9884844
Parallel multilevel block ILU preconditioning techniques for large sparse linear systems
We present a class of parallel preconditioning strategies built on a multilevel block incomplete LU (ILU)factorization technique to solve large sparse linear systems on distributed memory parallel computers. The preconditioners are constructed by using the concept of block independent sets. Two algorithms for constructing block independent sets of a distributed sparse matrix are proposed. We compare a few implementations of the parallel multilevel ILU preconditioners with different block independent set algorithms and different coarse level solution strategies. We also use some diagonal thresholding and perturbation strategies to enhance factorization stability. Numerical experiments indicate that our parallel multilevel block ILU preconditioners are robust and efficient.
39282722
Parallel Multilevel Sparse Approximate Inverse Preconditioners in Large Sparse Matrix Computations
2595116
A Class of New Parallel Preconditioning Strategies for Solving Large Sparse Linear Systems
31845675
Alternating Fixpoint Theory for Logic Programs with Priority
van Gelder's alternating fixpoint theory has proven to be a very useful tool for unifying and characterizing various semantics for logic programs without priority. In this paper we propose an extension of van Gelder's alternating fixpoint theory and show that it can be used as a general semantic framework for logic programs with priority. Specifically, we define three declarative and model-theoretic semantics in this framework for prioritied logic programs: prioritized answer sets, prioritized regular extensions and prioritized well-founded model. We show that all of these semantics are natural generalizations of the corresponding semantics for logic programs without priority. We also show that these semantics have some other desirable properties. In particular, they can handle conflicts caused indirectly by the priorities.
4537262
A semantic framework for preference handling in answer set programming
We provide a semantic framework for preference handling in answer set programming. To this end, we introduce preference preserving consequence operators. The resulting fixpoint characterizations provide us with a uniform semantic framework for characterizing preference handling in existing approaches. Although our approach is extensible to other semantics by means of an alternating fixpoint theory, we focus here on the elaboration of preferences under answer set semantics. Alternatively, we show how these approaches can be characterized by the concept of order preservation. These uniform semantic characterizations provide us with new insights about inter-relationships and moreover about ways of implementation.
12496214
Anomaly Detection in Computer Security and an Application to File System Accesses
We present an overview of anomaly detection used in computer security, and provide a detailed example of a host-based Intrusion Detection System that monitors file systems to detect abnormal accesses. The File Wrapper Anomaly Detector (FWRAP) has two parts, a sensor that audits file systems, and an unsupervised machine learning system that computes normal models of those accesses. FWRAP employs the Probabilistic Anomaly Detection (PAD) algorithm previously reported in our work on Windows Registry Anomaly Detection. FWRAP represents a general approach to anomaly detection. The detector is first trained by operating the host computer for some amount of time and a model specific to the target machine is automatically computed by PAD. The model is then deployed to a real-time detector. In this paper we describe the feature set used to model file system accesses, and the performance results of a set of experiments using the sensor while attacking a Linux host with a variety of malware exploits. The PAD detector achieved impressive detection rates in some cases over 95% and about a 2% false positive rate when alarming on anomalous processes.
1889167
An Extension to GCWA and Query Evaluation for Disjunctive Deductive Databases
We present a simple and intuitive extension GCWAG of the generalized closed world assumption (GCWA) from positive disjunctive deductive databases to general disjunctive deductive databases (with default negation). This semantics is defined in terms of unfounded sets and possesses an argumentation-theoretic characterization. We also provide a top-down procedure for GCWAG, which is sound and complete with respect to GCWAG. We investigate two query evaluation methods for GCWAG: database partition, and database splitting. The basic idea of these methods is to divide the original deductive database into several smaller sub-databases and the query evaluation in the original database is transformed into the problem of query evaluation in smaller or simplified components. We prove that these two methods of query evaluation are all sound with respect to GCWAG.
12098916
Binary decision diagram with minimum expected path length
We present methods to generate a Binary Decision Diagram (BDD) with minimum expected path length. A BDD is a generic data structure which is widely used in several fields. One important application is the representation of Boolean functions. A BDD representation enables us to evaluate a Boolean function: Simply traverse the BDD from the root node to the terminal node and retrieve the value in the terminal node. For a BDD with minimum expected path length will be also minimized the evaluation time for the corresponding Boolean function. Three efficient algorithms for constructing BDDs with minimum expected path length are proposed.