André Mayers

The Intelligent Habitat And Everyday Life Activity Support

Dementia causes cognitive deficits producing functional impairments. Continuous care and monitoring are thus compulsory to keep at home elders suffering from dementia. Intelligent habitat can play a central role toward a global and integrated solution and alleviate relatives from the care burden. The general idea is twofold. On the one hand, the physical environment could supplement elder cognitive impairments by providing personalized environmental cues that assist him in achieving his tasks. On the other hand, the intelligent house could maintain a link with relatives and medical care system to inform them of the evolution of the disease and to alert them in case of emergency. This paper shows how intelligent houses can deliver such cognitive assistance to elders, prolonging the time they can remain at home. First we derive the requirements for cognitive assistance by an intelligent habitat from the impact of the Alzheimer disease in the daily living of elders. Subsequently we describe the layered computer infrastructure needed to implement a distributed intelligent house information system. The implementation of such a pervasive system raises many issues that are not trivial from a computer science perspective. In this paper, we focus on modelling issues. Finally a simple scenario is used to exemplify the interactions between the intelligent house and the elders.

The Role Of Intelligent Habitats In Upholding Elders In Residence

The intelligent habitat is made of fixed components (movements detectors and intelligent electric household appliances) and small mobile processors worn by the elder. Fixed and mobile components communicate to assist the elder in performing his tasks and to intervene in case of risk. The system has two types of features: those carried out inside the residence (information acquisition, cognitive help like sound or visual cues when everyday life activity is carried out in an incomplete or dangerous way) and those reporting to the relatives and the external care network major risk events or evolution of the elder health state. The system intervention with the elder must be personalized according to the incurred risk gravity, his health state, his life habits and his preferred interaction mode: image, text, sound, voice ...

DHCC: Divisive hierarchical clustering of categorical data

Clustering categorical data poses two challenges defining an inherently meaningful similarity measure, and effectively dealing with clusters which are often embedded in different subspaces. In this paper, we propose a novel divisive hierarchical clustering algorithm for categorical data, named DHCC. We view the task of clustering categorical data from an optimization perspective, and propose effective procedures to initialize and refine the splitting of clusters. The initialization of the splitting is based on multiple correspondence analysis (MCA). We also devise a strategy for deciding when to terminate the splitting process. The proposed algorithm has five merits. First, due to its hierarchical nature, our algorithm yields a dendrogram representing nested groupings of patterns and similarity levels at different granularities. Second, it is parameter-free, fully automatic and, in particular, requires no assumption regarding the number of clusters. Third, it is independent of the order in which the data is processed. Fourth, it is scalable to large data sets. And finally, our algorithm is capable of seamlessly discovering clusters embedded in subspaces, thanks to its use of a novel data representation and Chi-square dissimilarity measures. Experiments on both synthetic and real data demonstrate the superior performance of our algorithm.

Personal bankruptcy prediction by mining credit card data

A personal bankruptcy prediction system running on credit card data is proposed. Personal bankruptcy, which usually results in significant losses to creditors, is a rapidly increasing yet little understood phenomenon. The most commonly used methods in personal bankruptcy prediction are credit scoring models. Some data mining models have also been investigated in this domain. Neither the scoring models nor the existing data mining methods adequately take sequence information in credit card data into account. In our system, sequence patterns, obtained by developing sequence mining techniques and applying them to credit card data from one major Canadian bank, are employed as main predictors. The mined sequence patterns, which we refer to as bankruptcy features, are represented in low-dimensional vector space. From the new feature space, which can be extended with some existing prediction-capable features (e.g., credit score), a support vector machine (SVM) classifier is built to combine these mined and already existing features. Our system is readily comprehensible and demonstrates promising prediction performance.

MIACE, a human cognitive architecture

Miace as a human cognitive architecture is a computational model that explains how a student acquires, encodes and uses domain knowledge. Because Miace takes into account the cognitive psychological laws and the environment in which the student works, it can be used as a virtual student in help systems dedicated to pedagogical formation, in intelligent tutoring systems, in cooperative learning applications and for the conception of didactic material. This paper describes the implementation of Miace and discusses the Miace theoretical components from three point of view: temporal, their roles in cognitive activity and their generic or functional forms. A comparison is done to show the originality and the contribution of Miace in user modeling.

Evaluating Spatial Representations and Skills in a Simulator-Based Tutoring System

Performing exercises in a simulation-based environment is a convenient and cost-effective way of learning spatial tasks. However, training systems that offer such environments lack models for the assessment of learners spatial representations and skills, which would allow the automatic generation of customized training scenarios and assistance. Our proposal aims at filling this gap by extending a model for representing learners cognitive processes in tutoring systems, based on findings from research on spatial cognition. This article describes how the model is applied to represent knowledge handled in complex and demanding tasks, namely, the manipulation of the robotic arm Canadarm2, and, more specifically, how a training system for Canadarm2 manipulation benefits from this model, both by its ability to assess spatial representations and skills and to generate customized assistance and exercises.

A Cognitive and Logic Based Model for Building Glass-Box Learning Objects

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.

Authoring Problem-Solving Tutors: A Comparison between ASTUS and CTAT

ASTUS is an Intelligent Tutoring System (ITS) framework for problem-solving domains. In this chapter we present a study we performed to evaluate the strengths and weaknesses of ASTUS compared to the well-known Cognitive Tutor Authoring Tools (CTAT) framework. To challenge their capacity to handle a comprehensive model of a well-defined task, we built a multi-column subtraction tutor (model and interface) with each framework. We incorporated into the model various pedagogically relevant procedural errors taken from the literature, to see how each framework deals with complex situations where remedial help may be needed. We successfully encoded the model with both frameworks and found situations in which we consider ASTUS to surpass CTAT. Examples of these include: ambiguous steps, errors with multiple (possibly correct) steps, composite errors, and off-path steps. Selected scenarios in the multi-column subtraction domain are presented to illustrate that ASTUS can show a more sophisticated behavior in these situations. ASTUS achieves this by relying on an examinable hierarchical knowledge representation system and a domain-independent MVC-based approach to build the tutors’ interface.

A New MCA-Based Divisive Hierarchical Algorithm for Clustering Categorical Data

Clustering categorical data faces two challenges, one is lacking of inherent similarity measure, and the other is that the clusters are prone to being embedded in different subspace. In this paper, we propose the first divisive hierarchical clustering algorithm for categorical data. The algorithm, which is based on Multiple Correspondence Analysis (MCA), is systematic, efficient and effective. In our algorithm, MCA plays an important role in analyzing the data globally. The proposed algorithm has five merits. First, our algorithm yields a dendrogram representing nested groupings of patterns and similarity levels at different granularities. Second, it is parameter-free, fully automatic and, most importantly, requires no assumption regarding the number of clusters. Third, it is independent of the order in which the data are processed. Forth, it is scalable to large data sets; and finally, using the novel data representation and Chi-square distance measures makes our algorithm capable of seamlessly discovering the clusters embedded in the subspaces. Experiments on both synthetic and real data demonstrate the superior performance of our algorithm.

How emotional mechanism helps episodic learning in a cognitive agent

In this paper we propose the CTS (Concious Tutoring System) technology, a biologically plausible cognitive agent based on human brain functions.This agent is capable of learning and remembering events and any related information such as corresponding procedures, stimuli and their emotional valences. Our proposed episodic memory and episodic learning mechanism are closer to the current multiple-trace theory in neuroscience, because they are inspired by it [5] contrary to other mechanisms that are incorporated in cognitive agents. This is because in our model emotions play a role in the encoding and remembering of events. This allows the agent to improve its behavior by remembering previously selected behaviors which are influenced by its emotional mechanism. Moreover, the architecture incorporates a realistic memory consolidation process based on a data mining algorithm.