The increasingly widespread use of digital tokens around the world has meant that businesses have been seeking clarity with respect to their tax liabilities from transactions involving digital tokens. However, as such transactions are relatively new, there is considerable uncertainty as to the appropriate tax treatment in what can be a rather messy field. Singapore has provided comprehensive guidance on the taxation of digital tokens in the form of e-tax guides. However, quite understandably, a good number of open questions still remain. This project aims to add to the available knowledge on the taxation of digital tokens in Singapore by providing a single comprehensive guide that can be easily referenced by businesses seeking clarity on their tax obligations. In particular, it will add value by looking at four areas that are not currently covered by the existing guidance and literature: 1) a clear theoretical map of the area; 2) the application of existing law (case law and statutes) to these new transactions; 3) a comparative approach, to determine how the tax treatment in Singapore differs from that of other leading jurisdictions; and 4) the stamp duty implications of transactions involving digital tokens. In addition, the project will cover the three most relevant taxes in this area: 1) income tax; 2) goods and services tax; and 3) stamp duties.
(This is a 6-month extension of the research collaboration with Fujitsu Ltd.) Under the Fujitsu-SMU Urban Computing and Engineering (UNiCEN) Corp Lab, SMU has undertaken the Digital Platform Experimentation (DigiPlex) project with Fujitsu. The project was carried out using the Digital Annealer (DA), a quantum inspired-technology inspired by Fujitsu. Through the DigiPlex project, certain challenges in solving constrained optimization problems using such technology, and promising methods on tuning of the underlying model parameters to improve run time performance, have been identified. This project aims at developing hyper parameter tuning methodology, machine learning techniques, operations research algorithms, and software tools to enhance quantum-inspired techniques for solving large scale real-world combinatorial optimization problems.
This project aims to develop a practical method for certifying real-world AI-based systems based on a novel combination of static and dynamic verification, targeting systems with a certification requirement similar to that of EAL 6-7 for traditional software systems. We accomplish this by developing a completely new set of algorithms, which are designed to battle the scalability limitation of static verification techniques and connect static and dynamic verification, and use the partial verification engine developed to solve the verification problem systematically.
The world is experiencing a rapid transition towards a digital society. Although huge number of Internet of Things (IoT) devices are being deployed to provide accurate and real-time sensing and observation of the environment, security and privacy concerns are becoming one of the major barriers for large scale adoption and deployment of IoT. To that end, this project aims to provide IoT devices with privacy-aware authentication and flexible authorisation capabilities to build trust in IoT.
With the widespread adoption of drones in civilian, business, and government applications nowadays, concerns for breaches of safety, security, and privacy by exploiting drone systems are also rising to the highest national level. Malicious entities have used drones to conduct physical and cyber-attacks such as unauthorized surveillance, drug smuggling, armed use, etc. In this project, the research team aims to develop methods and tools for analysing a list of drones to audit drones for detecting anomalies such as malware, data leak, software bugs that could be exploited to conduct criminal/malicious drone activities. The research team will analyse at least five different drone-related criminal/malicious activities from their collaborator and demonstrate how ADrone can assist Drone forensic analysts with the detection of the root causes of activities.
The first objective of this programme evaluation endeavour is to produce a ‘Roadmap Report’ that will aid Alzheimer’s Disease Association (ADA)’s efforts to improve subsequent iterations of the Voice for Hope (VFH) programme. The second objective of this research study is to co-create an ‘Amplification Article’ with collaborators at ADA, with the goal of sharing valuable insights from the VFH programme in Singapore with the larger ecosystem in Asia and beyond. This effort will allow for the curation of further evidence-based programmes that benefit persons with dementia and their caregivers in the future.
This project focuses on the interaction among individuals that occurs within the framework of an institution. For an institution to deliver socially desirable outcomes, the design of the institution has to provide the individuals with the right incentives. Implementation theory is considered a natural framework for us to ensure the correct incentive structure in the design of institutions. The objective of this project is to design effective, that is, simple and practically usable institutions, by imposing various robustness requirements on the class of institutions to be considered. The project thus aims to generate critical insights on the robust design of institutions, which would in turn significantly enhance our ability to design effective institutions for economic, political, and social situations.
Reliable forecasting based on observational evidence is useful in individual and firm decision-making, as well as governmental planning, regulation, and many other activities that rely on future economic projections. For example, central banks need information about a future path of inflation in order to adjust its interest rate. Companies forecast future sales in order to adjust their production. This project sets out to develop three new machine learning (ML) methods that address structural instabilities and nonstationarities that are well known to be present in economic and financial systems and that complicate variable selection and tuning parameter choices. By virtue of their attention to these characteristics, the new methods are expected to outperform traditional econometric methods and existing ML methods and produce more accurate economic and financial forecasting, which in turns leads to better decisions.
In this Human-Computer Interaction research, the research team will design a novel system that addresses the low wage of online crowd work—also known as online gig-economy. By using knowledge from mechanism design in the economics literature, the research team will design and develop user interfaces through machine learning models that:
- Present information to encourage crowdsourcing requesters pay a fairer wage to online workers; and
- Use nudging messages and information visualization to persuade workers to submit high-quality work.
This research collaboration with IBM aims to develop the optimisation capabilities to build a cutting-edge resilient supply chain, leveraging data science to preserve the continuity and consistency of product supply and meet business obligations for product delivery and service to customers in the face of both short-term operational and longer-term strategic disruptions. In this project, the team seeks to leverage IBM’s relevant internal, supplier-provided, public and subscription data sources to improve operational decision-making capability to proactively anticipate and respond to disruptive events, and to enable resiliency evaluations for products, product families, or tiered supply networks.
