Matrix Factorization Techniques for Recommender Systems

The development of new technologies enables learners to learn more effectively, efficiently, flexibly and comfortably. Learners utilize smart devices to access digital resources through wireless network and to immerse in both personalized and seamless learning. Smart education, a concept that describes learning in digital age, has gained increased attention. This paper discusses the definition of smart education and presents a conceptual framework. A four-tier framework of smart pedagogies and ten key features of smart learning environments are proposed for foster smart learners who need master knowledge and skills of the 21st century learning. The smart pedagogy framework includes class-based differentiated instruction, group-based collaborative learning, individual-based personalized learning and mass-based generative learning. Furthermore, a technological architecture of smart education, which emphasizes the role of smart computing, is proposed. The tri-tier architecture and key functions are all presented. Finally, challenges of smart education are discussed.

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A research framework of smart education

(1) Background: The sharing economy has emerged as a phenomenon widely described by academic literature to promote more sustainable consumption practices such as access over ownership. However, there exists great semantic confusion within academic literature surrounding the term “sharing economy,” which threatens the realisation of its purported sustainability potential. (2) Objective: The aim of this paper is to synthesise the existing academic definitions and propose a definition of the sharing economy from the perspective of sustainability science in order to indicate sharing practices that are consistent with the sustainability claims attributed to the sharing economy. (3) Methods: We conduct a database search to collect relevant academic articles. Then, we leverage qualitative content analysis in order to analyse the authors’ definitions and to synthesise the broad dimensions of the sharing economy in the discourse. (4) Results: We propose the following characteristics, or semantic properties, of the sharing economy for sustainability: ICT-mediated, non-pecuniary motivation for ownership, temporary access, rivalrous and tangible goods. (5) Conclusion: The semantic properties that inform our definition of the sharing economy for sustainability indicate those sharing practices that promote sustainable consumption compared to purely market-based exchanges. This definition is relevant for academics studying the sustainability impacts of the sharing economy in order to promote comparability and compatibility in research. Furthermore, the definition is useful for policy-makers, entrepreneurs, managers and consumers that have the sharing economy on the agenda in order to promote social enterprise and support sustainable consumption.

https://www.mdpi.com/2071-1050/11/3/567/pdf

Defining the Sharing Economy for Sustainability

Mobile Cloud Computing (MCC) has been introduced as a viable solution to the inherited limitations of mobile computing. These limitations include battery lifetime, processing power, and storage capacity. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be returned to the mobile device. This will reduce the required power and time for completing such intensive jobs. However, connecting mobile devices with the cloud suffers from the high network latency and the huge transmission power consumption especially when using 3G/LTE connections. In this paper, we introduce a Cloudlet based MCC system aiming to reduce the power consumption and the network delay while using MCC. We merged the MCC concepts with the proposed Cloudlet framework and propose a new framework for the MCC model. Our practical experimental results showed that using the proposed model reduces the power consumption from the mobile device, besides reducing the communication latency when the mobile device requests a job to take place remotely while keeping high quality of service stander.

Resource Efficient Mobile Computing Using Cloudlet Infrastructure

The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.

A systematic review: machine learning based recommendation systems for e-learning

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

https://www.mdpi.com/1424-8220/11/8/7835/pdf

Smart Learning Services Based on Smart Cloud Computing

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.

/pdf/recommendation-system-for-sharing-economy-based-on-54ox5enkq6.pdf

Recommendation system for sharing economy based on multidimensional trust model

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.

Video Customization System Using Mpeg Standards

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.

/pdf/ambient-intelligence-middleware-architecture-based-on-4zg8mlotru.pdf

Ambient intelligence middleware architecture based on awareness-cognition framework

With the development of the Internet of Things (IoT), the amount of data is growing and becoming more diverse. There are several problems when transferring data to the cloud, such as limitations on network bandwidth and latency. That has generated considerable interest in the study of edge computing, which processes and analyzes data near the network terminals where data is causing. The edge computing can extract insight data from a large number of data and provide fast essential services through simple analysis. The edge computing has a real-time advantage, but also has disadvantages, such as limited edge node capacity. The edge node for edge computing causes overload and delays in completing the task. In this paper, we proposes an efficient offloading model through collaboration between edge nodes for the prevention of overload and response to potential danger quickly in emergencies. In the proposed offloading model, the functions of edge computing are divided into data-centric and task-centric offloading. The offloading model can reduce the edge node overload based on a centralized, inefficient distribution and trade-off occurring in the edge node. That is the leading cause of edge node overload. So, this paper shows a collaborative offloading model in edge computing that guarantees real-time and prevention overload prevention based on data-centric offloading and task-centric offloading. Also, we present an intelligent offloading model based on several scenarios of forest fire ignition.

Svetlana Kim

Papers

Smart Learning Services Based on Smart Cloud Computing

Recommendation system for sharing economy based on multidimensional trust model

Video Customization System Using Mpeg Standards

Ambient intelligence middleware architecture based on awareness-cognition framework

Dynamic Offloading Model for Distributed Collaboration in Edge Computing: A Use Case on Forest Fires Management