LGAug 14, 2019
Towards Linearization Machine Learning AlgorithmsSteve Tueno
This paper is about a machine learning approach based on the multilinear projection of an unknown function (or probability distribution) to be estimated towards a linear (or multilinear) dimensional space E'. The proposal transforms the problem of predicting the target of an observation x into a problem of determining a consensus among the k nearest neighbors of x's image within the dimensional space E'. The algorithms that concretize it allow both regression and binary classification. Implementations carried out using Scala/Spark and assessed on a dozen LIBSVM datasets have demonstrated improvements in prediction accuracies in comparison with other prediction algorithms implemented within Spark MLLib such as multilayer perceptrons, logistic regression classifiers and random forests.
SEDec 20, 2017
Formal Representation of SysML/KAOS Domain Model (Complete Version)Steve Tueno, Régine Laleau, Amel Mammar et al.
Nowadays, the usefulness of a formal language for ensuring the consistency of requirements is well established. The work presented here is part of the definition of a formally-grounded, model-based requirements engineering method for critical and complex systems. Requirements are captured through the SysML/KAOS method and the targeted formal specification is written using the Event-B method. Firstly, an Event-B skeleton is produced from the goal hierarchy provided by the SysML/KAOS goal model. This skeleton is then completed in a second step by the Event-B specification obtained from system application domain properties that gives rise to the system structure. Considering that the domain is represented using ontologies through the SysML/KAOS Domain Model method, is it possible to automatically produce the structural part of system Event-B models ? This paper proposes a set of generic rules that translate SysML/KAOS domain ontologies into an Event-B specification. The rules have been expressed, verified and validated through the Rodin tool using the Event-B method. They are illustrated through a case study dealing with a landing gear system. Our proposition makes it possible to automatically obtain, from a representation of the system application domain in the form of ontologies, the structural part of the Event-B specification which will be used to formally validate the consistency of system requirements.
SEOct 2, 2017
The SysML/KAOS Domain Modeling ApproachSteve Tueno, Régine Laleau, Amel Mammar et al.
A means of building safe critical systems consists of formally modeling the requirements formulated by stakeholders and ensuring their consistency with respect to application domain properties. This paper proposes a metamodel for an ontology modeling formalism based on OWL and PLIB. This modeling formalism is part of a method for modeling the domain of systems whose requirements are captured through SysML/KAOS. The formal semantics of SysML/KAOS goals are represented using Event-B specifications. Goals provide the set of events, while domain models will provide the structure of the system state of the Event-B specification. Our proposal is illustrated through a case study dealing with a Cycab localization component specification. The case study deals with the specification of a localization software component that uses GPS,Wi-Fi and sensor technologies for the realtime localization of the Cycab vehicle, an autonomous ground transportation system designed to be robust and completely independent.