A Directed Hypergraph Approach for the Verification of Rule-Based Expert Systems.

Rule-based representation techniques have become popular for storing and manipulation of domain knowledge in expert systems. It is important that systems using such a representation are verified for accuracy before implementation. In recent years, graphical techniques have been found to provide a good framework for the detection of errors that may appear in a rule base. In this dissertation, we develop a technique that uses a directed hypergraph to accurately detect all the different types of errors that appear in a rule base. This technique overcomes limitations of existing graphical techniques that are unable to accurately detect all the errors that appear in a rule base, without misdiagnosing error-free instances. The directed hypergraph technique allows rules to be represented in a manner that clearly identifies complex dependencies across compound clauses in the rule base. Since connectivity across compound clauses are accurately represented, the verification procedure can detect errors in an accurate fashion. We have developed a verification procedure that uses the adjacency matrix of the directed hypergraph. The procedure detects different types of errors by using simple operations on the adjacency matrix. In practice, expert systems are often used to make inferences based on multiple observed facts. Most existing techniques have ignored this aspect, since the selection of valid combinations of rule antecedents from a large number of rule antecedents to be considered is difficult. To address this issue, the directed hypergraph technique has been extended to perform verification checks when sets of feasible multiple assertions are made available to the system. As the size of the rule base increases, execution of the algorithm can be hard due to storage and computational considerations. It has been empirically found that sets of rules in large rule bases are sufficiently separated to allow decomposition into smaller sets. The directed hypergraph technique has been enhanced to accurately detect all errors in large rule bases by performing verification checks over the smaller groups of rules separately, and propagating the results from one group to other linked groups

A machine learning approach with verification of predictions and assisted supervision for a rule-based network intrusion detection system

Network security is a branch of network management in which network intrusion detection systems provide attack detection features by monitorization of traffic data. Rule-based misuse detection systems use a set of rules or signatures to detect attacks that exploit a particular vulnerability. These rules have to be handcoded by experts to properly identify vulnerabilities, which results in misuse detection systems having limited extensibility. This paper proposes a machine learning layer on top of a rule-based misuse detection system that provides automatic generation of detection rules, prediction verification and assisted classification of new data. Our system offers an overall good performance, while adding an heuristic and adaptive approach to existing rule-based misuse detection systems

Fingerprint verification by fusion of optical and capacitive sensors

A few works have been presented so far on information fusion for fingerprint verification. None, however, have explicitly investigated the use of multi-sensor fusion, in other words, the integration of the information provided by multiple devices to capture fingerprint images. In this paper, a multi-sensor fingerprint verification system based on the fusion of optical and capacitive sensors is presented. Reported results show that such a multi-sensor system can perform better than traditional fingerprint matchers based on a single sensor. (C) 2004 Elsevier B.V. All rights reserved

An enhanced intelligent database engine by neural network and data mining

An Intelligent Database Engine (IDE) is developed to solve any classification problem by providing two integrated features: decision-making by a backpropagation (BP) neural network (NN) and decision support by Apriori, a data mining (DM) algorithm. Previous experimental results show the accuracy of NN (90%) and DM (60%) to be drastically distinct. Thus, efforts to improve DM accuracy is crucial to ensure a well-balanced hybrid architecture. The poor DM performance is caused by either too few rules or too many poor rules which are generated in the classifier. Thus, the first problem is curbed by generating multiple level rules, by incorporating multiple attribute support and level confidence to the initial Apriori. The second problem is tackled by implementing two strengthening procedures, confidence and Bayes verification to filter out the unpredictive rules. Experiments with more datasets are carried out to compare the performance of initial and improved Apriori. Great improvement is obtained for the latte

Overview of Proposed Exchange, Medicaid and IRS Regulations

Explains the implications of draft regulations on Medicaid, health insurance exchanges, and premium tax credits under healthcare reform, including eligibility criteria, enrollment, and verification; minimum essential coverage; and credit computation

Thread-Modular Static Analysis for Relaxed Memory Models

We propose a memory-model-aware static program analysis method for accurately analyzing the behavior of concurrent software running on processors with weak consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of our method is a unified framework for deciding the feasibility of inter-thread interferences to avoid propagating spurious data flows during static analysis and thus boost the performance of the static analyzer. We formulate the checking of interference feasibility as a set of Datalog rules which are both efficiently solvable and general enough to capture a range of hardware-level memory models. Compared to existing techniques, our method can significantly reduce the number of bogus alarms as well as unsound proofs. We implemented the method and evaluated it on a large set of multithreaded C programs. Our experiments showthe method significantly outperforms state-of-the-art techniques in terms of accuracy with only moderate run-time overhead.Comment: revised version of the ESEC/FSE 2017 pape