In its most basic form, advertising video production communicates a message about a product or service to the public. In the age of digital marketing, where the most popular way to connect with audiences is through advertising videos. However, advertising video production is a costly and complicated process from creation, material shooting, editing to the final commercial video. Therefore, producing qualified advertising videos is a capital and talent-intensive task, which poses a huge challenge for start-ups or inexperienced ad creators. paper proposes an intelligent advertising video production system driven by multi-modal retrieval, which only requires the input of descriptive copy. This system can automatically generate scripts, then extract key queries, retrieve related short video materials in the video library, and finally synthesize short advertising videos. The whole process minimizes human input, greatly reduces the threshold for advertising video production and greatly improves output and efficiency. It has a modular design to encourage the study of new multi-modal algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. The proposed system has been fully verified and has broad prospects in the production of short videos for commodity advertisements within Alibaba.

Offloading application to cloud can augment mobile devices' computation capabilities for emerging resource-hungry mobile applications, however it can consume both much time and energy for mobile devices offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve the above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. Finally, the simulation results show that, compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.