Infographics deal with representing data or information visually in a perceptually compelling manner. Recently, infographics have gained widespread popularity, giving rise to automated infographics synthesis from texts. Our research follows an ontological approach to automatically extract the necessary indicators from an input sentence and synthesize an infographic corresponding to it. This work includes (1) the creation of a dataset, (2) an end-to-end domain-agnostic framework, and (3) demonstrating the application of the proposed framework. The results demonstrate our framework's ability to extract the necessary textual cues from real-world textual descriptions (from various domains) and synthesize meaningful infographics.

Cycle accurate simulator is a critical tool for processor design and as the complexity and the core count of the processor increase, the simulation becomes extremely time and resource consuming and hence not very practical. Accurate multi-core performance estimation in realistic time is needed for making the right design choices and make high quality performance projections. In this work we present a multi-core simulation model called Follower Core, that helps us to approximate the multi-core simulations by simulating some cores in detail and abstracting out the other cores without reducing the overall activities at the shared resources. This enables us to simulate all the critical shared resources in the multi-core system accurately and hence the detailed core can provide correct performance estimation. The approach is applied over existing simulation models and it reduces the simulation time significantly, especially for long running workloads. The 'Follower Core' model provides an average speed up of 3x compared to baseline and is an accurate approximation of detailed multi-core simulations with a maximum error of 2% with the baseline model and extends our capabilities by improving our coverage and providing flexibilities to run mixed workloads.