This research project investigates how transnational judicial training can benefit participating judges and judicial officers. It will also inquire whether and how the delivery of such training to foreign participants can enhance the reputation of the host institution and the host country’s legal system. The research findings will contribute to what we know about incentives to organise or partake in transnational judicial interactions, while also broadening our understanding of the conditions under which transnational judicial education is a promising modality for pursuing such interactions.
Through usage of city-scale commuting data (e.g., public transport records captured by smart card EZLink) to perform multimodal data analysis, this project seeks to answer following questions:
- Question 1: when and where do people commute (related to trip prediction)
- Question 2: how they commute (related to recovery of the exact routes taken by commuters inside the MRT network)
- Question 3: why they commute (related to inference of trip purposes)
A novel parallel framework named CONQUEROR is proposed in this project to support large scale concurrent graph query processing. The framework is built upon the popular heterogeneous architecture, which consists of both CPUs (central processing units) and GPUs (graphics processing units), and they aim to develop a set of novel parallel approaches to fully harness the unique characteristics of the heterogenous platform for processing millions of graph queries concurrently.
As the number of people turning to social media for information and news continues to increase, the spread of fabricated and inaccurate news becomes easier and more common. This has raised concerns about the authenticity of information shared on social media, such that regulators (including China, France and Singapore) have passed anti-fake news laws. This project therefore aims to study whether these laws are helpful in increasing the credibility of information in capital markets, from various stakeholders' perspectives.
This research aims to investigate the characteristics of quantum computing for distributed ledger technologies, which include Distributed Ledgers. Distributed Ledgers are now live and in use and there are many issues and limitations such as in scalability, security and determinism. Areas under consideration for improvement is the consensus mechanism and interoperability between Distributed Ledgers and the aim of this research is to investigate quantum mechanisms for these areas.
The proposal covers both the assessment and intervention for hyperglycemia, hypertension, and hyperlipidemia (3H) patients. The system periodically assesses the status of 3H patients, as well as identifies pre-3H persons based on early behavioural patterns, health symptoms and other non-medical factors. The system will also provide individual and group-based adaptive, long-term interventions through gamification.
The Research Programme in Computational Law will develop a common language (Domain Specific Language) for writing laws, contracts, and business logic in code. DSL will allow end-users to automate legal processes, generate computable contracts, expedite legal services, and even offer entirely new legal services / products. A new Centre will also be established to conduct this research and to develop cutting-edge digital and data solutions for legal practice.
The Spectre vulnerability has recently been reported to affect most modern processors. Attackers can extract information about the private data using a timing attack. This is an example of side channel attacks, where secure information flow through side channels unintentionally. How to systematically mitigate such attacks is an important and challenging research problem. This project proposes to automatically synthesise mitigation of side channel attacks using well-developed verification techniques. Given a system with design parameters which can be tuned to mitigate side channel attacks, this approach will automatically generate provable “secure” valuations of the parameters.
The objective of the project is to build a predictive machine learning model implemented on a quantum computer and a simulated quantum computer which has the potential to improve credit scoring accuracy. Credit scoring provides lenders and counterparties better transparency of the credit risk they are taking when dealing with a counterparty. Machine learning approaches allow for automated credit scoring feasible for a broad coverage of small companies. The current approaches rely on classical machine learning algorithms applied to broad datasets that combine company, accounting, and socio-economic information. Improving the learning algorithms is thus an important element to providing credit risk transparency.
This project will explore the use of quantum algorithms which cannot be implemented on classical machines today. This may open the route to a practical quantum supremacy for a financial application and create business advantages for the financial industry as quantum computing continues to improve.
The objective of this project is to provide new methods and systems for taxi queue management to better manage and predict taxi demand and supply at the Changi Airport. Models and systems that could more accurately predict the current and future taxi demand and drivers’ waiting times at different terminals would be developed, to predict taxi supply, passenger demand and driver waiting times at all taxi queues. Through deriving segmented and personalised models on how drivers react to incentives and related information, the project will also study how to best balance the supply and demand at all terminals in a workable & cost effective way. The team will determine whether there is a shortage of taxi supply either now or in the near term, and if so, construct real-time/pre-emptive plans to attract the right number of drivers based on individual driver’s reaction & behaviour model.
In addition, this project should provide useful and timely information to passengers (via different means such as digital display, mobile app alerts, SMS alerts, etc.) when there are long queues at taxi stands that cannot be resolved within the next 10 to 15 minutes. The system should provide alternative and available transport options nearby (i.e. MRT, private-hire cars, limousine, shuttle bus, etc.) with related way finding, costing and traveling time information.Mobile devices and mobile applications are increasingly important for people's daily life, and their security and privacy are raising more and more concerns. "Do you allow the app to access your contacts, photos, media, files, messages, …" becomes a common question faced by users when they start to use an app, but such permission control mechanisms put too much burden on users. Most users may not understand well the purposes of the accesses and the implications of granting permissions, and simply grant the permissions most of the time, leading to significant misuses of their privacy-sensitive data by apps. This proposal aims to build up the capabilities that enable automated, finer-grained, and customisable permission controls, which will promote a privacy ecosystem that keeps users aware, while reducing the burden on users, and pushes app developers to improve the privacy protection grades of their apps.
