Develops a method for online assembly-language debugging which greatly simplifies several of the bookkeeping tasks characteristically associated with that process. Implementation in a program for the UNIVAC M-460 comp...
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Develops a method for online assembly-language debugging which greatly simplifies several of the bookkeeping tasks characteristically associated with that process. Implementation in a program for the UNIVAC M-460 computer at Air Force Cambridge Research Laboratories.
Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm ...
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Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm which is based on the multivariate dynamic time warping measure. First of all, we propose a Mahalanobis distance based dynamic time warping measure which can compute the similarity of multivariate time series (MTS) efficiently and accurately. Then, a PM-FD framework which consists of data preprocessing, metric learning, MTS pieces building, and MTS classification is presented. After that, we conduct experiments on industrial benchmark of Tennessee Eastman (TE) process. The experimental results demonstrate the improved performance of the proposed algorithm when compared with other classical PM-FD classical methods.
Deterministic execution offers many benefits for debugging, fault tolerance, and security. Current methods of executing parallel programs deterministically, however, often incur high costs, allow misbehaved software t...
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Deterministic execution offers many benefits for debugging, fault tolerance, and security. Current methods of executing parallel programs deterministically, however, often incur high costs, allow misbehaved software to defeat repeatability, and transform time-dependent races into input- or path-dependent races without eliminating them. We introduce a new parallel programming model addressing these issues, and use Determinator, a proof-of-concept OS, to demonstrate the model's practicality. Determinator's microkernel application programming interface (API) provides only "shared-nothing" address spaces and deterministic inter-process communication primitives to make execution of all unprivileged code-well-behaved or not-precisely repeatable. Atop this microkernel, Determinator's user-level run-time offers a private workspace model for both thread-level and process-level parallel programming. This model avoids the introduction of read/write data races, and converts write/write races into reliably detected conflicts. Coarse-grained parallel benchmarks perform and scale comparably to nondeterministic systems, both on multicore PCs and across nodes in a distributed cluster.
There is a minor error in Section 3 wherein it is stated that ?acc 0_ _ k loops in_nitely, even if k succeeds on input _.? This statement is not correct, and should be replaced by ?If k returns false on input cs, then...
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There is a minor error in Section 3 wherein it is stated that ?acc 0_ _ k loops in_nitely, even if k succeeds on input _.? This statement is not correct, and should be replaced by ?If k returns false on input cs, then acc 1_ cs k loops in_nitely.?The author is grateful to Derek Dreyer for pointing out this mistake, and suggesting the above-mentioned correction. [ABSTRACT FROM AUTHOR]
Predictive models are fundamental to engineering reliable software systems. However, designing conservative, computable approximations for the behavior of programs (static analyses) remains a difficult and error-prone...
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Predictive models are fundamental to engineering reliable software systems. However, designing conservative, computable approximations for the behavior of programs (static analyses) remains a difficult and error-prone process for modern high-level programming languages. What analysis designers need is a principled method for navigating the gap between semantics and analytic models: analysis designers need a method that tames the interaction of complex languages features such as higher-order functions, recursion, exceptions, continuations, objects and dynamic allocation. We contribute a systematic approach to program analysis that yields novel and transparently sound static analyses. Our approach relies on existing derivational techniques to transform high-level language semantics into low-level deterministic state-transition systems (with potentially infinite state spaces). We then perform a series of simple machine refactorings to obtain a sound, computable approximation, which takes the form of a non-deterministic state-transition systems with finite state spaces. The approach scales up uniformly to enable program analysis of realistic language features, including higher-order functions, tail calls, conditionals, side effects, exceptions, first-class continuations, and even garbage collection.
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