7664269
Management of Conflicting Obligations in Self-Protecting Policy-Based Systems
Policy-based management of business systems is increasingly becoming the norm for autonomic computing since these systems can adapt to the changing needs and increasing complexity of the underlying organizations or enterprises. One of the vital characteristics of these systems is self-protection, ie., the ability to secure information and resources, by anticipating, detecting, identifying and protecting against any form of unauthorised access and permitting all authorised accesses based on the users' roles and pre-established policies. In this paper, we focus on one aspect of self-protecting autonomous systems, which is, how to automatically enforce privacy policies related to data handling, for compliance and auditing purposes. The automatic management of privacy sensitive information based on enterprise policies that are driven by a combination of user preferences, internal objectives and external regulations is a key aspect to any enterprise to prevent misuse of this information. These policies extend beyond simple authorization rules, and also mandate obligations to be enforced under certain conditions. One issue in the automatic enforcement of obligations is the presence of conflicts among different obligations which mandate different actions on the same resource, based on different conditions in which the resource is accessed. In this paper we propose algorithms for detecting and resolving conflicts among obligations in both static and runtime environments. We then briefly describe our prototype obligation management system with the conflict resolution module that achieves the automated enforcement of obligations for data-handling based on privacy policies
195894838
Dynamic Access Control Framework Based On Events.
8951467
Heuristics for selecting robust database structures with dynamic query patterns
The success of a company increasingly depends on timely information (internal or external) being available to the right person at the right time for crucial managerial decision-making. Achieving such a ‘‘right time/right place’’ duet depends directly on database performance. A database system has been a core component that supports modern business system such as enterprise resource planning (ERP) system that integrates and supports all enterprise processes including product designing and engineering, manufacturing, and other business functions to achieve highest efficiency and effectiveness of operations. We develop and demonstrate through a proof-of-concept case study, a new ‘‘query-driven’’ heuristics for database design that seeks to identify database structures that perform robustly in dynamic settings with dynamic queries. Our focus is the design of efficient structures to process read-only queries in complex environments. Our heuristics begins with detailed analysis of relationships between diverse queries and the performance of different database structures. These relationships are then used in a series of steps that identify ‘‘robust’’ database structures that maintain high performance levels for a wide range of query patterns. We conjecture that our heuristics can facilitate efficient operations and effective decision-making of companies in todays dynamic environment. � 2004 Elsevier B.V. All rights reserved.
30968721
Tracking Deformable Templates Using a Shortest Path Algorithm
This paper proposes a new technique to track deformable templates. We extend the typical graph algorithms that have been used for active contour recovery to incorporate shape information. The advantage of graph algorithms is that they are guaranteed to find the global minimum of the energy function. The difficulty with their traditional use for active contours is that they consider only two pixels at a time when recovering the contour, making it impossible to enforce shape constraints. We define the deformable template as a polygonal contour, demonstrate the proper mapping between the image, the contour, and a graph, and show how to apply Dijkstra's algorithm to track contours in image sequences. Examples are shown for deforming contours, articulated objects, and smooth contours being tracked in simple and complicated backgrounds. We also provide an analysis of the computational requirements.
2044368
AGATHA: An Integrated Expert System to Test and Diagnose Complex Personal Computer Boards
PA-RISC is Hewlett-Packard's (HP) reduced instruction set computer (RISC) architecture that is used in its high-performance computer systems (Mahon et al. 1986). Implementations of this architecture have produced some of the most complex processor boards that HP makes (Robinson et al. 1987; Gassman et al. 1987): They can contain as many as 8 very large scale integrated (VLSI) chips—most of them custom, from central processing units to bus controllers to floating-point pro-cessors—several high-speed random-access memory arrays, one or more high-speed buses with over 100 lines, and many other components. In large part because of this complexity, the testing of PA-RISC processor boards became a bottleneck, resulting in an undesirable backlog of undiagnosed boards, growing at a rate of 10 percent each month.
62972305
On the complexity of computation and learning in neural networks
This dissertation is on the complexity of computation and learning in Neural Networks. One of the reasons for studying neural networks is that they provide a computational model which seems closer to the computational framework employed by the human brain for information processing than the conventional computers. Neural networks are highly simplified model of the human brain and one hopes that by attaining a proper understanding of the computational aspects in neural networks, one can gain significant insights into the computational characteristics of the human brain. 
In this dissertation, we have considered three different kinds of neural networks, namely, the Boltzmann Machines, the Hopfield Nets and the Feedforward Neural Nets. We studied the computational and learning abilities of these networks from a complexity theory point of view. We also related the computational power of various neural nets, in particular, the Boltzmann Machines and the Feedforward Neural Nets which are also known as Threshold circuits in the complexity theory literature.
270982
A Method for the Efficient Design of Boltzmann Machines for Classiffication Problems
We introduce a method for the efficient design of a Boltzmann machine (or a Hopfield net) that computes an arbitrary given Boolean function f. This method is based on an efficient simulation of acyclic circuits with threshold gates by Boltzmann machines. As a consequence we can show that various concrete Boolean functions f that are relevant for classification problems can be computed by scalable Boltzmann machines that are guaranteed to converge to their global maximum configuration with high probability after constantly many steps.