By Liming Chen, Chris D. Nugent, Jit Biswas, Jesse Hoey
This publication contains a couple of chapters addressing assorted elements of job reputation, approximately in 3 major different types of themes. the 1st subject should be enthusiastic about task modeling, illustration and reasoning utilizing mathematical versions, wisdom illustration formalisms and AI recommendations. the second one subject will pay attention to job acceptance equipment and algorithms. except conventional equipment in line with facts mining and computer studying, we're really attracted to novel ways, similar to the ontology-based method, that facilitate information integration, sharing and automatic/automated processing. within the 3rd subject we intend to hide novel architectures and frameworks for job reputation, that are scalable and appropriate to giant scale dispensed dynamic environments. additionally, this subject also will comprise the underpinning technological infrastructure, i.e. instruments and APIs, that helps function/capability sharing and reuse, and fast improvement and deployment of technological ideas. The fourth classification of subject can be devoted to consultant functions of task popularity in clever environments, which deal with the existence cycle of task popularity and their use for novel services of the end-user structures with entire implementation, prototyping and review. this may contain quite a lot of program situations, reminiscent of clever houses, clever convention venues and cars.
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Extra info for Activity Recognition in Pervasive Intelligent Environments
The results which are returned are displayed in the Super classes, Sub classes, Descendant classes, Instances and Equivalent classes panes, which can be interpreted as follows: • If a class in the Super classes pane is exactly the same as the one in the Sub classes pane, then the class can be regarded as the ongoing ADL. • If a class in the Super classes is different from the one in the Sub classes, then the inferred ADL can be viewed as an intermediate ADL between a superclass and a subclass. In our example, the situation can be interpreted as that a type of MakeHotDrink ADL has been performed.
22, No. 8, pp. 747– 757. , (2003), Vision based human tracking and activity recognition, In Proceedings of the 11th Mediterranean Conference on Control and Automation. , (2008), Multi-Camera Human Activity Monitoring, Journal of Intelligent and Robotic Systems, Vol. 52, No. 1, pp. 5–43.  Bao L. , (2004), Activity recognition from userannotated acceleration data, In 28 Activity Recognition in Pervasive Intelligent Environments Proc. Pervasive, LNCS3001, pp. 1–17. , (2008), Human Activity Recognition with Wearable Sensors, PhD thesis, TU Darmstadt.
But they all are normal activities. Another kind of activity that we should pay attention is abnormal activity. Detecting abnormal activities is a particularly important task in security monitoring and healthcare applications. Nevertheless it is challenging to solve the problem. First what is an abnormal activity; we might have a variety of definitions. For instance everyone do activity A, one person does activity B; we can call it an abnormal activity. Yin et al.  defined abnormal activities as events that occur rarely and have not been expected in advance.