Jun Yang

People around the world are more connected today than ever before. By making phone calls, sending text messages and participating in online chats, mobile users are frequently interacting with their social connections through multiple communication channels. This trend is expected to continue with the emergence of immensely popular communication apps on mobile devices. Intuitively, these interactions on users' mobile phones can reveal valuable information regarding their social relationship with… Show more

People around the world are more connected today than ever before. By making phone calls, sending text messages and participating in online chats, mobile users are frequently interacting with their social connections through multiple communication channels. This trend is expected to continue with the emergence of immensely popular communication apps on mobile devices. Intuitively, these interactions on users' mobile phones can reveal valuable information regarding their social relationship with their phone contacts. Understanding such relationship can help provide new services and improve users' mobile experience. In this paper, we explore the opportunity to deeply understand these social relationship through mining mobile communication data. By building an on-device mining framework called Commsense, we show that automatically learning and understanding such relationship can efficiently support useful applications such as categorizing mobile contacts, identifying their relative importance, and automatically managing mobile contacts with very little human interference. Show less

Energy conservation on mobile devices is now more important than ever due to the increasing benefits that smartphones and tablets provide to our daily life. However, most existing power management approaches either focus narrowly on a particular sub-system of the mobile device such as the sensor system, the LCD display, or the communication system, or use heuristic approaches to maximize energy efficiency at the cost of user experience. In this paper, we present Boe, a context-aware global… Show more

Energy conservation on mobile devices is now more important than ever due to the increasing benefits that smartphones and tablets provide to our daily life. However, most existing power management approaches either focus narrowly on a particular sub-system of the mobile device such as the sensor system, the LCD display, or the communication system, or use heuristic approaches to maximize energy efficiency at the cost of user experience. In this paper, we present Boe, a context-aware global power management scheme for mobile devices Balancing battery outage and user experience. To meet the mobile device's expected battery life while sacrificing end user experience as little as possible. Boe takes into account the users' phone usage patterns and activities to dynamically adjust the device's global power management policy to minimize outage time and maximize user experience. We demonstrate our proposed technique by controlling display brightness level and GPS sampling rate on smartphones. We evaluate our approach through real world smartphone data from 10 users over two months. Compared to the best fixed user experience policies, we show that: (i) Boe eliminates all frustrating battery outage events for light, moderate, and heavy phone users, and (ii) Boe improves user experience by 20% for light users, maintains the same user experience for moderate users, and degrades user experience by 23% for heavy smartphone users. Show less

Mobile devices collect a variety of information about their environments, recording “digital footprints” about the locations and activities of their human owners. These footprints come from physical sensors such as GPS, WiFi, and Bluetooth, as well as social behavior logs like phone calls, application usage, etc. Existing studies analyze mobile device footprints to infer daily activities like driving/running/walking, etc. and social contexts such as personality traits and
emotional states… Show more

Mobile devices collect a variety of information about their environments, recording “digital footprints” about the locations and activities of their human owners. These footprints come from physical sensors such as GPS, WiFi, and Bluetooth, as well as social behavior logs like phone calls, application usage, etc. Existing studies analyze mobile device footprints to infer daily activities like driving/running/walking, etc. and social contexts such as personality traits and
emotional states. In this paper, we propose a different approach that uses multimodal mobile sensor and log data to build a novel user modeling framework called mFingerprint that can effectively and uniquely depict users. mFingerprint does not expose raw sensitive information from the mobile device, e.g., the exact location, WiFi access points, or apps installed, but computes privacy-preserving statistical features to model the user. These descriptive features obscure sensitive information, and thus can be shared, transmitted, and reused with fewer privacy concerns. By testing on 22 users’ mobile phone data collected over 2 months, we demonstrate the effectiveness of mFingerprint in user modeling and identification, with our proposed statistics achieving 81% accuracy across 22 users over 10-day intervals. Show less

Due to the dramatical increase in popularity of mobile devices in the last decade, more sensitive user information is stored and accessed on these devices everyday. However, most existing technologies for user authentication only cover the login stage or only work in restricted controlled environments or GUIs in the post login stage. In this work, we present TIPS, a Touch based Identity Protection Service that implicitly and unobtrusively authenticates users in the background by continuously… Show more

Due to the dramatical increase in popularity of mobile devices in the last decade, more sensitive user information is stored and accessed on these devices everyday. However, most existing technologies for user authentication only cover the login stage or only work in restricted controlled environments or GUIs in the post login stage. In this work, we present TIPS, a Touch based Identity Protection Service that implicitly and unobtrusively authenticates users in the background by continuously analyzing touch screen gestures in the context of a running application. To the best of our knowledge, this is the first work to incorporate contextual app information to improve user authentication. We evaluate TIPS over data collected from 23 phone owners and deployed it to 13 of them with 100 guest users. TIPS can achieve over 90% accuracy in real-life naturalistic conditions within a small amount of computational overhead and 6% of battery usage. Show less

Supporting continuous sensing applications on mobile phones is challenging because of the resource demands of long-term sensing, inference and communication algorithms. We present the design, implementation and evaluation of the Jigsaw continuous sensing engine, which balances the performance needs of the application and the resource demands of continuous sensing on the phone. Jigsaw comprises a set of sensing pipelines for the accelerometer, microphone and GPS sensors, which are built in a… Show more

Supporting continuous sensing applications on mobile phones is challenging because of the resource demands of long-term sensing, inference and communication algorithms. We present the design, implementation and evaluation of the Jigsaw continuous sensing engine, which balances the performance needs of the application and the resource demands of continuous sensing on the phone. Jigsaw comprises a set of sensing pipelines for the accelerometer, microphone and GPS sensors, which are built in a plug and play manner to support: i) resilient accelerometer data processing, which allows inferences to be robust to different phone hardware, orientation and body positions; ii) smart admission control and on-demand processing for the microphone and accelerometer data, which adaptively throttles the depth and sophistication of sensing pipelines when the input data is low quality or uninformative; and iii) adaptive pipeline processing, which judiciously triggers power hungry pipeline stages (e.g., sampling the GPS) taking into account the mobility and behavioral patterns of the user to drive down energy costs. We implement and evaluate Jigsaw on the Nokia N95 and the Apple iPhone, two popular smartphone platforms, to demonstrate its capability to recognize user activities and perform long term GPS tracking in an energy-efficient manner. Show less