Empirical studies of programming language learnability and usability have thus far depended on indirect measures of human cognitive performance, attempting to capture what is at its essence a purely cognitive exercise through various indicators of comprehension, such as the time spent working out the meaning of code and producing acceptable solutions. We present evidence of the relative contribution of experience and the individual alpha frequency (IAF) to achieving correct performance during program comprehension tasks, specifically that more experience and higher IAF are both associated with an increased likelihood of correct task performance, with experience playing the greater part.

Abstract-Recent research in ubiquitous and mobile computing uses mobile phones and wearable accelerometers to monitor individuals' physical activities for personalized and proactive health care. The goal of this project is to measure and reduce the energy demand placed on mobile phones that monitor individuals' physical activities for extended periods of time with limited access to battery recharging and mobile phone reception. Many issues must be addressed before mobile phones become a viable platform for remote health monitoring, including: security, reliability, privacy, and, most importantly, energy. Mobile phones are battery-operated, making energy a critical resource that must be carefully managed to ensure the longest running time before the battery is depleted. In a sense, all other issues are secondary, since the mobile phone will simply not function without energy. In this project, we therefore focus on understanding the energy consumption of a mobile phone that runs physical activity monitoring applications and consider ways to reduce its energy consumption.

Abstract-Empirical studies of programming language learnability and usability have thus far depended on indirect measures of human cognitive performance, attempting to capture what is at its essence a purely cognitive exercise through various indicators of comprehension, such as the correctness of coding tasks or the time spent working out the meaning of code and producing acceptable solutions. Understanding program comprehension is essential to understanding the inherent complexity of programming languages, and ultimately, having a measure of mental effort based on direct observation of the brain at work will illuminate the nature of the work of programming. We provide evidence of direct observation of the cognitive effort associated with programming tasks, through a carefully constructed empirical study using a cross-section of undergraduate computer science students and an inexpensive, off-the-shelf brain-computer interface device. This study presents a link between expertise and programming language comprehension, draws conclusions about the observed indicators of cognitive effort using recent cognitive theories, and proposes directions for future work that is now possible.

Wireless Network Interface Cards (WNICs) are part of every portable device, where efficient energy management plays a significant role in extending the device's battery life. The goal of efficient energy management is to match the performance of the WNIC to the network activity shaped by a running application. In the case of interactive applications on mobile systems, network I/O is largely driven by user interactions. Current solutions either require application modifications or lack a sufficient context of execution that is crucial in making accurate and timely predictions. This paper proposes a range of user-interaction-aware mechanisms that utilize a novel approach of monitoring a user's interaction with applications through the capture and classification of mouse events. This approach yields considerable improvements in energy savings and delay reductions of the WNIC, while significantly improving the accuracy, timeliness, and computational overhead of predictions when compared to existing state-of-the-art solutions.

Increasingly power-hungry processors have reinforced the need for aggressive power management. Dynamic voltage scaling has become a common design consideration allowing for energy efficient CPUs by matching CPU performance with the computational demand of running processes. In this paper, we propose Interaction-Aware Dynamic Voltage Scaling (IADVS), a novel fine-grained approach to managing CPU power during interactive workloads, which account for the bulk of the processing demand on modern mobile or desktop systems. IADVS is built upon a transparent, fine-grained interaction capture system. Able to track CPU usage for each user interface event, the proposed system sets the CPU performance level to the one that best matches the predicted CPU demand. Compared to the state-of-the-art approach of user-interactionbased CPU energy management, we show that IADVS improves prediction accuracy by 37%, reduces processing delays by 17%, and reduces energy consumed of the CPU by as much as 4%. The proposed design is evaluated with both a detailed trace-based simulation as well as implementation on a real system, verifying the simulation findings.