Current Search: Yuan, Xiaojing (x)
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Title
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The relationship between memory and social judgement: A dynamical perspective.
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Creator
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Yuan, Xiaojing, Florida Atlantic University, Vallacher, Robin R.
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Abstract/Description
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This study explored the relationship between memory and social judgment. Subjects evaluated someone who was described in both desirable and undesirable terms in a taped conversation. They used a computer mouse to express their judgments on a moment-to-moment basis for 90 sec. under one of three instructional sets: memory-based (mouse judgment upon completion of the conversation, based on their recall of information), on-line (mouse judgment while listening to the conversation), and off-line ...
Show moreThis study explored the relationship between memory and social judgment. Subjects evaluated someone who was described in both desirable and undesirable terms in a taped conversation. They used a computer mouse to express their judgments on a moment-to-moment basis for 90 sec. under one of three instructional sets: memory-based (mouse judgment upon completion of the conversation, based on their recall of information), on-line (mouse judgment while listening to the conversation), and off-line (mouse judgment upon completion of the conversation, based on their judgments formed while listening to the conversation). Half the subjects believed their judgments were relevant to the person's fate (high importance), half believed their judgments were not relevant to his fate (low importance). Subjects in the off-line/important condition demonstrated sustained oscillation in their mouse judgments throughout the judgment period in accord with dynamic integration. In all other conditions, subjects converged on a stable judgment relatively quickly, in accord with static integration.
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Date Issued
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1997
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PURL
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http://purl.flvc.org/fcla/dt/15472
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Subject Headings
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Social values, Social perception, Memory, Judgment (Logic)
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Format
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Document (PDF)
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Title
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Modeling software quality with TREEDISC algorithm.
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Creator
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Yuan, Xiaojing, Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
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Abstract/Description
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Software quality is crucial both to software makers and customers. However, in reality, improvement of quality and reduction of costs are often at odds. Software modeling can help us to detect fault-prone software modules based on software metrics, so that we can focus our limited resources on fewer modules and lower the cost but still achieve high quality. In the present study, a tree classification modeling technique---TREEDISC was applied to three case studies. Several major contributions...
Show moreSoftware quality is crucial both to software makers and customers. However, in reality, improvement of quality and reduction of costs are often at odds. Software modeling can help us to detect fault-prone software modules based on software metrics, so that we can focus our limited resources on fewer modules and lower the cost but still achieve high quality. In the present study, a tree classification modeling technique---TREEDISC was applied to three case studies. Several major contributions have been made. First, preprocessing of raw data was adopted to solve the computer memory problem and improve the models. Secondly, TREEDISC was thoroughly explored by examining the roles of important parameters in modeling. Thirdly, a generalized classification rule was introduced to balance misclassification rates and decrease type II error, which is considered more costly than type I error. Fourthly, certainty of classification was addressed. Fifthly, TREEDISC modeling was validated over multiple releases of software product.
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Date Issued
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1999
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PURL
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http://purl.flvc.org/fcla/dt/15718
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Subject Headings
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Computer software--Quality control, Computer simulation, Software engineering
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Format
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Document (PDF)
