Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification. One obstacle that prevents networks from reasoning more deeply about complex scenes and situations, and from integrating visual knowledge with natural language, like humans do, is their lack of common sense knowledge about the physical world. Videos, unlike still images, contain a wealth of detailed information about the physical world. However, most labelled video datasets represent high-level concepts rather than detailed physical aspects about actions and scenes. In this work, we describe our ongoing collection of the “something-something” database of video prediction tasks whose solutions require a common sense understanding of the depicted situation. The database currently contains more than 100,000 videos across 174 classes, which are defined as caption-templates. We also describe the challenges in crowd-sourcing this data at scale.

In this paper we show how high-level UML models of mobile computing applications can be analysed for classical performance measures such as throughput. The approach proceeds by compiling the UML model into a representation in the formally-defined modelling language of PEPA nets. The compilation process and subsequent performance analysis based on numerical solution of a continuous-time Markov chain is supported by a software tool, the Choreographer design platform. Choreographer interoperates with popular UML tools by reading and writing UML models in the XML Metadata Interchange format (XMI).