Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

Crawling Facebook for Social Network Analysis Purposes

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.\u

The Viuva Negra crawler

This report discusses architectural aspects of web crawlers and details the design, implementation and evaluation of the Viuva Negra (VN) crawler. VN has been used for 4 years, feeding a search engine and an archive of the Portuguese web. In our experiments it crawled over 2 million documents per day, correspondent to 63 GB of data. We describe hazardous situations to crawling found on the web and the adopted solutions to mitigate their effects. The gathered information was integrated in a web warehouse that provides support for its automatic processing by text mining applications

Design, implementation and experiment of a YeSQL Web Crawler

We describe a novel, "focusable", scalable, distributed web crawler based on GNU/Linux and PostgreSQL that we designed to be easily extendible and which we have released under a GNU public licence. We also report a first use case related to an analysis of Twitter's streams about the french 2012 presidential elections and the URL's it contains