By Soraya Kouadri Mostefaoui, Zakaria Maamar, George M. Giaglis
The improvement and availability of recent computing and communique units, and the elevated connectivity among those units, due to stressed out and instant networks, are allowing new possibilities for individuals to accomplish their operations wherever and every time. This technological growth has constructed a mess of demanding situations that call for additional learn.
,b>Advances in Ubiquitous Computing: destiny Paradigms and instructions investigates the know-how foreground of ubiquitous computing, the rising purposes and prone, in addition to yes social concerns which are important for the winning deployment of a ubiquitous computing software, via a multi-perspective analytical lens. offering teachers and practitioners with top of the range, authoritative content material on such subject matters as equipment layout, instant conversation, situation sensing, privateness matters, recognition concentration, multi-person interplay, and direct interplay, paintings styles, this best Reference resource is a must have addition to library collections all over the world.
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Extra info for Advances in Ubiquitous Computing: Future Paradigms and Directions
One reason is that POLPO supports dynamic downloading of new applications on mobile phones. The expandability of the software installed on the user’s device is a key factor in a pervasive computing environment. In fact, the set of services available to users must grow as new devices or software systems are added to the environment. If the functionalities of the system are statically defined, users are forced to upgrade their devices continuously. A second reason is that our system is based on the JME platform that eases the deployment of new services and makes it suitable, in the near future, for a much larger user base than the one afforded by other systems.
For instance, some inference rules associated with the learner ontology are used to infer pre-requisite knowledge and learner preferences, while rules for device ontology can infer information for content and presentation adaptation. Similarly, domain ontology rules are used to infer the necessary learning content, learning granularity, and the learning sequence. Rules from the three ontologies are applied in the order shown in the learning cycle given in Figure 6. Finally, the bottom layer includes the interaction manager which deals with the learner’s queries and controls the learning sequence.
The author emphasized that while concepts may be understandable to people, defining contexts for each user using ontologies Copyright © 2008, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Context-Aware Mobile Learning on the Semantic Web is a more complex problem. To solve the context modeling problem, the author proposed a solution based on the fact that user contexts can be easily classified and thus identified through unsupervised learning which processes the information available in the user’s world and builds up a representation of the user’s context.