Formal semantics offers a complete and rigorous definition of a language. It is important to define different semantic models for a language and different models serve different purposes. Building equivalence between different semantic models of a language strengthen its formal foundation. This paper shows the derivation of denotational semantics from operational semantics of the language cCSP. The aim is to show the correspondence between operational and trace semantics. We extract traces from operational rules and use induction over traces to show the correspondence between the two semantics of cCSP.
The semantic correspondence presented here is based on the technique of applying structural induction. A similar approach is also applied by S. Schneider @cite_11 , where an equivalence relation was established between the operational and denotational semantics of timed CSP @cite_0 @cite_12 . Operational rules are defined for timed CSP and then timed traces and refusals are extracted from the transition rules of a program, and it is shown that the pertinent information corresponds to the semantics obtained from the denotational semantic function. By applying structural induction over the terms of timed CSP, it was proved that the behaviour of the transition system is identical to those provided by the denotational semantics.
@cite_0 @cite_12 @cite_11
2037230951 1909063750 1499638282 1973673849
An operational semantics is defined for the language of timed CSP, in terms of two relations: an evolution relation, which describes when a process becomes another simply by allowing time to pass; and a timed transition relation, which describes when a process may become another by performing an action at a particular time. It is shown how the timed behaviours used as the basis for the denotational models of the language may be extracted from the operational semantics. Finally, the failures model for timed CSP is shown to be equivalent to may-testing and, thus, to trace congruence. In this paper we introduce a model for a wide class of computational systems, whose behaviour can be described by certain rewriting rules. We gathered our inspiration both from the world of term rewriting, in particular from the rewriting logic framework Mes92 , and of concurrency theory: among the others, the structured operational semantics Plo81 , the context systems LX90 and the structured transition systems CM92 approaches. Our model recollects many properties of these sources: first, it provides a compositional way to describe both the states and the sequences of transitions performed by a given system, stressing their distributed nature. Second, a suitable notion of typed proof allows to take into account also those formalisms relying on the notions of synchronization and side-effects to determine the actual behaviour of a system. Finally, an equivalence relation over sequences of transitions is defined, equipping the system under analysis with a concurrent semantics, where each equivalence class denotes a family of computationally equivalent'''' behaviours, intended to correspond to the execution of the same set of (causally unrelated) events. As a further abstraction step, our model is conveniently represented using double-categories: its operational semantics is recovered with a free construction, by means of a suitable adjunction. In this paper operational equivalence of simple functional programs is defined, and certain basic theorems proved thereupon. These basic theorems include congruence, least fixed-point, an analogue to continuity, and fixed-point induction. We then show how any ordering on programs for which these theorems hold can be easily extended to give a fully abstract cpo for the language, giving evidence that any operational semantics with these basic theorems proven is complete with respect to a denotational semantics. Furthermore, the mathematical tools used in the paper are minimal, the techniques should be applicable to a wide class of languages, and all proofs are constructive. In the field of infrastructures’ surveillance and protection, it is important to make decisions based on activities occurring in the environment and its local context and conditions. In this paper we use an active rule based event processing architecture in order to make sense of situations from the combination of different signals received by the rule engine. However obtaining some high level information automatically is not without risks, especially in sensitive environments, and detection mistakes can happen for various reasons: the signal’s source can be defective, whether it is human—miss-interpretation of the signal—or computed—material malfunction; the aggregation rules can be wrong syntaxically, for example when a rule will never be triggered or a situation never detected; the interpretation given to the combination of signals does not correspond to the reality on the field—because the knowledge of the rule designer is subjective or because the environment evolves over-time—the rules are therefore incorrect semantically. In this paper, a new approach is proposed to avoid the third kind of error sources. We present a hybrid machine learning technique adapted to the complexity of the rules’ representation, in order to create a system more conform to reality. The proposed approach uses a combination of an Association Rule Mining algorithm and Inductive Logic Programming for rule induction. Empirical studies on simulated datasets demonstrate how our method can contribute to sensible systems such as the security of a public or semi-public place.