RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)

Overview

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation

This is the implementation of RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation.

Code

To run our code, please use the following commands:

g++ RATE.cpp -o RATE -std=c++11
./RATE [Training File] [Test File] [L, optional, default = 30] [T, optional, default = 1]

For example,

g++ RATE.cpp -o RATE -std=c++11
./RATE Dataset/train.txt Dataset/test.txt 40 1

The prediction results will be in ./result.txt (the first row is the classification result). Then you can run

python eval.py

to obtain evaluation metrics.

Dataset

We release the Europe dataset (Dataset/data.json), where each line is a json file with tweet text and metadata. Due to privacy issues, we have anonymized the whole dataset by representing each word/feature as an integer. An example is shown below.

{ 
   "label":0,
   "language":"3",
   "timezone":"5",
   "offset":"7",
   "userlang":"5",
   "latitude":"36.8901",
   "longitude":"30.6809",
   "text":"3332 2608 29"
}

Given the json file, one can run

cd Dataset/
python preprocess.py

to get training and testing data (Dataset/train.txt and Dataset/test.txt).

Result

Method Micro-F1 (Acc) Macro-F1 Mean Distance Error (km) [email protected]
RATE 0.8905 0.5230 365.16 0.4315

Citation

@inproceedings{zhang2017rate,
  title={RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation},
  author={Zhang, Yu and Wei, Wei and Huang, Binxuan and Carley, Kathleen M and Zhang, Yan},
  booktitle={Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
  pages={2423--2426},
  year={2017},
  organization={ACM}
}
Owner
Yu Zhang
CS Ph.D. student at UIUC; Data Mining
Yu Zhang
This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut

Microsoft 408 Dec 30, 2022
This is the code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields".

HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields This is the code for "HyperNeRF: A Higher-Dimensional

Google 702 Jan 02, 2023
Yolox-bytetrack-sample - Python sample of MOT (Multiple Object Tracking) using YOLOX and ByteTrack

yolox-bytetrack-sample YOLOXとByteTrackを用いたMOT(Multiple Object Tracking)のPythonサン

KazuhitoTakahashi 12 Nov 09, 2022
Language-Driven Semantic Segmentation

Language-driven Semantic Segmentation (LSeg) The repo contains official PyTorch Implementation of paper Language-driven Semantic Segmentation. Authors

Intelligent Systems Lab Org 416 Jan 03, 2023
QuadTree Attention for Vision Transformers (ICLR2022)

This repository contains codes for quadtree attention. This repo contains codes for feature matching, image classficiation, object detection and seman

tangshitao 222 Dec 28, 2022
PlaidML is a framework for making deep learning work everywhere.

A platform for making deep learning work everywhere. Documentation | Installation Instructions | Building PlaidML | Contributing | Troubleshooting | R

PlaidML 4.5k Jan 02, 2023
Progressive Growing of GANs for Improved Quality, Stability, and Variation

Progressive Growing of GANs for Improved Quality, Stability, and Variation — Official TensorFlow implementation of the ICLR 2018 paper Tero Karras (NV

Tero Karras 5.9k Jan 05, 2023
A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

ICT.MIRACLE lab 75 Dec 26, 2022
Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments

Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments Paper: arXiv (ICRA 2021) Video : https://youtu.be/CC

Sachini Herath 68 Jan 03, 2023
Streaming over lightweight data transformations

Description Data augmentation libarary for Deep Learning, which supports images, segmentation masks, labels and keypoints. Furthermore, SOLT is fast a

Research Unit of Medical Imaging, Physics and Technology 256 Jan 08, 2023
[ICSE2020] MemLock: Memory Usage Guided Fuzzing

MemLock: Memory Usage Guided Fuzzing This repository provides the tool and the evaluation subjects for the paper "MemLock: Memory Usage Guided Fuzzing

Cheng Wen 54 Jan 07, 2023
PyTorch implementation of 'Gen-LaneNet: a generalized and scalable approach for 3D lane detection'

(pytorch) Gen-LaneNet: a generalized and scalable approach for 3D lane detection Introduction This is a pytorch implementation of Gen-LaneNet, which p

Yuliang Guo 233 Jan 06, 2023
Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe

Traductor de señas Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe Requerimientos 🔧 Python 3.8 o inferior para evitar

Jahaziel Hernandez Hoyos 3 Nov 12, 2022
Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning :rocket:

MLJAR Automated Machine Learning Documentation: https://supervised.mljar.com/ Source Code: https://github.com/mljar/mljar-supervised Table of Contents

MLJAR 2.4k Dec 31, 2022
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research int

阿才 73 Dec 16, 2022
Real-time ground filtering algorithm of cloud points acquired using Terrestrial Laser Scanner (TLS)

This repository contains tools to simulate the ground filtering process of a registered point cloud. The repository contains two filtering methods. The first method uses a normal vector, and fit to p

5 Aug 25, 2022
Codebase for Diffusion Models Beat GANS on Image Synthesis.

Codebase for Diffusion Models Beat GANS on Image Synthesis.

Katherine Crowson 128 Dec 02, 2022
Implementation of paper "Towards a Unified View of Parameter-Efficient Transfer Learning"

A Unified Framework for Parameter-Efficient Transfer Learning This is the official implementation of the paper: Towards a Unified View of Parameter-Ef

Junxian He 216 Dec 29, 2022
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Unbiased Teacher for Semi-Supervised Object Detection This is the PyTorch implementation of our paper: Unbiased Teacher for Semi-Supervised Object Detection

Facebook Research 366 Dec 28, 2022
Implement object segmentation on images using HOG algorithm proposed in CVPR 2005

HOG Algorithm Implementation Description HOG (Histograms of Oriented Gradients) Algorithm is an algorithm aiming to realize object segmentation (edge

Leo Hsieh 2 Mar 12, 2022