9351295
Looking Algebraically at Tractable Quantified Boolean Formulas
We make use of the algebraic theory that has been used to study the complexity of constraint satisfaction problems, to investigate tractable quantified boolean formulas. We present a pair of results: the first is a new and simple algebraic proof of the tractability of quantified 2-satisfiability; the second is a purely algebraic characterization of models for quantified Horn formulas that were given by Kleine Buning, Subramani, and Zhao, and described proof-theoretically.
958592
Inverse Circumscription
Inverse (or identification) problems involve deciding whether or not an explicitly given set of data points have an implicit description, for instance, in the form of a constraint network. Such problems provide insight into the relationships among various representations of knowledge, which may have differing computational properties. This paper formalizes and studies the inverse circumscription problem, which (roughly speaking) is to decide, given a set of models, if there exists a formula whose circumscription describes the input set.
1194024
Hierarchical classification of Web content
This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level.
We use support vector machine (SVM) classifiers, which have been shown to be efficient and effective for classification, but not previously explored in the context of hierarchical classification. We found small advantages in accuracy for hierarchical models over flat models. For the hierarchical approach, we found the same accuracy using a sequential Boolean decision rule and a multiplicative decision rule. Since the sequential approach is much more efficient, requiring only 14%-16% of the comparisons used in the other approaches, we find it to be a good choice for classifying text into large hierarchical structures.
9507980
Using build-integrated static checking to preserve correctness invariants
A key missing link in the creation of secure and robust systems is finding a ost effective way to demonstrate and preserve correspondence between a software design and its implementation. This paper explores the use of software model checking techniques to validate selected design invariants in the EROS operating system kernel. Several global consistency policies in the EROS kernel can be expressed as finite state automata. Using the MOPS static hecker, we have been able to validate the EROS kernel implementation against these automata. In the process, we have confirmed the practical utility of the basic verification technique, identified a number of desirable enhancements in MOPS, and located bugs in the EROS implementation.
 A key contribution of this paper is establishing that it is practical to integrate software model checking into normal development lifestyle. Model hecking is efficient enough that it does not add noticeably to our build times. This allows us to view it as a tool for error prevention rather than <i>detection</i>. Our work with EROS and MOPS suggests that domain specific application of software model hecking is a practical and powerful technique for software assurance and maintenance.
33677672
A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting
In this paper we present an application of hybrid neural network approaches and an assessment of the effects of missing data on motorway traffic flow forecasting. Two hybrid approaches are developed using a Self-Organising Map (SOM) to initially classify traffic into different states. The first hybrid approach includes four Auto-Regressive Integrated Moving Average (ARIMA) models, whilst the second uses two Multi-Layer Perception (MLP) models. It was found that the SOM/ARIMA hybrid approach out-performs all individual ARIMA models, whilst the SOM/MLP hybrid approach achieves superior forecasting performance to all models used in this study, including three naïve models. The effects of different proportions of missing data on Neural Network (NN) performance when forecasting traffic flow are assessed and several initial substitution options to replace missing data are discussed. Over-all, it is shown that ARIMA models are more sensitive to the percentage of missing data than neural networks in this context.
1463101
RDF/RDFS-based Relational Database Integration
We study the problem of answering queries through a RDF/RDFS ontology, given a set of view-based mappings between one or more relational schemas and this target ontology. Particularly, we consider a set of RDFS semantic constraints such as rdfs:subClassof, rdfs:subPropertyof, rdfs:domain, and rdfs:range, which are present in RDF model but neither XML nor relational models. We formally define the query semantics in such an integration scenario, and design a novel query rewriting algorithm to implement the semantics. On our approach, we highlight the important role played by RDF Blank Node in representing incomplete semantics of relational data. A set of semantic tools supporting relational data integration by RDF are also introduced. The approach have been used to integrate 70 relational databases at China Academy of Traditional Chinese Medicine.
6389142
Optimal confidence interval for the largest normal mean with unknown variance
A single-sample procedure for obtaining an optimal confidence interval for the largest or smallest mean of several independent normal populations is proposed. It is assumed that the common variance is unknown. It has been found that the optimal confidence interval is uniformly better than any other existing one-sample confidence interval in the sense of a reduced interval width. This optimal confidence interval is obtained by maximizing the coverage probability with the expected confidence width being fixed at a least favorable configuration of means. Tables of the critical values are given for the optimal confidence interval.