5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grantbased (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this paper. Specifically, we present a spatiotemporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.

Abstract-Massive open online courses (MOOCs) are the latest e-learning initiative to attain widespread popularity in the world. Unfortunately, nearly all of these studies are built from western insights. Thus, there is a need for a thematic and practical analysis of related studies to bring a better understanding of MOOCs with eastern insights. In this paper, a study is conducted focusing on the use of MOOCs from the eastern perspective. Our primary goal is to summarize the motivations and challenges of using MOOCs, as well as to identify issues that haven't yet to be fully addressed or resolved. In this paper, we employed different methodologies that included the use of a questionnaire survey and activity data analysis. The results of this study may enhance our understanding of challenges of teaching and learning in MOOCs and help educators develop MOOCs with eastern insights. Index Terms-Eastern insights, human engagement, massive open online courses (MOOCs), motivations and challenges ofMOOCs.

How science and technology attitudes vary across the United States, China, South Korea and Japan - all of which top Bloomberg's list of high-tech centralization - is explored through data from the sixth wave of the World Values Survey (2010-2014). The following study examines the presence of different types of attitudinal groups using latent profile analysis. Not only do unique attitudinal groups exist in each country, but each group is uniquely influenced by select demographic characteristics, including education, age, gender, religiosity, employment status and individual interaction with technology. The findings provide insight into public attitudes towards science and technology across social and cultural contexts and generate nuanced understandings of similar and different attitudinal groups in East Asia and the United States.

KDD Cup 2007 focuses on movie rating behaviors. The goal of the task "Who Rated What" is to predict whether "existing" users will review "existing" movies in the future. We cast the task as a link prediction problem and address it via a simple classification approach. Compared with other applications for link prediction, there are two major challenges in our task: (1) the huge size of the Netflix data; (2) the prediction target is complicated by many factors, such as a general decrease of interest in old movies and more tendency to review more movies by Netflix users due to the success of the internet DVD rental industries. We address the first challenge by "selective" subsampling and the second by combining information from the review scores, movie contents and graph topology effectively.

This paper presents an analytical framework to estimate the Total Allowable Area for Coastal Reclamation (TAACR) to provide scientific support for the implementation of a coastal reclamation restriction mechanism. The logic of the framework is to maximize the net benefits of coastal reclamation subject to a set of constraints. Various benefits and costs, including the ecological and environmental costs of coastal reclamation, are systematically quantified in the framework. Model simulations are developed using data from Tongan Bay of Xiamen. The results suggest that the TAACR in Tongan Bay is 5.67 km 2 , and the area of the Bay should be maintained at least at 87.52 km 2 .