Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (4514)
- Computer Sciences (4439)
- Social and Behavioral Sciences (2792)
- Business (1978)
- Databases and Information Systems (1611)
-
- Software Engineering (1295)
- Economics (980)
- International and Area Studies (867)
- Asian Studies (845)
- Artificial Intelligence and Robotics (833)
- Information Security (765)
- Law (737)
- Econometrics (505)
- Graphics and Human Computer Interfaces (503)
- Numerical Analysis and Scientific Computing (467)
- Finance and Financial Management (446)
- Psychology (429)
- Organizational Behavior and Theory (421)
- Engineering (386)
- Accounting (300)
- Programming Languages and Compilers (288)
- Sociology (252)
- Public Affairs, Public Policy and Public Administration (250)
- Computer Engineering (245)
- Corporate Finance (239)
- Arts and Humanities (232)
- Communication (222)
- Political Science (221)
- Theory and Algorithms (220)
- Education (210)
- Keyword
-
- Singapore (232)
- Machine learning (82)
- Deep learning (80)
- China (73)
- Artificial intelligence (53)
-
- Privacy (49)
- Social media (45)
- Empirical study (42)
- Trust (41)
- Innovation (39)
- Blockchain (37)
- Deep Learning (37)
- Security (37)
- Training (37)
- Data mining (36)
- Corporate governance (35)
- COVID-19 (34)
- Creativity (33)
- Natural language processing (33)
- Software engineering (33)
- Culture (32)
- Task analysis (32)
- Cloud computing (31)
- Classification (30)
- Neural networks (30)
- Reinforcement learning (30)
- Sustainability (30)
- Large Language Models (29)
- Robustness (29)
- Large language models (28)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (4285)
- Research Collection Lee Kong Chian School Of Business (1089)
- Research Collection School Of Economics (779)
- Research Collection School of Social Sciences (713)
- Research Collection Yong Pung How School Of Law (672)
-
- Dissertations and Theses Collection (Open Access) (360)
- Research Collection School Of Accountancy (276)
- Research Collection College of Integrative Studies (136)
- Knowledge@SMU (55)
- Perspectives@SMU (47)
- Asian Management Insights (35)
- Research Collection Library (30)
- Dissertations and Theses Collection (18)
- Digital Narratives of Asia (17)
- Singapore Law Journal (Lexicon) (16)
- Sim Kee Boon Institute for Financial Economics (15)
- Social Space (14)
- CCX Research (11)
- Oral History Collection (11)
- Research Collection Lee Kong Chian School of Business (11)
- FORCE 2026 (8)
- Research Collection BNP Paribas Hedge Fund Centre (7)
- Centre for AI & Data Governance (2019-2025) (6)
- ROSA Research Briefs (6)
- Research Collection School of Economics (6)
- Lien Centre for Social Innovation: Research (5)
- PhD Student’s Publications Collection (5)
- SMU Corporate Reports (5)
- 2009 Yong Pung How Professorship of Law Lecture (4)
- Research Collection Centre for English Communication (4)
- Publication Type
Articles 2131 - 2160 of 8706
Full-Text Articles in Entire DC Network
Housing Wealth Shocks, Home Equity Withdrawal, And The Claiming Of Social Security Retirement Benefits, Naqun Huang, Li Jing, Amanda Ross
Housing Wealth Shocks, Home Equity Withdrawal, And The Claiming Of Social Security Retirement Benefits, Naqun Huang, Li Jing, Amanda Ross
Research Collection School Of Economics
This paper examines the impact of changes in house prices on when eligible individuals start receiving Social Security benefits. If house prices increase, financially constrained households may draw upon the additional home equity to finance expenses and delay receipt of Social Security in order to have increased lifetime monthly benefits. To address concerns that house price changes are correlated with unobserved local demand shocks, we use a control function approach and employ two different instrumental variables. We find that individuals delay Social Security claiming when house prices increase during the housing boom. The probability of claiming within two years after …
The Grid Bootstrap For Continuous Time Models, Yiu Lim Lui, Weilin Xiao, Jun Yu
The Grid Bootstrap For Continuous Time Models, Yiu Lim Lui, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This article proposes the new grid bootstrap to construct confidence intervals (CI) for the persistence parameter in a class of continuous-time models. It is different from the standard grid bootstrap of Hansen in dealing with the initial condition. The asymptotic validity of the CI is discussed under the in-fill scheme. The modified grid bootstrap leads to uniform inferences on the persistence parameter. Its improvement over in-fill asymptotics is achieved by expanding the coefficient-based statistic around its in-fill asymptotic distribution that is non-pivotal and depends on the initial condition. Monte Carlo studies show that the modified grid bootstrap performs better than …
The Effects Of The Affordable Care Act Dependent Coverage Mandate On Parents’ Labor Market Outcomes, Kim, Koh Kanghyock
The Effects Of The Affordable Care Act Dependent Coverage Mandate On Parents’ Labor Market Outcomes, Kim, Koh Kanghyock
Research Collection School Of Economics
We examine the labor market impacts of the Affordable Care Act dependent mandate (ACA-DM), which has significantly increased dependent children's health insurance coverage through parents’ employer-sponsored health benefits. Using data from the American Community Survey, we find that the ACA-DM reduced parents’ annual wages by about $2,600. However, the probability of employment and working hours only decreased marginally. The back-of-the-envelope calculation indicates that the magnitude of the estimated wage impact is similar to the increased insurance premium of a family plan due to the ACA-DM. These findings imply that a deadweight loss associated with the expansion of dependent health coverage …
Hearing, Darius Chan, Gerome Goh
Hearing, Darius Chan, Gerome Goh
Research Collection Yong Pung How School Of Law
In certain international commercial arbitrations, for instance, in expedited proceedings under the auspices of various institutional rules, the tribunal may decide the dispute based on documentary evidence only. However, in most cases, hearings are generally conducted for the tribunal to hear examination of any factual or expert witness and/or for oral argument. It is not uncommon for there to be multiple hearings in a single arbitration, with each hearing focusing on specific procedural or substantive issues. The hearing is therefore a forum for the parties to adduce evidence and/or put forward their legal views in direct confrontation with each other. …
Collaboration Scope And Product Innovation In B2b Markets: Are There Too Many Cooks Or Is It The Customer Who Spoils The Broth?, Erik Mooi, Ernst C. Osinga, Carlos D. Santos
Collaboration Scope And Product Innovation In B2b Markets: Are There Too Many Cooks Or Is It The Customer Who Spoils The Broth?, Erik Mooi, Ernst C. Osinga, Carlos D. Santos
Research Collection Lee Kong Chian School Of Business
Purpose: Product innovations are often the result of combinations of internal and external knowledge. A significant amount of Open Innovation literature has argued that working with external partners can be beneficial, in particular when this is complemented by internal R&D yet a wholesale shift to Open Innovation has not occurred. Our work demonstrates two new limits of openness, grounded in attention-based theory, that help explain why such a shift has not occurred. We argue that specific combinations of identities a firm collaborates with, i.e., whether a partner is classified as a customer, supplier, competitor, or university and/or technological center predictably …
Growing Pains: The Effect Of Generational Product Innovation On Mobile Games Performance, Liang Chen, Pengxiang Zhang, Sali Li, Scott F. Turner
Growing Pains: The Effect Of Generational Product Innovation On Mobile Games Performance, Liang Chen, Pengxiang Zhang, Sali Li, Scott F. Turner
Research Collection Lee Kong Chian School Of Business
Research Summary: Strategy research advises firms to capture generative value by continually introducing generational improvements on their existing products. This article considers a potential dark side of such strategy. We argue that generational innovation can elicit a negative near-term response from customers, as it distorts their ingrained behavioral patterns and imposes learning costs. Further, we propose that this negative effect of generational innovation will diminish when the product has a leading market position; and it will be more severe as the product's technological legacy lengthens. Using a difference-in-differences research design based on mobile game apps that multihome on two platforms, …
Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators, Shijia Li, Chunfu Jia, Pengda Qiu, Qiyuan Chen, Jiang Ming, Debin Gao
Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators, Shijia Li, Chunfu Jia, Pengda Qiu, Qiyuan Chen, Jiang Ming, Debin Gao
Research Collection School Of Computing and Information Systems
—Code virtualization is a well-known sophisticated obfuscation technique that uses custom virtual machines (VM) to emulate the semantics of original native instructions. Commercial VM-based obfuscators (e.g., Themida and VMProtect) are often abused by malware developers to conceal malicious behaviors. Since the internal mechanism of commercial obfuscators is a black box, it is a daunting challenge for the analyst to understand the behavior of virtualized programs. To figure out the code virtualization mechanism and design deobfuscation techniques, the analyst has to perform reverse-engineering on large-scale highly obfuscated programs. This knowledge learning process suffers from painful cost and imprecision. In this project, …
A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin
A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin
Research Collection School Of Computing and Information Systems
In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Then, researchers started to focus on machine learning models due to their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its …
User Satisfaction Estimation With Sequential Dialogue Act Modeling In Goal-Oriented Conversational Systems, Yang Deng, Wenxuan Zhang, Wai Lam, Hong Cheng, Helen Meng
User Satisfaction Estimation With Sequential Dialogue Act Modeling In Goal-Oriented Conversational Systems, Yang Deng, Wenxuan Zhang, Wai Lam, Hong Cheng, Helen Meng
Research Collection School Of Computing and Information Systems
User Satisfaction Estimation (USE) is an important yet challenging task in goal-oriented conversational systems. Whether the user is satisfied with the system largely depends on the fulfillment of the user’s needs, which can be implicitly reflected by users’ dialogue acts. However, existing studies often neglect the sequential transitions of dialogue act or rely heavily on annotated dialogue act labels when utilizing dialogue acts to facilitate USE. In this paper, we propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction, by jointly learning User Satisfaction Estimation and Dialogue Act Recognition tasks. In …
Investor Sentiment And Paradigm Shifts In Equity Premium Forecasting, Liya Chu, Kai Li, Tony Xue-Zhong He, Jun Tu
Investor Sentiment And Paradigm Shifts In Equity Premium Forecasting, Liya Chu, Kai Li, Tony Xue-Zhong He, Jun Tu
Research Collection Lee Kong Chian School Of Business
This study investigates the impact of investor sentiment on excess equity return forecasting. A high (low) investor sentiment may weaken the connection between fundamental economic (behavioral-based non-fundamental) predictors and market returns. We find that although fundamental variables can be strong predictors when sentiment is low, they tend to lose their predictive power when investor sentiment is high. Non-fundamental predictors perform well during high-sentiment periods while their predictive ability deteriorates when investor sentiment is low. These paradigm shifts in equity return forecasting provide a key to understanding and resolving the lack of predictive power for both fundamental and non-fundamental variables debated …
Do Underwriters Short-Change Corporations Issuing Bonds?, Choo Yong, Jeremy Goh, Lisa Yang
Do Underwriters Short-Change Corporations Issuing Bonds?, Choo Yong, Jeremy Goh, Lisa Yang
Research Collection Lee Kong Chian School Of Business
We confirm prior evidence that bonds on average are offered at prices below their immediate post-offer secondary market prices. However, in cases where banks lead-manage their own bond offerings the underpricing is significantly less as compared to other non-self-marketed offerings. These findings are robust across various matched samples and selection models. Our results suggest that the bond offering process is characterized by substantive agency conflicts between shareholders of corporations (issuers) and underwriters.
Why Commonality Persists?, Chyng Wen Tee, Raja Velu, Zhaoque Zhou
Why Commonality Persists?, Chyng Wen Tee, Raja Velu, Zhaoque Zhou
Research Collection Lee Kong Chian School Of Business
We show that order flows do not exhibit predictive power on asset returns, and their relationships have been static over time. We use a reduced-rank regression formulation to model both returns and the order flows as endogenous variables, and use investors' sentiment and attention as exogenous factors. We provide empiricalevidence to demonstrate that cross-sectional commonality in attention (sentiment) is linearly (nonlinearly) associated with both returns and order flows at the intraday level, while the sentiment and attentionmeasures themselves exhibit a nonlinear mutual relationship, thus revealing the multi-dimensional aspect of the commonality relationship.
Note To Self: How I Can Be A Better Reviewer?, Reddi Kotha
Note To Self: How I Can Be A Better Reviewer?, Reddi Kotha
Research Collection Lee Kong Chian School of Business
In this article I review some of the common errors I have made as a reviewer and suggest ways to avoid them.The progress of science depends on reviewers as much as it depends upon researchers developing new science. Reviewers provide impartial, anonymous, and expert advice to researchers, and they screen which research gets published in scientific journals. When the review process works well, it acts as an effective filter and enables dissemination of high-quality and rigorous scientific work. As so much of scientific progress depends upon review, it is surprising that reviewers learn this science by doing. Of course, there …
It's So Boring - Or Is It? Examining The Role Of Mindfulness For Work Performance And Attitudes In Monotonous Jobs, Andreas Wihler, Ute R. Hulsheger, Jochen Reb, Jochen I. Menges
It's So Boring - Or Is It? Examining The Role Of Mindfulness For Work Performance And Attitudes In Monotonous Jobs, Andreas Wihler, Ute R. Hulsheger, Jochen Reb, Jochen I. Menges
Research Collection Lee Kong Chian School Of Business
We examine the role of employee mindfulness in the context of highly monotonous work conditions. Integrating research on task monotony with theorizing on mindfulness, we hypothesized that mindfulness is negatively associated with the extent to which employees feel generally bored by their jobs. We further hypothesized that this lower employee boredom would relate to downstream outcomes in the form of job attitudes (job satisfaction and turnover intentions) and task performance. We examined both objective task performance quality and quantity to shed light on the complexity of the mindfulness-task performance relation, which has so far mostly been investigated using subjective supervisor …
On The Test Accuracy And Effective Control Of The Covid-19 Pandemic: A Case Study In Singapore, Guang Cheng, Sarah Yini Gao, Yancheng Yuan, Chenxiao Zhang, Zhichao Zheng
On The Test Accuracy And Effective Control Of The Covid-19 Pandemic: A Case Study In Singapore, Guang Cheng, Sarah Yini Gao, Yancheng Yuan, Chenxiao Zhang, Zhichao Zheng
Research Collection Lee Kong Chian School Of Business
This study examines the impact of coronavirus disease 2019 (COVID-19) test accuracy (i.e., sensitivity and specificity) on the progression of the pandemic under two scenarios of limited and unlimited test capacity. We extend the classic susceptible–exposed–infectious–recovered model to incorporate test accuracy and compare the progression of the pandemic under various sensitivities and specificities. We find that high-sensitivity tests effectively reduce the total number of infections only with sufficient testing capacity. Nevertheless, with limited test capacity and a relatively high cross-infection rate, the total number of infected cases may increase when sensitivity is above a certain threshold. Despite the potential for …
Wifitrace: Network-Based Contact Tracing For Infectious Diseases Using Passive Wifi Sensing, Amee Trivedi, Camellia Zakaria, Rajesh Krishna Balan, Ann Becker, George Corey, Prashant Shenoy
Wifitrace: Network-Based Contact Tracing For Infectious Diseases Using Passive Wifi Sensing, Amee Trivedi, Camellia Zakaria, Rajesh Krishna Balan, Ann Becker, George Corey, Prashant Shenoy
Research Collection School Of Computing and Information Systems
Contact tracing is a well-established and effective approach for the containment of the spread of infectious diseases. While Bluetooth-based contact tracing method using phones has become popular recently, these approaches suffer from the need for a critical mass adoption to be effective. In this paper, we present WiFiTrace, a network-centric approach for contact tracing that relies on passive WiFi sensing with no client-side involvement. Our approach exploits WiFi network logs gathered by enterprise networks for performance and security monitoring, and utilizes them for reconstructing device trajectories for contact tracing. Our approach is specifically designed to enhance the efficacy of traditional …
Mask-Guided Deformation Adaptive Network For Human Parsing, Aihua Mao, Yuan Liang, Jianbo Jiao, Yongtuo Liu, Shengfeng He
Mask-Guided Deformation Adaptive Network For Human Parsing, Aihua Mao, Yuan Liang, Jianbo Jiao, Yongtuo Liu, Shengfeng He
Research Collection School Of Computing and Information Systems
Due to the challenges of densely compacted body parts, nonrigid clothing items, and severe overlap in crowd scenes, human parsing needs to focus more on multilevel feature representations compared to general scene parsing tasks. Based on this observation, we propose to introduce the auxiliary task of human mask and edge detection to facilitate human parsing. Different from human parsing, which exploits the discriminative features of each category, human mask and edge detection emphasizes the boundaries of semantic parsing regions and the difference between foreground humans and background clutter, which benefits the parsing predictions of crowd scenes and small human parts. …
Jscsp: A Novel Policy-Based Xss Defense Mechanism For Browsers, Guangquan Xu, Xiaofei Xie, Shuhan Huang, Jun Zhang, Lei Pan, Wei Lou, Kaitai Liang
Jscsp: A Novel Policy-Based Xss Defense Mechanism For Browsers, Guangquan Xu, Xiaofei Xie, Shuhan Huang, Jun Zhang, Lei Pan, Wei Lou, Kaitai Liang
Research Collection School Of Computing and Information Systems
To mitigate cross-site scripting attacks (XSS), the W3C group recommends web service providers to employ a computer security standard called Content Security Policy (CSP). However, less than 3.7 percent of real-world websites are equipped with CSP according to Google’s survey. The low scalability of CSP is incurred by the difficulty of deployment and non-compatibility for state-of-art browsers. To explore the scalability of CSP, in this article, we propose JavaScript based CSP (JSCSP), which is able to support most of real-world browsers but also to generate security policies automatically. Specifically, JSCSP offers a novel self-defined security policy which enforces essential confinements …
Analyzing Offline Social Engagements: An Empirical Study Of Meetup Events Related To Software Development, Abhishek Sharma, Gede Artha Azriadi Prana, Anamika Sawhney, Nachiappan Nagappan, David Lo
Analyzing Offline Social Engagements: An Empirical Study Of Meetup Events Related To Software Development, Abhishek Sharma, Gede Artha Azriadi Prana, Anamika Sawhney, Nachiappan Nagappan, David Lo
Research Collection School Of Computing and Information Systems
Software developers use a variety of social mediachannels and tools in order to keep themselves up to date,collaborate with other developers, and find projects to contributeto. Meetup is one of such social media used by softwaredevelopers to organize community gatherings. We in this work,investigate the dynamics of Meetup groups and events relatedto software development. Our work is different from previouswork as we focus on the actual event and group data that wascollected using Meetup API.In this work, we performed an empirical study of eventsand groups present on Meetup which are related to softwaredevelopment. First, we identified 6,327 Meetup groups related …
On The Influence Of Biases In Bug Localization: Evaluation And Benchmark, Ratnadira Widyasari, Stefanus Agus Haryono, Ferdian Thung, Jieke Shi, Constance Tan, Fiona Wee, Jack Phan, David Lo
On The Influence Of Biases In Bug Localization: Evaluation And Benchmark, Ratnadira Widyasari, Stefanus Agus Haryono, Ferdian Thung, Jieke Shi, Constance Tan, Fiona Wee, Jack Phan, David Lo
Research Collection School Of Computing and Information Systems
Bug localization is the task of identifying parts of thesource code that needs to be changed to resolve a bug report.As this task is difficult, automatic bug localization tools havebeen proposed. The development and evaluation of these toolsrely on the availability of high-quality bug report datasets. In2014, Kochhar et al. identified three biases in datasets used toevaluate bug localization techniques: (1) misclassified bug report,(2) already localized bug report, and (3) incorrect ground truthfile in a bug report. They reported that already localized bugreports statistically significantly and substantially impact buglocalization results, and thus should be removed. However, theirevaluation is still limited, …
Interpretable Knowledge Tracing: Simple And Efficient Student Modeling With Causal Relations, Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Feida Zhu
Interpretable Knowledge Tracing: Simple And Efficient Student Modeling With Causal Relations, Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Feida Zhu
Research Collection School Of Computing and Information Systems
Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have shown significant predictive performance compared with traditional models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are …
Sdac: A Slow-Aging Solution For Android Malware Detection Using Semantic Distance Based Api Clustering, Jiayun Xu, Yingjiu Li, Robert H. Deng, Xu Ke
Sdac: A Slow-Aging Solution For Android Malware Detection Using Semantic Distance Based Api Clustering, Jiayun Xu, Yingjiu Li, Robert H. Deng, Xu Ke
Research Collection School Of Computing and Information Systems
A novel slow-aging solution named SDAC is proposed to address the model aging problem in Android malware detection, which is due to the lack of adapting to the changes in Android specifications during malware detection. Different from periodic retraining of detection models in existing solutions, SDAC evolves effectively by evaluating new APIs' contributions to malware detection according to existing API's contributions. In SDAC, the contributions of APIs are evaluated by their contexts in the API call sequences extracted from Android apps. A neural network is applied on the sequences to assign APIs to vectors, among which the differences of API …
Debiasing Nlu Models Via Causal Intervention And Counterfactual Reasoning, Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing
Debiasing Nlu Models Via Causal Intervention And Counterfactual Reasoning, Bing Tian, Yixin Cao, Yong Zhang, Chunxiao Xing
Research Collection School Of Computing and Information Systems
Recent studies have shown that strong Natural Language Understanding (NLU) models are prone to relying on annotation biases of the datasets as a shortcut, which goes against the underlying mechanisms of the task of interest. To reduce such biases, several recent works introduce debiasing methods to regularize the training process of targeted NLU models. In this paper, we provide a new perspective with causal inference to fnd out the bias. On the one hand, we show that there is an unobserved confounder for the natural language utterances and their respective classes, leading to spurious correlations from training data. To remove …
State Graph Reasoning For Multimodal Conversational Recommendation, Yuxia Wu, Lizi Liao, Gangyi Zhang, Wenqiang Lei, Guoshuai Zhao, Xueming Qian, Tat-Seng Chua
State Graph Reasoning For Multimodal Conversational Recommendation, Yuxia Wu, Lizi Liao, Gangyi Zhang, Wenqiang Lei, Guoshuai Zhao, Xueming Qian, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Conversational recommendation system (CRS) attracts increasing attention in various application domains such as retail and travel. It offers an effective way to capture users’ dynamic preferences with multi-turn conversations. However, most current studies center on the recommendation aspect while over-simplifying the conversation process. The negligence of complexity in data structure and conversation flow hinders their practicality and utility. In reality, there exist various relationships among slots and values, while users’ requirements may dynamically adjust or change. Moreover, the conversation often involves visual modality to facilitate the conversation. These actually call for a more advanced internal state representation of the dialogue …
Towards Efficient Annotations For A Human-Ai Collaborative, Clinical Decision Support System: A Case Study On Physical Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia
Towards Efficient Annotations For A Human-Ai Collaborative, Clinical Decision Support System: A Case Study On Physical Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being explored to support various decision-making tasks in health (e.g. rehabilitation assessment). However, the development of such AI/ML-based decision support systems is challenging due to the expensive process to collect an annotated dataset. In this paper, we describe the development process of a human-AI collaborative, clinical decision support system that augments an ML model with a rule-based (RB) model from domain experts. We conducted its empirical evaluation in the context of assessing physical stroke rehabilitation with the dataset of three exercises from 15 post-stroke survivors and therapists. Our results bring …
Match In My Way: Fine-Grained Bilateral Access Control For Secure Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Yingjiu Li, Yinghui Zhang, Guowen Xu, Xinyi Huang, Robert H. Deng
Match In My Way: Fine-Grained Bilateral Access Control For Secure Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Yingjiu Li, Yinghui Zhang, Guowen Xu, Xinyi Huang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Cloud-fog computing is a novel paradigm to extend the functionality of cloud computing to provide a variety of on demand data services via the edge network. Many cryptographic tools have been introduced to preserve data confidentiality against the untrustworthy network and cloud servers. However, how to efficiently identify and retrieve useful data from a large number of ciphertexts without a costly decryption mechanism remains a challenging problem. In this paper, we introduce a cloud fog-device data sharing system (CFDS) with data confidentiality and data source identification simultaneously based on a new cryptographic primitive named matchmaking attribute-based encryption (MABE) by extending …
Scaled Pca: A New Approach To Dimension Reduction, Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou
Scaled Pca: A New Approach To Dimension Reduction, Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou
Research Collection Lee Kong Chian School Of Business
This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to …
Mwptoolkit: An Open-Source Framework For Deep Learning-Based Math Word Problem Solvers, Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim
Mwptoolkit: An Open-Source Framework For Deep Learning-Based Math Word Problem Solvers, Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bing Tian Dai, Yan Wang, Dongxiang Zhang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Duplicate record, see https://ink.library.smu.edu.sg/sis_research/7680/. Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs. However, most existing methods are benchmarked solely on one or two datasets, varying in different configurations, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods. This paper presents MWPToolkit, the first open-source framework for solving MWPs. In MWPToolkit, we decompose the procedure of existing MWP solvers into multiple core components and decouple …
Neuron Coverage-Guided Domain Generalization, Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang
Neuron Coverage-Guided Domain Generalization, Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang
Research Collection School Of Computing and Information Systems
This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN can help to explore possible defects of DNN (i.e.,misclassification). More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization …
Learning Program Semantics With Code Representations: An Empirical Study, Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu
Learning Program Semantics With Code Representations: An Empirical Study, Jing Kai Siow, Shangqing Liu, Xiaofei Xie, Guozhu Meng, Yang Liu
Research Collection School Of Computing and Information Systems
Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works propose diverse approaches to learn the program semantics for different tasks and these works have achieved state-of-the-art performance. However, currently, a comprehensive and systematic study on evaluating different program representation techniques across diverse tasks is still missed. From this starting point, in this paper, we conduct an empirical study to evaluate different program representation techniques. Specifically, we categorize current mainstream code representation techniques into four categories i.e., Feature-based, Sequence-based, Tree-based, and Graph-based program representation technique and …