发布求助
文献互助 智能选刊 最新文献

LatinX in AI at Neural Information Processing Systems Conference 2020最新文献

筛选
英文 中文
Inferring Aggressive Driving Behavior from Smartphone Data – Smartphone’s sensors meet Inception 从智能手机数据推断攻击性驾驶行为——智能手机的传感器符合《盗梦空间》
LatinX in AI at Neural Information Processing Systems Conference 2020 Pub Date : 2020-12-12 DOI: 10.52591/lxai202012127
Aarón H. Narváez, L. González, Johan Wahlström
{"title":"Inferring Aggressive Driving Behavior from Smartphone Data – Smartphone’s sensors meet Inception","authors":"Aarón H. Narváez, L. González, Johan Wahlström","doi":"10.52591/lxai202012127","DOIUrl":"https://doi.org/10.52591/lxai202012127","url":null,"abstract":"","PeriodicalId":301818,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2020","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133720554","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
Graph Neural Networks Learn Twitter Bot Behaviour 图神经网络学习推特机器人行为
LatinX in AI at Neural Information Processing Systems Conference 2020 Pub Date : 2020-12-12 DOI: 10.52591/lxai2020121213
Albert Orozco, Sacha Lévy, Reihaneh Rabbany
{"title":"Graph Neural Networks Learn Twitter Bot Behaviour","authors":"Albert Orozco, Sacha Lévy, Reihaneh Rabbany","doi":"10.52591/lxai2020121213","DOIUrl":"https://doi.org/10.52591/lxai2020121213","url":null,"abstract":"Social media trends are increasingly taking a significant role for the understanding of modern social dynamics. In this work, we take a look at how the Twitter landscape gets constantly shaped by automatically generated content. Twitter bot activity can be traced via network abstractions which, we hypothesize, can be learned through state-of-the-art graph neural network techniques. We employ a large bot database, continuously updated by Twitter, to learn how likely is that a user is mentioned by a bot, as well as, for a hashtag. Thus, we model this likelihood as a link prediction task between the set of users and hashtags. Moreover, we contrast our results by performing similar experiments on a crawled data set of real users.","PeriodicalId":301818,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2020","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116785202","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
Overcoming Transformer Fine-Tuning process to improve Twitter Sentiment Analysis for Spanish Dialects 克服变压器微调过程，提高西班牙语方言的Twitter情感分析
LatinX in AI at Neural Information Processing Systems Conference 2020 Pub Date : 2020-12-12 DOI: 10.52591/lxai202012124
Daniel Palomino
{"title":"Overcoming Transformer Fine-Tuning process to improve Twitter Sentiment Analysis for Spanish Dialects","authors":"Daniel Palomino","doi":"10.52591/lxai202012124","DOIUrl":"https://doi.org/10.52591/lxai202012124","url":null,"abstract":"Is there an effective Spanish Sentiment Analysis algorithm? The aim of this paper is to answer this question. The task is challenging because there are several dialects for the Spanish Language. Thus, identically written words could have several meanings and polarities regarding Spanish speaking countries. To tackle this multidialect issue we rely on a transfer learning approach. To do so, we train a BERT language model to “transfer” general features of the Spanish language. Then, we fine-tune the language model to specific dialects. BERT is also used to generate contextual data augmentation aimed to prevent overfitting. Finally, we build the polarity classifier and propose a fine-tuning step using groups of layers. Our design choices allow us to achieve state-of-the-art results regarding multidialect benchmark datasets.","PeriodicalId":301818,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2020","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117317848","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
Detecting Damaged Regions after Natural Disasters using Mobile Phone Data: The Case of Ecuador 利用移动电话数据在自然灾害后检测受损地区:厄瓜多尔的案例
LatinX in AI at Neural Information Processing Systems Conference 2020 Pub Date : 2020-12-12 DOI: 10.52591/lxai202012125
Maria-Belen Guaranda, Galo Castillo-López, Fabricio Layedra, Carmen Vaca
{"title":"Detecting Damaged Regions after Natural Disasters using Mobile Phone Data: The Case of Ecuador","authors":"Maria-Belen Guaranda, Galo Castillo-López, Fabricio Layedra, Carmen Vaca","doi":"10.52591/lxai202012125","DOIUrl":"https://doi.org/10.52591/lxai202012125","url":null,"abstract":"In this work, we use mobile phone activity data to infer the affected zones in the Ecuadorian province of Manabí, after the 2016 earthquake, with epicenter in the same province. We calculate a series of features to train a classifier based on the K-Nearest Neighbors algorithm to detect affected zones with a 75% of precision. We compare our results with official reports published two months after the disaster.","PeriodicalId":301818,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2020","volume":"307 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124370321","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
Study, Attend and Predict: Academic Performance Prediction using Transformers 学习，参加和预测:使用变压器预测学习成绩
LatinX in AI at Neural Information Processing Systems Conference 2020 Pub Date : 2020-12-12 DOI: 10.52591/lxai202012122
Nicolas Araque, Mario Catapano, M. Gennaro
{"title":"Study, Attend and Predict: Academic Performance Prediction using Transformers","authors":"Nicolas Araque, Mario Catapano, M. Gennaro","doi":"10.52591/lxai202012122","DOIUrl":"https://doi.org/10.52591/lxai202012122","url":null,"abstract":"","PeriodicalId":301818,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2020","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126425485","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
Automatic Georeferencing of Map Images Using Unsupervised Learning and Graph Analysis 使用无监督学习和图分析的地图图像自动地理参考
LatinX in AI at Neural Information Processing Systems Conference 2020 Pub Date : 2020-12-12 DOI: 10.52591/lxai202012129
Enrique Arriaga-Varela, Toru Takahashi
{"title":"Automatic Georeferencing of Map Images Using Unsupervised Learning and Graph Analysis","authors":"Enrique Arriaga-Varela, Toru Takahashi","doi":"10.52591/lxai202012129","DOIUrl":"https://doi.org/10.52591/lxai202012129","url":null,"abstract":"We present a novel method for the automatic georeferencing of heterogeneous map images based on the analysis of the spatial relationships between their lines of text and the geographical locations they depict. Our approach differs from previous work in that the only input provided is the raster image, as it does not require additional hints or metadata. The method is also designed to be highly tolerant of maps with different art styles, scales, orientations, and cartographic projections. To accomplish this task, we leverage the power of modern OCR (Optical Character Recognition) and geocoding services to generate a series of candidate ground control points (GCP) and then discriminate between them using a combination of clustering algorithms and graph analysis. Experimental results for 359 map images demonstrate the viability of the proposed method. We achieved a precision ranging from 81.19% to 97.56% and a recall from 55.71% to 71.15%.","PeriodicalId":301818,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2020","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134099659","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
Towards forensic speaker identification in Spanish using triplet loss 用三连音丢失法鉴定西班牙语的说话人
LatinX in AI at Neural Information Processing Systems Conference 2020 Pub Date : 2020-12-12 DOI: 10.52591/lxai2020121210
Emmanuel Maqueda, Javier Alvarez, Iván V. Meza
{"title":"Towards forensic speaker identification in Spanish using triplet loss","authors":"Emmanuel Maqueda, Javier Alvarez, Iván V. Meza","doi":"10.52591/lxai2020121210","DOIUrl":"https://doi.org/10.52591/lxai2020121210","url":null,"abstract":"This work explores the use of a triplet loss deep network setting for the forensic identification of speakers in Spanish. Within the framework, we train a convolutional network to produce vector representations of speech spectrogram slices. Then we test how similar these vectors are for a given speaker and how dissimilar are compared with other speakers. Based on these metrics we propose the calculation of the Likelihood Radio which is a cornerstone for forensic identification.","PeriodicalId":301818,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2020","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129580609","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信