This paper aims to address the problem of anomaly detection and discrimination in complex behaviours, where anomalies are subtle and difficult to detect owing to the complex temporal dynamics and correlations among multiple objects’ behaviours. Specifically, we decompose a complex behaviour pattern according to its temporal characteristics or spatial-temporal visual contexts. The decomposed beh...

In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. T...

This paper describes a novel method for allowing an autonomous ground vehicle to predict the intent of other agents in an urban environment. This method, termed the cognitive driving framework, models both the intent and the potentially false beliefs of an obstacle vehicle. By modeling the relationships between these variables as a dynamic Bayesian network, filtering can be performed to calcula...

In addition to being accurate, it is important that diagnostic systems for use in automobiles also have low development and hardware costs. Model-based methods have shown promise at reducing hardware costs since they use analytical redundancy to reduce physical redundancy. In addition to requiring no extra sensors, the diagnostic system presented in this paper also allows for high accuracy and ...

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Learning from observation (LfO), also known as learning from demonstration, studies how computers can learn to perform complex tasks by observing and thereafter imitating the performance of a human actor. Although there has been a significant amount of research in this area, there is no agreement on a unified terminology or evaluation procedure. In this paper, we present a theoretical framework...

Affective states play a significant role in students’ learning behaviour. Positive affective states can enhance learning, while negative ones can inhibit it. This paper describes the development of an affective state reasoner that is able to adapt the feedback type according to students’ affective states in order to evoke positive affective states and as such improve their learning experience. ...

In order to solve the problem of modeling and inference for a vast complicated system, a new concept of Hierarchy DDBN was proposed here through the layer analysis method, and worked out the inference algorithm of the Hierarchy DDBN based on the strict probability theory. In order to test the validity of the inference algorithm, a series of simulation were conducted. The result showed that the ...

HCI field began to see more studies in exploring not only the rational characteristics of human users in making decisions but also the “extra-rational” aspects in interacting with their environment and devices. The first task to exploit one such facet, human affect, is to accurately recognize and assess the affective state in real-time. This paper first serves as a survey of the state-of-the-ar...