We here present the first whole genome analysis of an anonymous Kinh Vietnamese (KHV) trio whose genomes were deeply sequenced to 30-fold average coverage. The resulting short reads covered 99.91 percent of the human reference genome (GRCh37d5). We identified 4,719,412 SNPs and 827,385 short indels that satisfied the Mendelian inheritance law. Among them, 109,914 (2.3 percent) SNPs and 59,119 (7.1 percent) short indels were novel. We also detected 30,171 structural variants of which 27,604 (91.5 percent) were large indels. There were 6,681 large indels in the range 0.1-100 kbp occurring in the child genome that were also confirmed in either the father or mother genome. We compared these large indels against the DGV database and found that 1,499 (22.44 percent) were KHV specific. De novo assembly of high-quality unmapped reads yielded 789 contigs with the length greater than or equal to 300 bp. There were 235 contigs from the child genome of which 199 (84.7 percent) were significantly matched with at least one contig from the father or mother genome. Blasting these 199 contigs against other alternative human genomes revealed 4 novel contigs. The novel variants identified from our study demonstrated the necessity of conducting more genome-wide studies not only for Kinh but also for other ethnic groups in Vietnam.

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Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

The conceptual model of smart university has been generalized as a digital transformation-oriented higher educational institution using digital infrastructure (digital legal, digital human resources, digital data, digital technologies and digital applications) to provide personalized learning services to learners of all generations in the country and around the world, meeting the lifelong learning requirements and sustainable development of individuals as well as nations. It is described through the V-SMARTH model, including 6 basic components of digital resources, open access learning materials, virtual learning environment, individualized education, interactive learning and digital platform. These elements come together in three pillars of digitization, digital learning model innovation and comprehensive digital transformation process. The study also approached the notion of the smartness and the readiness level of the smart university. In particular, the issue of performance metrics of smart universities has been developed and implemented with the UPM rating for the VNU University of Engineering and Technology.