Neuroevolution is a powerful and general technique for evolving the structure and weights of artificial neural networks. Though neuroevolutionary approaches such as NeuroEvolution of Augmenting Topologies (NEAT) have been successfully applied to various problems including classification, regression, and reinforcement learning problems, little work has explored application of these techniques to larger-scale multiclass classification problems. In this paper, NEAT is evaluated in several multiclass classification problems, and then extended via two ensemble approaches: One-vs-All and One-vs-One. These approaches decompose multiclass classification problems into a set of binary classification problems, in which each binary problem is solved by an instance of NEAT. These ensemble models exhibit reduced variance and increasingly superior accuracy as the number of classes increases. Additionally, higher accuracy is achieved early in training, even when artificially constrained for the sake of fair comparison with standard NEAT. However, because the approach can be trivially distributed, it can be applied quickly at large scale to solve real problems. In fact, these approaches are incorporated into DarwinTM, an enterprise automatic machine learning solution that also incorporates various other algorithmic enhancements to NEAT. The resulting complete system has proven robust to a wide variety of client datasets.

The Web has certainly demonstrated the power of personalization with offers based on interests and prior purchasing behavior. Most studies indicate a 10 to 1 improvement in sale conversion using some degree of personalization in printed marketing collateral. Until recently, the cost of providing personalization in high-quality printed collateral has been prohibitive. Delivering a combined campaign of web and print is a logistics nightmare requiring parallel workflows, parallel content management, and custom synchronization mechanisms.While digital offset presses (such as the HP Indigo) can address fulfillment of high-quality personalized pages, the upstream work processes including layout, typography, content merge, proofing and color are struggling to capture and represent these new types of jobs. In this talk, I'll describe the capabilities of the new digital offset presses, some of the market forces that are driving the new types of pages, and highlight the challenges being faced in creating these new workflows.

A new approach to testing component-based applications is presented, which exploits the practice in component-based systems of generating stub/skeleton modules and using these stubs/skeletons to construct a global perspective of end-to-end causality of inter-component communication. This global causality is captured regardless of reentrancy, callbacks, thread and process boundaries, and unsynchronized clocks. The captured logs created from the interception points are used to construct a system-wide component interaction model that can expose the inter-component dependencies usually hidden in static analysis of application code. These discovered dependencies are used to create a test boundary for applying a component test harness for that component and the set of dependent components. Similarly, the discovered dependencies can be applied to pruning the available test cases to identify those cases that are best suited to exposing defects when one or more components are changed. A particular advantage of the approach has been the ability to isolate the sequence of events that led up to a crash or a deadlock condition and view the entire system behavior (not just a particular thread's perspective or a linear log of intercepted messages).

Abstract NetBuild is a suite of tools which automate the process of selecting, locating, downloading, configuring, and installing computational software libraries from over the Internet, and which aid in the construction and cataloging of such libraries. Unlike many other tools, NetBuild is designed to work across a wide variety of computing platforms, and perform fine‐grained matching to find the most suitable version of a library for a given target platform. We describe the architecture of NetBuild and its initial implementation. Copyright © 2002 John Wiley & Sons, Ltd.

SNIPE is a metacomputing system that aims to provide a reliable, secure, fault-tolerant environment for long-term distributed computing applications and data stores across the global InterNet. This system combines global naming and replication of both processing and data to support large scale information processing applications leading to better availablity and reliability than currently available with typical cluster computing and/or distributed computer environments.