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There are many scenarios in which multi-instance learning problems may be difficult to solve because of a lack of correctly labeled examples for algorithm training. Labeled examples may be difficult or expensive to obtain because human effort is often needed to produce labels and because there may be limitations on the ability to collect large samples for training from a homogeneous population. In...
This paper proposes a quantitative model for assessing cyber security risk in information security. The model can be used to evaluate the security readiness of firms in the marketplace through qualitative and quantitative tools. We propose a Bayesian network methodology that can be used to generate a cyber security risk score that takes as input a firm's security profile and data breach statistics...
There are a number of ways to evaluate a teacher's aptitude as an instructor, but these methods require a trained expert for the analysis. This paper investigates a method of teacher evaluation that draws on a teacher's own self assessment of what constitutes ldquogood teachingrdquo. This method is compared to a trained expert's own assessment of ldquogood teachingrdquo and an existing scoring system...
We consider decentralized Bayesian search problems that involve a team of multiple autonomous agents searching for targets on a network of search points operating under the following constraints: 1) interagent communication is limited; 2) the agents do not have the opportunity to agree in advance on how to resolve equivalent but incompatible strategies; and 3) each agent lacks the ability to control...
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