OPEN DOORS AND OPEN MINDS: WHAT FACULTY AUTHORS CAN DO TO ENSURE OPEN ACCESS TO THEIR WORK THROUGH THEIR INSTITUTION

The Internet has brought unparalleled opportunities for expanding availability of research by bringing down economic and physical barriers to sharing. The digitally networked environment promises to democratize access, carry knowledge beyond traditional research niches, accelerate discovery, encourage new and interdisciplinary approaches to ever more complex research challenges, and enable new computational research strategies. However, despite these opportunities for increasing access to knowledge, the prices of scholarly journals have risen sharply over the past two decades, often forcing libraries to cancel subscriptions. Today even the wealthiest institutions cannot afford to sustain all of the journals needed by their faculties and students. To take advantage of the opportunities created by the Internet and to further their mission of creating, preserving, and disseminating knowledge, many academic institutions are taking steps to capture the benefits of more open research sharing. Colleges and universities have built digital repositories to preserve and distribute faculty scholarly articles and other research outputs. Many individual authors have taken steps to retain the rights they need, under copyright law, to allow their work to be made freely available on the Internet and in their institutionâ s repository. And, faculties at some institutions have adopted resolutions endorsing more open access to scholarly articles. Most recently, on February 12, 2008, the Faculty of Arts and Sciences (FAS) at Harvard University took a landmark step. The faculty voted to adopt a policy requiring that faculty authors send an electronic copy of their scholarly articles to the universityâ s digital repository and that faculty authors automatically grant copyright permission to the university to archive and to distribute these articles unless a faculty member has waived the policy for a particular article. Essentially, the faculty voted to make open access to the results of their published journal articles the default policy for the Faculty of Arts and Sciences of Harvard University. As of March 2008, a proposal is also under consideration in the University of California system by which faculty authors would commit routinely to grant copyright permission to the university to make copies of the facultyâ s scholarly work openly accessible over the Internet. Inspired by the example set by the Harvard faculty, this White Paper is addressed to the faculty and administrators of academic institutions who support equitable access to scholarly research and knowledge, and who believe that the institution can play an important role as steward of the scholarly literature produced by its faculty. This paper discusses both the motivation and the process for establishing a binding institutional policy that automatically grants a copyright license from each faculty member to permit deposit of his or her peer-reviewed scholarly articles in institutional repositories, from which the works become available for others to read and cite.

Web Mediators for Accessible Browsing

We present a highly accurate method for classifying web pages based on link percentage, which is the percentage of text characters that are parts of links normalized by the number of all text characters on a web page. K-means clustering is used to create unique thresholds to differentiate index pages and article pages on individual web sites. Index pages contain mostly links to articles and other indices, while article pages contain mostly text. We also present a novel link grouping algorithm using agglomerative hierarchical clustering that groups links in the same spatial neighborhood together while preserving link structure. Grouping allows users with severe disabilities to use a scan-based mechanism to tab through a web page and select items. In experiments, we saw up to a 40-fold reduction in the number of commands needed to click on a link with a scan-based interface, which shows that we can vastly improve the rate of communication for users with disabilities. We used web page classification and link grouping to alter web page display on an accessible web browser that we developed to make a usable browsing interface for users with disabilities. Our classification method consistently outperformed a baseline classifier even when using minimal data to generate article and index clusters, and achieved classification accuracy of 94.0% on web sites with well-formed or slightly malformed HTML, compared with 80.1% accuracy for the baseline classifier.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.