Digital Commons Network^™

Owner-Free Distributed Symmetric Searchable Encryption Supporting Conjunctive Queries, Qiuyun Tong, Xinghua Li, Yinbin Miao, Yunwei Wang, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an Owner-free Distributed Symmetric searchable encryption supporting Conjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) …

Diffuse3d: Wide-Angle 3d Photography Via Bilateral Diffusion, Yutao Jiang, Yang Zhou, Yuan Liang, Wenxi Liu, Jianbo Jiao, Yuhui Quan, Shengfeng He

Research Collection School Of Computing and Information Systems

This paper aims to resolve the challenging problem of wide-angle novel view synthesis from a single image, a.k.a. wide-angle 3D photography. Existing approaches rely on local context and treat them equally to inpaint occluded RGB and depth regions, which fail to deal with large-region occlusion (i.e., observing from an extreme angle) and foreground layers might blend into background inpainting. To address the above issues, we propose Diffuse3D which employs a pre-trained diffusion model for global synthesis, while amending the model to activate depth-aware inference. Our key insight is to alter the convolution mechanism in the denoising process. We inject depth …

Learning Provably Stabilizing Neural Controllers For Discrete-Time Stochastic Systems, Matin Ansaripour, Krishnendu Chatterjee, A. Thomas Henzinger, Mathias Lechner, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problem of learning control policies in discrete-time stochastic systems which guarantee that the system stabilizes within some specified stabilization region with probability 1. Our approach is based on the novel notion of stabilizing ranking supermartingales (sRSMs) that we introduce in this work. Our sRSMs overcome the limitation of methods proposed in previous works whose applicability is restricted to systems in which the stabilizing region cannot be left once entered under any control policy. We present a learning procedure that learns a control policy together with an sRSM that formally certifies probability 1 stability, both learned as neural …

Does The Federal Reserve Obtain Competitive And Appropriate Prices In Monetary Policy Implementation?, Yu An, Zhaogang Song

Research Collection Lee Kong Chian School Of Business

Many of the Federal Reserve’s (the Fed’s) monetary policy operations involve trading with primary dealers. We find that, for agency MBS, dealers charge 2.5 cents (per $100 face value) higher selling to the Fed than to non-Fed customers. Controlling for the same dealer, same security, and same trading time, this discriminatory pricing likely arises from dealers’ market power rather than inventory costs. Further, matching trade size reduces the price differential by more than half, implying that dealers’ market power greatly relates to the Fed’s purchases in large amounts, whereas the Fed’s limited breadth of counterparty choice also plays some role.

Visilience: An Interactive Visualization Framework For Resilience Analysis Using Control-Flow Graph, Hailong Jiang, Shaolun Ruan, Bo Fang, Yong Wang, Qiang Guan

Research Collection School Of Computing and Information Systems

Soft errors have become one of the main concerns for the resilience of HPC applications, as these errors can cause HPC applications to generate serious outcomes such as silent data corruption (SDC). Many approaches have been proposed to analyze the resilience of HPC applications. However, existing studies rarely address the challenges of analysis result perception. Specifically, resilience analysis techniques often produce a massive volume of unstructured data, making it difficult for programmers to perform resilience analysis due to non-intuitive raw data. Furthermore, different analysis models produce diverse results with multiple levels of detail, which can create obstacles to compare and …

Towards An Effective And Interpretable Refinement Approach For Dnn Verification, Jiaying Li, Guangdong Bai, Long H. Pham, Jun Sun

Research Collection School Of Computing and Information Systems

Recently, several abstraction refinement techniques have been proposed to improve the verification precision for deep neural networks (DNNs). However, these techniques usually take many refinement steps to verify a property and the refinement decision in each step is hard to interpret, thus hindering their analysis, reasoning and optimization.In this work, we propose SURGEON, a novel DNN verification refinement approach that is both effective and interpretable, allowing analyst to understand why and how each refinement decision is made. The main insight is to leverage the ‘interpretable’ nature of debugging processes and formulate the verification refinement problem as a debugging problem. Given …

Hallucination Detection: Robustly Discerning Reliable Answers In Large Language Models, Yuyuan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects …

Sense Of Control Mediates The Relation Between Discrimination And Executive Functions In Middle-Aged And Older Adults: A Structural Equation Modeling Approach, Wee Qin Ng, Gilaine Rui Ng, Hwajin Yang

Research Collection School of Social Sciences

Background Despite previous findings of a negative association between everyday discrimination and executive functions (EF) - a set of domain-general cognitive control processes - in middle-aged and older adults, less is known about the underlying mechanism. Thus, we focused on sense on control and its two facets - perceived constraints and personal mastery - as potential psychosocial mediators of this relation. Methods By analyzing a nationally representative adult cohort from the Midlife Development in the United States (MIDUS) 2 study, we examined two mediational models: a single mediation model with sense of control and a parallel mediation model with perceived …

When Planetary Cosmopolitanism Meets The Buddhist Ethic: Recycling, Karma And Popular Ecology In Singapore, Siew Ying Shee, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

By thinking with and through Buddhist cosmology, this paper explores the emergence of an ethical sensibility—what we call planetary cosmopolitanism—that is based on not just a spatially expanded ethic of care to ecological worlds, but also a temporally extended sense of justice to the future Earth. This transtemporal sense of ethical becoming reflects how the possibility of future ‘rebirth’ and accountability for past actions can motivate new ecological consciousness in the present. We forge these ideas through an empirical focus on popular Buddhist ecological practices in Singapore, where green recovery visions have primarily been driven by a secular and technocratic …

Why Do Farmers’ Cooperatives Fail In A Market Economy? Rediscovering Chayanov With The Chinese Experience, Zhanping Hu, Qian Forrest Zhang, John A. Donaldson

Research Collection School of Social Sciences

In The Theory of Peasant Cooperatives, Chayanov develops the theories of differential optima and vertical integration, which stress the vulnerability of peasant farming in capitalist markets, and argues that cooperatives can support smallholders only if they operate as ‘a cooperative movement’, are buttressed by a strong ‘cooperative culture’, and achieve ‘vertical integration’. Based on extensive fieldwork in China, we identify six major obstacles that explain the failure of most cooperatives. Chayanov’s arguments caution us to not only the vital importance of cooperatives to the resilience of peasant farming, but also the apparently insurmountable obstacles that cooperatives face in market economies.

Motivations, Policies And Performance: A Review Of China’S Culture ‘Going Out’ Strategy, Jihua Yang, David Ocon

Research Collection School of Social Sciences

Few countries have advanced their culture globally as China has done in the last two decades with its culture ‘going out’ strategy. Promoting cultural goods and services overseas and fostering cultural exchanges, the strategy is also part of the country’s efforts to advance its soft power abroad. Broadly, there are two contrasting perspectives on the strategy’s performance. Because of its stability and financial muscle, local analysts generally praise it, while the overarching official involvement provokes suspicion in some international contexts. This article provides a neutral assessment of the implementation, achievements, and impact of China’s culture ‘going out’ strategy, investigating its …

Using The Typescript Compiler To Fix Erroneous Node.Js Snippets, Brittany Reid, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Most online code snippets do not run. This means that developers looking to reuse code from online sources must manually find and fix errors. We present an approach for automatically evaluating and correcting errors in Node.js code snippets: Node Code Correction (NCC). NCC leverages the ability of the TypeScript compiler to generate errors and inform code corrections through the combination of TypeScript’s builtin codefixes, our own targeted fixes, and deletion of erroneous lines. Compared to existing approaches using linters, our findings suggest that NCC is capable of detecting a larger number of errors per snippet and more error types, and …

Understanding The Effect Of Counterfactual Explanations On Trust And Reliance On Ai For Human-Ai Collaborative Clinical Decision Making, Min Hun Lee, Chong Jun Chew

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) is increasingly being considered to assist human decision-making in high-stake domains (e.g. health). However, researchers have discussed an issue that humans can over-rely on wrong suggestions of the AI model instead of achieving human AI complementary performance. In this work, we utilized salient feature explanations along with what-if, counterfactual explanations to make humans review AI suggestions more analytically to reduce overreliance on AI and explored the effect of these explanations on trust and reliance on AI during clinical decision-making. We conducted an experiment with seven therapists and ten laypersons on the task of assessing post-stroke survivors' quality …

Tackling Misperceptions About Immigrants With Fact-Checking Interventions: A Randomized Survey Experiment, Syngjoo Choi, Chung-Yoon Choi, Kim

Research Collection School Of Economics

We conduct a randomized online survey experiment to study the impact of fact-checking offers and financial incentives on misperceptions about immigrants. We find that natives overestimate the number of immigrants and the social and economic costs of immigration. Offering a free check of the factual information about immigrants reduces these misperceptions; it becomes more effective when combined with financial incentives. However, more than half of the participants never took up offers to check factual information. Using a model of information search with limited attention, we identify the presence of non-negligible costs of information search and processing, which limits the effectiveness …

Limit Theory For Locally Flat Functional Coefficient Regression, Peter C. B. Phillips, Ying Wang

Research Collection School Of Economics

Functional coefficient (FC) regressions allow for systematic flexibility in the responsiveness of a dependent variable to movements in the regressors, making them attractive in applications where marginal effects may depend on covariates. Such models are commonly estimated by local kernel regression methods. This paper explores situations where responsiveness to covariates is locally flat or fixed. The paper develops new asymptotics that take account of shape characteristics of the function in the locality of the point of estimation. Both stationary and integrated regressor cases are examined. The limit theory of FC kernel regression is shown to depend intimately on functional shape …

Boosting Adversarial Training In Safety-Critical Systems Through Boundary Data Selection, Yifan Jia, Christopher M. Poskitt, Peixin Zhang, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay

Research Collection School Of Computing and Information Systems

AI-enabled collaborative robots are designed to be used in close collaboration with humans, thus requiring stringent safety standards and quick response times. Adversarial attacks pose a significant threat to the deep learning models of these systems, making it crucial to develop methods to improve the models' robustness against them. Adversarial training is one approach to improve their robustness: it works by augmenting the training data with adversarial examples. This, unfortunately, comes with the cost of increased computational overhead and extended training times. In this work, we balance the need for additional adversarial data with the goal of minimizing the training …

Ubisurface: A Robotic Touch Surface For Supporting Mid-Air Planar Interactions In Room-Scale Vr, Ryota Gomi, Kazuki Takashima, Yuki Onishi, Kazuyuki Fujita, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

Room-scale VR has been considered an alternative to physical office workspaces. For office activities, users frequently require planar input methods, such as typing or handwriting, to quickly record annotations to virtual content. However, current off-The-shelf VR HMD setups rely on mid-Air interactions, which can cause arm fatigue and decrease input accuracy. To address this issue, we propose UbiSurface, a robotic touch surface that can automatically reposition itself to physically present a virtual planar input surface (VR whiteboard, VR canvas, etc.) to users and to permit them to achieve accurate and fatigue-less input while walking around a virtual room. We design …

Invariant Training 2d-3d Joint Hard Samples For Few-Shot Point Cloud Recognition, Xuanyu Yi, Jiajun Deng, Qianru Sun, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang

Research Collection School Of Computing and Information Systems

We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-pretrained 2D model. Surprisingly, such an ensemble, though seems trivial, has hardly been shown effective in recent 2D-3D models. We find out the crux is the less effective training for the “joint hard samples”, which have high confidence prediction on different wrong labels, implying that the 2D and 3D models do not collaborate well. To this end, our proposed invariant training strategy, called INVJOINT, does not only emphasize the training more on the hard …

Configuring Timing Parameters To Ensure Execution-Time Opacity In Timed Automata, Étienne André, Engel Lefaucheux, Didier Lime, Dylan Marinho, Jun Sun

Research Collection School Of Computing and Information Systems

Timing information leakage occurs whenever an attacker successfully deduces confidential internal information by observing some timed information such as events with timestamps. Timed automata are an extension of finite-state automata with a set of clocks evolving linearly and that can be tested or reset, making this formalism able to reason on systems involving concurrency and timing constraints. In this paper, we summarize a recent line of works using timed automata as the input formalism, in which we assume that the attacker has access (only) to the system execution time. First, we address the following execution-time opacity problem: given a timed …

Flacgec: A Chinese Grammatical Error Correction Dataset With Fine-Grained Linguistic Annotation, Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu

Research Collection School Of Computing and Information Systems

Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences …

Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein

Research Collection School Of Computing and Information Systems

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts ( e.g. , source code or executable code) into a form that is suitable for learning. Traditionally, researchers and practitioners have relied on manually selected features, based on expert knowledge, for the task at hand. Such knowledge is sometimes imprecise and generally incomplete. To overcome this limitation, many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable …

Evlog: Identifying Anomalous Logs Over Software Evolution, Yintong Huo, Cheryl Lee, Yuxin Su, Shiwen Shan, Jinyang Liu, Michael Lyu

Research Collection School Of Computing and Information Systems

Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving …

Reachability Poorman Discrete-Bidding Games, Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider bidding games, a class of two-player zerosum graph games. The game proceeds as follows. Both players have bounded budgets. A token is placed on a vertex of a graph, in each turn the players simultaneously submit bids, and the higher bidder moves the token, where we break bidding ties in favor of Player 1. Player 1 wins the game iff the token visits a designated target vertex. Weconsider, for the first time, poorman discrete-bidding in which the granularity of the bids is restricted and the higher bid is paid to the bank. Previous work either did not impose …

Face Recognition Under Adverse Viewing Conditions: Implications For Eyewitness Testimony, Charles C. F. Or, Denise Y. Lim, Siyuan Chen, Alan L. F. Lee

Research Collection Yong Pung How School Of Law

Eyewitness testimony forms an important component in deciding whether a case can be prosecuted. Yet, many criminal perpetrators deliberately conceal their faces with disguises or under dim lighting, undermining eyewitness accuracy. This article reviews recent studies to characterize the factors that impair face recognition performance, specifically, various forms of face disguise (e.g., face masks, sunglasses) and different lighting conditions. Research shows that identification accuracy, alongside eyewitness confidence and decision bias, all affect the reliability of eyewitness accounts. A consistent finding across studies is that face-identification accuracy can be improved by matching the viewing conditions during the police lineup with those …

Unpacking Constitutional Literacy, De Maartje Visser, Brian Christopher Jones

Research Collection Yong Pung How School Of Law

The contemporary crisis in relation to constitutional literacy relates not to the lack of knowledge that citizens possess about fundamental constitutional texts, but to the considerable lack of development in relation to what constitutional literacy itself entails. This article accordingly unpacks the notion of constitutional literacy: its importance, its characteristics, its variable nature, and its potential downsides. Using a comparative lens, the article invites reflection on the role that we expect citizens to play in our democracies, and especially the associated knowledge and skills required for successful state performance. We suggest that constitutional literacy is exceptionally multifaceted and fluid in …

Toolkit For The Evaluation Of Crypto Tax Risks (Outline), Vincent Ooi

Research Collection Yong Pung How School Of Law

This Toolkit seeks to provide a practical, structured framework for the identification and assessment of crypto tax risks that can be used by tax administrations. It has three main parts. Firstly, an introduction to the Toolkit and how it should be used. Secondly, a series of questionnaires to complete. Thirdly, a commentary to provide additional context and details on each part of the Toolkit and its application. As tax administrations go through the questionnaires, they can rely on the Commentary to complement their existing knowledge and expertise to accurately identify the crypto tax risks facing their domestic tax systems.

Esg News, Future Cash Flows, And Firm Value, Francois Derrin, Philipp Krueger, Augustin Landier, Tianhao Yao

Research Collection Lee Kong Chian School Of Business

We investigate the expected consequences of negative ESG news on firms’ future profits. After learning about negative ESG news, analysts significantly downgrade their forecasts at short and longer horizons. Negative ESG news affect forecasts more strongly at longer horizons than other types of negative corporate news. The negative revisions of earnings forecasts following negative ESG news reflect expectations of lower future sales (rather than higher future costs). Quantitatively, forecast revisions can explain most of the negative impacts of ESG news on firm value. Analysts are correct to revise forecasts downward following negative ESG news and ESG sensitive analysts tend to …

Smu Libraries Annual Report 2022-2023: Transforming Knowledge Into Lasting Connections, Singapore Management University

SMU Corporate Reports

The annual report gives a compilation of SMU Libraries’ key initiatives for the academic year 2022 – 2023. It is one way of communicating the positive impact that libraries have on the teaching, learning and research needs of the SMU community, and I hope the following pages give you a comprehensive view of the myriad ways in which the Libraries play a role in the academic journey of our community.

Ai In Universities And Libraries, Aaron Tay

Research Collection Library

Everyone is familiar with ChatGPT. But can ChatGPT alone be used for information retrieval? In this Keynote speech, Aaron argues that ChatGPT or similar large language models alone is not sufficient for information retrieval. As databases such as Scopus, Dimensions announce new beta search tools that leverage generative AI, he provides a overview of how these tools that blend search with generative AI (large language models) work (using a technique known as Retrieval Augmented Generation or RAG) and explains the pros and cons of this new class of search tools.

He ends with a prediction of what the next 3 …

Singapore Resilience Study, Paulin T. Straughan, Grace Cheong, Wensi Lim, Rachel Ngu, Yan Er Tan, Yen Cong Wong

ROSA Research Briefs

This study was conducted in partnership with Income Insurance and ROSA to establish the baseline measurement of resilience in the Singapore population so as to provide the benchmark for future longitudinal studies and render visible the significance of resilience to well-being. In view of the COVID19 pandemic, notable emphasis has been placed on resilience as a vital quality to better prepare our population against future uncertainties and maintain well-being in spite of adversity. Through this study, a set of validated measures were developed to assess resilience levels across the four domains – mental, social, physical and financial resilience. The present …