In Summer 2009 I participated in the NSF-funded SURF-IT (Summer Undergraduate Research Fellowship in Information Technology) at the University of California, Santa Cruz. This REU (Research Experience for Undergraduates) involved creating an iPhone/iPod Touch application called Teenvity that is designed to motivate teenagers to exercise by playing games that require movement. The application is molded to the user’s personality; the system selects an agent, motivational phrases, and games to suggest to the user based on a short personality test.

A web crawler is a program that scours the Internet moving from website to website. Web crawlers have many different purposes, such as sending out junk mail, finding dead links within a domain, and searching websites and databases for relevant information like that of Google. This project focuses on using a web crawler to map the hierarchy of links within a particular domain. Starting at the St. Lawrence University’s home page, the web crawler gathers all the links that are found while crawling the St. Lawrence domain.

Given the increased interest of educational institutions to raise awareness of environmental issues, there is a desire to inform students of their personal usage of resources. Generally, this is in the form of the quantity of carbon dioxide and other greenhouse gases that are produced by the lifestyle that they lead. The volume of carbon produced either directly or indirectly by an individual’s lifestyle is dependant on a wide range of factors and is almost impossible to precisely calculate.

sc pipes is a SystemC package for building and simulating models of pipelined systems. To use sc pipes, a designer familiar with SystemC should have very little to learn. To create a pipeline simulation with sc pipes, the user builds an sc module for each stage to be used in the pipeline, with only minor di erences from the way such a module would be built in a SystemC program without sc pipes. The user then builds an scp pipeline object, passing to its constructor an expression describing the pipeline's con guration.

When presented with a dataset, it is beneficial to identify any relationships or trends. One way in which we can accomplish this is through the application of cluster analysis, a method for developing taxonomies within a set of observations. While this technique is beneficial in marketing, research, or any profession requiring data analysis, there are many algorithms for dfining clusters in a dataset. As a result, we raise the question, which clustering algorithm is the best in various scenarios?

Classification trees are used with a categorical response variable. The goal of a classification tree is to derive a model that predicts to which category a particular subject or individual belongs, based one or more explanatory factors. For example, we could use a classification tree to predict the diagnosis (Benign or Malignant) of a particular patient based upon information obtained by doctors through scanned images. These classification trees are displayed as a decision tree that has a start node which then branches into other nodes.

The Central Limit Theorem (CLT) states that the distribution of the sample mean of independent and identically distributed random variables converges to the normal distribution as the sample size increases. A common rule of thumb is to consider sample sizes greater than 30 as "large enough" samples to use the CLT as an approximation. However, the "large enough" depends on how non-normal the individual observations are distributed.

Article 2 of the Universal Declaration of Human Rights (UDHR) states that the freedoms and livelihoods of people throughout the world cannot be compromised or denied based on “colour, sex, language, religion, political or other opinion, national or social origin, property, birth or other status.” The list of characteristics enumerated in the UDHR seems to protect members of all possible social categories; however, Article 2 fails to explicitly mention sexual orientation as a personal trait protected from discrimination and violence.

Baseball is the great American pastime. In this study we examine different aspects of baseball games to determine what factors play a role in predicting the winning team for a specific game or an entire season. To predict who is likely to win individual games, we consider factors such as each team’s offensive or defensive ability, past game scores, and previous winning percentage. In particular, we examine the extent to which a team playing at home has an advantage over the visiting team.

Explores issues of identity formation at Grinnell College during the years 2007-2010 during which time several homophobic incidents took place.