Abstract-The ability to automatically recognize a person's behavioral context can contribute to health monitoring, aging care and many other domains. Validating context recognition in-the-wild is crucial to promote practical applications that work in real-life settings. We collected over 300k minutes of sensor data with context labels from 60 subjects. Unlike previous studies, our subjects used their own personal phone, in any way that was convenient to them, and engaged in their routine in their natural environments. Unscripted behavior and unconstrained phone usage resulted in situations that are harder to recognize. We demonstrate how fusion of multi-modal sensors is important for resolving such cases. We present a baseline system, and encourage researchers to use our public dataset to compare methods and improve context recognition in-the-wild.

This study explores the potential and the limits of medium-resolution satellite data as a proxy for economic activity at small geographic units. Using a commune-level dataset from Vietnam, it compares the performance of commonly used nightlight data and higher resolution Landsat imagery, which measures daytime light reflection. The analysis suggests that Landsat outperforms nighttime lights at predicting enterprise counts, employment, and expenditure in simple regression models. A parsimonious combination of the first two moments of the Landsat spectral bands can explain a reasonable share of the variation in economic activity in the cross-section. There is, however, poor prediction power of either satellite measure for changes over time.

Digital music has become prolific in the web in recent decades. Automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience. When manual annotations and user preference data is lacking (e.g. for new artists) these systems must rely on content based methods. Besides powerful machine learning tools for classification and retrieval, a key component for successful recommendation is the audio content representation.Good representations should capture informative musical patterns in the audio signal of songs. These representations should be concise, to enable efficient (low storage, easy indexing, fast search) management of huge music repositories, and should also be easy and fast to compute, to enable real-time interaction with a user supplying new songs to the system. Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations. We experiment with different encoding methods, namely the LASSO, vector quantization (VQ) and cosine similarity (CS). We evaluate the representations' quality in two music information retrieval applications: queryby-tag and query-by-example. Our results show that concise representations can be used for successful performance in both applications. We recommend using top-τ VQ encoding, which consistently performs well in both applications, and requires much less computation time than the LASSO.

No abstract

Automatic recognition of behavioral context (location, activities, body-posture etc.) can serve health monitoring, aging care, and many other domains. Recognizing context in-the-wild is challenging because of great variability in behavioral patterns, and it requires a complex mapping from sensor features to predicted labels. Data collected in-the-wild may be unbalanced and incomplete, with cases of missing labels or missing sensors. We propose using the multiple layer perceptron (MLP) as a multi-task model for context recognition. Based on features from multi-modal sensors, the model simultaneously predicts many diverse context labels. We analyze the advantages of the model's hidden layers, which are shared among all sensors and all labels, and provide insight to the behavioral patterns that these hidden layers may capture. We demonstrate how recognition of new labels can be improved when utilizing a model that was trained for an initial set of labels, and show how to train the model to withstand missing sensors. We evaluate context recognition on the previously published ExtraSensory Dataset, which was collected in-the-wild. Compared to previously suggested models, the MLP improves recognition, even with fewer parameters than a linear model. The ability to train a good model using data that has incomplete, unbalanced labeling and missing sensors encourages further research with uncontrolled, in-the-wild behavior.