User Identification via Keystroke Characteristics of Typed Names using Neural Networks
A method for identifying computer users by analysing keystroking patterns with neural networks and a simple geometric distance is presented. A model of each user's normal typing style was created and compared with later typing samples. Preliminary results demonstrate complete exclusion of imposters and a reasonably low false alarm rate when the sample text was limited to the user's name.

title: The literate-programming paradigm
abstract: Literate programming, a technique for coding software systems that promotes readability and comprehension, is examined in detail. The current literate-programming paradigm is reviewed by looking at two sample literate programs. A critique of literate programming as it is currently used is presented, and methods for enhancing the process are explored. A number of new facilities are proposed, and restrictions on current literate-programming practices are suggested.<<ETX>>

title: Literate Programming Applied to Conventional Software Design

title: Geometrically correct imagery for teleconferencing
abstract: Current camera-monitor teleconferencing applications produce unrealistic imagery and break any sense of presence for the participants. Other capture/display technologies can be used to provide more compelling teleconferencing. However, complex geometries in capture/display systems make producing geometrically correct imagery difficult. It is usually impractical to detect, model and compensate for all effects introduced by the capture/display system. Most applications simply ignore these issues and rely on the user acceptance of the camera-monitor paradigm.
This paper presents a new and simple technique for producing geometrically correct imagery for teleconferencing environments. The necessary image transformations are derived by finding a mapping between a capture and display device for a fixed viewer location. The capture/display relationship is computed directly in device coordinates and completely avoids the need for any intermediate, complex representations of screen geometry, capture and display distortions, and viewer location. We describe our approach and demonstrate it via several prototype implementations that operate in real-time and provide a substantially more compelling sense of presence than the standard teleconferencing paradigm.

title: Building large-format displays for digital libraries
abstract: COMMUNICATIONS OF THE ACM May 2001/Vol. 44, No. 5 57 Digital libraries house media of ever-increasing complexity, from models of statues to virtual archeological landscapes. Future digital archives will only increase in resolution and complexity as acquisition hardware improves and data becomes more intricate. Although digital content is constantly improving, the typical display device (the desktop monitor) remains relatively unchanged. The problem is the spatial resolution and color quality of digital media surpasses the display real estate of typical monitors. It is common for objects such as artwork and manuscripts to be digitized at a much higher resolution than can be displayed on even the best monitors. This forces the data to be down-sampled, or restricts viewing to selected regions of interest at a time. Even with improved resolution, small display areas are often less than compelling. For example, the high-resolution facsimile of Michelangelo’s 17-foot statue of David [4] is somewhat less spectacular when displayed on a high-resolution 20-inch monitor. The simple fact is monitors have become under-powered technologies for the visualization capabilities now needed to fully appreciate digital archives. The immediate reaction is to enlist the help of large-format displays for visualization available as video walls, domes, and immersive environments such as the CAVE. These environments provide a compelling sense of presence and help break the “window on worlds” paradigm. Unfortunately, these technologies also present serious challenges that limit their accessibility and usefulness to a small number of institutions. Of primary concern is the cost. Initial costs of projectors, rendering hardware, and subsequent recurring operational expenses are fiscally prohibitive. A second concern is the renovation necessary for installing a large-format display system. Because most systems rely on mechanically aligned projectors and rigidly constructed display surfaces, room modification and projection surface infrastructure are necessary. An additional difficulty is the complexity of continuous operation. These systems are not for novice users and can quickly become unusable without expert maintenance and tuning. Our goal is to bring large-format displays to the digital library community by breaking down the barriers that make them expensive and difficult to set up and run. We are attacking the price/performance barrier by engineering displays built entirely from commodity components and assembled in a scalable configuration. Specifically, commodity light projectors, unlike monitors, can be positioned collectively to form a single logical desktop . Inexpensive video cards can perform at the level required to drive a projector. Groups of PCs, each driving a projector and communicating via a local area network, are inexpensive and powerful [2, 6]. While the physical alignment of projectors creates a very compelling display, it is tedious and requires major effort. Within the digital library community, where libraries must operate and maintain equipment, it is important to enable novice technicians to build up and maintain a display by a more casual placement of projectors. Michael S. Brown, W. Brent Seales, Stephen B. Webb, and Christopher O. Jaynes { {

title: Small Subunit Ribosomal RNA Modeling Using Stochastic Context-Free Grammars
abstract: We introduce a model based on stochastic context-free grammars (SCFGs) that can construct small subunit ribosomal RNA (SSU rRNA) multiple alignments. The method takes into account both primary sequence and secondary structure basepairing interactions. We show that this method produces multiple alignments of quality close to hand edited ones and outperforms several other methods. We also introduce a method of SCFG constraints that dramatically reduces the required computer resources needed to effectively use SCFGs on large problems such as SSU rRNA. Without such constraints, the required computer resources are infeasible for most computers. This work has applications to fields such as phylogenetic tree construction.

title: Model of Familiarity Discrimination in the Perirhinal Cortex
abstract: Much evidence indicates that recognition memory involves two separable processes, recollection and familiarity discrimination, with familiarity discrimination being dependent on the perirhinal cortex of the temporal lobe. Here, we describe a new neural network model designed to mimic the response patterns of perirhinal neurons that signal information concerning the novelty or familiarity of stimuli. The model achieves very fast and accurate familiarity discrimination while employing biologically plausible parameters and Hebbian learning rules. The fact that the activity patterns of the model's simulated neurons are closely similar to those of neurons recorded from the primate perirhinal cortex indicates that this brain region could discriminate familiarity using principles akin to those of the model. If so, the capacity of the model establishes that the perirhinal cortex alone may discriminate the familiarity of many more stimuli than current neural network models indicate could be recalled (recollected) by all the remaining areas of the cerebral cortex. This efficiency and speed of detecting novelty provides an evolutionary advantage, thereby providing a reason for the existence of a familiarity discrimination network in addition to networks used for recollection.

title: Multi-image matching using multi-scale oriented patches
abstract: This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 /spl times/ 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.