By Liming Chen, Chris D. Nugent, Jit Biswas, Jesse Hoey
This publication contains a couple of chapters addressing various points of job attractiveness, approximately in 3 major different types of themes. the 1st subject should be inquisitive about job modeling, illustration and reasoning utilizing mathematical versions, wisdom illustration formalisms and AI strategies. the second one subject will pay attention to task reputation equipment and algorithms. except conventional tools in line with facts mining and desktop studying, we're quite drawn to novel ways, akin to the ontology-based procedure, that facilitate facts integration, sharing and automatic/automated processing. within the 3rd subject we intend to hide novel architectures and frameworks for job attractiveness, that are scalable and acceptable to massive scale dispensed dynamic environments. additionally, this subject also will contain the underpinning technological infrastructure, i.e. instruments and APIs, that helps function/capability sharing and reuse, and fast improvement and deployment of technological suggestions. The fourth type of subject might be devoted to consultant purposes of task reputation in clever environments, which handle the lifestyles cycle of task attractiveness and their use for novel capabilities of the end-user platforms with entire implementation, prototyping and overview. this can comprise quite a lot of software situations, similar to shrewdpermanent houses, clever convention venues and cars.
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Additional info for Activity Recognition in Pervasive Intelligent Environments
Objects, environment elements and events, facilitates interoperability, reusability and portability of the models between different systems and application domains. 3 Semantic sensor metadata creation In a SH sensor data are generated continuously, and activity assistance needs to be provided dynamically, both along a timeline. This requires that semantic enrichment of sensor data should be done in real time so that the activity inference can take place. To this end, domain specific dedicated lightweight annotation mechanisms and tools are required.
We first input the situational context described above into the class expression pane using a simplified OWL DL query syntax. When the Execute button is pressed, the specified context is passed onto the backend FaCT++ reasoner to reason against the ontological ADL models. 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.
Sensor-Based Human Activity Recognition in a Multi-user Scenario, AmI 2009, LNCS 5859, pp. 78–87, 2009. , Recognizing independent and joint activities among multiple residents in smart environments. Ambient Intelligence and Humanized Computing Journal, 1(1):57–63, 2010. , homeML - An open standard for the exchange of data within smart environments, Proceedings of 5th International Conference on Smart homes and health Telematics, Lecture Notes in Computer Science (Vol. 4541), pp. 121–129, 2007.