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We describe several new bottom-up approaches to problems in role engineering for Role-Based Access Control (RBAC). The salient problems are all NP-complete, even to approximate, yet we find that in instances that arise in practice these problems can be solved in minutes. We first consider role minimization, the process of finding a smallest collection of… (More)

- Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles
- IEEE Trans. Neural Networks
- 1996

It has previously been shown that gradient-descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. We show that the long-term dependencies problem is lessened for a class of architectures… (More)

- Bill G. Horne, Lesley R. Matheson, Casey Sheehan, Robert E. Tarjan
- Digital Rights Management Workshop
- 2001

We describe a software self-checking mechanism designed to improve the tamper resistance of large programs. The mechanism consists of a number of testers that redundantly test for changes in the executable code as it is running and report modifications. The mechanism is built to be compatible with copy-specific static watermarking and other… (More)

- Bill G. Horne, Don R. Hush
- Neural Networks
- 1993

In this paper the efficiency of recurrent neural network implementations of m-state finite state machines will be explored. Specifically, it will be shown that the node complexity for the unrestricted case can be bounded above by 0 ( fo) . It will also be shown that the node complexity is 0 (y'm log m) when the weights and thresholds are restricted to the… (More)

- Bill G. Horne, Don R. Hush
- Neural Networks
- 1994

- Hava T. Siegelmann, Bill G. Horne, C. Lee Giles
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 1997

Recently, fully connected recurrent neural networks have been proven to be computationally rich-at least as powerful as Turing machines. This work focuses on another network which is popular in control applications and has been found to be very effective at learning a variety of problems. These networks are based upon Nonlinear AutoRegressive models with… (More)

- Bill G. Horne, C. Lee Giles
- NIPS
- 1994

Many different discrete-time recurrent neural network architectures have been proposed. However, there has been virtually no effort to compare these arch:tectures experimentally. In this paper we review and categorize many of these architectures and compare how they perform on various classes of simple problems including grammatical inference and nonlinear… (More)

- Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles
- NIPS
- 1995

Bill G. Horne NEC Research Institute 4 Independence Way Princeton, NJ 08540 c. Lee Gilest NEC Research Institute 4 Independence Way Princeton, N J 08540 It has recently been shown that gradient descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies. In this paper we explore this problem for… (More)

- Don R. Hush, Bill G. Horne, John M. Salas
- IEEE Trans. Systems, Man, and Cybernetics
- 1992

The paper explores the characteristics of error surfaces for the multilayer perceptron neural network. These characteristics help explain why learning techniques that use hill climbing methods are so slow in these networks. They also help provide insights into techniques that may help speed learning. Several important characteristics are revealed. First,… (More)