IEEE Intelligent Systems最新文献_第10页

IEEE Computer Society Career Center 电气和电子工程师学会计算机协会职业中心

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-12-08 DOI: 10.1109/mis.2023.3328527

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Over the Rainbow: 21st Century Security & Privacy Podcast 彩虹之上：21 世纪安全与隐私播客

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-12-08 DOI: 10.1109/mis.2023.3329551

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An Operational Framework for Guiding Human Evaluation in Explainable and Trustworthy Artificial Intelligence 在可解释和可信赖的人工智能中指导人类评估的操作框架

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-11-20 DOI: 10.1109/mis.2023.3334639

Roberto Confalonieri, Jose Maria Alonso-Moral

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IEEE CS - Call for Papers IEEE CS - 征稿启事

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-11-01 DOI: 10.1109/mis.2023.3329553

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IEEE Computer Society D & I Fund: Drive Diversity & Inclusion in Computing IEEE 计算机协会 D & I 基金：推动计算机领域的多样性和包容性

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-11-01 DOI: 10.1109/mis.2023.3328519

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IEEE Computing in Science & Engineering IEEE 科学与工程计算

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-11-01 DOI: 10.1109/mis.2023.3329549

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CRule: Category-Aware Symbolic Multihop Reasoning on Knowledge Graphs 知识图上的类别感知符号多跳推理

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-09-01 DOI: 10.1109/MIS.2023.3291567

Zikang Wang, Linjing Li, Jinlin Li, Pengfei Zhao, D. Zeng

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Effectively Modeling Sentence Interactions With Factorization Machines for Fact Verification 利用因子分解机对句子交互进行有效建模以进行事实验证

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-09-01 DOI: 10.1109/MIS.2023.3301170

Zhen-Heng Chen, Fuzhen Zhuang, Lejian Liao, Meihuizi Jia, Jiaqi Li, Heyan Huang

{"title":"Effectively Modeling Sentence Interactions With Factorization Machines for Fact Verification","authors":"Zhen-Heng Chen, Fuzhen Zhuang, Lejian Liao, Meihuizi Jia, Jiaqi Li, Heyan Huang","doi":"10.1109/MIS.2023.3301170","DOIUrl":"https://doi.org/10.1109/MIS.2023.3301170","url":null,"abstract":"Fact verification is a very challenging task that requires retrieving multiple evidence sentences from a reliable corpus to authenticate claims. Many claims require the simultaneous integration and reasoning of several pieces of evidence for verification. Existing models exhibit limitations in two aspects: 1) during the sentence selection stage, they only consider the interaction between the claim and the evidence, disregarding the intersentence information, and 2) most fusion strategies employed in current research, such as addition, concatenation, or simple neural networks, fail to capture the relationships and logical information among the evidence. To alleviate these problems, we propose select and fact verification modeling (SFVM). Our model utilizes a multihead self-attention mechanism combined with a gating mechanism to facilitate sentence interaction and enhance sentence embeddings. Then, we utilize factorization machines to effectively express the compressed alignment vectors, which are then used to expand the representations of the base evidence. To distinguish the importance of features, we use the evidence fusion network to determine the importance of various feature interactions. Results from experiments on the two public datasets showed that SFVM can utilize richer information between the claim and the evidence for fact verification and achieve competitive performance on the FEVER dataset.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"38 1","pages":"18-27"},"PeriodicalIF":6.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44360647","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}

引用次数: 0

Effective Interpretable Policy Distillation via Critical Experience Point Identification 通过关键经验点识别进行有效的可解释政策提炼

IF 6.4 3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-09-01 DOI: 10.1109/MIS.2023.3265868

Xiao Liu, Shuyang Liu, Bo An, Yang Gao, Shangdong Yang, Wenbin Li

{"title":"Effective Interpretable Policy Distillation via Critical Experience Point Identification","authors":"Xiao Liu, Shuyang Liu, Bo An, Yang Gao, Shangdong Yang, Wenbin Li","doi":"10.1109/MIS.2023.3265868","DOIUrl":"https://doi.org/10.1109/MIS.2023.3265868","url":null,"abstract":"Interpretable policy distillation aims to imitate a deep reinforcement learning (DRL) policy into a self-explainable model. However, the distilled policy usually does not generalize well to complex tasks. To investigate this phenomenon, we examine the experience pools of DRL tasks and find that these interactive experience distributions are heavy tailed. However, this critical issue is largely ignored by existing approaches, and, thus, they do not fully unitize the less frequent but very critical experience points. To address this issue, we propose characterizing decision boundaries via the minimum experience retention to deal with the heavy-tailed experience distributions. Our method identifies critical experience points that are close to the model’s decision boundaries, and such experience points are more critical because they portray the prerequisite of a model to take an action. As a result, our method distills the DRL policy to a self-explainable structure without a neural structure and ambiguous intermediate parameters. Through experiments on six games, we show that our method outperforms the state-of-the-art baselines in cumulative rewards, stability, and faithfulness.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"38 1","pages":"28-36"},"PeriodicalIF":6.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41930464","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}

引用次数: 1

IEEE Diversity and Inclusion Fund IEEE多样性和包容性基金

3区计算机科学

IEEE Intelligent Systems Pub Date : 2023-09-01 DOI: 10.1109/mis.2023.3313500

引用次数: 0