Learn the best optimizations across multiple programs and architectures based on the correlation between program features, Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically Programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly Machine learning has been proposed to tune optimizations across However, the large number of evaluations required for each program has prevented iterativeĬompilation from widespread take-up in production compilers. Iterative optimization is a popular approach to adapting programs to a new architecture automatically Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new In particular, case study applications implemented using CAreDroid are shown to have: (1) at least half lines of code fewer and (2) at least 10× more efficient in execution time compared to equivalent context-aware applications that use only standard Android APIs. By pushing context monitoring and adaptation into the runtime system, CAreDroid eases the development of context-aware applications and increases their efficiency. At run time, CAreDroid monitors the context of the physical environment and intercepts calls to sensitive methods, activating only the blocks of code that best fit the current physical context.ĬAreDroid is implemented as part of the Android runtime system. In this framework, developers are required-only-to focus on the application logic by providing a list of methods that are sensitive to certain contexts along with the permissible operating ranges under those contexts. In this paper, we introduce CAreDroid, which is a framework that is designed to decouple the application logic from the complex adaptation decisions in Android context-aware applications. Because of this, application developers must build their own context-awareness adaptation engines, dealing directly with sensors and polluting application code with complex adaptation decisions. Nevertheless, there is little systematic support for context-awareness in contemporary mobile operating systems. Many contemporary mobile applications adapt to changing locations, connectivity states, available computational and energy resources, and proximity to other users and devices. 235Ĭhapter 1: Introduction to Statistics Section 1-2 1.Context-awareness is the ability of software systems to sense and adapt to their physical environment. Statistical significance is indicated when methods of statistics are used to reach a conclusion that some treatment or finding is effective, but common sense might suggest that the treatment or finding does not make enough of a difference to justify its use or to be practical. Yes, it is possible for a study to have statistical significance but not a practical significance. If the source of the data can benefit from the results of the study, it is possible that an element of bias is introduced so that the results are favorable to the source.Ī voluntary response sample is a sample in which the subjects themselves decide whether to be included in the study. A voluntary response sample is generally not suitable for a statistical study because the sample may have a bias resulting from participation by those with a special interest in the topic being studied.Įven if we conduct a study and find that there is a correlation, or association, between two variables, we cannot conclude that one of the variables is the cause of the other. There does appear to be a potential to create a bias. There does not appear to be a potential to create a bias. There does appear a potential to create a bias. The sample is a voluntary response sample and is therefore flawed.ġ0. The sample is a voluntary response sample and is therefore flawed. Because there is a 30% chance of getting such results with a diet that has no effect, it does not appear to have statistical significance, but the average loss of 45 pounds does appear to have practical significance. Because there is only a 1% chance of getting the results by chance, the method appears to have a statistical significance. The result of 540 boys in 1000 births is above the approximately 50% rate expected by chance, but it does not appear to be high enough to have practical significance.
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