To avoid hidden safety problems in future large scale systems, we must be able to identify the crucial assumptions underlying the development of their components and to enunciate straightforward rules for safe component interconnection. Keyword Codes: K.4.1; K.6.5; J.7

Classification is one of the data mining problems receiving great attention recently in the database community. This paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or inte...

In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and ob...

Knowledge-based neural networks are networks whose topology is determined by mapping the dependencies of a domain-speciic rulebase into a neural network. However, existing network training methods lack the ability to add new rules to the (reformulated) rulebases. Thus, on domain theories that are lacking rules, generalization is poor, and training can corrupt the original rules, even those that...

Inducing association rules is one of the central tasks in data mining applications. Quantitative association rules induced from databases describe rich and hidden relationships to be found within data that can prove useful for various application purposes (e.g., market basket analysis, customer profiling, and others). Although association rules are quite widely used in practice, a thorough anal...

Classification is one of the data mining problems receiving great attention recently in the database community. This paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or inte...

In this article, we study the mass spectrum of the scalar hidden charmed and bottomed tetraquark states with the QCD sum rules. The numerical results are compared with the corresponding ones from the potential quark model. PACS number: 12.39.Mk, 12.38.Lg

We introduce a new family of judgment aggregation rules, called the binomial rules, designed to account for hidden dependencies between some of the issues being judged. To place them within the landscape of judgment aggregation rules, we analyse both their axiomatic properties and their computational complexity, and we show that they contain both the well-known distance-based rule and the basic...

This paper describes about the development of a two stage hybrid Named Entity Recognition (NER) system for Indian Languages particularly for Hindi, Oriya, Bengali and Telugu. We have used both statistical Maximum Entropy Model (MaxEnt) and Hidden Markov Model (HMM) in this system. We have used variety of features and contextual information for predicting the various Named Entity (NE) classes. T...

Many models of meta-stable supersymmetry (SUSY) breaking lead to a very light scalar pseudo-Nambu Goldstone boson (PNGB), P, associated with spontaneous breakdown of a baryon number like symmetry in the hidden sector. Current particle physics data provide no useful constraints on the existence of P. For example, the predicted decay rates for both K → π + P and Υ → γ + P are many orders of magni...