IT Project Variables in the Balance: A Bayesian Approach to Prediction of Support Costs

2011 ◽  
Author(s):  
Bruno L. Dalmazo ◽  
Abraham L. R. de Sousa ◽  
Weverton L. Cordeiro ◽  
Juliano A. Wickboldt ◽  
Roben C. Lunardi ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Support Costs ◽  
It Project

Leveraging IT project lifecycle data to predict support costs

2011 ◽  
Author(s):  
Bruno L. Dalmazo ◽  
Weverton L. Cordeiro ◽  
Lincoln Rabelo ◽  
Juliano A. Wickboldt ◽  
Roben C. Lunardi ◽  
...  
Keyword(s):  
Support Costs ◽  
It Project ◽  
Project Lifecycle

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

Architecture of a distributed system for processing heterogeneous data from social networks

2020 ◽  
Vol 4 ◽  
pp. 97-100
Author(s):  
A.P. Pronichev ◽  
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Distributed System ◽  
Heterogeneous Data ◽  
Analysis Process ◽  
Support Costs

The article discusses the architecture of a system for collecting and analyzing heterogeneous data from social networks. This architecture is a distributed system of subsystem modules, each of which is responsible for a separate task. The system also allows you to use external systems for data analysis, providing the necessary interface abstraction for connection. This allows for more flexible customization of the data analysis process and reduces development, implementation and support costs.

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