G
Gregg T. Vesonder
Researcher at Stevens Institute of Technology
Publications - 51
Citations - 1480
Gregg T. Vesonder is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Expert system & Recall. The author has an hindex of 14, co-authored 51 publications receiving 1418 citations. Previous affiliations of Gregg T. Vesonder include AT&T & Bell Labs.
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Text processing of domain-related information for individuals with high and low domain knowledge
TL;DR: This paper presents the detailed propositional analyses of the text employed in a study that compared the contents of recall protocols of highAnowledde (HK) and by knowledge (LK1 groups) and the results are presented.
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Text generation and recall by high-knowledge and low-knowledge individuals
TL;DR: The findings support the idea that domain-related knowledge has a similar influence upon the processes of text generation and comprehension, that such processes may be viewed in terms of a problem-solving framework, and that HK—LK performance differences are due to differences in the respective problem spaces.
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A knowledge-based configurator that supports sales, engineering, and manufacturing at AT&T Network Systems
Jon R. Wright,Elia S. Weixelbaum,Gregg T. Vesonder,Karen E. Brown,Stephen R. Palmer,Jay I. Berman,Harry H. Moore +6 more
TL;DR: The PROSE architecture is general and is not tied to any specific telecommunications product, as such, it is being reused to develop configurators for several different products.
Nfsight: netflow-based network awareness tool
TL;DR: The internal architecture of Nfsight, the evaluation of the service, and intrusion detection algorithms are presented, and several case studies conducted by security administrators on a large university network are illustrated.
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On the ability to predict one's own responses while learning
Gregg T. Vesonder,James F. Voss +1 more
TL;DR: In this article, a multiple-trial, paired associate procedure, subjects predicted "yes" or "no" on each trial, and significant prediction accuracy was attributed to knowledge of items correctly recalled on Trial N-1, leading to “yes” predictions and correct recall on Trial n, and estimates of item difficulty.