A major challenge for many database management tasks including admission control, query scheduling, progress monitoring and self-driving data storage systems is to enhance queries performances which are based on computational models known as database cost models. One of the most challenging aspects of developing accurate database cost models is identifying their parameters and capturing their relationships, consequently we can derive the query execution cost on the basis of a specific database hosted on a given platform. Furthermore, the highly dynamic workload (i.e., a set of queries) and the query execution variation lead to performance degradation risk, therefore cost models need to be improved by considering newer software configuration and future workload characteristics. In this article, we propose a framework called DeepCM that is based on a min-max optimization for building robust database cost model against uncertainty parameters. Furthermore, our framework is based on Robust Deep Neural Networks to build database cost models that guarantee a high accuracy regardless of variations from software configuration and workload characteristics.Several experiments have been done to evaluate the robustness of produced cost models and findings show that DeepCM provides a high cost model prediction accuracy and stable performance.
This study proposes a computer-assisted system with a design based on Polya's problem-solving model. The system is designed to help average and low-achieving second graders in mathematics with word-based addition and subtraction questions. The emphasis of using the specific model was on dividing the problem-solving procedure into stages and the concentration on the stages that are problematic for students.Specifically, we compared mathematical word problem-solving performance and computational skills of students who utilized the computer-assisted system with students who employed general strategy instruction. Participants consisted of 52 second-grade students randomly assigned to treatment conditions. Students were pretested and posttested with mathematical problem-solving and computation tests, and repeated measures of their progress with respect to word problem solving were registered.The results showed there was a significant difference between the experimental and control groups in terms of the word problem-solving progress measure, favouring the experimental group. This confirms that providing students with a computerassisted system offered the opportunity to explore all stages of the problem-solving procedure as one possible way to enhance their problem-solving skills.
In this paper we present an agent based evaluation mechanism in a context of multi-agent participatory simulation applied to rodent control. Our training environment is based on (1) an agent based ecosystems simulation as well as actor's avatar interacting with the simulation; and (2) a negotiation based skill evaluation of the actors' behaviors during the simulation. This paper highlights how the combination of these two approaches can help improving rodent control efficiency.
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