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Deep learning for fake news detection: A comprehensive survey 深度学习在假新闻检测中的应用综述
AI Open Pub Date : 2022-10-01 DOI: 10.1016/j.aiopen.2022.09.001
Linmei Hu, Siqi Wei, Ziwang Zhao, Bin Wu
{"title":"Deep learning for fake news detection: A comprehensive survey","authors":"Linmei Hu, Siqi Wei, Ziwang Zhao, Bin Wu","doi":"10.1016/j.aiopen.2022.09.001","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.09.001","url":null,"abstract":"","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"44 1","pages":"133-155"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91013077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
Debiased Recommendation with Neural Stratification 神经分层去偏见推荐
AI Open Pub Date : 2022-08-15 DOI: 10.48550/arXiv.2208.07281
Quanyu Dai, Zhenhua Dong, Xu Chen
{"title":"Debiased Recommendation with Neural Stratification","authors":"Quanyu Dai, Zhenhua Dong, Xu Chen","doi":"10.48550/arXiv.2208.07281","DOIUrl":"https://doi.org/10.48550/arXiv.2208.07281","url":null,"abstract":"Debiased recommender models have recently attracted increasing attention from the academic and industry communities. Existing models are mostly based on the technique of inverse propensity score (IPS). However, in the recommendation domain, IPS can be hard to estimate given the sparse and noisy nature of the observed user-item exposure data. To alleviate this problem, in this paper, we assume that the user preference can be dominated by a small amount of latent factors, and propose to cluster the users for computing more accurate IPS via increasing the exposure densities. Basically, such method is similar with the spirit of stratification models in applied statistics. However, unlike previous heuristic stratification strategy, we learn the cluster criterion by presenting the users with low ranking embeddings, which are future shared with the user representations in the recommender model. At last, we find that our model has strong connections with the previous two types of debiased recommender models. We conduct extensive experiments based on real-world datasets to demonstrate the effectiveness of the proposed method.","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"7 1","pages":"213-217"},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79579881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Hierarchical label with imbalance and attributed network structure fusion for network embedding 基于不平衡的分层标签和属性网络结构融合的网络嵌入
AI Open Pub Date : 2022-08-01 DOI: 10.1016/j.aiopen.2022.07.002
Shu Zhao, Jialin Chen, Jie Chen, Yanping Zhang, Jie Tang
{"title":"Hierarchical label with imbalance and attributed network structure fusion for network embedding","authors":"Shu Zhao, Jialin Chen, Jie Chen, Yanping Zhang, Jie Tang","doi":"10.1016/j.aiopen.2022.07.002","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.07.002","url":null,"abstract":"","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"15 1","pages":"91-100"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76855558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 on heterogeneous information network based recommender systems: Concepts, methods, applications and resources 基于异构信息网络的推荐系统综述:概念、方法、应用和资源
AI Open Pub Date : 2022-04-01 DOI: 10.1016/j.aiopen.2022.03.002
Jiawei Liu, Chuan Shi, Cheng Yang, Zhiyuan Lu, Philip S. Yu
{"title":"A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources","authors":"Jiawei Liu, Chuan Shi, Cheng Yang, Zhiyuan Lu, Philip S. Yu","doi":"10.1016/j.aiopen.2022.03.002","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.03.002","url":null,"abstract":"","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"1 1","pages":"40-57"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75296641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Self-directed Machine Learning 自主机器学习
AI Open Pub Date : 2022-01-04 DOI: 10.1016/j.aiopen.2022.06.001
Wenwu Zhu, Xin Wang, P. Xie
{"title":"Self-directed Machine Learning","authors":"Wenwu Zhu, Xin Wang, P. Xie","doi":"10.1016/j.aiopen.2022.06.001","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.06.001","url":null,"abstract":"","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"64 1","pages":"58-70"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75237207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Domain generalization by class-aware negative sampling-based contrastive learning 基于类别感知的负抽样对比学习的领域泛化
AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.11.004
Mengwei Xie, Suyun Zhao, Hong Chen, Cuiping Li
{"title":"Domain generalization by class-aware negative sampling-based contrastive learning","authors":"Mengwei Xie,&nbsp;Suyun Zhao,&nbsp;Hong Chen,&nbsp;Cuiping Li","doi":"10.1016/j.aiopen.2022.11.004","DOIUrl":"10.1016/j.aiopen.2022.11.004","url":null,"abstract":"<div><p>When faced with the issue of different feature distribution between training and test data, the test data may differ in style and background from the training data due to the collection sources or privacy protection. That is, the transfer generalization problem. Contrastive learning, which is currently the most successful unsupervised learning method, provides good generalization performance for the various distributions of data and can use labeled data more effectively without overfitting. This study demonstrates how contrast can enhance a model’s ability to generalize, how joint contrastive learning and supervised learning can strengthen one another, and how this approach can be broadly used in various disciplines.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 200-207"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000195/pdfft?md5=d1beea40105807161328cdcc4aa5b211&pid=1-s2.0-S2666651022000195-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83293382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
StackVAE-G: An efficient and interpretable model for time series anomaly detection StackVAE-G:一种高效且可解释的时间序列异常检测模型
AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.07.001
Wenkai Li , Wenbo Hu , Ting Chen , Ning Chen , Cheng Feng
{"title":"StackVAE-G: An efficient and interpretable model for time series anomaly detection","authors":"Wenkai Li ,&nbsp;Wenbo Hu ,&nbsp;Ting Chen ,&nbsp;Ning Chen ,&nbsp;Cheng Feng","doi":"10.1016/j.aiopen.2022.07.001","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.07.001","url":null,"abstract":"<div><p>Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsupervised manner. In this work, we propose a novel autoencoder-based model, named StackVAE-G that can significantly bring the efficiency and interpretability to multivariate time series anomaly detection. Specifically, we utilize the similarities across the time series channels by the stacking block-wise reconstruction with a weight-sharing scheme to reduce the size of learned models and also relieve the overfitting to unknown noises in the training data. We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels. Combining these two modules, we introduce the stacking block-wise VAE (variational autoencoder) with GNN (graph neural network) model for multivariate time series anomaly detection. We conduct extensive experiments on three commonly used public datasets, showing that our model achieves comparable (even better) performance with the state-of-the-art models and meanwhile requires much less computation and memory cost. Furthermore, we demonstrate that the adjacency matrix learned by our model accurately captures the interrelation among multiple channels, and can provide valuable information for failure diagnosis applications.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 101-110"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000110/pdfft?md5=1bdde12e6a6cbde8b1220840197923b8&pid=1-s2.0-S2666651022000110-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72282566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Augmented and challenging datasets with multi-step reasoning and multi-span questions for Chinese judicial reading comprehension 具有多步骤推理和多跨度问题的中国司法阅读理解扩充和挑战性数据集
AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.12.001
Qingye Meng , Ziyue Wang , Hang Chen , Xianzhen Luo , Baoxin Wang , Zhipeng Chen , Yiming Cui , Dayong Wu , Zhigang Chen , Shijin Wang
{"title":"Augmented and challenging datasets with multi-step reasoning and multi-span questions for Chinese judicial reading comprehension","authors":"Qingye Meng ,&nbsp;Ziyue Wang ,&nbsp;Hang Chen ,&nbsp;Xianzhen Luo ,&nbsp;Baoxin Wang ,&nbsp;Zhipeng Chen ,&nbsp;Yiming Cui ,&nbsp;Dayong Wu ,&nbsp;Zhigang Chen ,&nbsp;Shijin Wang","doi":"10.1016/j.aiopen.2022.12.001","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.12.001","url":null,"abstract":"","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"193-199"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000225/pdfft?md5=b1c460292acbffd5098c88c36eca4487&pid=1-s2.0-S2666651022000225-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72286079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Survey: Transformer based video-language pre-training 基于Transformer的视频语言预训练
AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.01.001
Ludan Ruan, Qin Jin
{"title":"Survey: Transformer based video-language pre-training","authors":"Ludan Ruan,&nbsp;Qin Jin","doi":"10.1016/j.aiopen.2022.01.001","DOIUrl":"10.1016/j.aiopen.2022.01.001","url":null,"abstract":"<div><p>Inspired by the success of transformer-based pre-training methods on natural language tasks and further computer vision tasks, researchers have started to apply transformer to video processing. This survey aims to provide a comprehensive overview of transformer-based pre-training methods for Video-Language learning. We first briefly introduce the transformer structure as the background knowledge, including attention mechanism, position encoding etc. We then describe the typical paradigm of pre-training &amp; fine-tuning on Video-Language processing in terms of proxy tasks, downstream tasks and commonly used video datasets. Next, we categorize transformer models into Single-Stream and Multi-Stream structures, highlight their innovations and compare their performances. Finally, we analyze and discuss the current challenges and possible future research directions for Video-Language pre-training.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 1-13"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000018/pdfft?md5=d7b4ae16eb4b58434223ebe8ccf64030&pid=1-s2.0-S2666651022000018-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77585167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 24
Learning towards conversational AI: A survey 学习对话式AI:一项调查
AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.02.001
Tingchen Fu , Shen Gao , Xueliang Zhao , Ji-rong Wen , Rui Yan
{"title":"Learning towards conversational AI: A survey","authors":"Tingchen Fu ,&nbsp;Shen Gao ,&nbsp;Xueliang Zhao ,&nbsp;Ji-rong Wen ,&nbsp;Rui Yan","doi":"10.1016/j.aiopen.2022.02.001","DOIUrl":"10.1016/j.aiopen.2022.02.001","url":null,"abstract":"<div><p>Recent years have witnessed a surge of interest in the field of open-domain dialogue. Thanks to the rapid development of social media, large dialogue corpus from the Internet builds up a fundamental premise for data-driven dialogue model. The breakthrough in neural network also brings new ideas to researchers in AI and NLP. A great number of new techniques and methods therefore came into being. In this paper, we review some of the most representative works in recent years and divide existing prevailing frameworks for a dialogue model into three categories. We further analyze the trend of development for open-domain dialogue and summarize the goal of an open-domain dialogue system in two aspects, informative and controllable. The methods we review in this paper are selected according to our unique perspectives and by no means complete. Rather, we hope this servery could benefit NLP community for future research in open-domain dialogue.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 14-28"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000079/pdfft?md5=a8c5cdae822d93f7d82a0ff336415b53&pid=1-s2.0-S2666651022000079-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85008120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
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