36,680 research outputs found
A Context-Aware Framework to Provide Personalized Mobile Assessment
In mobile learning environment, context-aware systems refer to applications that employs contextual information to provide appropriate services to the leaners or other applications to perform a specific task. An important challenge in such applications is context modeling, using ontologies to model context information and to reason about context at a semantic level has attracted a lot of interest in the research community. Semantic Web technologies have been applied in recent years with different purposes in education. But, their applications for generating useful personalized mobile assessment resources have not been researched enough so far.In this paper, we introduce a context-aware approach that makes use of Semantic Web technologies to support personalized assessment in mobile environments. We propose a Service-based framework for bringing assessment techniques to mobile environment. We provide a formal description for our mobile assessment framework and detail the functionalities of its various layers. We have carried out also an experiment with computer science university students to evaluate our mobile assessment framework
Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G
By caching content at network edges close to the users, the content-centric
networking (CCN) has been considered to enforce efficient content retrieval and
distribution in the fifth generation (5G) networks. Due to the volume,
velocity, and variety of data generated by various 5G users, an urgent and
strategic issue is how to elevate the cognitive ability of the CCN to realize
context-awareness, timely response, and traffic offloading for 5G applications.
In this article, we envision that the fundamental work of designing a cognitive
CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to
associatively learn and control the states of edge devices (such as phones,
vehicles, and base stations) and in-network resources (computing, networking,
and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework
for C-CCN in 5G, which can aggregate the idle computing resources of the
neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive
learning tasks. By leveraging artificial intelligence (AI) to jointly
processing sensed environmental data, dealing with the massive content
statistics, and enforcing the mobility control at network edges, the FEL makes
it possible for mobile users to cognitively share their data over the C-CCN in
5G. To validate the feasibility of proposed framework, we design two
FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network
acceleration, 2) enhanced mobility management. Simultaneously, we present the
simulations to show the FEL's efficiency on serving for the mobile users'
delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201
Factors Influencing the Quality of the User Experience in Ubiquitous Recommender Systems
The use of mobile devices and the rapid growth of the internet and networking
infrastructure has brought the necessity of using Ubiquitous recommender
systems. However in mobile devices there are different factors that need to be
considered in order to get more useful recommendations and increase the quality
of the user experience. This paper gives an overview of the factors related to
the quality and proposes a new hybrid recommendation model.Comment: The final publication is available at www.springerlink.com
Distributed, Ambient, and Pervasive Interactions Lecture Notes in Computer
Science Volume 8530, 2014, pp 369-37
A Model for Using Physiological Conditions for Proactive Tourist Recommendations
Mobile proactive tourist recommender systems can support tourists by
recommending the best choice depending on different contexts related to herself
and the environment. In this paper, we propose to utilize wearable sensors to
gather health information about a tourist and use them for recommending tourist
activities. We discuss a range of wearable devices, sensors to infer
physiological conditions of the users, and exemplify the feasibility using a
popular self-quantification mobile app. Our main contribution then comprises a
data model to derive relations between the parameters measured by the wearable
sensors, such as heart rate, body temperature, blood pressure, and use them to
infer the physiological condition of a user. This model can then be used to
derive classes of tourist activities that determine which items should be
recommended
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