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Introduction 2 • Smartphones feature numerous sensors • Many users concerned about GPS sensors leaking location privacy • Mobile OS companies were reported to collect GPS location information of their customers • Other sensors (e.g., accelerometers) considered benign • Should we consider acceleration data (accelerometer) to be privacy sensitive? Yes, we(More)
A variety of real-life mobile sensing applications are becoming available , especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity(More)
Activity recognition (AR) has become an essential component of many applications present in our everyday lives such as life-logging, fitness tracking, health and wellbeing monitoring. To build an AR system, one needs to first identify a set of activities of interest and collect labeled training data for these activities. However, activities of interest are(More)
Activity recognition (AR) systems are typically built to recognize a predefined set of common activities. However, these systems need to be able to learn new activities to adapt to a user's needs. Learning new activities is especially challenging in practical scenarios when a user provides only a few annotations for training an AR model. In this work, we(More)
—Location privacy has become one of the critical issues in the smartphone era. Since users carry their phones everywhere and all the time, leaking users' location information can have dangerous implications. In this paper, we leverage the idea that Wi-Fi parameters not considered to be " sensitive " in the Android platform can be exploited to learn users'(More)
Activity recognition (AR) systems are typically built and evaluated on a predefined set of activities. AR systems work best if the test data contains and only contains these predefined activities. In real world applications, AR systems trained in this manner generate serious false positives, for example if "smoking" is one of the activities in the training(More)
—In spite of extensive research in the last decade, activity recognition still faces many challenges for real-world applications. On one hand, when attempting to recognize various activities, different sensors play different on different activity classes. This heterogeneity raises the necessity of learning the optimal combination of sensor modalities for(More)
—Knowledge of working professionals gained through years of experience is invaluable for any organization. Extracting this knowledge allows an organization to optimize internal processes and facilitate training of new hires. Therefore, there has been a significant research effort in developing techniques for automated knowledge mining at workplaces.(More)