Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Measuring Readers Flow State with a High Medium Interactivity Online News Story

The rapid maturation of the Internet has enabled journalists and news media companies to push the boundaries of traditional journalistic narrative by utilizing the techniques of literary journalism to integrate pictures, audio, video and other multimedia elements into interactive and immersive multimedia rich stories. These immersive stories, while aesthetically stunning, are neither easy nor cheap to create. This study uses expectation confirmation theory and the theory of flow, with uses and gratifications as an umbrella theory to examine whether individuals who read interactive stories experience higher levels of media disorientation than readers of stories presented in a traditional online story format. The study results demonstrated that medium interactivity of an online news story does not impact a participant\u27s state of flow

Chinese International Students\u27 Informal Second Language (L2) Learning Through Technology for Enhancing Lived Experiences in Canada

An increasing amount of attention has been drawn to international students’ academic development in the context of studying abroad; however, few studies shed light on students’ studying and lived experiences outside of school. This thesis explores how technology can enhance Chinese international students’ informal acquisition of second language (L2) and their lived experiences in Canada. Through a qualitative case study, I describe what language difficulties newly arrived Chinese international students encounter, and how they cope with those language difficulties through technology-assisted informal L2 learning. Data sources include in-depth interviews and follow-up interviews, participants’ personal narratives, and researchers’ reflective journals. Theories of multiliteracies, basic interpersonal communicative skills and cognitive academic language proficiency distinction, as well as a communicative competence framework have been adopted as the theoretical frameworks for data analysis. The findings show that newly arrived Chinese international students’ major language difficulties includes lack of non-academic vocabulary, lack of understanding of sociocultural differences, and unfamiliarity with informal context embedded phrases. To overcome these language difficulties, they creatively design informal L2 learning experiences through the combinational use of technology tools. The results have significant implications for newly arrived Chinese international students’ informal L2 learning