Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surrounding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an average accuracy of 96.60%.

This paper is concerned with an ITS designed for augmenting a student's capability in problem solving. Discussions are concentrated on helping students acquire strategic knowledge and assisting them to build it in their heads. In this paper, many kinds of strategies are treated from a unified point of view. Based on this consideration, a teaching paradigm of strategic knowledge is presented. The paradigm is realized in an ITS as a training environment for strategic knowledge. Assisting students to learn strategic knowledge, the system sets up an appropriate environment and gives them some appropriate advice in each environment. It is realized as a function of giving them appropriate problems and hints about it. In general, strategic knowledge is a kind of heuristics so that it is not easy to describe their application conditions deterministically and explicitly. For this reason, an ITS for strategic knowledge is required to be designed so as to cover not only the case where expertise is represented explicitly as an executable model but also the case where it is represented only implicitly. To realize this teaching paradigm, situation-dependent knowledge called reminding pattern is prepared in the system. It is represented by a triple of a strategy, a situation, and a key symbol in the situation. It denotes that the key usually reminds students of the strategy in the situation. The system gives students problems including positive/negative examples of applications of each strategy in its problem solving process and hints which remind them of an appropriate strategy and makes them resume the problem solving when they fall into an impasse. In this paper, the structure of the system realizing this teaching paradigm is explained in the domain of proving propositional formulas.

Creating an ontology from multidisciplinary knowledge is a challenge because it needs a number of various domain experts to collaborate in knowledge construction and verify the semantic meanings of the cross-domain concepts. Confusions and misinterpretations of concepts during knowledge creation are usually caused by having different perspectives and different business goals from different domain experts. In this paper, we propose a community-driven ontology-based application management (CD-OAM) framework that provides a collaborative environment with supporting features to enable collaborative knowledge creation. It can also reduce confusions and misinterpretations among domain stakeholders during knowledge construction process. We selected one of the multidisciplinary domains, which is Life Cycle Assessment (LCA) for our scenario-based knowledge construction. Constructing the LCA knowledge requires many concepts from various fields including environment protection, economic development, social development, etc. The output of this collaborative knowledge construction is called MLCA (multidisciplinary LCA) ontology. Based on our scenario-based experiment, it shows that CD-OAM framework can support the collaborative activities for MLCA knowledge construction and also reduce confusions and misinterpretations of cross-domain concepts that usually presents in general approach.

Student contradictions are the essentials of concepts and knowledge acquisition processes of a student, in the course of tutoring. This paper presents a new perspective to represent student contradictions and a student modeling architecture to capture them. The formulation of a student modeling mechanism enables flexible decision making by using information obtained from students. A nonmonotonic and inductive student model inference system HSMIS has been developed and formulated to cope with modeling contradictions, which basically embodies advanced representation power, sufficiently high adaptability and generality. The HSMIS is evaluated and compared with other representative systems in order to demonstrate its effectiveness.