No abstract

Accurate traffic characterization by packet source is needed to predict network behavior and to properly allocate network resources to achieve a desired Quality of Service for all network users. As networks have become faster, the processing load required for complete packet sampling has also grown. In some cases, for example Gigabit Ethernet, the network can deliver packets faster than a network management subsystem can process them. In order to prevent inaccurate traffic statistics due to "clipping" of traffic peaks, Claffy et al. applied several static sampling strategies to network traffic characterization. As shown in this paper, static sampling may produce inaccurate traffic statistics. In this paper, adaptive sampling methods are developed and evaluated to address inaccuracies of static sampling. In addition, the estimation of the Hurst parameter, a measure of traffic self-similarity, is studied for static and adaptive sampling. It is shown that adaptive sampling results in a more accurate estimation of the mean, variance, and Hurst parameter for packet counts.

In this digital era, a fundamental challenge is to design digital reading materials in such a way that they improve children's reading skills. Since reading books is challenging for many fifth graders-particularly for those genetically susceptible to attention problems-the researchers hypothesized that guidance from a digital Pedagogical Agent (PA) could improve students' reading motivation and incidental vocabulary learning. Using a sample of 147 fifth-grade students, the researchers carried out a randomized control trial with three groups of students reading: (a) hardcopy (print) books, (b) digital books, and (c) digital books with a PA. For students with a genetic predisposition to attention problems, carriers of the DRD4 seven-repeat allele, the PA supported their incidental vocabulary learning. For noncarriers, there were no effects of the digital reading materials or the PA.