38,316 research outputs found
Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)
Dynamic data race prediction aims to identify races based on a single program
run represented by a trace. The challenge is to remain efficient while being as
sound and as complete as possible. Efficient means a linear run-time as
otherwise the method unlikely scales for real-world programs. We introduce an
efficient, near complete and often sound dynamic data race prediction method
that combines the lockset method with several improvements made in the area of
happens-before methods. By near complete we mean that the method is complete in
theory but for efficiency reasons the implementation applies some optimizations
that may result in incompleteness. The method can be shown to be sound for two
threads but is unsound in general. We provide extensive experimental data that
shows that our method works well in practice.Comment: typos, appendi
SmartTrack: Efficient Predictive Race Detection
Widely used data race detectors, including the state-of-the-art FastTrack
algorithm, incur performance costs that are acceptable for regular in-house
testing, but miss races detectable from the analyzed execution. Predictive
analyses detect more data races in an analyzed execution than FastTrack
detects, but at significantly higher performance cost.
This paper presents SmartTrack, an algorithm that optimizes predictive race
detection analyses, including two analyses from prior work and a new analysis
introduced in this paper. SmartTrack's algorithm incorporates two main
optimizations: (1) epoch and ownership optimizations from prior work, applied
to predictive analysis for the first time; and (2) novel conflicting critical
section optimizations introduced by this paper. Our evaluation shows that
SmartTrack achieves performance competitive with FastTrack-a qualitative
improvement in the state of the art for data race detection.Comment: Extended arXiv version of PLDI 2020 paper (adds Appendices A-E) #228
SmartTrack: Efficient Predictive Race Detectio
A Two-Process Model for Control of Legato Articulation Across a Wide Range of Tempos During Piano Performance
Prior reports indicated a non-linear increase in key overlap times (KOTs) as tempo slows for scales/arpeggios performed at internote intervals (INIs) of I00-1000 ms. Simulations illustrate that this function can be explained by a two-process model. An oscillating neural network based on dynamics of the vector-integration-to-endpoint model for central generation of voluntary actions, allows performers to compute an estimate of the time remaining before the oscillator's next cycle onset. At fixed successive threshold values of this estimate they first launch keystroke n+l and then lift keystroke n. As tempo slows, time required to pass between threshold crossings elongates, and KOT increases. If only this process prevailed, performers would produce longer than observed KOTs at the slowest tempo. The full data set is explicable if subjects lift keystroke n whenever they cross the second threshold or receive sensory feedback from stroke n+l, whichever comes earlier.Fulbright grant; Office of Naval Research (N00014-92-J-1309, N0014-95-1-0409
Predictive Monitoring against Pattern Regular Languages
In this paper, we focus on the problem of dynamically analysing concurrent
software against high-level temporal specifications. Existing techniques for
runtime monitoring against such specifications are primarily designed for
sequential software and remain inadequate in the presence of concurrency --
violations may be observed only in intricate thread interleavings, requiring
many re-runs of the underlying software. Towards this, we study the problem of
predictive runtime monitoring, inspired by the analogous problem of predictive
data race detection studied extensively recently. The predictive runtime
monitoring question asks, given an execution , if it can be soundly
reordered to expose violations of a specification.
In this paper, we focus on specifications that are given in regular
languages. Our notion of reorderings is trace equivalence, where an execution
is considered a reordering of another if it can be obtained from the latter by
successively commuting adjacent independent actions. We first show that the
problem of predictive admits a super-linear lower bound of , where
is the number of events in the execution, and is a parameter
describing the degree of commutativity. As a result, predictive runtime
monitoring even in this setting is unlikely to be efficiently solvable.
Towards this, we identify a sub-class of regular languages, called pattern
languages (and their extension generalized pattern languages). Pattern
languages can naturally express specific ordering of some number of (labelled)
events, and have been inspired by popular empirical hypotheses, the `small bug
depth' hypothesis. More importantly, we show that for pattern (and generalized
pattern) languages, the predictive monitoring problem can be solved using a
constant-space streaming linear-time algorithm
Fault Detection Analysis in Ball Bearings using Machine Learning Techniques
The Bearing element is very essential component of any rotating equipment. Any defect in the bearings lead to instable performance of the machinery. To avoid such malfunction and breakdown of the machinery equipment due to misalignment is review critically in this research paper and various machine learning techniques to tackle the issue is highlighted. This review article finds the basis for developing an effective system in order to reduce the breakdown of machinery or equipment. Conventional Machine Learning methods, like Artificial neural network, Decision Tree, Random Forest, Support Vector Machines(SVM) have been applied to detecting categorizing fault, while the application of Deep Learning methods has ignited great interest in the industry
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