Digital Commons Network^™

Multiple Proceedings And Abuse Of Procedure On Bri Disputes, Mariam Gotsiridze

Research Collection Yong Pung How School Of Law

This article argues that specific mechanisms and best practices should be adopted by different players in the Belt and Road Initiative (BRI) dispute resolution ecosystem to mitigate the risks of multiple proceedings and abuse of procedure. Specifically, (i) states should enhance their multilateral and bilateral treaty practices; (ii) arbitral institutions should enhance their arbitral rules as well as cooperate with each other; and (iii) states should direct more efforts to dispute prevention and amicable dispute settlement. This article will explain what these best practices and mechanisms are.

Not Such Massively Discretionary Trusts: Proper Purposes Doctrine And Protectors As Means Of Control, Man Yip, Hang Wu Tang

Research Collection Yong Pung How School Of Law

Innovation in drafting trust deeds has been central to international trust practice resulting in the discretionary trusts being the norm in modern wealth management. In a seminal article, Lionel Smith observes that these trust drafting practices have ‘led to an increase in the dispositive discretions held by trustees’. His analysis deprecates this development where the ‘trustees’ dispositive discretions effectively govern the whole trust structure’—which he labels ‘massively discretionary trusts’. Smith goes on to detail the various legal risks entailed in massively discretionary trusts which generally arise from the fact that the explicitly identified residuary or default beneficiaries are usually not …

Loose Ends In Singapore Equal Protection Doctrine, Wei Yao, Kenny Chng

Research Collection Yong Pung How School Of Law

A trilogy of landmark Singapore Court of Appeal decisions has defined the landscape of constitutional equal protection doctrine in Singapore: Lim Meng Suang, Syed Suhail and Tan Seng Kee. While this trio of cases has laid the doctrinal foundation for the constitutional right to equality in Singapore, three loose ends remain for clarification. First, what is the relationship between the legal tests articulated in Syed Suhail and Lim Meng Suang? Second, what is the relationship between both steps in the Syed Suhail test? Third, what is the distinction between the Syed Suhail test and the common law judicial review ground …

Constitutional, Governance Or Market Failures: China, Climate Change And Energy Transition, Henry S. Gao, Weihuan Zhou

Research Collection Yong Pung How School Of Law

Jinping announced China’s plan to further scale up its Intended Nationally Determined Contributions, aiming at achieving CO2 emissions peak before 2030 and carbon neutrality before 2060.2 A central element of the plan is reducing China’s heavy reliance on coal power. As a result, China’s use of coal already saw a steady decrease between 2013 and 2018. While coal demand increased in 2019 and 2020, new coal power plants approved in 2021 declined by approximately 58% compared to 2020.3 However, the good progress in energy transition was interrupted by the power outage sprawling over 20 provinces in China in September 2021, …

Generative Ai In Finance: Risks And Potential Solutions, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

Generative Artificial Intelligence captured the attention of academics, policymakers, the private sector, and some regulators in 2022 after the launch of ChatGPT and its widespread adoption worldwide. Then, during the World Economic Forum session in 2023, Microsoft Chairman and Chief Executive Officer Satya Nadella said that the ‘golden age’ of AI is underway and generative AI is set to play a big role in it. Accordingly, Microsoft has invested billions of dollars into OpenAI, the company behind the launch of ChatGPT. Additionally, Google and Meta have also created their own generative AI models. Given the multiplicity of potential use cases …

Within-Development Density And Housing Prices In Singapore, Eric Fesselmeyer, Haoming Liu, Louisa Poco

Research Collection College of Integrative Studies

This paper measures how much more households pay for less density in their immediate surroundings. Using transaction and administrative data and exploiting the introduction of a regulation that restricted the number of housing units for certain land lots, we find that households discount density: a 10% increase in within-development density decreases the price per square meter by 5%. Further, the mean price per square meter of the average development increased by 1%–3% after the regulation was introduced, while the amount of built-up space remained constant. The increase in total revenue suggests developers may underestimate the externality caused by density.

Measurement Of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning, Seung Jun Choi, Junfeng Jiao

Research Collection College of Integrative Studies

This study explores the socioeconomic disparities observed in the early adoption of Electric Vehicles (EVs) in the United States. A multiagent deep reinforcement learning-based policy simulator was developed to address the disparities. The model, tested using data from Austin, Texas, indicates that neighborhoods with higher incomes and a predominantly White demographic are leading in EV adoption. To help low-income communities keep pace, we introduced tiered subsidies and incrementally increased their amounts. In our environment, with the reward and policy design implemented, the adoption gap began to narrow when the incentive was equivalent to an increase in promotion from 20% to …

Infrared Investigation In Singapore (Iris) Observatory: Urban Heat Island Contributors And Mitigators Analysis Using Neighborhood-Scale Thermal Imaging, Clayton Miller, Vasantha Ramani, Clayton Miller

Research Collection College of Integrative Studies

This paper studies heat fluxes from contributors and mitigators of urban heat islands using thermal images and weather data. Thermal images were collected by a rooftop observatory between November 2021 and April 2022. Over the same period, weather stations were operating at several locations on a university campus in Singapore. From collected data, a method was defined to estimate sensible and latent heat fluxes from building façades, vegetation, and traffic. Before analyzing heat fluxes using the method, thermal images were calibrated against measurements made with contact surface sensors. Results show that the method can be used to study heat fluxes …

Faster Rates For Compressed Federated Learning With Client-Variance Reduction, Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtarik

Research Collection School Of Computing and Information Systems

Due to the communication bottleneck in distributed and federated learning applications, algorithms using communication compression have attracted significant attention and are widely used in practice. Moreover, the huge number, high heterogeneity, and limited availability of clients result in high client -variance. This paper addresses these two issues together by proposing compressed and clientvariance reduced methods COFIG and FRECON. We prove an O( (1+\omega)3/2\surdN+ (1+\omega)N2/3 S\epsilon2 S\epsilon2 ) bound on the number of communication rounds of COFIG in the nonconvex setting, where N is the total number of clients, S is the number of clients participating in each round, \epsilon is …

Reward Penalties On Augmented States For Solving Richly Constrained Rl Effectively, Jiang Hao, Tien Mai, Pradeep Varakanthan, Minh Huy Hoang

Research Collection School Of Computing and Information Systems

Constrained Reinforcement Learning employs trajectory-based cost constraints (such as expected cost, Value at Risk, or Conditional VaR cost) to compute safe policies. The challenge lies in handling these constraints effectively while optimizing expected reward. Existing methods convert such trajectory-based constraints into local cost constraints, but they rely on cost estimates, leading to either aggressive or conservative solutions with regards to cost. We propose an unconstrained formulation that employs reward penalties over states augmented with costs to compute safe policies. Unlike standard primal-dual methods, our approach penalizes only infeasible trajectories through state augmentation. This ensures that increasing the penalty parameter always …

Decentralized Multimedia Data Sharing In Iov: A Learning-Based Equilibrium Of Supply And Demand, Jiani Fan, Minrui Xu, Jiale Guo, Lwin Khin Shar, Jiawen Kang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. However, decentralized data sharing may not achieve the expected efficiency if there are IoV users who only want to consume the shared data but are not willing to contribute their own data to the community, resulting in incomplete information observed by other vehicles and infrastructure, which can introduce additional transmission latency. Therefore, in this paper, by modeling the …

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Stopguess: A Framework For Public-Key Authenticated Encryption With Keyword Search, Tao Xiang, Zhongming Wang, Biwen Chen, Xiaoguo Li, Peng Wang, Fei Chen

Research Collection School Of Computing and Information Systems

Public key encryption with keyword search (PEKS) allows users to search on encrypted data without leaking the keyword information from the ciphertexts. But it does not preserve keyword privacy within the trapdoors, because an adversary (e.g., untrusted server) might launch inside keyword-guessing attacks (IKGA) to guess keywords from the trapdoors. In recent years, public key authenticated encryption with keyword search (PAEKS) has become a promising primitive to counter the IKGA. However, existing PAEKS schemes focus on the concrete construction of PAEKS, making them unable to support modular construction, intuitive proof, or flexible extension. In this paper, our proposal called “StopGuess” …

Multiobjective Stochastic Optimization: A Case Of Real-Time Matching In Ride-Sourcing Markets, Guodong Lyu, Wang Chi Cheung, Chung-Piaw Teo, Hai Wang

Research Collection School Of Computing and Information Systems

Problem Definition: The job of any marketplace is to facilitate the matching of supply with demand in real-time. Success is often measured using various metrics. The challenge is to design matching algorithms to balance the trade-offs among multiple objectives in a stochastic environment, to arrive at a “compromise” solution, which minimizes say the ℓp-norm-based distance function (for some 1 ≤p ≤∞) between the attained performance metrics and the target performances.Methodology/Results: We observe that the sample-average-approximation formulation of this multi-objective stochastic optimization problem can be solved by an online algorithm that uses only gradient information from “historical” (i.e., past) sample information, …

Leveraging Multimodal Features And Item‑Level User Feedback For Bundle Construction, Yunshan Ma, Xiaohao Liu, Yinwei Wei, Zhulin Tao, Xiang Wang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of existing bundles. However, it is hard to acquire large-scale well-curated bundle dataset, especially for those platforms that have not offered bundle services before. Even for platforms with mature bundle services, there are still many items that are included in few or even zero bundles, which give rise to sparsity and cold-start challenges in the bundle construction models. To tackle these issues, we target at leveraging multimodal features, item-level user feedback signals, and the bundle composition information, …

Ddos Family: A Novel Perspective For Massive Types Of Ddos Attacks, Ziming Zhao, Zhaoxuan Li, Zhihao Zhou, Jiongchi Yu, Zhuoxue Song, Xiaofei Xie, Fan Zhang, Rui Zhang

Research Collection School Of Computing and Information Systems

Distributed Denial of Service (DDoS) defense is a profound research problem. In recent years, adversaries tend to complicate their attack strategies by crafting vast DDoS variants. On the one hand, this trend exacerbates both extremes of classification granularity (i.e., binary and attack level) in existing machine learning methods. On the other hand, massive attack categories make the filter rule table bulky, as well as cause problems of slow reaction presented in the recent state-of-the-art DDoS mitigation system. Therefore, we propose the concept of a DDoS family to reconcile/cope with these issues. The specific technical roadmap includes traffic pattern characterization, attack …

From Asset Flow To Status, Action And Intention Discovery: Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Cryptocurrency has been subject to illicit activities probably more often than traditional financial assets due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all three critical properties of early detection, good interpretability, and versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without interpretability and are only available for retrospective analysis of a specific illicit type. To tackle all these challenges, we propose Intention Monitor for early malice detection in Bitcoin, where the on-chain record data for a certain …

Understanding Newcomers' Onboarding Process In Deep Learning Projects, Junxiao Han, Jiahao Zhang, David Lo, Xin Xia, Shuigang Deng, Minghui Wu

Research Collection School Of Computing and Information Systems

Attracting and retaining newcomers are critical for the sustainable development of Open Source Software (OSS) projects. Considerable efforts have been made to help newcomers identify and overcome barriers in the onboarding process. However, fewer studies focus on newcomers’ activities before their successful onboarding. Given the rising popularity of deep learning (DL) techniques, we wonder what the onboarding process of DL newcomers is, and if there exist commonalities or differences in the onboarding process for DL and non-DL newcomers. Therefore, we reported a study to understand the growth trends of DL and non-DL newcomers, mine DL and non-DL newcomers’ activities before …

Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we re-examine the Markov property in the context of neural machine translation. We design a Markov Autoregressive Transformer (MAT) and undertake a comprehensive assessment of its performance across four WMT benchmarks. Our findings indicate that MAT with an order larger than 4 can generate translations with quality on par with that of conventional autoregressive transformers. In addition, counter-intuitively, we also find that the advantages of utilizing a higher-order MAT do not specifically contribute to the translation of longer sentences.

Knowledge Generation For Zero-Shot Knowledge-Based Vqa, Rui Cao, Jing Jiang

Research Collection School Of Computing and Information Systems

Previous solutions to knowledge-based visual question answering (K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model. Recently pre-trained LLMs have been used as both a knowledge source and a zero-shot QA model for K-VQA and demonstrated promising results. However, these recent methods do not explicitly show the knowledge needed to answer the questions and thus lack interpretability. Inspired by recent work on knowledge generation from LLMs for text-based QA, in this work we propose and test a similar knowledge-generation-based K-VQA method, which first generates knowledge from an LLM and then incorporates the generated …

Temporal Implicit Multimodal Networks For Investment And Risk Management, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a …

Attack As Detection: Using Adversarial Attack Methods To Detect Abnormal Examples, Zhe Zhao, Guangke Chen, Tong Liu, Taishan Li, Fu Song, Jingyi Wang, Jun Sun

Research Collection School Of Computing and Information Systems

As a new programming paradigm, deep learning (DL) has achieved impressive performance in areas such as image processing and speech recognition, and has expanded its application to solve many real-world problems. However, neural networks and DL are normally black-box systems; even worse, DL-based software are vulnerable to threats from abnormal examples, such as adversarial and backdoored examples constructed by attackers with malicious intentions as well as unintentionally mislabeled samples. Therefore, it is important and urgent to detect such abnormal examples. Although various detection approaches have been proposed respectively addressing some specific types of abnormal examples, they suffer from some limitations; …

Xfuzz: Machine Learning Guided Cross-Contract Fuzzing, Yinxing Xue, Jiaming Ye, Wei Zhang, Jun Sun, Lei Ma, Haijun Wang, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present xFuzz , a machine learning guided smart contract fuzzing framework. …

Representation Learning For Stack Overflow Posts: How Far Are We?, Junda He, Xin Zhou, Bowen Xu, Ting Zhang, Kisub Kim, Zhou Yang, Thung Ferdian, Ivana Clairine Irsan, David Lo

Research Collection School Of Computing and Information Systems

The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on the selection of representation models for Stack Overflow posts. As the volume of literature on Stack Overflow continues to burgeon, it highlights the need for a powerful Stack Overflow post representation model and drives researchers’ interest in developing specialized representation models that can adeptly capture the intricacies of Stack Overflow posts. The state-of-the-art (SOTA) Stack Overflow post representation models are Post2Vec and BERTOverflow, which are built …

The Influence Of Societal Nationalist Sentiment On Trade Flows, Douglas Dow, Ilya Cuypers

Research Collection Lee Kong Chian School Of Business

In recent years, the world has witnessed a backlash against globalization and a rise in populist and nationalist movements around the world. However, there appears to be little empirical research concerning how these movements, and especially nationalist sentiment, actually influence trade. Therefore, we explore how and when nationalist sentiment within a country influences trade. Our results indicate that the effect of nationalist sentiment on imports is mediated by lower participation in free trade agreements (FTAs) but not via tariffs. Furthermore, we are unable to confirm support for a direct effect of nationalist sentiment on imports, as predicted by the consumer …

Demand Pull Versus Resource Push Training Approaches To Entrepreneurship: A Field Experiment, Simone Santamaria, Niloofar Abolfathi, Ishtiag Pasha Mahmood

Research Collection Lee Kong Chian School Of Business

We compare the efficacy of two broad approaches to entrepreneurship training: a training prioritizing demand-side activities versus a training prioritizing resource-side activities. We do so by running a field experiment inside a 6-month entrepreneurship program involving 236 early-stage entrepreneurs. Inspired by our training, the first group invested more time interacting with potential customers and developing a deep understanding of customer needs and problems. The other group, in contrast, spent more time identifying and exploiting their core resources such as their network. Our results reveal that the training prioritizing demand-side activities is substantially more effective. At the end of the program, …

Reproducibility In Management Science, MiloˇS Fišar, Ben Greiner, Christoph Huber, Elena Katok, Ali I. Ozkes, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample …

Math Word Problem Generation Via Disentangled Memory Retrieval, Wei Qin, Xiaowei Wang, Zhenzhen Hu, Lei Wang, Yunshi Lan, Richang Hong

Research Collection Lee Kong Chian School Of Business

The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers’ attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based on the simple memory retrieval module. To this end, we first disentangle the training MWPs into logical description and scenario description and then record them in respective memory modules. Later, we use the given equation and topic words as …

Analyzing Global Utilization And Missed Opportunities In Debt-For-Nature Swaps With Generative Ai, Nataliya Tkachenko, Simon Frieder, Ryan-Rhys Griffiths, Christoph Nedopil

Sim Kee Boon Institute for Financial Economics

We deploy a prompt-augmented GPT-4 model to distill comprehensive datasets on the global application of debt-for-nature swaps (DNS), a pivotal financial tool for environmental conservation. Our analysis includes 195 nations and identifies 21 countries that have not yet used DNS before as prime candidates for DNS. A significant proportion demonstrates consistent commitments to conservation finance (0.86 accuracy as compared to historical swaps records). Conversely, 35 countries previously active in DNS before 2010 have since been identified as unsuitable. Notably, Argentina, grappling with soaring inflation and a substantial sovereign debt crisis, and Poland, which has achieved economic stability and gained access …

Impacts Of The E-Centralized Procurement Platforms On Business Operations - Based On A Study Of Hebei Construction Group, Baozhong Li

Dissertations and Theses Collection (Open Access)

Thanks to the rapid development of Internet technology, enterprises make an important innovation and they launch e-centralized procurement platforms in procurement activities. Taking Hebei Construction Group for a case study, this paper aims to explore the impact of E-Centralized Procurement Platforms on business operations, and comprehensively evaluates the impact of E-Centralized Procurement Platforms on enterprises’ performance by means of theoretical review, case study, empirical research, and questionnaire.

Chapter 1 firstly presents the problems of this paper and introduces the background and significance of the selected topic. Hebei Construction Group is primarily engaged in the construction industry, so this paper is …