11,412 research outputs found
Randomization Adaptive Self-Stabilization
We present a scheme to convert self-stabilizing algorithms that use
randomization during and following convergence to self-stabilizing algorithms
that use randomization only during convergence. We thus reduce the number of
random bits from an infinite number to a bounded number. The scheme is
applicable to the cases in which there exits a local predicate for each node,
such that global consistency is implied by the union of the local predicates.
We demonstrate our scheme over the token circulation algorithm of Herman and
the recent constant time Byzantine self-stabilizing clock synchronization
algorithm by Ben-Or, Dolev and Hoch. The application of our scheme results in
the first constant time Byzantine self-stabilizing clock synchronization
algorithm that uses a bounded number of random bits
Adaptive just-in-time code diversification
We present a method to regenerate diversified code dynamically in a Java bytecode JIT compiler, and to update the diversification frequently during the execution of the program. This way, we can significantly reduce the time frame in which attackers can let a program leak useful address space information and subsequently use the leaked information in memory exploits. A proof of concept implementation is evaluated, showing that even though code is recompiled frequently, we can achieved smaller overheads than the previous state of the art, which generated diversity only once during the whole execution of a program
Handling Covariates in the Design of Clinical Trials
There has been a split in the statistics community about the need for taking
covariates into account in the design phase of a clinical trial. There are many
advocates of using stratification and covariate-adaptive randomization to
promote balance on certain known covariates. However, balance does not always
promote efficiency or ensure more patients are assigned to the better
treatment. We describe these procedures, including model-based procedures, for
incorporating covariates into the design of clinical trials, and give examples
where balance, efficiency and ethical considerations may be in conflict. We
advocate a new class of procedures, covariate-adjusted response-adaptive (CARA)
randomization procedures that attempt to optimize both efficiency and ethical
considerations, while maintaining randomization. We review all these
procedures, present a few new simulation studies, and conclude with our
philosophy.Comment: Published in at http://dx.doi.org/10.1214/08-STS269 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Developing a Framework for Creating mHealth Surveys
Various issues in the design of surveys for mobile health (mHealth) research projects yet exist. As mHealth solutions become more popular, new issues are brought into consideration. Researchers need to collect some critical information from participants in these mHealth studies. These mHealth studies require a specialized framework to create surveys, track progress and analyze user data. In these procedures, mHealth’s needs differ from other studies. Therefore, there has to be a new framework that satisfies needs of mHealth research studies. Although there are studies for creating efficient, robust and user-friendly surveys, there is no solution or study, which is specialized in mHealth area and solves specific problems of mHealth research studies. mHealth research studies sometimes require real-time access to user data. Reward systems may play a key role in their study. Most importantly, storing user information securely plays a key role in these studies. There is no such solution or study, which covers all these areas. In this thesis, we present guidelines for developing a framework for creating mHealth surveys. In doing this, we hope that we propose a solution for problems of creating and using of surveys in mHealth studies
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