Maintenance scheduling of generating units is very important for the reliable operation of units. This paper presents a hybrid evolutionary algorithm to tackle the Generator Maintenance Scheduling (GMS) problem. The paper assumes a reliability objective function for the GMS problem. A new local search method which is derived from Extremal Optimization (EO) and Genetic Algorithm (GA) is presented. The proposed method, Hill Climbing Technique (HCT) and EO are applied to different location in GA. The selected locations are initial population, mating pool, in the offspring created by the crossover operator and in the offspring created by the mutation operator. Combination of the proposed method with HCT is also applied to the selected locations in the GA. The discussed methods are applied to a test case study and implementation and performance of the applied methods are presented. The obtained results show that the proposed method in combination with HCT yields the best results in comparison with other local search methods.

a b s t r a c tThe need for intelligent HCI has been reinforced by the increasing numbers of human-centered applications in our daily life. However, in order to respond adequately, intelligent applications must first interpret users' actions. Identifying the context in which users' interactions occur is an important step toward automatic interpretation of behavior. In order to address a part of this context-sensing problem, we propose a generic and application-independent framework for activity recognition of users interacting with a computer interface. Our approach uses Layered Hidden Markov Models (LHMM) and is based on eye-gaze movements along with keyboard and mouse interactions. The main contribution of the proposed framework is the ability to relate users' interactions to a task model in variant applications and for different monitoring purposes. Experimental results from two user studies show that our activity recognition technique is able to achieve good predictive accuracy with a relatively small amount of training data.

In the field of e-learning, a popular solution to make teaching material reusable is to represent it as learning object (LO). However, building better adaptive educational software also takes an explicit model of the learner's cognitive process related to LOs. This paper presents a three layers model that explicitly connect the description of learners' cognitive processes to LOs. The first layer describes the knowledge from a logical and ontological perspective. The second describes cognitive processes. The third builds LOs upon the two first layers. The proposed model has been successfully implemented in an intelligent tutoring system for teaching Boolean reduction that provides highly tailored instruction thanks to the model.

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The article describes a recognition approach of undertaken activities of daily living (ADLs) performed by memory and/or cognitively impaired elders in smart homes. The proposed technique is materialized via a recognition module inserted in a modular generic architecture which aims to offer a framework to conceive intelligent ADLs assistance systems.

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