Recent extensions of the Event Calculus resulted in powerful formalisms, able to reason about a multitude of commonsense phenomena in causal domains, involving epistemic notions, functional fluents and probabilistic aspects, among others. Surprisingly, little attention has been paid to the problem of automatically revising (correcting) a Knowledge Base when an observation contradicts prediction...

In this article we describe Professor, a new program for tuning model parameters of Monte Carlo event generators to experimental data by parameterising the per-bin generator response to parameter variations and numerically optimising the parameterised behaviour. Simulated experimental analysis data is obtained using the Rivet analysis toolkit. This paper presents the Professor procedure and imp...

People frequently underestimate how long it will take them to complete a task. The prevailing view is that during the prediction process, people incorrectly use their memories of how long similar tasks have taken in the past because they take an overly optimistic outlook. A variety of evidence is reviewed in this article that points to a different, although not mutually exclusive, explanation: ...

Event-based systems are a popular substrate for distributing information derived from sensors to be used in driving adaptive behaviour. We argue that event systems only provide a poor model of context, and that a hybrid approach that uses events to populate and maintain a knowledge base provides a more stable solution. The inherent uncertainties imply that traditional knowledgebased system tech...

Ontologies play a key role in the realization of the Semantic Web. An ontology is used as an explicit specification of a shared conceptualization of a given domain. When such a domain evolves, the describing ontology needs to evolve too. In this paper, we present an approach that allows tracing evolution on the instance level. We use event types as an abstraction mechanism to define the semanti...

The Event-based Aspect-Oriented Programming model (EAOP) makes it possible to define pointcuts in terms of sequences of events emitted by the base program. The current formalization of the model relies on a monolithic entity, the monitor, which observes the execution of the base program and executes the actions associated to the matching pointcut. This model is not intrinsically sequential but ...

The organization of the knowledge base for monitoring and the related principles for knowledge representation are presented. The knowledge base consists of conguration, sensor, dynamic and statistical databases. The con guration database represents the system architecture of the network. The sensor database is set up based on the sensor con guration in the network. The dynamic database represen...

In this paper, we propose an evolutionary emergency model of home network environment (EEMHNE). This model not only can do self-tuning to fit the home network environment but also detect the emergency event automatically. There are three modules in this model, namely, emergency process module (EPM), emergency knowledge base (EKB), and knowledge base evolution module (KBEM). EKB includes three c...

The A virtually linked knowledge base is designed to utilize a pre-constructed knowledge base in a dynamic mode when it is in actual use. An algorithm is proposed for causality reasoning based on a set of lexical knowledge bases that contain information about such items as event role, is-a hierarchy, relevant relation, antonym, and other features. These lexical knowledge bases have mainly made ...

Plasma endothelin 1 concentrations were determined in infants with meconium stained amniotic fluid. Plasma endothelin 1 concentration in the study group (5.23 pg/ml) was statistically higher than that in the control group (1.12 pg/ml). Cord blood pH and base excess values were significantly lower in infants with meconium stained amniotic fluid when compared with the control group. There was no ...