Bilingual dictionaries present several challenges, especially for sign languages and oral languages, where multimodality plays a role. We deployed and tested the first bilingual Peruvian Sign Language (LSP) - Spanish Online Dictionary. The first feature allows the user to introduce a text and receive as a result a list of videos whose glosses are related to the input text or Spanish word. The second feature allows the user to sign in front of the camera and shows the five most probable Spanish translations based on the similarity between the input sign and gloss-labeled sign videos used to train a machine learning model. These features are constructed in a design and architecture that differentiates among the coincidence for the Spanish text searched, the sign gloss, and Spanish translation. We explain in depth how these concepts or database columns impact the search. Similarly, we share the challenges of deploying a real-world machine learning model for isolated sign language recognition through Amazon Web Services (AWS).

Thoroughly understanding how energy consumption is disaggregated into individual appliances can help reduce household expenses, integrate renewable sources of energy, and lead to efficient use of energy. In this work, we propose a deep latent generative model based on variational recurrent neural networks (VRNNs) for energy disaggregation. Our model jointly disaggregates the aggregated energy signal into individual appliance signals, achieving superior performance when compared to the state-of-the-art models for energy disaggregation, yielding a 29% and 41% performance improvement on two energy datasets, respectively, without explicitly encoding temporal/contextual information or heuristics. Our model also achieves better prediction performance on lowpower appliances, paving the way for a more nuanced disaggregation model. The structured output prediction in our model helps in accurately discerning which appliance(s) contribute to the aggregated power consumption, thus providing a more useful and meaningful disaggregation model.

Energy and water related problems are becoming more relevant due to their huge impact on our environment. The limited availability of resources necessitates the development of machine learning prediction models that can help in predicting demand and consumption of these resources. We follow a data-driven approach that takes advantage of the data collected about the demand and usage of these resources. Our prediction models help in the decision making processes involved in the management of these resources. Our research focuses on developing deep learning and probabilistic models for sequential data generation and prediction. More specifically, we are focusing in water quality and availability prediction and in the energy disaggregation problem. In the following paragraphs we describe the problems, methods, data sets and some of the results of these ongoing projects. The models applied to these two problems can be extended to other smart living problem such as water demand and distribution, traffic prediction, and transportation demand.

Water availability and management is an important problem plaguing many developing and under-developed countries. Many factors including geographic, political, management, and environmental factors affect the availability of water in these regions. In this paper, we develop an ensemble-learning based predictive-analytics framework for smart water management to predict: i) water pump operation status (e.g., functional, non functional), ii) water quality, and iii) quantity. In the predictive-analytics framework, we first perform feature engineering to select relevant features, use them to develop the XGBoost and Random Forest ensemble learning models, and then perform extensive feature analysis to identify the most predictive features, for each prediction problem mentioned above. We evaluate our framework on two publicly available smart water management datasets pertaining to Tanzania and Nigeria and show that our proposed models outperform several baseline approaches, including logistic regression, SVMs, and multi-layer perceptrons in terms of precision, recall and F1 score. We also demonstrate that our models are able to achieve a superior prediction performance for predicting water pump operation status for different water extraction methods. We conduct a detailed feature analysis to investigate the importance of the various feature groups (e.g., geographic, management) on the performance of the models for predicting water pump operation status, water quality and quantity. We then perform a fine-grained feature analysis to identify how individual features, not just feature groups, impact performance. We identify that among individual features, location (x, y, z coordinates) has the maximum impact on performance. Our analysis is helpful in understanding the types of data that should be collected in future for accurately predicting the different water problems.