Issues on Context Modelling

Abstract. Context modelling is a subject that has attracted much attention in recent years. Currently most of the effort is directed in developing rather specific models for various domain applications. In this paper we take a more generic view of the problem and even though we do not present a concrete model we provide a framework as a starting point for building context models for a variety of domain applications. We discuss in some detail core issues that arise in context modelling.

An Attention-Based Architecture for Context Switch Detection

Interaction Design, System Infrastructures and Applications for Smart EnvironmentsIn the era of Pervasive Computing (Weiser 1991), software applications, hidden in information appliances (Birnbaum 1997), will be continuously running, in an invisible manner (Weiser 1993), aiming at the best fulfilment of human users’ needs. These applications should be characterized by interaction transparency and context-awareness (Abowd 1999). Interaction transparency means that the human users are not aware that there is a computing module embedded in a tool or device that they are using. It contrasts with the actual transparency of current interactions with computers: both traditional input-output devices such as mice and keyboards and manipulations such as launching browsers and entering authentication information (by using a login and a password) are purely computer oriented. Context awareness refers to adaptation of the behaviour of an application depending on its current environment. This environment can be characterized as a physical location, an orientation or a user profile. A context-aware application can sense the environment and interpret the events that occur within it. Sensing the environment is very important for adapting the provided to the user services

Leaf Disease Recognition in Vine Plants Based on Local Binary Patterns and One Class Support Vector Machines

Part 7: Optimization-SVM (OPSVM)International audienceThe current application concerns a new approach for disease recognition of vine leaves based on Local Binary Patterns (LBPs). The LBP approach was applied on color digital pictures with a natural complex background that contained infected leaves. The pictures were captured with a smartphone camera from vine plants. A 32-bin histogram was calculated by the LBP characteristic features that resulted from a Hue plane. Moreover, four One Class Support Vector Machines (OCSVMs) were trained with a training set of 8 pictures from each disease including healthy, Powdery Mildew and Black Rot and Downy Mildew. The trained OCSVMs were tested with 100 infected vine leaf pictures corresponding to each disease which were capable of generalizing correctly, when presented with vine leave which was infected by the same disease. The recognition percentage reached 97 %, 95 % and 93 % for each disease respectively while healthy plants were recognized with an accuracy rate of 100 %

Drawing attention to the dangerous

ICANN/ICONIP 2003,Istanbul,TurkeyIn this paper we present an architecture of attention-based control for artificial agents. The agent is responsible for monitoring adaptively the user in order to detect context switches in his state. Assuming a successful detection appropriate action will be taken. Simulation results based on a simple scenario show that Attention is an appropriate mechanism for implementing context switch detector systems

Attention-driven artificial agents

In many (if not all) of the domains that are related with machines’ interaction with humans the human model is considered as the ideal prototype. Adaptation of the behaviour of an application as a function of its current environment known as context awareness is clearly one of these domains. The environment can be characterized as a physical location, an orientation or a user profile. A context-aware application can sense the environment and interpret the events that occur within. In this paper we present an attention-based model, inspired from the human brain, for constructing artificial agents. In this model adaptation is achieved through focusing to irregular patterns, so as to identify possible context switches, and adapting the behaviour goals accordingly. Simulation results, obtained using a health-monitoring scenario, are presented showing the efficiency of the proposed model