Le T. Nguyen

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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)
More and more people express their opinions on social media such as Facebook and Twitter. Predictive analysis on social media time-series allows the stake-holders to leverage this immediate, accessible and vast reachable communication channel to react and proact against the public opinion. In particular, understanding and predicting the sentiment change of(More)
The security and privacy risks posed by smartphone sensors such as microphones and cameras have been well documented. However, the importance of accelerometers have been largely ignored. We show that accelerometer readings can be used to infer the trajectory and starting point of an individual who is driving. This raises concerns for two main reasons.(More)
Efficient Wi-Fi probing has been demonstrated to leak sensitive user information. During the probing process, Wi-Fi clients transmit the names of previously known Wi-Fi access points (APs) in plaintext. An eavesdropper can easily collect the information leaked by this Wi-Fi probing process to mount numerous attacks, such as fake AP or revealing hidden APs,(More)
Mobile devices have become people's indispensable companion, since they allow each individual to be constantly connected with the outside world. In order to keep connected, the devices periodically send out data, which reveal some information about the device owner. Data sent by these devices can be captured by any external observer. Since the observer can(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(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)