Svetlana Kim

Smart learning services based on smart cloud computing.

Context-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user’s behavior. To add those technologies into existing e-learning services, a service architecture model is needed to transform the existing e-learning environment, which is situation-aware, into the environment that understands context as well. The context-awareness in e-learning may include the awareness of user profile and terminal context. In this paper, we propose a new notion of service that provides context-awareness to smart learning content in a cloud computing environment. We suggest the elastic four smarts (E4S)—smart pull, smart prospect, smart content, and smart push—concept to the cloud services so smart learning services are possible. The E4S focuses on meeting the users’ needs by collecting and analyzing users’ behavior, prospecting future services, building corresponding contents, and delivering the contents through cloud computing environment. Users’ behavior can be collected through mobile devices such as smart phones that have built-in sensors. As results, the proposed smart e-learning model in cloud computing environment provides personalized and customized learning services to its users.

Video Customization System Using Mpeg Standards

To generate dynamically a personalized video summary, a video customization system is designed and implemented incorporating usage environment. The customization system uses the three tier architecture to adapt and deliver some media contents for the user. The multimedia contents delivery includes today many challenges. There are increasing the diversity of user devices and network interface devices, and the pressure to satisfy the user preferences. These situations encourage the need of the customized contents for the user. In this paper, we describe the customization of multimedia contents, like video. For the customization of content, we propose the Hybrid Multimedia Access (HMA) model that uses the multimedia content descriptions such as MPEG-7 standard and MPEG-21 multimedia framework.

Universal Video Adaptation Model for Contents Delivery in Ubiquitous Computing

A video personalization system is designed and implemented an incorporating usage environment to dynamically generate a personalized video summary. The personalization systems adopt and deliver media contents to each user. The video content delivery chain poses today many challenges. There are an increasing terminal diversity, a network heterogeneity and a pressure to satisfy the user preferences. The situation encourages the need for the customized contents in order to provide the user in the best possible experience. In this paper, we address the problem of video customization. For the customized content, we suggest the UVA(Universal Video Adaptation) model that uses the video content description in MPEG-7 standard and MPEG-21 multimedia framework.

The evolution of standardization for mobile cloud

The cloud computing is emerging in several ICT areas, including the mobile service industry. This development is knows as mobile cloud computing. Mobile cloud can be considered a kind of cloud computing with assistance computing for mobility such as location-awareness, computing capability, and data backup. This paper introduces an evolution of standardization for mobile cloud computing.

Universal Multimedia Access Model for Video Delivery

The multimedia content delivery chain poses today many challenges. There are increasing terminal diversity, network heterogeneity and the pressure to satisfy the user preferences. The situation encourages the need for the customized contents in order to provide the user in the best possible experience. In this paper, we address the problem of multimedia customization. For the customized content, we suggest the UMA (universal multimedia access) model that uses the multimedia content description MPEG-7 standard and the MPEG-21 multimedia framework.

MRTensorCube: tensor factorization with data reduction for context-aware recommendations

Context information can be an important factor of user behavior modeling and various context recognition recommendations. However, state-of-the-art context modeling methods cannot deal with contexts of other dimensions such as those of users and items and cannot extract special semantics. On the other hand, some tasks for predicting multidimensional relationships can be used to recommend context recognition, but there is a problem with the generation recommendations based on a variety of context information. In this paper, we propose MRTensorCube, which is a large-scale data cube calculation based on distributed parallel computing using MapReduce computation framework and supports efficient context recognition. The basic idea of MRTensorCube is the reduction of continuous data combined partial filter and slice when calculating using a four-way algorithm. From the experimental results, it is clear that MRTensor is superior to all other algorithms.

Recommendation system for sharing economy based on multidimensional trust model

The recommendation system are widely adopted in today’s mainstream online sharing services, providing useful prediction of user’s rating or user’s preferences of sharing items (such as products, movies, books, and news articles). A key challenge of recommendation systems in sharing economy is to employ prediction algorithms to estimate the matching items with considering their interests and needs. The environment-context has been recognized as an important factor to consider in personalized recommender systems. Since dynamic information in environment-context describes the situation of items and users, the information affects the user’s decision process essentially to apply in recommender systems. However, most model-based collaborative filtering approaches such as Matrix Factorization do not provide an easy way of integrating context information into the model. In this paper, we introduce a Multidimensional Trust model based on Tensor Factorization. The generalization of Matrix Factorization allows for a flexible and generic integration of contextual information. According to the different types of context, the Multidimensional Trust model considers the additional dimensions for the representation of the data as a tensor. This is achieved by going through the collecting user’s behavior based on rating analysis and identification of users’ historical activity and viewing patterns. The benefits behavior solutions, which use the handle intelligently to meet the users’ needs, are the focus of this paper.

Ambient intelligence middleware architecture based on awareness-cognition framework

Ambient Intelligence refers to environments that consisting of smart sensor devices that can sense and respond to the existence of people. Through context awareness, ambient intelligence may deliver accurate detection of a user’s situation, predict future events, and support real-time decision making that requires intelligent analysis of large amounts of context data gathered from various sensing devices. This paper presents a context awareness framework called AC (awareness-cognition) for ambient intelligence that also solves problems pertaining to predictions by discovering personalized knowledge through combining multiple contexts.

A Model of Smart Learning System Based on Elastic Computing

In recently, learners have always mobile devices including smart phones so that is collecting users behavior by sensors mounted on the devices. This paper proposes a new notion for smart learning system by using the concept of elastic computing in cloud computing. The notion is Elastic 4S (Smart Pull, Smart Prospect, Smart Content and Smart Push) for the smart service. We focus on the benefits of smart computing for e-learning solution using handle intelligently to meet users needs through collecting users behaviors, prospecting, building, delivering, and rendering steps. The proposed smart-learning model will show the personalized and customized learning services to be possible in various fields.

Architecture of 4-way tensor factorization for context-aware recommendations

Contextual information has been recognized as an important factor to consider in user-aware Recommendation Systems. Since contextual information can be used as a significant factor in modeling user behavior, various context-aware recommendation methods are proposed. However, the state-of-the-art context modeling methods treat contexts as other dimensions similar to the dimensions of users and items, and cannot extract the special semantic operation of contexts. On the other hand, some works on multi-domain relation prediction can be used for the context-aware recommendation, but they have problems in generating recommendation under a large amount of contextual information. In this paper, we propose the 4-way Tensor, a parallel tensor factorization algorithm, to accelerate the tensor factorization of large datasets to support efficient context-aware recommendations. The basic idea of this algorithm is to partition a tensor into partition and then exploit the inherent parallelism to perform tensor related operations in parallel.