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Events for July 27, 2016
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Six Sigma Green Belt for Process ImprovementWed, Jul 27, 2016
DEN@Viterbi, Executive Education
Conferences, Lectures, & Seminars
Abstract: Course Dates: July 26-28, 2016
Available: On-campus or Online with Interactivity
This program, an introductory course in Six Sigma, will give you a thorough understanding of Six Sigma and its focus on eliminating defects through fundamental process knowledge. Topics covered in addition to DMAIIC and Six Sigma philosophy include basic statistics, statistical process control, process capability, financial implications and root cause analysis.
More Info: https://gapp.usc.edu/professional-programs/short-courses/industrial-systems/six-sigma-green-belt-process-improvement
Audiences: Registered Attendees
Contact: Viterbi Professional Programs
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PhD Defense Ding Li
Wed, Jul 27, 2016 @ 03:00 AM - 06:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ding Li, PhD Candidate
Talk Title: Energy Optimization of Mobile Applications
Abstract: Energy is a critical resource for mobile devices. Many techniques have been proposed to optimize the energy consumption of mobile devices at the hardware and system levels. However, only optimizations at the hardware and system level are insufficient. Poorly designed applications can still waste the energy of mobile devices even with fully optimized hardware and system support. In my dissertation work, I proposed multiple techniques to help developers to create energy efficient apps. Particularly, my dissertation work addresses three problems in creating energy efficient apps. The first problem in my dissertation is "where is energy consumed." Modern mobile apps are very complex. They may contain more than 500,000 lines of code. Thus, it is important to know which part of the code consumes more energy. To address this problem, I developed a source line level energy measurement technique that can report the energy consumption of mobile apps with a very fine granularity. My technique achieved 91% accuracy during the measurement. The second problem in my dissertation is "what to optimize." Modern mobile apps may use different libraries and invoke thousands of APIs. It is also important to know what kind of libraries and APIs can consume more energy. To address this problem, I conducted an empirical study with 405 Android market apps about how these Android apps consume energy. In this study, I evaluated ten research questions that have motivated my following energy optimization techniques. The third problem is "how to optimize." After knowing where is energy consumed and what to optimize, it is also important to design effective techniques to optimize the energy consumption of mobile apps. To address this problem, I developed two automated techniques. The first one can automatically optimize the display energy for mobile web apps and the second one can optimize HTTP energy for Android apps. My display energy optimization technique reduced the energy by 25% and my HTTP energy optimization technique achieved 15% energy savings. Besides the energy optimization techniques, I also improved the flexibility, accuracy, and efficiency of the string analysis technique, which is very important to my optimization techniques. In summary, my techniques and the empirical evaluation show that program analysis techniques can help developers to understand how energy is consumed in mobile apps and can also help to optimize the energy consumption of mobile apps.
Host: Ding Li
Location: 213
Audiences: Everyone Is Invited
Contact: Ryan Rozan
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PhD Defense Ding Li
Wed, Jul 27, 2016 @ 03:00 AM - 06:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ding Li, PhD Candidate
Talk Title: Energy Optimization of Mobile Applications
Abstract: Energy is a critical resource for mobile devices. Many techniques have been proposed to optimize the energy consumption of mobile devices at the hardware and system levels. However, only optimizations at the hardware and system level are insufficient. Poorly designed applications can still waste the energy of mobile devices even with fully optimized hardware and system support. In my dissertation work, I proposed multiple techniques to help developers to create energy efficient apps. Particularly, my dissertation work addresses three problems in creating energy efficient apps. The first problem in my dissertation is "where is energy consumed." Modern mobile apps are very complex. They may contain more than 500,000 lines of code. Thus, it is important to know which part of the code consumes more energy. To address this problem, I developed a source line level energy measurement technique that can report the energy consumption of mobile apps with a very fine granularity. My technique achieved 91% accuracy during the measurement. The second problem in my dissertation is "what to optimize." Modern mobile apps may use different libraries and invoke thousands of APIs. It is also important to know what kind of libraries and APIs can consume more energy. To address this problem, I conducted an empirical study with 405 Android market apps about how these Android apps consume energy. In this study, I evaluated ten research questions that have motivated my following energy optimization techniques. The third problem is "how to optimize." After knowing where is energy consumed and what to optimize, it is also important to design effective techniques to optimize the energy consumption of mobile apps. To address this problem, I developed two automated techniques. The first one can automatically optimize the display energy for mobile web apps and the second one can optimize HTTP energy for Android apps. My display energy optimization technique reduced the energy by 25% and my HTTP energy optimization technique achieved 15% energy savings. Besides the energy optimization techniques, I also improved the flexibility, accuracy, and efficiency of the string analysis technique, which is very important to my optimization techniques. In summary, my techniques and the empirical evaluation show that program analysis techniques can help developers to understand how energy is consumed in mobile apps and can also help to optimize the energy consumption of mobile apps.
Host: Ding Li
Location: 213
Audiences: Everyone Is Invited
Contact: Ryan Rozan
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PhD Defense Farshad Kooti
Wed, Jul 27, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Farshad Kooti, PhD Candidate
Talk Title: Predicting and Modeling Human Behavioral Changes Using Digital Traces
Abstract: People are increasingly spending more time online. Finding and understanding the patterns that exist in online behavior is essential for improving user experience. One of the main characteristics of online activity is diurnal, weekly, and monthly patterns, reflecting human circadian rhythms, sleep cycles, as well as work and leisure schedules. These patterns range from mood changes reflected on Twitter at different times of the days to reading stories on news aggregator websites. Using large scale data from multiple online social networks, we uncover temporal patterns that take place at far shorter time scales. Specifically, we demonstrate short-term, within-session behavioral changes, where a session is defined as a period of time during which a person engages continuously with the online social network without a long break. On Twitter, we show that people prefer easier tasks such as retweeting over more complicated tasks such as posting an original tweet later in a session. Also, tweets posted later in a session are shorter and are more likely to contain a spelling mistake. We focus on information consumption on Facebook and show that the people spend less time reading a story as they spend more time in the session. More interestingly, the rate of the change depends on the type of the content and people are more likely to spend time on photos and videos later in the session compared to textual posts. We also found changes in the quality of the content generated on Reddit and found that comments that are posted later in a session get lower scores from other users, receive fewer replies, and have lower readability. All these findings are evidence for short-term behavioral changes in the type of activity that users perform. Moreover, we identify the factors that affect these short-term behavior changes; age of the person being the most significant factor. The trends that we found can be used to predict the online behavior of individuals with much higher accuracy than competitive baselines. E.g., we can predict the length of the activity sessions or the length of breaks on Facebook.
Our observations are compatible with the cognitive depletion theories, suggesting that people's performance drop as they perform sustained activity for a period of time, and verify small scale, laboratory studies conducted by psychologists. We also investigate more general behavioral changes than short-term behavioral changes in the context of consumer behavior, specifically online shopping and iPhone purchases. We show that there is a significant heterogeneity in these large scale datasets and not considering and handling this heterogeneity can result in false findings. We present an approach to test for the false findings using randomization and show in a case of a mistake, how it could be solved.
Host: Farshad Kooti
Location: 322
Audiences: Everyone Is Invited
Contact: Ryan Rozan
