Gallery Items tagged Two-column

Regular scanner vs Lex
comparison of lex and a c scanner
Jesus Alvarez

WAF 2016 template
Template for the 17th Workshop of Physical Agents WAF 2016.
luis.manso@gmail.com

Using ResNet for Pulmonary Nodule Classification
Classifying pulmonary nodule CT images as either benign or malignant, using a trained Residual Neural Network.
Owen Li

WPGEC 2017 Paper Template
This document is the LaTeX template for submitting articles at the WPGEC (Workshop de Pós-graduação de Engenharia da Computação) from the University of São Paulo in its sixth edition.
This document allows the writing of articles in both portuguese and english version.
Collaborator: Rosalia Caya
WPGEC - Escola Politécnica - USP

Compiler Lab 4 Report: Lexical analyzer using lex
Report of the laboratory 04 of compiler class
Gerardo Juarez

Green Lab report template
This document represents the template of the final experiment report structure for the Green Lab course at the Vrije Universiteit Amsterdam, The Netherlands. It is based on the acmart proceedings template.
The Green Lab course Students allows students to work in teams to perform experiments on software energy consumption in a controlled environment.
Ivano Malavolta

Survey on Bi-LSTM CNNs CRF for Italian Sequence Labeling and Multi-Task Learning
In the last few years the resolution of NLP tasks with architectures composed of neural models has taken vogue. There are many advantages to using these approaches especially because there is no need to do features engineering. In this paper, we make a survey of a Deep Learning architecture that propose a resolutive approach to some classical tasks of the NLP. The Deep Learning architecture is based on a cutting-edge model that exploits both word-level and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture has provided cutting-edge performance in several sequential labeling activities for the English language. The architecture that will be treated uses the same approach for the Italian language. The same guideline is extended to perform a multi-task learning involving PoS labeling and sentiment analysis. The results show that the system performs well and achieves good results in all activities. In some cases it exceeds the best systems previously developed for Italian.
leo.ranaldi

Simultaneous Localization And Mapping (SLAM) using RTAB-Map
This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.
Sagarnil Das

ESQUEMA DE PUBLICACION PAPER - UNSA
Esquema para la publicación de artículos o papers de la Universidad Nacional de San Agustín.
EditorialUNSA