Streaming and congestion control using scalable video coding based on H.264/AVC

INTRODUCTION Streaming of multimedia data over the Internet has rapidly increased in recent years. All commercial applications and most research in video streaming use conventional hybrid video coding. To adapt the data transmission rate on the server to the varying bandwidth caused by congestion in the Internet or to different available bandwidths of different clients, the simulcast solution is widely applied (Balk et al., 2003; Feamster et al., 2001; Schierl and Wiegand, 2004). A larg…     

Anticipation to Social and Nonsocial Dynamic Cues in Preschool-Age Children

The ability to learn from expectations is foundational to social and nonsocial learning in children. However, we know little about the brain basis of reward expectation in development. Here, 3- to 4-year-olds (N = 26) were shown a passive associative learning paradigm with dynamic stimuli. Anticipation for reward-related stimuli was measured via the stimulus preceding negativity (SPN). To our knowledge, this is the first study to measure an SPN in children younger than age 6. Our findings reveal distinct anticipatory neural signatures for social versus nonsocial stimuli, consistent with previous research in older children. This study suggests an SPN can be elicited in preschoolers and is larger for social than nonsocial stimuli.     

Back to school in Afghanistan: Determinants of school enrollment

One of the first achievements of post-conflict Afghanistan was to bring almost 4 million children back to school. Issues remain daunting, however, with low primary enrollment especially for girls and in rural areas and very weak learning achievements. We review some key features of the education system in Afghanistan. By matching household and school data, we assess the impact of various factors on enrollment. Overall, the analysis indicates that further increasing supply alone is unlikely to lead to higher enrollment. The analysis confirms the importance of demand factors such as the education of parents, the family language, and other community and ethnic factors.     

Learning analytics to uncover inequality in behavioural engagement and academic attainment in a distance learning setting

Abstract

Although the attainment gap between black and minority ethnic (BME) students and white students has persisted for decades, the potential causes of these disparities are highly debated. The emergence of learning analytics allows researchers to understand how students engage in learning activities based on their digital traces in a naturalistic setting. This study investigates the attainment gap by analysing the differences in behavioural engagement between different ethnic groups. Using multilevel models of academic performance, demographics, and online traces of 149,672 students enrolled in 401 modules in a distance learning setting, we confirmed the existing attainment gap. After controlling for other demographics, module characteristics and engagement, BME students were between 19% and 79% less likely to complete, pass or achieve an excellent grade compared to white students. Given the same academic performance, BME students spent 4-12% more time on studying than white students. While the attainment gap remained persistent after controlling for academic engagement, our study further highlighted the inequality of attainment between BME and white students.     

Evaluating the Benefits and Values of Textbooks: Perceptions of Vietnamese Business Students

This study aims to examine students’ perceived benefits and values of textbooks, with a specific focus on emerging markets. A paper-based survey was used to collect data from 459 Vietnamese students in three business disciplines including accounting, international business and marketing. The findings reveal that Vietnamese business students view textbooks as having greater short-term benefits than long-term ones. They also believe that textbooks have relatively low economic value. Interestingly, marketing students perceive that textbooks provide primarily benefits of improving class performance, while accounting students tend to view textbooks to have benefits of clearer understanding of relevant concepts. Implications for key stakeholders in the higher education book publishing industry are discussed and future research directions presented.     

Causal Mediation Analysis With a Binary Outcome and Multiple Continuous or Ordinal Mediators: Simulations and Application to an Alcohol Intervention

We investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: (a) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, (b) predict potential outcome probabilities, and (c) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying the outcome to address residual mediator variance and covariance. We evaluate the estimation of risk-difference- and risk-ratio-based effects (RDs, RRs) using the maximum likelihood (ML), mean-and-variance-adjusted weighted least squares (WLSMV) and Bayes estimators in Mplus. Across most variations in path-coefficient and mediator-residual-correlation signs and strengths, and confounding situations investigated, the method performs well with all estimators, but favors ML/WLSMV for RDs with continuous mediators, and Bayes for RRs with ordinal mediators. Bayes outperforms ML/WLSMV regardless of mediator type when estimating RRs with small potential outcome probabilities and in two other special cases. An adolescent alcohol prevention study is used for illustration.     

Improving the performance of personal name disambiguation using web directories

Frequent requests from users to search engines on the World Wide Web are to search for information about people using personal names. Current search engines only return sets of documents containing the name queried, but, as several people usually share a personal name, the resulting sets often contain documents relevant to several people. It is necessary to disambiguate people in these result sets in order to to help users find the person of interest more readily. In the task of name disambiguation, effective measurement of similarities in the documents is a crucial step towards the final disambiguation. We propose a new method that uses web directories as a knowledge base to find common contexts in documents and uses the common contexts measure to determine document similarities. Experiments, conducted on documents mentioning real people on the web, together with several famous web directory structures, suggest that there are significant advantages in using web directories to disambiguate people compared with other conventional methods.