Intelligent route generation: discovery and search of correlation between shared resources

Abstract

Sharing information and resources on the Internet has become an important activity for education. The use of ubiquitous devices makes it possible for learning participants to be engaged in an open and connected social environment, and also allows the learning activities to be performed at any time and any place. In this study, the discovery of correlation among shared resources is concentrated. A hypothetical scenario is considered that the information, such as photos and thoughts, is applicable to be shared with implicit context (i.e., geographical information) by learners through a practical implementation, PadSCORM, on a mobile device. Two major contributions are achieved. First, the correlations among resources are determined through usage experiences mining and geographical information adjustment. It then assists learners in filtering out redundant information by highlighting the significance of resources. Second, an intelligent searching algorithm is proposed to visualize adaptive routes to facilitate search process and to enrich the learning activity. The empirical experiments revealing the feasibility and performance (e.g., accuracy and effectiveness) are conducted in the universities in North Taiwan.Social network analysis concentrates on the discovery of implicit correlation via the explicit exchange of resources among participants. The participant, including the human user and/or object,

Evaluation of Device-Independent Internet Spatial Location

“…The location is estimated based on the knowledge of device IP address. Such IP-based location is used for a great number of location-aware services and applications, including web content and social network personalization [1]; user behaviour analysis [2] (including visitor maps for websites); load balancing by redirecting users to geographically close data/resource replicas [3]; geographical-based data collection from a large number of users [4]; spam filtering by sender location; detection of ID and password sharing; detection of credit card online fraud [5]; and law enforcement on media distribution by delivery restrictions.…”

Building the Multi-layer Theory of Association Semantic based on the Power-law Distribution of Linking Keywords

“…With the rapid development of information technology, human kinds are more likely to read and share information by similar intelligent applications. For example, the distributed and collaborative learning [7], semantic representation of scientific documents for supporting elearning [8], discovering and searching of correlation between shared resources [9], and smart component technologies for human centric computing [10],etc.…”

Sampling from social network to maintain community structure

“…The other is 'the internal nodes interconnect closely; nodes between the communities interconnect sparsely' [2]. However, there is a common problem in these algorithm is that they cannot assure the precision and speed simultaneously, the community detection algorithm which has a low precision does not help us analyze the community structure much, yet some algorithms with high precision may not apply to such large scale of datasets [11,23,24]. Community detection algorithm, as the method that can reveal the network community structure precisely, has been researched extensively and deeply in the past decades.…”