Knowledgezoom for java: A concept-based exam study tool with a zoomable open student model

This paper presents our attempt to develop a personalized exam preparation tool for Java/OOP classes based on a fine-grained concept model of Java knowledge. Our goal was to explore two most popular student model-based approaches: open student modeling and problem sequencing. The result of our work is a Java exam preparation tool, Knowledge Zoom. The tool combines an open concept-level student model component, Knowledge Explorer and a concept-based sequencing component, Knowledge Maximizer into a single interface. This paper presents both components of Knowledge Zoom, reports results of its evaluation, and discusses lessons learned. © 2013 IEEE

New bounds on the signed total domination number of graphs

In this paper, we study the signed total domination number in graphs and present new sharp lower and upper bounds for this parameter. For example by making use of the classic theorem of Turan, we present a sharp lower bound on this parameter for graphs with no complete graph of order r+1 as a subgraph. Also, we prove that n-2(s-s') is an upper bound on the signed total domination number of any tree of order n with s support vertices and s' support vertives of degree two. Moreover, we characterize all trees attainig this bound.Comment: This paper contains 11 pages and one figur

Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture

Statistical Multiplexing of Semi-Markov Modulated Sources

It is shown that when a continuous buffer is driven by a semi-Markox modulated fluid flow source(s), the stationary distribution of the buffer content is governed by the same differential equation describing the distribution for continuous time Markov modulated fluid source(s) [1]. It is also shown that the same techniques can be utilized to decompose and solve the eigenvalue problem associated with the differential equation [6]. Finally it is shown that the stationary distribution of buffer content depends only on the mean time spent by each multiplexed semi-Markov source in each state