A Dynamic Replica Placement Mechanism Based on Response Time Measure

2010 ◽  
Author(s):  
Wenfeng Wang ◽  
Wenhong Wei
Keyword(s):  
Response Time ◽  
Replica Placement ◽  
Response Time Measure

Response-Time Measures as a Means of Exploring Tonal Hierarchies

1988 ◽  
Vol 6 (2) ◽  
pp. 161-172 ◽  
Author(s):  
Petr Janata ◽  
Daniel Reisberg
Keyword(s):  
Response Time ◽  
Music Perception ◽  
Recency Effect ◽  
Music Listening ◽  
Tonal Hierarchy ◽  
Report Data ◽  
Tonic Triad ◽  
Response Time Measure

We explore the possibility of studying music perception with responsetime measures. Subjects heard either a chord (tonic triad) or scale prime, followed by a single note, and indicated whether the note did or did not belong in the primed key. Overall, the data resemble the tonal hierarchy previously demonstrated with other methods, thus establishing the validity of the response-time measure. In addition, the scale primes superimpose a recency effect on the standard hierarchy, as would be expected from a serially presented stimulus. We discuss what this implies about tonal hierarchies, and the use of response-time measures to study the online processes of music listening. We also report data for nondiatonic tones.

Validation of a simple response-time measure of listening effort

2015 ◽  
Vol 138 (3) ◽  
pp. EL187-EL192 ◽  
Author(s):  
Carina Pals ◽  
Anastasios Sarampalis ◽  
Hedderik van Rijn ◽  
Deniz Başkent
Keyword(s):  
Response Time ◽  
Listening Effort ◽  
Response Time Measure

Adaptive Replica Creation and Selection Strategies for Latency-Aware Application in Collaborative Edge-Cloud System

2019 ◽  
Vol 63 (9) ◽  
pp. 1338-1354
Author(s):  
Chunlin Li ◽  
YiHan Zhang ◽  
Youlong Luo
Keyword(s):  
Response Time ◽  
Storage Capacity ◽  
Replica Placement ◽  
File Access ◽  
User Request ◽  
Replica Selection ◽  
Replica Management ◽  
Research Problems ◽  
Two Parameters ◽  
And Storage

Abstract There are many research problems in cloud replica management such as low data reliability, unbalanced node load and large resource consumption. The strategy and status of replica creation, replica placement and replica selection are analyzed. The replica creation based on access tendency (DRC-AT), the replica placement based on user request response time and storage capacity (DRP-RS) and the replica selection based on response time (DRS-RT) are proposed. The DRC-AT algorithm introduces the two parameters of file popularity and period value of file popularity, calculates the file access tendency periodically and decides the creation and deletion of the replica of the file according to the size of the file access tendency. The DRP-RS algorithm evaluates the user’s request response time and storage capacity to select the best node set to place the replica. The DRS-RT algorithm returns to the user the node with the strongest service capability that contains the user’s requested data. Experiments show that the algorithm can improve the speed of data reading by the client, improve the resource utilization, balance the load of the node and improve the overall performance of the system.

scholarly journals Reconsidering the Automaticity of Visual Statistical Learning

2019 ◽  
Author(s):  
Kevin D Himberger ◽  
Amy Finn ◽  
Christopher John Honey
Keyword(s):  
Response Time ◽  
Statistical Learning ◽  
Active Engagement ◽  
Indirect Response ◽  
Visual Statistical Learning ◽  
Incidental Exposure ◽  
Statistical Regularities ◽  
Forced Choice Task ◽  
Direct Measures ◽  
Response Time Measure

Statistical learning refers to the process of extracting regularities from the world without feedback. What are the necessary conditions for statistical learning to arise? It has been argued that visual statistical learning (VSL) is “automatic”, such that subjects will passively and even unconsciously extract statistical regularities from streams of visual input as long as they attend to the stimuli. In contrast, our data indicate that simply attending to stimuli is not, on its own, sufficient for learning. In Experiments 1 & 2, we provided incidental exposure to regularities in a stream of images and observed little to zero VSL across a range of conditions. In Experiment 3, we found that explicitly instructing participants to seek regularities dramatically improved their performance on direct measures of learning, but not on an indirect response time measure. Finally, in Experiments 4 & 5, we demonstrated that a methodological confound in prior work using the indirect response time measure could account for some previous evidence of automatic and implicit VSL.Overall, we found very little evidence of learning using direct measures of VSL, and no evidence of learning using an indirect response time measure. Participants who recognized visual sequence regularities in a forced-choice task could also often recreate the sequences when explicitly probed, indicating their knowledge was not entirely implicit. We suggest that some form of active engagement with stimuli may be needed to extract sequential regularities, and that VSL does not occur automatically.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

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