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.
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.
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.
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. CRITICAL: Do Not Use Symbols, Special Characters, or Math in Paper Title or Abstract.
This document is a combination of the SC18 IEEE proceedings format and a modified SC18 artifact descriptor to be used for the HPCSYSPROS18 CFP. More information here: https://github.com/HPCSYSPROS/CFP18