The project seeks to identify misinformation on the web by studying the content and propagation patterns of known cases of misinformation. Through the analysis of these misinformation, the research team seeks to propose methods that will allow for automatic flagging of suspicious pieces of information circulating on the web which can be sent to experts for verification.
The project is a joint collaboration with Acronis Asia Research and Development Pte Ltd, and it aims to develop a non-intrusive monitor that leverages artificial intelligence to achieve protection against data-infringing malware on Android devices. A dynamic analysis framework would be built to perform dynamic analysis of Android apps on un-rooted Android phones to achieve the feature of non-intrusiveness, while a deep learning solution would be developed to identify the specific Android app responsible for performing sensitive operations.
Awarded with a second year funding, the project looks into building fundamental data fusion techniques to combine data from both physical urban sensors and social media sensing to generate improved insights into the evolution of urban events, and a software library of tools that extract and combine analysis techniques across multiple socio-physical sensing channels.
The project, led by Trampolene Limited, aims to design a scalable, self-sustaining system that can collect, classify and determine accessible point-to-point routes that are suitable for barrier-free access.
The project aims to understand MRT events and commuting experience through sentiment analysis of public tweets related to MRT events generated by Singapore's Twitter users.
