Spanish is one of the most spoken languages in the globe, but not necessarily Spanish is written and spoken in the same way in different countries. Understanding local language variations can help to improve model performances on regional tasks, both understanding local structures and also improving the message's content. For instance, think about a machine learning engineer who automatizes some language classification task on a particular region or a social scientist trying to understand a regional event with echoes on social media; both can take advantage of dialect-based language models to understand what is happening with more contextual information hence more precision. This manuscript presents and describes a set of regionalized resources for the Spanish language built on four-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities; as well as examples of using regional resources on message classification tasks.

We consider human activity recognition (HAR) from wearable sensor data in manual-work processes, like warehouse order-picking. Such structured domains can often be partitioned into distinct process steps, e.g., packaging or transporting. Each process step can have a different prior distribution over activity classes, e.g., standing or walking, and different system dynamics. Here, we show how such context information can be integrated systematically into a deep neural network-based HAR system. Specifically, we propose a hybrid architecture that combines a deep neural network-that estimates high-level movement descriptors, attributes, from the raw-sensor data-and a shallow classifier, which predicts activity classes from the estimated attributes and (optional) context information, like the currently executed process step. We empirically show that our proposed architecture increases HAR performance, compared to state-of-the-art methods. Additionally, we show that HAR performance can be further increased when information about process steps is incorporated, even when that information is only partially correct.

Every day for over four years, Ramses woke up in his home in Barquisimeto, Venezuela, turned on his computer, and began labeling images that will help make self-driving cars ubiquitous one day. Through a microtasking platform called Remotasks, he would identify mundane objects that line the streets everywhere -- trees, lampposts, pedestrians, stop signs -- so that autonomous vehicles could learn to notice them, too. Like many Venezuelans, Ramses turned to microtasking when his country plunged into economic turmoil. The gig gave him the opportunity to earn American dollars instead of the local currency, which is subject to extraordinarily high inflation. "I would work Sunday to Sunday," Ramses, who asked to use only his first name for privacy reasons, told Rest of World over WhatsApp.