AI Open Pub Date : 2022-01-01 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":"<div><p>Network embedding (NE) aims to learn low-dimensional vectors for nodes while preserving the network’s essential properties (e.g., attributes and structure). Previous methods have been proposed to learn node representations with encouraging achievements. Recent research has shown that the hierarchical label has potential value in seeking latent hierarchical structures and learning more effective classification information. Nevertheless, most existing network embedding methods either focus on the network without the hierarchical label, or the learning process of hierarchical structure for labels is separate from the network structure. Learning node embedding with the hierarchical label suffers from two challenges: (1) Fusing hierarchical labels and network is still an arduous task. (2) The data volume imbalance under different hierarchical labels is more noticeable than flat labels. This paper proposes a <strong>H</strong>ierarchical Label and <strong>A</strong>ttributed <strong>N</strong>etwork <strong>S</strong>tructure Fusion model(HANS), which realizes the fusion of hierarchical labels and nodes through attributes and the attention-based fusion module. Particularly, HANS designs a directed hierarchy structure encoder for modeling label dependencies in three directions (parent–child, child–parent, and sibling) to strengthen the co-occurrence information between labels of different frequencies and reduce the impact of the label imbalance. Experiments on real-world datasets demonstrate that the proposed method achieves significantly better performance than the state-of-the-art algorithms.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 91-100"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000122/pdfft?md5=b0971b7ac0f357e13fd0e41f95f6412d&pid=1-s2.0-S2666651022000122-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72246448","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

Self-directed machine learning 自主机器学习

AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.06.001

Wenwu Zhu , Xin Wang , Pengtao Xie

{"title":"Self-directed machine learning","authors":"Wenwu Zhu , Xin Wang , Pengtao Xie","doi":"10.1016/j.aiopen.2022.06.001","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.06.001","url":null,"abstract":"<div><p>Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation metrics, which is labor-intensive, time-consuming, and cannot learn autonomously like humans. In education science, self-directed learning, where human learners select learning tasks and materials on their own without requiring hands-on guidance, has been shown to be more effective than passive teacher-guided learning. Inspired by the concept of self-directed human learning, we introduce the principal concept of Self-directed Machine Learning (SDML) and propose a framework for SDML. Specifically, we design SDML as a self-directed learning process guided by self-awareness, including internal awareness and external awareness. Our proposed SDML process benefits from self task selection, self data selection, self model selection, self optimization strategy selection and self evaluation metric selection through self-awareness without human guidance. Meanwhile, the learning performance of the SDML process serves as feedback to further improve self-awareness. We propose a mathematical formulation for SDML based on multi-level optimization. Furthermore, we present case studies together with potential applications of SDML, followed by discussing future research directions. We expect that SDML could enable machines to conduct human-like self-directed learning and provide a new perspective towards artificial general intelligence.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 58-70"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000109/pdfft?md5=5480e0d544d9f6d6307d44ca29f5d00c&pid=1-s2.0-S2666651022000109-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72282567","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

Deep learning for fake news detection: A comprehensive survey 深度学习用于假新闻检测：一项综合调查

AI Open Pub Date : 2022-01-01 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":"<div><p>The information age enables people to obtain news online through various channels, yet in the meanwhile making false news spread at unprecedented speed. Fake news exerts detrimental effects for it impairs social stability and public trust, which calls for increasing demand for fake news detection (FND). As deep learning (DL) achieves tremendous success in various domains, it has also been leveraged in FND tasks and surpasses traditional machine learning based methods, yielding state-of-the-art performance. In this survey, we present a complete review and analysis of existing DL based FND methods that focus on various features such as news content, social context, and external knowledge. We review the methods under the lines of supervised, weakly supervised, and unsupervised methods. For each line, we systematically survey the representative methods utilizing different features. Then, we introduce several commonly used FND datasets and give a quantitative analysis of the performance of the DL based FND methods over these datasets. Finally, we analyze the remaining limitations of current approaches and highlight some promising future directions.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 133-155"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000134/pdfft?md5=d2d9826705629e3762ea484a2d93d29d&pid=1-s2.0-S2666651022000134-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72286084","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: 25

Human motion modeling with deep learning: A survey 基于深度学习的人体运动建模:综述

AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2021.12.002

Zijie Ye, Haozhe Wu, Jia Jia

引用次数: 8

On the distribution alignment of propagation in graph neural networks 关于图神经网络中传播的分布对齐

AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.11.006

Qinkai Zheng , Xiao Xia , Kun Zhang , Evgeny Kharlamov , Yuxiao Dong

引用次数: 0

HSSDA: Hierarchical relation aided Semi-Supervised Domain Adaptation HSSDA：层次关系辅助的半监督域自适应

AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.11.001

Xiechao Guo , Ruiping Liu , Dandan Song

{"title":"HSSDA: Hierarchical relation aided Semi-Supervised Domain Adaptation","authors":"Xiechao Guo , Ruiping Liu , Dandan Song","doi":"10.1016/j.aiopen.2022.11.001","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.11.001","url":null,"abstract":"<div><p>The mainstream domain adaptation (DA) methods transfer the supervised source domain knowledge to the unsupervised or semi-supervised target domain, so as to assist the classification task in the target domain. Usually the supervision only contains the class label of the object. However, when human beings recognize a new object, they will not only learn the class label of the object, but also correlate the object to its parent class, and use this information to learn the similarities and differences between child classes. Our model utilizes hierarchical relations via making the parent class label of labeled data (all the source domain data and part of target domain data) as a part of supervision to guide prototype learning module vbfd to learn the parent class information encoding, so that the prototypes of the same parent class are closer in the prototype space, which leads to better classification results. Inspired by this mechanism, we propose a <strong>Hierarchical relation aided Semi-Supervised Domain Adaptation (HSSDA)</strong> method which incorporates the hierarchical relations into the Semi-Supervised Domain Adaptation (SSDA) method to improve the classification results of the model. Our model performs well on the DomainNet dataset, and gets the state-of-the-art results in the semi-supervised DA problem.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 156-161"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266665102200016X/pdfft?md5=acdf10fbc8ecc16b703bc63cf409d5c7&pid=1-s2.0-S266665102200016X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72286082","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

CAILIE 1.0: A dataset for Challenge of AI in Law - Information Extraction V1.0 CAILIE 1.0：人工智能法律挑战数据集-信息提取V1.0

AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.12.002

Yu Cao, Yuanyuan Sun, Ce Xu, Chunnan Li, Jinming Du, Hongfei Lin

引用次数: 1

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 : 2022-01-01 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":"<div><p>As an important way to alleviate information overload, a recommender system aims to filter out irrelevant information for users and provides them items that they may be interested in. In recent years, an increasing amount of works have been proposed to introduce auxiliary information in recommender systems to alleviate data sparsity and cold-start problems. Among them, heterogeneous information networks (HIN)-based recommender systems provide a unified approach to fuse various auxiliary information, which can be combined with mainstream recommendation algorithms to effectively enhance the performance and interpretability of models, and thus have been applied in many kinds of recommendation tasks. This paper provides a comprehensive and systematic survey of HIN-based recommender systems, including four aspects: concepts, methods, applications, and resources. Specifically, we firstly introduce the concepts related to recommender systems, heterogeneous information networks and HIN-based recommendation. Secondly, we present more than 70 methods categorized according to models or application scenarios, and describe representative methods symbolically. Thirdly, we summarize the benchmark datasets and open source code. Finally, we discuss several potential research directions and conclude our survey.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 40-57"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000092/pdfft?md5=6df8ab165a626a41bbcb77bcaac40c0f&pid=1-s2.0-S2666651022000092-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72246449","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: 12

Data augmentation approaches in natural language processing: A survey 自然语言处理中的数据扩充方法：一项调查

AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.03.001

Bohan Li, Yutai Hou, Wanxiang Che

引用次数: 118

PTR: Prompt Tuning with Rules for Text Classification PTR：使用文本分类规则进行提示调整

AI Open Pub Date : 2022-01-01 DOI: 10.1016/j.aiopen.2022.11.003

Xu Han , Weilin Zhao , Ning Ding , Zhiyuan Liu , Maosong Sun

{"title":"PTR: Prompt Tuning with Rules for Text Classification","authors":"Xu Han , Weilin Zhao , Ning Ding , Zhiyuan Liu , Maosong Sun","doi":"10.1016/j.aiopen.2022.11.003","DOIUrl":"https://doi.org/10.1016/j.aiopen.2022.11.003","url":null,"abstract":"<div><p>Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved promising results on some few-class classification tasks, such as sentiment classification and natural language inference, manually designing prompts is cumbersome. Meanwhile, generating prompts automatically is also difficult and time-consuming. Therefore, obtaining effective prompts for complex many-class classification tasks still remains a challenge. In this paper, we propose to encode the prior knowledge of a classification task into rules, then design sub-prompts according to the rules, and finally combine the sub-prompts to handle the task. We name this <strong>P</strong>rompt <strong>T</strong>uning method with <strong>R</strong>ules “<strong>PTR</strong>”. Compared with existing prompt-based methods, PTR achieves a good trade-off between effectiveness and efficiency in building prompts. We conduct experiments on three many-class classification tasks, including relation classification, entity typing, and intent classification. The results show that PTR outperforms both vanilla and prompt tuning baselines, indicating the effectiveness of utilizing rules for prompt tuning. The source code of PTR is available at <span>https://github.com/thunlp/PTR</span><svg><path></path></svg>.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 182-192"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000183/pdfft?md5=00c56e1aac330e25c378cff01e2ca394&pid=1-s2.0-S2666651022000183-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72286080","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: 291

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