report that provides an understanding, analysis, and comparison of different quantization approaches. Consequently, we present a comprehensive survey of quantization concepts and methods, with a focus on image classification. We describe clustering-based quantization methods and explore the use of a scale factor parameter for approximating full-precision values. Moreover, we thoroughly review the training of a quantized DNN, including the use of a straight-through estimator and quantization regularization. We explain the replacement of floating-point operations with low-cost bitwise operations in a quantized DNN and the sensitivity of different layers in quantization. Furthermore, we highlight the evaluation metrics for quantization methods and important benchmarks in the image classification task. We also present the accuracy of the state-of-the-art methods on CIFAR-10 and ImageNet. This article attempts to make the readers familiar with the basic and advanced concepts of quantization, introduce important works in DNN quantization, and highlight challenges for future research in this field.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"125 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138529993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding 用于医学概念嵌入的分类本体和多关系本体与电子病历数据的自适应集成

IF 5 4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-14 DOI: 10.1145/3625224

Chin Wang Cheong, Kejing Yin, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon

{"title":"Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding","authors":"Chin Wang Cheong, Kejing Yin, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon","doi":"10.1145/3625224","DOIUrl":"https://doi.org/10.1145/3625224","url":null,"abstract":"Representation learning has been applied to Electronic Health Records (EHR) for medical concept embedding and the downstream predictive analytics tasks with promising results. Medical ontologies can also be integrated to guide the learning so the embedding space can better align with existing medical knowledge. Yet, properly carrying out the integration is non-trivial. Medical concepts that are similar according to a medical ontology may not be necessarily close in the embedding space learned from the EHR data, as medical ontologies organize medical concepts for their own specific objectives. Any integration methodology without considering the underlying inconsistency will result in sub-optimal medical concept embedding and, in turn, degrade the performance of the downstream tasks. In this article, we propose a novel representation learning framework called ADORE (ADaptive Ontological REpresentations) that allows the medical ontologies to adapt their structures for more robust integrating with the EHR data. ADORE first learns multiple embeddings for each category in the ontology via an attention mechanism. At the same time, it supports an adaptive integration of categorical and multi-relational ontologies in the embedding space using a category-aware graph attention network. We evaluate the performance of ADORE on a number of predictive analytics tasks using two EHR datasets. Our experimental results show that the medical concept embeddings obtained by ADORE can outperform the state-of-the-art methods for all the tasks. More importantly, it can result in clinically meaningful sub-categorization of the existing ontological categories and yield attention values that can further enhance the model interpretability.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"33 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138530000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Performing Cancer Diagnosis via an Isoform Expression Ranking-based LSTM Model 通过基于异构体表达排序的LSTM模型进行癌症诊断

IF 5 4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-14 DOI: 10.1145/3625237

Óscar Reyes, Eduardo Pérez

{"title":"Performing Cancer Diagnosis via an Isoform Expression Ranking-based LSTM Model","authors":"Óscar Reyes, Eduardo Pérez","doi":"10.1145/3625237","DOIUrl":"https://doi.org/10.1145/3625237","url":null,"abstract":"The known set of genetic factors involved in the development of several types of cancer has considerably been expanded, thus easing to devise and implement better therapeutic strategies. The automatic diagnosis of cancer, however, remains as a complex task because of the high heterogeneity of tumors and the biological variability between samples. In this work, a long short-term memory network-based model is proposed for diagnosing cancer from transcript-base data. An efficient method that transforms data into gene/isoform expression-based rankings was formulated, allowing us to directly embed important information in the relative order of the elements of a ranking that can subsequently ease the classification of samples. The proposed predictive model leverages the power of deep recurrent neural networks, being able to learn existing patterns on the individual rankings of isoforms describing each sample of the population. To evaluate the suitability of the proposal, an extensive experimental study was conducted on 17 transcript-based datasets, and the results showed the effectiveness of this novel approach and also indicated the gene/isoforms expression-based rankings contained valuable information that can lead to a more effective cancer diagnosis.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"80 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138529963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Memory Network-Based Interpreter of User Preferences in Content-Aware Recommender Systems 内容感知推荐系统中基于记忆网络的用户偏好解释器

IF 5 4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-14 DOI: 10.1145/3625239

Nhu-Thuat Tran, Hady W. Lauw

{"title":"Memory Network-Based Interpreter of User Preferences in Content-Aware Recommender Systems","authors":"Nhu-Thuat Tran, Hady W. Lauw","doi":"10.1145/3625239","DOIUrl":"https://doi.org/10.1145/3625239","url":null,"abstract":"This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user preferences with a set of human-understandable attributes, each is described by a single word, enabling comprehension of user interests behind item adoptions. This is achieved via a dedicated architecture, which is interpretable by design, involving two components for recommendation and interpretation. In particular, we seek an interpreter, which accepts holistic user’s representation from a recommender to output a set of activated attributes describing user preferences. Besides encoding interpretability properties such as fidelity, conciseness and diversity, the proposed memory network-based interpreter enables the generalization of user representation by discovering relevant attributes that go beyond her adopted items’ textual content. We design experiments involving both human- and functionally-grounded evaluations of interpretability. Results on four real-world datasets show that our proposed model not only discovers highly relevant attributes for interpreting user preferences, but also enjoys comparable or better recommendation accuracy than a series of baselines.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"13 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138529965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles 基于深度强化学习的串通车辆交通信号控制系统对抗性攻击

IF 5 4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-14 DOI: 10.1145/3625236

Ao Qu, Yihong Tang, Wei Ma

{"title":"Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles","authors":"Ao Qu, Yihong Tang, Wei Ma","doi":"10.1145/3625236","DOIUrl":"https://doi.org/10.1145/3625236","url":null,"abstract":"The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) models produce state-of-the-art performance and have great potential for practical applications. In the existing DRL-based ATCS, the controlled signals collect traffic state information from nearby vehicles, and then optimal actions (e.g., switching phases) can be determined based on the collected information. The DRL models fully “trust” that vehicles are sending the true information to the traffic signals, making the ATCS vulnerable to adversarial attacks with falsified information. In view of this, this article first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to “cheat” DRL-based ATCS in order to save their total travel time. To solve the proposed task, we develop CollusionVeh, a generic and effective vehicle-colluding framework composed of a road situation encoder, a vehicle interpreter, and a communication mechanism. We employ our framework to attack established DRL-based ATCS and demonstrate that the total travel time for the colluding vehicles can be significantly reduced with a reasonable number of learning episodes, and the colluding effect will decrease if the number of colluding vehicles increases. Additionally, insights and suggestions for the real-world deployment of DRL-based ATCS are provided. The research outcomes could help improve the reliability and robustness of the ATCS and better protect the smart mobility systems.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"126 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138529970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Demand-Driven Urban Facility Visit Prediction 需求驱动的城市设施访问预测

4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-09 DOI: 10.1145/3625233

Yunke Zhang, Tong Li, Yuan Yuan, Fengli Xu, Fan Yang, Funing Sun, Yong Li

{"title":"Demand-Driven Urban Facility Visit Prediction","authors":"Yunke Zhang, Tong Li, Yuan Yuan, Fengli Xu, Fan Yang, Funing Sun, Yong Li","doi":"10.1145/3625233","DOIUrl":"https://doi.org/10.1145/3625233","url":null,"abstract":"Predicting citizens’ visiting behaviors to urban facilities is instrumental for city governors and planners to detect inequalities in urban opportunities and optimize the distribution of facilities and resources. Previous works predict facility visits simply using observed visit behavior, yet citizens’ intrinsic demands for facilities are not characterized explicitly, causing potential incorrect learned relations in the prediction results. In this paper, to make up for this deficiency, we present a demand-driven urban facility visit prediction method that decomposes citizens’ visits to facilities into their unobservable demands and their capability to fulfill them. Demands are expressed as the function of regional demographic attributes by a neural network, and the fulfillment capability is determined by the urban region’s spatial accessibility to facilities. Extensive evaluations of datasets of three large cities confirm the efficiency and rationality of our model. Our method outperforms the best state-of-the-art model by 8.28% on average in facility visit prediction tasks. Further analyses demonstrate the reasonableness of recovered facility demands and their relationship with citizen demographics. For instance, senior citizens tend to have higher medical demands but lower shopping demands. Meanwhile, estimated capabilities and accessibilities provide deeper insights into the decaying accessibility with respect to spatial distance and facilities’ diverse functions in the urban environment. Our findings shed light on demand-driven urban data mining and demand-based urban facility planning.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":" 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Quantifying Levels of Influence and Causal Responsibility in Dynamic Decision Making Events 动态决策事件中影响和因果责任的量化水平

4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-03 DOI: 10.1145/3631611

Yossef Saad, Joachim Meyer

{"title":"Quantifying Levels of Influence and Causal Responsibility in Dynamic Decision Making Events","authors":"Yossef Saad, Joachim Meyer","doi":"10.1145/3631611","DOIUrl":"https://doi.org/10.1145/3631611","url":null,"abstract":"Intelligent systems support human operators’ decision-making processes, many of which are dynamic and involve temporal changes in the decision-related parameters. As we increasingly depend on automation, it becomes imperative to understand and quantify its influence on the operator’s decisions and to evaluate its implications for the human’s causal responsibility for outcomes. Past studies proposed a model for human responsibility in static decision-making processes involving intelligent systems. We present a model for dynamic, non-stationary decision-making events based on the concept of causation strength. We apply it to a test case of a dynamic binary categorization decision. The results show that for automation to influence humans significantly, it must have high detection sensitivity. However, this condition is insufficient since it is unlikely that automation, irrespective of its sensitivity, will sway humans with high detection sensitivity away from their original position. Specific combinations of automation and human detection sensitivities are required for automation to have a major influence. Moreover, the automation influence and the human causal responsibility that can be derived from it are sensitive to possible changes in the human’s detection capabilities due to fatigue or other factors, creating a ”Responsibility Cliff.” This should be considered during system design and when policies and regulations are defined. This model constitutes a basis for further analyses of complex events in which human and automation sensitivity levels change over time and for evaluating human involvement in such events.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"20 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Second-order Confidence Network for Early Classification of Time Series 时间序列早期分类的二阶置信网络

4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-02 DOI: 10.1145/3631531

Junwei Lv, Yuqi Chu, Jun Hu, Peipei Li, Xuegang Hu

{"title":"Second-order Confidence Network for Early Classification of Time Series","authors":"Junwei Lv, Yuqi Chu, Jun Hu, Peipei Li, Xuegang Hu","doi":"10.1145/3631531","DOIUrl":"https://doi.org/10.1145/3631531","url":null,"abstract":"Time series data are ubiquitous in a variety of disciplines. Early classification of time series, which aims to predict the class label of a time series as early and accurately as possible, is a significant but challenging task in many time-sensitive applications. Existing approaches mainly utilize heuristic stopping rules to capture stopping signals from the prediction results of time series classifiers. However, heuristic stopping rules can only capture obvious stopping signals, which makes these approaches give either correct but late predictions or early but incorrect predictions. To tackle the problem, we propose a novel second-order confidence network for early classification of time series, which can automatically learn to capture implicit stopping signals in early time series in a unified framework. The proposed model leverages deep neural models to capture temporal patterns and outputs second-order confidence to reflect the implicit stopping signals. Specifically, our model not only exploits the data from a time step but from the probability sequence to capture stopping signals. By combining stopping signals from the classifier output and the second-order confidence, we design a more robust trigger to decide whether or not to request more observations from future time steps. Experimental results show that our approach can achieve superior in early classification compared to state-of-the-art approaches.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"4 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135876640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring 安全联邦学习中的水印:基于客户端后门的验证框架

4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-10-30 DOI: 10.1145/3630636

Wenyuan Yang, Shuo Shao, Yue Yang, Xiyao Liu, Ximeng Liu, Zhihua Xia, Gerald Schaefer, Hui Fang

{"title":"Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring","authors":"Wenyuan Yang, Shuo Shao, Yue Yang, Xiyao Liu, Ximeng Liu, Zhihua Xia, Gerald Schaefer, Hui Fang","doi":"10.1145/3630636","DOIUrl":"https://doi.org/10.1145/3630636","url":null,"abstract":"Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to the jointly trained model. Application of homomorphic encryption (HE) in secure FL framework prevents the central server from accessing plaintext models. Thus, it is no longer feasible to embed the watermark at the central server using existing watermarking schemes. In this paper, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE. To our best knowledge, it is the first scheme to embed the watermark to models under the Secure FL environment. We design a black-box watermarking scheme based on client-side backdooring to embed a pre-designed trigger set into an FL model by a gradient-enhanced embedding method. Additionally, we propose a trigger set construction mechanism to ensure the watermark cannot be forged. Experimental results demonstrate that our proposed scheme delivers outstanding protection performance and robustness against various watermark removal attacks and ambiguity attack.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"549 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136018958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 3

Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach 时间序列域的非分布检测:一种新的季节比率评分方法

4区计算机科学

ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-10-30 DOI: 10.1145/3630633

Taha Belkhouja, Yan Yan, Janardhan Rao Doppa

{"title":"Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach","authors":"Taha Belkhouja, Yan Yan, Janardhan Rao Doppa","doi":"10.1145/3630633","DOIUrl":"https://doi.org/10.1145/3630633","url":null,"abstract":"Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data which is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed by time-series data and explain why prior methods from the image domain will perform poorly. Motivated by these challenges, this paper proposes a novel Seasonal Ratio Scoring (SRS) approach. SRS consists of three key algorithmic steps. First, each input is decomposed into class-wise semantic component and remainder. Second, this decomposition is employed to estimate the class-wise conditional likelihoods of the input and remainder using deep generative models. The seasonal ratio score is computed from these estimates. Third, a threshold interval is identified from the in-distribution data to detect OOD examples. Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"637 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136019728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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