A C++ Frame Library : User Manual and Implementation Notes

This document briefly describes FrameLib, a C++ library to manipulate "frames" as they are used in the Artificial Intelligence world. The library provides a general framework (!) for creating new frames (!!) by inheritance. The intended use is to generate new frames automatically, from a frame description language. But nothing prevents from using this library "manually". This document should be sufficient for directly using the library or for using it on an automatic generation basis. It also presents some design issues for those who are interested in the guts of the library. Note that FrameLib requires a C++ compiler supporting templates, ANSI exceptions, and RTTI (Run Time Type Information

Dynamic Reconfiguration of Feature Models: an Algorithm and its Evaluation

This paper deals with dynamic adaption of software architecture in response to context changes. In the line of “models at run time”, we keep a model of the system and its context in parallel with the running system itself. We adopted an enriched Feature Model approach to express the variability of the architecture as well as of the context. A context change is transformed into a set of feature modifications (selection/deselection) that we validate against the feature model to yield a new suitable and valid architecture configuration. Then we update the model view of the configuration and the running system architecture accordingly. The paper focuses on the feature model reconfiguration step and details the algorithms and heuristics that implement our adaptation rules. The approach is illustrated with a simple example borrowed from the videosurveillance domain. The efficiency of the algorithm is evaluated on randomly generated feature models (from 60 to 1400 features). Our results show that in our target applications (video analysis), the processing time of a context change may be considered negligible

Tackling High Variability in Video Surveillance Systems through a Model Transformation Approach

taux acceptation 44%International audienceThis work explores how model-driven engineering techniques can support the configuration of systems in domains presenting multiple variability factors. Video surveillance is a good candidate for which we have an extensive experience. Ultimately, we wish to automatically generate a software component assembly from an application specification, using model to model transformations. The challenge is to cope with variability both at the specification and at the implementation levels. Our approach advocates a clear separation of concerns. More precisely, we propose two feature models, one for task specification and the other for software components. The first model can be transformed into one or several valid component configurations through step-wise specialization. This paper outlines our approach, focusing on the two feature models and their relations. We particularly insist on variability and constraint modeling in order to achieve the mapping from domain variability to software variability through model transformations

Towards Formalizing Behavorial Substitutability in Component Frameworks

International audienceWhen using a component framework, developers need to respect the behavior implemented by the components. Static information about the component interface is not sufficient. Dynamic information such as the description of valid sequences of operations is required. In this paper we propose a mathematical model and a formal language to describe the knowledge about behavior. We rely on a hierarchical model of deterministic finite state-machines. The execution model of these state-machines follows the Synchronous Paradigm. We focus on extension of components, owing to the notion of behavioral substitutability. A formal semantics for the language is defined and a composition-ality result allows us to get modular model-checking facilities. From the language and the model, we can draw practical design rules that are sufficient to preserve behavorial substitutability. Associated tools may ensure correct (re)use of components, as well as automatic simulation and verification , code generation, and run-time checks

A Behavioral Model of Component Frameworks

When using a component framework developers need to respect the behavior implemented by the components. Static information about the component interface is not sufficient. Dynamic information such as the description of valid sequences of operations is required. Instead of being in some external documentation, this information should be formally represented and embedded within the components themselves, so that it can be used by automatic tools. We propose a mathematical model and a formal language to describe the knowledge about behavior. We rely on a hierarchical model of deterministic finite state-machines. The communication between the machines follows the Synchronous Paradigm. We favor a structural approach allowing incremental simulation, automatic verification, code generation, and run-time checks. Associated tools may ensure correct and safe reuse of the components. We focus on extension of components through inheritance (in the sense of sub-typing), owing to the notion of behavioral refinement

BLOCKS, a Component Framework with Checking Facilities for Knowledge-Based Systems

équipe PULSARInternational audienceBLOCKS is an answer to the software engineering needs of the design of knowledge-based system engines. It is a framework composed of reusable and adaptable software components. However , its safe and correct use is complex and we supply formal models and associated tools to assist using it. These models and tools are based on behavioral description of components and on model checking techniques. They ensure a safe reuse of the components, especially when extending them through inheritance, owing to the notion of behavioral refinement

Girgit: A Lightweight Framework for building Dynamically Adaptive Systems

Many modern systems must run in continually changing context. For example, a computer vision system to detect vandalism in train stations must function during the day and at night. The software components for image acquisition and people detection used during daytime may not be the same as those used at night.The system must adapt to the changing context by replacing running components such as image acquisition from color to infra-red. This adaptation involves context detection, decision on change in components, followed by seamless execution of a new configuration of components. All this must occur while minimizing the impact of dynamic change on continuity and loss in performance. We present Girgit, a lightweight Python-based framework for building dynamic adaptive systems. We evaluate it by building a dynamically adaptive vision system followed by performing experiments to determine its continuity and performance.Sociedad Argentina de Informática e Investigación Operativ

Tackling High Variability in Video Surveillance Systems through a Model Transformation Approach

taux acceptation 44%International audienceThis work explores how model-driven engineering techniques can support the configuration of systems in domains presenting multiple variability factors. Video surveillance is a good candidate for which we have an extensive experience. Ultimately, we wish to automatically generate a software component assembly from an application specification, using model to model transformations. The challenge is to cope with variability both at the specification and at the implementation levels. Our approach advocates a clear separation of concerns. More precisely, we propose two feature models, one for task specification and the other for software components. The first model can be transformed into one or several valid component configurations through step-wise specialization. This paper outlines our approach, focusing on the two feature models and their relations. We particularly insist on variability and constraint modeling in order to achieve the mapping from domain variability to software variability through model transformations

Towards Lightweight Dynamic Adaptation : A Framework and its Evaluation

Many modern systems must run in continually changing contexts. For example, a computer vision system to detect vandalism in train stations must function during the day and at night. The software components for image acquisition and people detection used during daytime may not be the same as those used at night. The system must adapt by replacing running components such as image acquisition from color to infra-red. This adaptation involves context detection, decision on change in components, followed by seamless execution of a new configuration of components. All this must occur at runtime while minimizing the impact of dynamic change on continuity and loss in performance. We present Girgit, a lightweight Python-based framework for building dynamic adaptive software systems. We evaluate it by building a dynamically adaptive vision system followed by performing rigorous experiments to determine its continuity and performance.Sociedad Argentina de Informática e Investigación Operativ

Reconnaissance d'Activités Probabilistes pour des Jeux Sérieux à Application Médicale

International audienceHuman activity recognition plays an important role especially in medical applications. This paper proposes a formal approach to model such activities, taking into account possible variations in human behavior. Starting from an activity description enriched with event occurrence probabilities, we translate it into a corresponding formal model based on discrete-time Markov chains (DTMCs). We use the PRISM framework and its model checking facilities to express and check interesting temporal logic properties (PCTL) concerning the dynamic evolution of activities. We illustrate our approach on the model of a serious game used by clinicians to monitor Alzheimer patients. We expect that such a modeling approach could provide new indications for interpreting patient performances. This paper addresses only the model definition and its suitability to check behavioral properties of interest. Indeed, this is mandatory before envisioning any clinical study.La reconnaissance d'activités humaines joue un rôle important, particulièrement dans les applications médicales. Cet article propose une approche formelle pour modéliser ces activités en prenant en compte la variabilité des comportements humains. En partant d'une description d'activité enrichie avec des probabilités sur l’occurrence d'évènements, nous la traduisons en un modèle formel fondé sur les chaînes de Markov à temps discret (DTMCs). Nous utilisons l'environnement de PRISM et son model checker pour exprimer et vérifier des propriétés d'intérêt en logique temporelle (PCTL) concernant l'évolution dynamique de l'activité. Nous illustrons notre approche avec le modèle d'un jeu sérieux utilisé par les cliniciens pour évaluer des patients atteints d'Alzheimer. Nous pensons que cette approche de modélisation peut apporter de nouvelles indications pour interpréter les performances des patients. Cet article ne se concentre que sur la définition du modèle et sa capacité à valider des propriétés d'intérêts. Cet étape est un passage obligé avant d'envisager des tests cliniques