Skip to main content

Google Summer of Code

Participante: Galo Castillo

Organizacion: Fundacion Linux SPDX

Fecha: Mayo 2018-Agosto 2018

El proyecto consistió en automatizar el proceso de registro de las licencias, ya que antes se los realizaba de manera manual y el costo de tiempo, era elevado.

La solución que propuso Galo Castillo fue que, a través de un formulario registrar una nueva licencia, luego de completar el mismo, se genera un archivo XML, con la información procesada, automáticamente se crea un pull/request en un repositorio de la fundación. Antes todo este proceso era de manera manual por lo que tomaba gran cantidad de tiempo al momento de autorizar la licencia.
Galo se encargó de crear todo el backend del proceso, y también de la interfaz web en la que se realizaba el formulario.

 

The Silence of the Cantons: Estimating Villages Socioeconomic Status Through Mobile Phones Data

Authors: Galo Castillo, Fabricio Layedra, Maria-Belen Guaranda, Paolo Lara, Carmen Vaca

Abstract:

The use of cellphones has deeply influenced the way how people communicate and live everyday. Because of mobile phones ubiquity, the geolocated information recorded by every activity carried on with them has been used in numerous studies in topics related to human mobility and their relation with socioeconomic indicators. Socioeconomic indicators like health, education and poverty provide insights about the welfare of a region. Subsequently, such geolocated records with their inherent fine granularity are key for a local government in order to take decisions over a region and promote their development. Analysis of CDRs to approximate these indicators has been mainly done over developed and emerging countries like India and Brazil, but there is still a lack of studies over countries in means of development. In the present study, we propose a method to predict three socioeconomic indices at a high granularity, in the context of a developing country. Our study uses the volume of mobile phones calls and SMS (Short Message Service) messages located in a province of Ecuador over different periods of time. Our results demonstrate that activities from mobile phones are an effective and accessible input for determining the economic status of a developing country's canton. We show that a high mobile phones activity frequency is linked to a population with higher incomes and education level.

 

Know your customer: Detection of Customer Experience (CX) in Social Platforms using Text Categorization

Authors: Leonardo Kuffo, Carmen Vaca, Edgar Izquierdo, Juan Carlos Bustamante

Abstract:

Customers nowadays are one online post away from their stores, specially when it comes to post-shopping experiences. This translates to large amounts of text messages to evaluate and process for big brands that aim to maintain a good quality of service as well as a digital channel of communication for their customers. Automating the understanding of this text data poses questions such as how large the corpus should be and which are the best algorithms to discriminate whether a social media post is related or not to customer experience (CX). In order to help answering these questions, first, we get hold of posts from three different platforms: Foursquare (77K) , Twitter (153K) and Facebook (2.2M). Such posts are directed to brands ranked in the ForeSee CX Index and the Forrester CX Index rankings. Second, we build a binary classifier using different algorithms to identify customer experience posts on a social platform. The accuracy of the best performing setting is 86.4% for Facebook and 91.2% for Twitter. Third, we explore the effect of increasing the number of training samples, and how a plateau is reached after 5K posts. Finally, we conduct experiments using different combinations of n-grams as features for the text mining process. As a result we observe that uni-grams and bi-grams are the best combination when we need to choose features for a classifier discriminating customer experience social media posts on Twitter and a combination of up to four-grams on Facebook.