2020 Science and Artificial Intelligence conference (S.A.I.ence)最新文献

S.A.I.ence 2020 Committees 科学科学2020委员会

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/s.a.i.ence50533.2020.9303214

引用次数: 0

Neural Networks and Classical Algorithms in Inverse Problems of Anomalous Diffusion 反常扩散反问题中的神经网络和经典算法

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303217

V. A. Dedok, T. V. Bugueva

引用次数: 0

Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian 俄文科技文本的实体识别与关系提取

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303196

E. Bruches, Alexey Pauls, Tatiana Batura, Vladimir Isachenko

{"title":"Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian","authors":"E. Bruches, Alexey Pauls, Tatiana Batura, Vladimir Isachenko","doi":"10.1109/S.A.I.ence50533.2020.9303196","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303196","url":null,"abstract":"This paper is devoted to the study of methods for information extraction (entity recognition and relation classification) from scientific texts on information technology. Scientific publications provide valuable information into cutting-edge scientific advances, but efficient processing of increasing amounts of data is a time-consuming task. In this paper, several modifications of methods for the Russian language are proposed. It also includes the results of experiments comparing a keyword extraction method, vocabulary method, and some methods based on neural networks. Text collections for these tasks exist for the English language and are actively used by the scientific community, but at present, such datasets in Russian are not publicly available. In this paper, we present a corpus of scientific texts in Russian, RuSERRC. This dataset consists of 1600 unlabeled documents and 80 labeled with entities and semantic relations (6 relation types were considered). The dataset and models are available at https://github.com/iis-research-team. We hope they can be useful for research purposes and development of information extraction systems.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129158392","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: 5

S.A.I.ence 2020 Index 2020年科学指数

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/s.a.i.ence50533.2020.9303179

引用次数: 0

Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction 基于少镜头学习技术的命名实体识别与关系提取

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303192

Ivan Bondarenko, S. Berezin, Alexey Pauls, Tatiana Batura, Yuliya Rubtsova, B. Tuchinov

引用次数: 3

Pattern recognition for bubbly flows with vapor or gas-liquid interfaces using U-Net architecture 基于U-Net结构的蒸汽或气液界面气泡流模式识别

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303175

A. Seredkin, I. Plokhikh, R. Mullyadzhanov, I. Malakhov, V. Serdyukov, A. Surtaev, Alexander Chinak, P. Lobanov, M. Tokarev

引用次数: 2

Two-Staged Self-Attention Based Neural Model For Lung Cancer Recognition 基于两阶段自我注意的肺癌识别神经模型

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303206

A. Samarin, A. Savelev, Valentin Malykh

引用次数: 2

Interpretable Natural Language Segmentation Based on Link Grammar 基于链接语法的可解释性自然语言切分

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303220

Vignav Ramesh, A. Kolonin

{"title":"Interpretable Natural Language Segmentation Based on Link Grammar","authors":"Vignav Ramesh, A. Kolonin","doi":"10.1109/S.A.I.ence50533.2020.9303220","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303220","url":null,"abstract":"Natural language segmentation (NLS), or text segmentation, refers to the process of dividing written text into meaningful units. Sentence segmentation, a subfield of text segmentation, is the problem of dividing a string of natural language text into its component sentences. Current methods of sentence segmentation are often either hardcoded—they require manual implementation of fixed grammar and segmentation rules—or require extensive training on labeled corpora and are not explainable—they are \"black box\" algorithms that cannot be understood by humans. In this paper, we present a novel explainable sentence segmentation method capable of separating bodies of text into grammatically valid sentences solely based on the grammatical relationships between individual words or tokens. The proposed NLS architecture can both automate the input query parsing and semantic query execution components of voice-activated question answering and information retrieval systems as well as enable automatic summarization, entity extraction, sentiment identification, and a variety of other natural language processing (NLP) algorithms that operate at the sentential level.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125165988","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

Selection of Potential Ligands for TRPM8 Using Deep Neural Networks and Intermolecular Docking by the "AUTODOCK" Software 基于深度神经网络和AUTODOCK软件的TRPM8潜在配体选择

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303180

P. D. Timkin, A. Chupalov, E. Timofeev, E. Borodin

{"title":"Selection of Potential Ligands for TRPM8 Using Deep Neural Networks and Intermolecular Docking by the \"AUTODOCK\" Software","authors":"P. D. Timkin, A. Chupalov, E. Timofeev, E. Borodin","doi":"10.1109/S.A.I.ence50533.2020.9303180","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303180","url":null,"abstract":"The article describes a strategy of ligands prediction for TRPM8, where a deep neural network is used to screen out ligands and reduce the list of candidate ligands, the remaining ones are checked via AutoDock software. Subsequent analysis of the minimum binding energy between the receptor site and the putative ligands, as well as possible reactive conformations. The docking control sites were: Y745 (tyrosine 745), a critical site for TRPM8. We also analyzed the intermolecular docking of TRPM8 with its sites of manifestation of the biological effect: R1008 (phenylalanine 1008) and L1009 (alanine 1009). About 10 potential ligands were predicted, which were further verified by the \"AUTODOCK\" method. Intermolecular docking, carried out using the AUTODOCK program, was carried out in coordinates for each of the sites set in the closest position to the docking point. The program identified the potential for successful interactions for eight out of ten predicted candidates for each of the sites. Two of the predicted ligands do not have the ability to successfully interact with TRPM8, the rest showed a high minimum binding energy and the number of reactive conformations compared to the classical ligand, menthol. In this work, we used the method of in silico selection of ligands using deep neural network, with further verification by the AUTODOCK program. This method will speed up the search for potential medicinal substances in the future.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133786088","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: 1

Learning phase transitions in the ferrimagnetic GdFeCo alloy 铁磁性GdFeCo合金的相变学习

2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303219

N. A. Koritsky, A. K. Fedorov, S. Solov’yov, A. K. Zvezdin

引用次数: 2