Suk Jai Seo

Intelligent tutoring system for CS-I and II laboratory

A Web-based adaptive tutoring system which dynamically adapts to each students needs and gives a student immediate feedback is being developed for our CS-I and CS-II closed laboratories. The system currently contains the question tutor, the program tutor, and the course management components. The tutoring components help students learn programming concepts through hands-on, self-paced exercises. The course management component helps teachers prepare and maintain the lab materials. Experiments have been conducted to evaluate the effectiveness of this new tutoring system and promising preliminary results were obtained.

Can algotutor change attitudes toward algorithms

The ability to design an algorithm is one of the most important learning outcomes of a computer science program. Unfortunately, not only is learning how to design algorithms a challenging task, but many students believe that algorithm design is not an important part of problem solving. To address this challenge and hopefully change student attitudes, we developed AlgoTutor, a web-based algorithm development tutoring system. AlgoTutors primary components are the algorithm composer and the algorithm tracer. A third component, ProgramPad, was added to show the connection between algorithms and code. This paper presents the results of experiments that assessed AlgoTutors effectiveness in changing student attitudes about algorithm development. The results show that students who used AlgoTutor in CS-I were more likely to realize the importance of algorithm design in problem solving and to have confidence in their own algorithm development abilities.

Open neighborhood locating-domination for infinite cylinders

For a graph G that models a facility or a multi-processor network, detection devices can be placed at the vertices so as to identify the location of an intruder such as a thief or saboteur or a faulty processor. Open neighborhood locating-dominating sets are of interest when the intruder/fault at a vertex precludes its detection by a device at that location. For example, a processor might be able to determine whether another processor directly connected to it is faulty, but it cannot be assumed to detect its own fault. In this paper we illustrate these concepts using cylindrical graphs as examples of multi-processor networks.

Algorithms for optimal orientations of a unicyclic graph

Let (<i>G</i>, <i>R</i>) denote the directed graph obtained from undirected graph <i>G</i> by an acyclic orientation <i>R</i> so that (<i>G</i>, <i>R</i>) contains no directed cycle. We consider acyclic orientations <i>R</i> of a unicyclic graph <i>G</i> which maximizes/minimizes the number of ordered pairs of non-adjacent vertices with directed paths in (<i>G</i>, <i>R</i>). These orientations are referred as optimal orientations. We present algorithms to determine optimal orientations of a unicyclic graph.

On open neighborhood locating-dominating in graphs

A set D of vertices in a graph G = (V (G), E(G)) is an open neighborhood locating-dominating set (OLD-set) for G if for every two vertices u, v of V (G) the sets N(u) ∩ D and N(v) ∩ D are non-empty and different. The open neighborhood locating-dominating number OLD(G) is the minimum cardinality of an OLD-set for G. In this paper we characterize graphs G of order n with OLD(G) = 2, 3, or n and graphs with minimum degree (G) ≥ 2 that are C4-free with OLD(G) = n-1.

A comparative study of text classification approaches for personalized retrieval in PubMed

Retrieval of the information relevant to ones need from PubMed is becoming increasingly challenging due to its large volume and rapid growth. The traditional information search techniques based on keyword matching are insufficient for large databases such as PubMed. A personalized article retrieval system that is tailored to individual researchers specific interests and selects only highly relevant articles can be a helpful tool in the field of Bioinformatics. The text classification methods developed in the text mining community have shown good results in differentiating relevant articles from the irrelevant ones. This study compares two text classification methods, Naïve Bayes and Support Vector Machines, in order to study the effectiveness of the two methods on classifying full text articles in the case when only a small set of training data is available. The comparison results show that the Naïve Bayes method is a better choice than Support Vector Machines in building a personalized article retrieval system which can learn (train) from a small set of full text articles.

An introduction to competition-reachability of a graph

Let G be an undirected graph. A directed graph D obtained from G by assigning a direction to each edge in G is called an oriented graph. The reachability r(D) of a directed graph D is the number of ordered pairs of distinct vertices (x, y) with a directed path from x to y. Consider a graphical game associated with a graph G=(V, E) involving two players (maximizer and minimizer) who alternately select edges and orient them. The maximizer attempts to maximize the reachability, while the minimizer attempts to minimize the reachability, of the resulting digraph. If both players play optimally, then the reachability is fixed. Parameters that assign a value to each graph in this manner are called competitive parameters. We determine the competitive-reachability for special classes of graphs.

An introduction to proper-coupled-domination in graphs

In this paper we introduce the proper-coupled-domination problem. Specifically, assume that we have (disjoint) subsets <i>S</i><inf>1</inf>, <i>S</i><inf>2</inf>, ..., <i>S<inf>t</inf></i> of the vertex set <i>V(G)</i> of graph <i>G</i>. One seeks to find the minimum cardinality of a dominating set <i>D</i> with the property that <i>D</i> ∩ <i>S<inf>i</inf></i> ≠ &phis; implies that <i>S<inf>i</inf></i> ⊆ <i>D</i> for 1 ≤ <i>i</i> ≤ <i>t</i>. We focus, in particular, on a coupled-domination parameter for which each <i>S<inf>i</inf></i> has cardinality at most two.

Competition-reachability of a graph

The reachability r(D) of a directed graph D is the number of ordered pairs of distinct vertices (x,y) with a directed path from x to y. Consider a game associated with a graph G=(V,E) involving two players (maximizer and minimizer) who alternately select edges and orient them. The maximizer attempts to maximize the reachability, while the minimizer attempts to minimize the reachability, of the resulting digraph. If both players play optimally, then the reachability is fixed. Parameters that assign a value to each graph in this manner are called competitive parameters. We determine the competitive-reachability for special classes of graphs and discuss which graphs achieve the minimum and maximum possible values of competitive-reachability.

Identifying training sets for personalized article retrieval system

Retrieving documents that are relevant to a particular researchers purpose is a big challenge, especially when searching through large database, such as PubMed. Researchers who use traditional keyword-based document retrieval systems often end up with a large collection of documents that are not directly relevant to their needs. What is needed is a personalized document retrieval system that can select only relevant articles for ones specific research interests. Obtaining an appropriate training data set is essential in building and testing personalized article retrieval systems. This study describes one approach to form such training data set based on articles categorized by domain experts under MeSH major topics. Text classifiers, learned using Support Vector Machines, were used to test to what degree the training set categories are differentiable. Preliminary results and analysis of the results are discussed.