Pub Date : 2026-01-22DOI: 10.1109/TETCI.2026.3651281
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2026.3651281","DOIUrl":"https://doi.org/10.1109/TETCI.2026.3651281","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 1","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11361309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1109/TETCI.2026.3651279
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2026.3651279","DOIUrl":"https://doi.org/10.1109/TETCI.2026.3651279","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 1","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11361312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1109/TETCI.2025.3638911
{"title":"2025 Index IEEE Transactions on Emerging Topics in Computational Intelligence","authors":"","doi":"10.1109/TETCI.2025.3638911","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3638911","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 6","pages":"4300-4370"},"PeriodicalIF":5.3,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11273028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1109/TETCI.2025.3629446
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2025.3629446","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3629446","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 6","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11267170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1109/TETCI.2025.3629444
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2025.3629444","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3629444","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 6","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11267168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep neural networks have demonstrated exceptional performance in extracting task-specific representations from datasets, earning widespread recognition and application. However, the internal representations often reside in abstract, high-dimensional spaces that are unsupervised and difficult to interpret. Additionally, their complex and tightly coupled structures hinder researchers' ability to understand the models effectively. To tackle these challenges, we introduce NeuronExplorer, an analytical framework that employs self-supervised techniques for learning high-dimensional information representations. NeuronExplorer analyzes the high-dimensional representations derived from the basic units, namely neurons, within the neural network, predicting the clusters to which these neurons belong. This process facilitates the ‘community’ of neurons, enhancing interpretability.Moreover, we refine this neuron community structure by assessing the causal effects of intervening in neuron outputs, allowing us to measure the impact on model performance. NeuronExplorer ultimately enables a deeper understanding of the internal information representation within deep neural networks. Comprehensive experiments conducted across multiple models demonstrate that NeuronExplorer effectively mines internal representations, thereby improving model transparency.
{"title":"Deep Neural Networks Internal Representation via Neuron Community Exploration","authors":"Guipeng Lan;Shuai Xiao;Jiachen Yang;Wen Lu;Qinggang Meng;Xinbo Gao","doi":"10.1109/TETCI.2025.3622647","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3622647","url":null,"abstract":"Deep neural networks have demonstrated exceptional performance in extracting task-specific representations from datasets, earning widespread recognition and application. However, the internal representations often reside in abstract, high-dimensional spaces that are unsupervised and difficult to interpret. Additionally, their complex and tightly coupled structures hinder researchers' ability to understand the models effectively. To tackle these challenges, we introduce NeuronExplorer, an analytical framework that employs self-supervised techniques for learning high-dimensional information representations. NeuronExplorer analyzes the high-dimensional representations derived from the basic units, namely neurons, within the neural network, predicting the clusters to which these neurons belong. This process facilitates the ‘community’ of neurons, enhancing interpretability.Moreover, we refine this neuron community structure by assessing the causal effects of intervening in neuron outputs, allowing us to measure the impact on model performance. NeuronExplorer ultimately enables a deeper understanding of the internal information representation within deep neural networks. Comprehensive experiments conducted across multiple models demonstrate that NeuronExplorer effectively mines internal representations, thereby improving model transparency.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 1","pages":"1038-1049"},"PeriodicalIF":5.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1109/TETCI.2025.3622664
Liping Gao;Feng Chu;Chao Chen
The development of intelligent transportation systems and the advancement of information technology bring new challenges to route planning, as shorter travel time may no longer be the travelers’ only preference for a route, and the preferences may also change over time which is overlooked in most prior work. In this paper, we study a new bi-objective planning problem with both time-dependent travel time and preference. The first objective is to maximize the total preference score and the second one is to minimize the total travel time. For the considered problem, an appropriate bi-objective integer linear model is formulated. Then, an exact $epsilon$-constraint method is proposed for small-sized instances, while a problem specific non-dominated sorting genetic algorithm-II (NSGA-II) is designed to handle large-sized instances. Specifically, novel region-based encoding and decoding methods are introduced to generate a set of solutions. Additionally, a feasibility condition and a repair strategy are incorporated to address cases where a chromosome is infeasible. We evaluate the proposed methods thoroughly based on 120 randomly generated road networks and 3 real-world road networks crawled via the OpenStreetMap platform. Results show that: (i) $epsilon$-constraint method obtains good performance on small-sized road networks; (ii) our problem-specific NSGA-II works well with large-sized road networks in obtaining the high-quality solutions while significantly saving computational time.
智能交通系统的发展和信息技术的进步给路线规划带来了新的挑战，因为较短的出行时间可能不再是旅行者对路线的唯一偏好，而且这种偏好也可能随着时间的推移而变化，这在大多数先前的工作中被忽视。本文研究了一种新的具有时间依赖的出行时间和出行偏好的双目标规划问题。第一个目标是使总偏好得分最大化，第二个目标是使总旅行时间最小化。对于所考虑的问题，建立了一个合适的双目标整数线性模型。然后，针对小型实例提出了一种精确的$epsilon$约束方法，针对大型实例设计了一种针对特定问题的非支配排序遗传算法- ii （NSGA-II）。具体来说，介绍了新的基于区域的编码和解码方法来生成一套解决方案。此外，可行性条件和修复策略被纳入解决染色体不可行的情况。我们基于120个随机生成的道路网络和3个通过OpenStreetMap平台抓取的现实世界道路网络，彻底评估了所提出的方法。结果表明：(i) $epsilon$-约束方法在小型路网上获得了良好的性能；（ii）我们针对特定问题的NSGA-II在处理大型道路网络时，能很好地获得高质量的解决方案，同时大大节省计算时间。
{"title":"Bi-Objective Optimization for Time-Dependent Preference-Driven Route Planning","authors":"Liping Gao;Feng Chu;Chao Chen","doi":"10.1109/TETCI.2025.3622664","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3622664","url":null,"abstract":"The development of intelligent transportation systems and the advancement of information technology bring new challenges to route planning, as shorter travel time may no longer be the travelers’ only preference for a route, and the preferences may also change over time which is overlooked in most prior work. In this paper, we study a new bi-objective planning problem with both time-dependent travel time and preference. The first objective is to maximize the total preference score and the second one is to minimize the total travel time. For the considered problem, an appropriate bi-objective integer linear model is formulated. Then, an exact <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-constraint method is proposed for small-sized instances, while a problem specific non-dominated sorting genetic algorithm-II (NSGA-II) is designed to handle large-sized instances. Specifically, novel region-based encoding and decoding methods are introduced to generate a set of solutions. Additionally, a feasibility condition and a repair strategy are incorporated to address cases where a chromosome is infeasible. We evaluate the proposed methods thoroughly based on 120 randomly generated road networks and 3 real-world road networks crawled via the OpenStreetMap platform. Results show that: (i) <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-constraint method obtains good performance on small-sized road networks; (ii) our problem-specific NSGA-II works well with large-sized road networks in obtaining the high-quality solutions while significantly saving computational time.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 1","pages":"1050-1068"},"PeriodicalIF":5.3,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thanks to higher power quality and performance efficiency, multilevel grid-tied inverters are the right choice for DC-to-AC conversion like the PV systems to the main power grid. However, the complexity of controlling the switching devices and capacitor voltages in these inverters presents significant stability challenges, particularly during grid-tied operation and when dealing with parameter mismatches. This paper proposes an optimized adaptive Active Disturbance Rejection Controller (ADRC) to stabilize the current of the grid-tied PEC9, serving as a multilevel inverter for PV applications. For this purpose, the PV system, connected to PEC9 as a main DC source to be integrated into the grid. The tunable coefficients of the ADRC controller are automatically adjusted using the on-policy reinforcement learning (RL) technique to effectively stabilize the grid-tied PEC9 with a PV inverter. In this approach, a reward function tailored to the inverter requirements guides the RL-agent in determining the optimal policy. Through maximizing the reward signal, the on-policy algorithm generates regulatory signals to adjust control gains accordingly. A laboratory prototype of PEC9 inverter is constructed by implementing OPAL-RT simulator to investigate the feasibility and applicability of suggested adaptive data-driven scheme. The experimental responses of grid-tied PEC9 equipped with the proposed adaptive ADRC demonstrate the effective performance under various operating conditions of grid-tied PV inverters, including change in the system’s references and parameter mismatches.
{"title":"On-Policy Machine Learning Based-Disturbance Rejection Control for Grid-Tied PEC9 Inverter Under Parameters Mismatch and Distorted Grid Voltage","authors":"Arman Fathollahi;Meysam Gheisarnejad;Mohammad Sharifzadeh;Eric Laurendeau;Björn Andresen;Kamal Al-Haddad","doi":"10.1109/TETCI.2025.3619574","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3619574","url":null,"abstract":"Thanks to higher power quality and performance efficiency, multilevel grid-tied inverters are the right choice for DC-to-AC conversion like the PV systems to the main power grid. However, the complexity of controlling the switching devices and capacitor voltages in these inverters presents significant stability challenges, particularly during grid-tied operation and when dealing with parameter mismatches. This paper proposes an optimized adaptive Active Disturbance Rejection Controller (ADRC) to stabilize the current of the grid-tied PEC9, serving as a multilevel inverter for PV applications. For this purpose, the PV system, connected to PEC9 as a main DC source to be integrated into the grid. The tunable coefficients of the ADRC controller are automatically adjusted using the on-policy reinforcement learning (RL) technique to effectively stabilize the grid-tied PEC9 with a PV inverter. In this approach, a reward function tailored to the inverter requirements guides the RL-agent in determining the optimal policy. Through maximizing the reward signal, the on-policy algorithm generates regulatory signals to adjust control gains accordingly. A laboratory prototype of PEC9 inverter is constructed by implementing OPAL-RT simulator to investigate the feasibility and applicability of suggested adaptive data-driven scheme. The experimental responses of grid-tied PEC9 equipped with the proposed adaptive ADRC demonstrate the effective performance under various operating conditions of grid-tied PV inverters, including change in the system’s references and parameter mismatches.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 1","pages":"1025-1037"},"PeriodicalIF":5.3,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-31DOI: 10.1109/TETCI.2025.3619564
Fumin Li;Rui Yang;Hanjing Cheng;Mengjie Huang;Fanglue Zhang;Fuad E. Alsaadi;Zidong Wang
In the recent years, researchers made significant progress in electroencephalogram (EEG) classification tasks using deep neural networks, especially in brain-computer interface (BCI) systems. BCI systems rely on EEG signals for effective human-computer interaction, and deep neural networks have shown excellent performance in processing EEG signals. However, backdoor attack have a significant impact on the security of EEG-based BCI systems. In this paper, a novel multi-scale Shapley adaptation pruning (MSAP) method is proposed to solve the security problem caused by backdoor attack. In the proposed MSAP, the multi-scale Shapley segmented mapping method is used to accurately locate the backdoor weights. Subsequently, the cost function is utilized to adaptively prune the backdoor weights to ensure normal classification. Ultimately, the validity of the experiments is verified on the BCI competition public datasets (BCI-III-IVb, BCI-III-IVa, and BCI-IV-1a). The results show that the proposed MSAP method outperforms other pruning methods in defending EEG-based BCI systems against backdoor attack, maintaining a high baseline classification accuracy while reducing the attack success rate.
近年来，研究人员利用深度神经网络，特别是脑机接口（BCI）系统，在脑电图（EEG）分类任务方面取得了重大进展。脑机接口系统依靠脑电信号进行有效的人机交互，深度神经网络在处理脑电信号方面表现出优异的性能。然而，后门攻击对基于脑电图的脑机接口系统的安全性产生了重大影响。针对后门攻击带来的安全问题，提出了一种新的多尺度Shapley自适应剪枝（MSAP）方法。在该算法中，采用多尺度Shapley分割映射方法精确定位后门权重。然后利用代价函数对后门权值进行自适应剪枝，保证正常分类。最后，在BCI竞争公开数据集（BCI- iii - ivb、BCI- iii - iva和BCI- iv -1a）上验证了实验的有效性。结果表明，本文提出的MSAP方法在防御基于eeg的BCI系统的后门攻击方面优于其他修剪方法，在降低攻击成功率的同时保持了较高的基线分类精度。
{"title":"Multi-Scale Shapley Adaptation Pruning: Realizing Backdoor Defense in Brain-Computer Interface With Shapley-Value-Based Neural Network Pruning","authors":"Fumin Li;Rui Yang;Hanjing Cheng;Mengjie Huang;Fanglue Zhang;Fuad E. Alsaadi;Zidong Wang","doi":"10.1109/TETCI.2025.3619564","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3619564","url":null,"abstract":"In the recent years, researchers made significant progress in electroencephalogram (EEG) classification tasks using deep neural networks, especially in brain-computer interface (BCI) systems. BCI systems rely on EEG signals for effective human-computer interaction, and deep neural networks have shown excellent performance in processing EEG signals. However, backdoor attack have a significant impact on the security of EEG-based BCI systems. In this paper, a novel multi-scale Shapley adaptation pruning (MSAP) method is proposed to solve the security problem caused by backdoor attack. In the proposed MSAP, the multi-scale Shapley segmented mapping method is used to accurately locate the backdoor weights. Subsequently, the cost function is utilized to adaptively prune the backdoor weights to ensure normal classification. Ultimately, the validity of the experiments is verified on the BCI competition public datasets (BCI-III-IVb, BCI-III-IVa, and BCI-IV-1a). The results show that the proposed MSAP method outperforms other pruning methods in defending EEG-based BCI systems against backdoor attack, maintaining a high baseline classification accuracy while reducing the attack success rate.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 1","pages":"967-981"},"PeriodicalIF":5.3,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-16DOI: 10.1109/TETCI.2025.3616051
Mehbooba P Shareef;Babita Roslind Jose;Jimson Mathew;Ramkumar P. B.
This paper presents a novel recommendation system designed to effectively suggest products to users by leveraging a neutrosophic fuzzy hypergraph structure, where users are represented as hyperedges and products as hypernodes. The approach incorporates a global partial order of items, derived from frequent pattern analysis, to establish an ordering framework over product recommendations. State vectors representing users are extracted and refined through a Graph Convolutional Neural Network (GCN), which captures the intricate relationships within the graph. Using a Deep Q Network (DQN)-based reinforcement learning model with indeterminacy-driven exploration-exploitation, the system learns optimal recommendation strategies from the feature representations of the neutrosophic fuzzy hypergraph. Reward signals are calculated by assessing how closely a new recommendation aligns with the partial ordering, as well as by using fuzzy rules generated from a domain-specific expert system. The recommendations are explained using paths extracted from the hypergraph. Our experimental evaluation on real-world datasets demonstrates that the proposed system outperforms state-of-the-art recommendation approaches in terms of Normalized Cumulative Discounted Gain(NDCG) and precision, indicating its strong suitability for practical applications in complex recommendation environments.
{"title":"Indeterminacy-Driven Trade-Off in Reinforcement Learning on Neutrosophic Fuzzy Hypergraphs for Explainable Item Recommendation With Path-Compliant Rewards","authors":"Mehbooba P Shareef;Babita Roslind Jose;Jimson Mathew;Ramkumar P. B.","doi":"10.1109/TETCI.2025.3616051","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3616051","url":null,"abstract":"This paper presents a novel recommendation system designed to effectively suggest products to users by leveraging a neutrosophic fuzzy hypergraph structure, where users are represented as hyperedges and products as hypernodes. The approach incorporates a global partial order of items, derived from frequent pattern analysis, to establish an ordering framework over product recommendations. State vectors representing users are extracted and refined through a Graph Convolutional Neural Network (GCN), which captures the intricate relationships within the graph. Using a Deep Q Network (DQN)-based reinforcement learning model with indeterminacy-driven exploration-exploitation, the system learns optimal recommendation strategies from the feature representations of the neutrosophic fuzzy hypergraph. Reward signals are calculated by assessing how closely a new recommendation aligns with the partial ordering, as well as by using fuzzy rules generated from a domain-specific expert system. The recommendations are explained using paths extracted from the hypergraph. Our experimental evaluation on real-world datasets demonstrates that the proposed system outperforms state-of-the-art recommendation approaches in terms of Normalized Cumulative Discounted Gain(NDCG) and precision, indicating its strong suitability for practical applications in complex recommendation environments.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"10 1","pages":"996-1008"},"PeriodicalIF":5.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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