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Given that “cross-border e-commerce + live streaming” has become an important driver of global trade but limited attention has been paid to this area, this study examines the impacts of live streaming features on the consumers’ cross-border purchase intention from the perspectives of consumers’ overall perceived value and overall perceived uncertainty based on the SOR theory. In addition, through investigating the moderating effects of saving money, this study reveals the impacts of amazing bargains in live streaming commerce. A total of 272 samples were collected by a questionnaire survey to test the proposed research model. The results show that live streaming features significantly increase consumers’ overall perceived value and purchase intention, and significantly reduce consumers’ overall perceived uncertainty; in addition, saving money further increases the impact of live streaming features on consumers’ overall perceived value. This study provides a theoretical basis and reference for cross-border e-commerce platforms and merchants to effectively leverage live streaming to influence consumers’ perception and purchase intention.

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Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.

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Abstract. WWW has posed itself as the largest data repository ever available in the history of humankind. Utilizing the Internet as a data source seems to be natural and many efforts have been made. In this paper we focus on establishing a robust system to collect structured recipe data from the Web incrementally, which, as we believe, is a critical step towards practical, continuous, reliable web data extraction systems and therefore utilizing WWW as data sources for various database applications. The reasons for advocating such an incremental approach are two-fold: (1) it is unpractical to crawl all the recipe pages from relevant web sites as the Web is highly dynamic; (2) it is almost impossible to induce a general wrapper for future extraction from the initial batch of recipe web pages. In this paper, we describe such a system called RecipeCrawler which targets at incrementally collecting recipe data from WWW. General issues in establishing an incremental data extraction system are considered and techniques are applied to recipe data collection from the Web. Our RecipeCrawler is actually used as the backend of a fully-fledged multimedia recipe database system being developed jointly by