An Enhanced Approach to Retrieve Learning Resources Over the Cloud

This study proposes AIREH (Architecture for Intelligent Retrieval of Educational content in Heterogeneous Environments) that is a model for the development of digital content retrieval based on the paradigm of virtual organizations of intelligent agents Learning objects have made it possible to create digital resources that can be reused in various didactic units. These resources are stored in repositories, and thus require a search process that allows them to be located and retrieved

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

QuizPower: a mobile app with app inventor and XAMPP service integration

This paper details the development of a mobile app for the Android operating system using MIT App Inventor language and development platform. The app, Quiz Power, provides students a way to study course material in an engaging and effective manner. At its current stage the app is intended strictly for use in a mobile app with App Inventor course, although it provides the facility to be adapted for other courses by simply changing the web data store. Development occurred during the spring semester of 2013. Students in the course played a vital role in providing feedback on course material, which would be the basis for the structure of the quiz as well as the questions. The significance of the project is the integration of the MIT App Inventor service with a web service implemented and managed by the department

Quantum autoencoders via quantum adders with genetic algorithms

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms