LatinX in AI at Neural Information Processing Systems Conference 2021最新文献

Flexible Learning of Sparse Neural Networks via Constrained L0 Regularizations 基于约束L0正则化的稀疏神经网络灵活学习

LatinX in AI at Neural Information Processing Systems Conference 2021 Pub Date : 2021-12-07 DOI: 10.52591/lxai202112071

Jose Gallego-Posada, Juan Ramirez de los Rios, Akram Erraqabi

引用次数: 2

Curating the Twitter Election Integrity Datasets for Better Online Troll Characterization 策划Twitter选举完整性数据集，以更好地在线喷子表征

LatinX in AI at Neural Information Processing Systems Conference 2021 Pub Date : 2021-12-07 DOI: 10.52591/202112076

Albert Orozco, Reihaneh Rabbany

引用次数: 0

A Pharmacovigilance Application of Social Media Mining: An Ensemble Approach for Automated Classification and Extraction of Drug 社交媒体挖掘在药物警戒中的应用:一种药物自动分类与提取的集成方法

LatinX in AI at Neural Information Processing Systems Conference 2021 Pub Date : 2021-12-07 DOI: 10.52591/202112075

L. Robles, Rajath Chikkatur, J. Banda

{"title":"A Pharmacovigilance Application of Social Media Mining: An Ensemble Approach for Automated Classification and Extraction of Drug","authors":"L. Robles, Rajath Chikkatur, J. Banda","doi":"10.52591/202112075","DOIUrl":"https://doi.org/10.52591/202112075","url":null,"abstract":"Researchers have extensively used social media platforms like Twitter for knowl-edge discovery purposes, as tweets are considered a wealth of information that provides unique insights. Recent developments have further enabled social media mining for various biomedical tasks such as pharmacovigilance. A first step towards identifying a use-case of Twitter for the pharmacovigilance domain is to extract medication/drug terminologies mentioned in the tweets, which is a challenging task due to several reasons. For example, drug mentions in tweets may be incorrectly written, making the identification of these mentions more difficult. In this work, we propose a two step approach, first, we focused on classifying tweets with drug mentions via an ensemble model (containing transformer models), second, we extract drug mentions (along with their span positions) using a text-tagging/dictionary based approach, and a Named Entity Recognition (NER) approach. By comparing these two entity identification approaches, we demonstrate that using only a dictionary-based approach is not enough.","PeriodicalId":355096,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2021","volume":"101 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116519977","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

On the Pitfalls of Label Differential Privacy 论标签差分隐私的缺陷

LatinX in AI at Neural Information Processing Systems Conference 2021 Pub Date : 2021-12-07 DOI: 10.52591/202112077

Andrés Muñoz

引用次数: 3

Vehicle Speed Estimation Using Computer Vision and Evolutionary Camera Calibration 基于计算机视觉和进化摄像机标定的车速估计

LatinX in AI at Neural Information Processing Systems Conference 2021 Pub Date : 2021-12-07 DOI: 10.52591/lxai202112072

Hector Mejia, E. Palomo, Ezequiel López-Rubio, Israel Pineda, R. Fonseca

{"title":"Vehicle Speed Estimation Using Computer Vision and Evolutionary Camera Calibration","authors":"Hector Mejia, E. Palomo, Ezequiel López-Rubio, Israel Pineda, R. Fonseca","doi":"10.52591/lxai202112072","DOIUrl":"https://doi.org/10.52591/lxai202112072","url":null,"abstract":"Currently, the standard for vehicle speed estimation is radar or lidar speed signs which can be costly to buy and maintain. However, most major cities already implement networks of traﬃc surveillance cameras that can be utilized for vehicle speed estimation using computer vision. This work implements such a system using homography estimation, YOLOv4 object detector, and an object tracker capable of vehicle speed estimation. The homography component uses world plane-image plane point correspondences, located by humans. Moreover, a new method is developed speciﬁcally for this use case, using the estimation of density evolutionary algorithm. It aims at correcting the points misalignment in between planes. In addition, a basic direct linear transformation (DLT) and a random sample consensus robust version of DLT are implemented for comparison. Finally, the results show that the proposed homography method reduces the projection error from world to image point by 97%, when compared to the other two methods, and the complete workﬂow can successfully estimate speed distributions expected from vehicles on urban traﬃc and handle dynamic changes in vehicle speed.","PeriodicalId":355096,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2021","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114686002","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