ICT & OTHERS
Principal Investigator: Jennifer Dodgson
Team Members: Wilson Chen, Jules Yim, Victor Mortreu
While traditional focus groups provide detailed information, they also suffer from researcher bias and low statistical representativeness due to the limited number of participants. Vox Dei’s Latent Categories Methodology provides a comparable level of nuance, while simultaneously producing actionable statistical insights. We use open-ended online surveys to collect large volumes of qualitative data on specific topics. Our linguistic AI then scans the results and compares them with existing online data to identify the top-level categories that are most important to the respondents. This eliminates researcher bias, and also produces an accurate relative factor weighting, enabling researchers to better describe current phenomena and predict future trends.
Team Members: Ye Luyao, Zhong Jun
The value proposition of our ‘Smart School’ Project is to enhance teaching and learning efficiency during class by utilizing deep-learning based Artificial Intelligence. By capturing students’ micro-expression during class, our software can provide prompt feedback such that the lecturer could get the information about the level of understanding on any subject. The teacher then can adjust the content and highlight whichever point the students find difficulties. Our target customers are tuition centers and public schools in mainland China. For tuition centers, our product could help them build reputations and bring in more students by improving students’ performance. For public schools, it helps enhance teaching efficiency and can therefore get government investment.
Principal Investigator: Assoc Prof Sanjib Kumar Panda
Team Members: Krishnanand Kaippilly Radhakrishnan, Hoang Duc Chinh, Beng Tiek Yap, Khoo Yin Le
Smart Electrical Outlet/Socket (SEOS) is an enabler for novel digitalized services within smart buildings. These socket outlets can perform plug-load management and can provide new energy services. Provisioned with instant plug-load identification mechanism, real-time control (on/off), and real-time electrical monitoring (voltage, current, power, energy, etc.), SEOS provided high-fidelity intelligence to building controls. Coupled with its software infrastructure, SEOS can uniquely identify billions of plugged in loads and can correspondingly map manufacturer’s information, electrical ratings, ownership, cost, health history – a plethora of metadata. Digitalization of these provide plug-load energy management solutions, demand response mechanisms, inventorying solutions, and unprecedented digital services suitable for a Smart Nation.
Principal Investigator: Toru Yoshikawa
Team Members: Nina Tan, Tang Gao Liang, Tiffany Lim
- Converts Financial and Behavioral data into Actionable Business Insights to improve business growth and profit margins.
- Tracks Financial and Behavioral Metrics over time.
- Unifies employees’ effort with the organization’s strategic goals.
- Understands user behavioral patterns.
Financial Analytics shows us WHAT happens, and Behavioral analytics shows us WHY it happens, by drawing predictive analysis between events (what people do) and the outcomes we expect (e.g. sales, gross margin and net profit). One of the most effective way management can use analytics is to start with setting of a clear strategic financial objective and reverse engineer to identify types of employee behavior patterns that could enhance the chances of success in reaching predetermined goal.
Team Members: Dylan Tan, Tan Yi Shu, Glen Ong, Dr. Hassan Hariri
ARgon is an Augmented Reality safety device and software platform that enhances information flow from systems to the human cognition through an optimised Optical Display System (ODS). Currently, ARgon is designed to attach onto motorcycle helmets to increase the situational awareness of motorcyclists. The ODS provides information to riders such as video feed of the rear, for navigation purposes. The versatile technology within ARgon also serves to allow businesses to customise our product to their needs
Team Members: Keith Wang, Debabrota Basu
Our team (5 engineers and a data scientist) is developing an AI prototype, Opinir, to analyse, score and sort customer reviews on the web that will enable users to make more informed purchase decisions through reliable and quality customer reviews. We aim to target the fashion industry after insights from 98 user interviews, to develop an app for reviewers to share their fashion purchase experiences and as a way to collect quality reviews data that will train and improve our AI model.