Serverless applications spanning the cloud and edge require flexible programming frameworks for expressing compositions across the different levels of deployment. Another critical aspect for applications with state is failure resilience beyond the scope of a single dataflow graph that is the current standard in data streaming systems. This paper presents Portals, an interactive, stateful dataflow composition framework with strong end-to-end guarantees. Portals enables event-driven, resilient applications that span across dataflow graphs and serverless deployments. The demonstration exhibits three scenarios in our multi-dataflow streaming-based system: dynamically composing a stateful serverless application; an interactive cloud and edge serverless application; and a Portals browser playground.

Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.Previous work focuses on automatic commenting based solely on textual content.However, in real-scenarios, online articles usually contain multiple modal contents.For instance, graphic news contains plenty of images in addition to text.Contents other than text are also vital because they are not only more attractive to the reader but also may provide critical information.To remedy this, we propose a new task: cross-model automatic commenting (CMAC), which aims to make comments by integrating multiple modal contents.We construct a largescale dataset for this task and explore several representative methods.Going a step further, an effective co-attention model is presented to capture the dependency between textual and visual information.Evaluation results show that our proposed model can achieve better performance than competitive baselines.1