Two Singapore Public Healthcare Ai Applications For National Screening Programs And Other Examples,
2022
Integrated Health Information Systems Pte Ltd
Two Singapore Public Healthcare Ai Applications For National Screening Programs And Other Examples, Andy Wee An Ta, Han Leong Goh, Christine Ang, Lian Yeow Koh, Ken Poon, Steven M. Miller
Research Collection School Of Computing and Information Systems
This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation-wide screening programs. The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases, targeting the rapidly increasing number of adults in the country with diabetes. In the second example, the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the …
Explanation Guided Contrastive Learning For Sequential Recommendation,
2022
Singapore Management University
Explanation Guided Contrastive Learning For Sequential Recommendation, Lei Wang, Ee-Peng Lim, Zhiwei Liu, Tianxiang Zhao
Research Collection Lee Kong Chian School Of Business
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions. Nevertheless, the existing contrastive learning-based methods fail to ensure that the positive (or negative) sequence obtained by some random augmentation (or sequence sampling) on a given anchor user sequence remains to be semantically similar (or different). When the positive and negative sequences turn out to be false positive and false negative respectively, it may lead to degraded recommendation performance. In this work, we address the above problem by proposing Explanation Guided Augmentations …
Artificial Intelligence, Consumers, And The Experience Economy,
2022
Singapore Management University
Artificial Intelligence, Consumers, And The Experience Economy, Hannah H. Chang, Anirban Mukherjee
Research Collection Lee Kong Chian School Of Business
The term Artificial Intelligence (AI) was first used by McCarthy, Minsky, Rochester, and Shannon in a proposal for a summer research project in 1955 (Solomonoff, 1985). It is widely and commonly defined to be “the science and engineering of making intelligent machines” (McCarthy, 2006). Recent technological advances and methodological developments have made AI pervasive in new marketing offerings, ranging from self-driving cars, intelligent voice assistants such as Amazon’s Alexa, to burger-making robots at restaurants and rack-moving robots inside warehouses such as Amazon’s family of robots (Kiva, Pegasus, Xanthus) and delivery drones. There is optimism, and perhaps even over-optimism, of the …
Using Machine Learning To Extract Insights From Consumer Data,
2022
Singapore Management University
Using Machine Learning To Extract Insights From Consumer Data, Hannah H. Chang, Anirban Mukherjee
Research Collection Lee Kong Chian School Of Business
Advances in digital technology have led to the digitization of everyday activities of billions of people around the world, generating vast amounts of data on human behavior. From what people buy, to what information they search for, to how they navigate the social, digital, and physical world, human behavior can now be measured at a scale and level of precision that human history has not witnessed before. These developments have created unprecedented opportunities for those interested in understanding observable human behavior–social scientists, businesses, and policymakers—to (re)examine theoretical and substantive questions regarding people’s behavior. Moreover, technology has led to the emergence …
Automatic Pull Request Title Generation,
2022
Singapore Management University
Automatic Pull Request Title Generation, Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, Donggyun Han, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Pull Requests (PRs) are a mechanism on modern collaborative coding platforms, such as GitHub. PRs allow developers to tell others that their code changes are available for merging into another branch in a repository. A PR needs to be reviewed and approved by the core team of the repository before the changes are merged into the branch. Usually, reviewers need to identify a PR that is in line with their interests before providing a review. By default, PRs are arranged in a list view that shows the titles of PRs. Therefore, it is desirable to have a precise and concise …
Social Access And Representation For Autistic Adult Livestreamers,
2022
University of Calgary
Social Access And Representation For Autistic Adult Livestreamers, Terrance Mok, Anthony Tang, Adam Mccrimmon, Lora Oehlberg
Research Collection School Of Computing and Information Systems
We interviewed 10 autistic livestreamers to understand their motivations for livestreaming on Twitch. Our participants explained that streaming helped them fulfill social desires by: supporting them in making meaningful social connections with others; giving them a safe space to practice social skills like “small talk”; and empowering them to be autistic role models and to share their true selves. This work offers an early report on how autistic individuals leverage livestreaming as a beneficial social platform while struggling with audience expectations.
Locally Varying Distance Transform For Unsupervised Visual Anomaly Detection,
2022
Singapore Management University
Locally Varying Distance Transform For Unsupervised Visual Anomaly Detection, Wen-Yan Lin, Zhonghang Liu, Siying Liu
Research Collection School Of Computing and Information Systems
Unsupervised anomaly detection on image data is notoriously unstable. We believe this is because many classical anomaly detectors implicitly assume data is low dimensional. However, image data is always high dimensional. Images can be projected to a low dimensional embedding but such projections rely on global transformations that truncate minor variations. As anomalies are rare, the final embedding often lacks the key variations needed to distinguish anomalies from normal instances. This paper proposes a new embedding using a set of locally varying data projections, with each projection responsible for persevering the variations that distinguish a local cluster of instances from …
Multi-Functional Job Roles To Support Operations In A Multi-Faceted Jewel Enabled By Ai And Digital Transformation,
2022
Singapore Management University
Multi-Functional Job Roles To Support Operations In A Multi-Faceted Jewel Enabled By Ai And Digital Transformation, Steven M. Miller
Research Collection School Of Computing and Information Systems
In this story, we highlight the way in which the use of AI enabled support systems, together with work process digital transformation and innovative approaches to job redesign, have combined to dramatically change the nature of the work of the front-line service staff who protect and support the facility and visitors at the world’s most iconic airport mall and lifestyle destination.
Field Experiments In Operations Management,
2022
Singapore Management University
Field Experiments In Operations Management, Yang Gao, Meng Li, Shujing Sun
Research Collection School Of Computing and Information Systems
While the field experiment is a powerful and well-established method to investigate causal relationships, operations management (OM) has embraced this methodology only in recent years. This paper provides a comprehensive review of the existing OM literature leveraging field experiments and serves as a one-stop guide for future application of field experiments in the OM area. We start by recapping the characteristics that distinguish field experiments from other common types of experiments and organizing the relevant OM studies by topic. Corresponding to the commonly overlooked issues in field experiment-based OM studies, we then provide a detailed roadmap, ranging from experimental design …
Equivariance And Invariance Inductive Bias For Learning From Insufficient Data,
2022
Nanyang Technological University
Equivariance And Invariance Inductive Bias For Learning From Insufficient Data, Tan Wang, Qianru Sun, Sugiri Pranata, Karlekar Jayashree, Hanwang Zhang
Research Collection School Of Computing and Information Systems
We are interested in learning robust models from insufficient data, without the need for any externally pre-trained model checkpoints. First, compared to sufficient data, we show why insufficient data renders the model more easily biased to the limited training environments that are usually different from testing. For example, if all the training "swan" samples are "white", the model may wrongly use the "white" environment to represent the intrinsic class "swan". Then, we justify that equivariance inductive bias can retain the class feature while invariance inductive bias can remove the environmental feature, leaving only the class feature that generalizes to any …
Class Is Invariant To Context And Vice Versa: On Learning Invariance For Out-Of-Distribution Generalization,
2022
Nanyang Technological University
Class Is Invariant To Context And Vice Versa: On Learning Invariance For Out-Of-Distribution Generalization, Jiaxin Qi, Kaihua Tang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang
Research Collection School Of Computing and Information Systems
Out-Of-Distribution generalization (OOD) is all about learning invariance against environmental changes. If the context in every class is evenly distributed, OOD would be trivial because the context can be easily removed due to an underlying principle: class is invariant to context. However, collecting such a balanced dataset is impractical. Learning on imbalanced data makes the model bias to context and thus hurts OOD. Therefore, the key to OOD is context balance.We argue that the widely adopted assumption in prior work—the context bias can be directly annotated or estimated from biased class prediction—renders the context incomplete or even incorrect. In contrast, …
Soci: A Toolkit For Secure Outsourced Computation On Integers,
2022
Singapore Management University
Soci: A Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Jiaming Yuan, Ximeng Liu, Yongdong Wu, Hwee Hwa Pang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Secure outsourced computation is a key technique for protecting data security and privacy in the cloud. Although fully homomorphic encryption (FHE) enables computations over encrypted data, it suffers from high computation costs in order to support an unlimited number of arithmetic operations. Recently, secure computations based on interactions of multiple computation servers and partially homomorphic encryption (PHE) were proposed in the literature, which enable an unbound number of addition and multiplication operations on encrypted data more efficiently than FHE and do not add any noise to encrypted data; however, these existing solutions are either limited in functionalities (e.g., computation on …
Improving Knowledge-Aware Recommendation With Multi-Level Interactive Contrastive Learning,
2022
Singapore Management University
Improving Knowledge-Aware Recommendation With Multi-Level Interactive Contrastive Learning, Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, Dangyang Chen
Research Collection School Of Computing and Information Systems
Incorporating Knowledge Graphs (KG) into recommeder system as side information has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the performance of the GNN-based models, from the following aspects: 1) the sparse interaction, itself, means inadequate supervision signals and limits the supervised GNN-based models; 2) the combination of sparse interactions (CF part) and redundant KG facts (KG part) further results in an unbalanced information utilization. Besides, the GNN paradigm aggregates local neighbors for node representation learning, while ignoring …
Toward Personalized Answer Generation In E-Commerce Via Multi-Perspective Preference Modeling,
2022
Singapore Management University
Toward Personalized Answer Generation In E-Commerce Via Multi-Perspective Preference Modeling, Yang Deng, Yaliang Li, Wenxuan Zhang, Bolin Ding, Wai Lam
Research Collection School Of Computing and Information Systems
Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience. Its key function, automatic answer generation for product-related questions, has been studied by aiming to generate content-preserving while question-related answers. However, an important characteristic of PQA, i.e., personalization, is neglected by existing methods. It is insufficient to provide the same “completely summarized” answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves, by taking into consideration their own preferences toward product aspects …
Evaluation Of Geo-Spebh Algorithm Based On Bandwidth For Big Data Retrieval In Cloud Computing,
2022
Department of Mathematical Sciences, Faculty of Science, Abubakar Tafawa Balewa University, Bauchi, Nigeria
Evaluation Of Geo-Spebh Algorithm Based On Bandwidth For Big Data Retrieval In Cloud Computing, Abubakar Usman Othman, Moses Timothy, Aisha Yahaya Umar, Abdullahi Salihu Audu, Boukari Souley, Abdulsalam Ya’U Gital
Al-Bahir
The fast increase in volume and speed of information created by mobile devices, along with the availability of web-based applications, has considerably contributed to the massive collection of data. Approximate Nearest Neighbor (ANN) is essential in big size databases for comparison search to offer the nearest neighbor of a given query in the field of computer vision and pattern recognition. Many hashing algorithms have been developed to improve data management and retrieval accuracy in huge databases. However, none of these algorithms took bandwidth into consideration, which is a significant aspect in information retrieval and pattern recognition. As a result, our …
Random Partition Based Adaptive Distributed Kernelized Svm For Big Data,
2022
Marshall University
Random Partition Based Adaptive Distributed Kernelized Svm For Big Data, Amrit Pal, Abishi Chowdhury, Satakshi, Husnu S. Narman, Arkabandhu Chowdhury, Manish Kumar
Computer Sciences and Electrical Engineering Faculty Research
In this paper, we present a distributed classification technique for big data by efficiently using distributed storage architecture and data processing units of a cluster. While handling such large data, the existing approaches consider specific data partitioning techniques which demand complete data be processed before partitioning. This leads to an excessive overhead of high computation and data communication. The proposed method does not require any pre-structured data partitioning technique and is also adaptive to big data mining tools. We hypothesize that an effective aggregation of the information generated from data partitions by subprocesses of the complete learning process can lead …
Distance Based Image Classification: A Solution To Generative Classification’S Conundrum?,
2022
Singapore Management University
Distance Based Image Classification: A Solution To Generative Classification’S Conundrum?, Wen-Yan Lin, Siying Liu, Bing Tian Dai, Hongdong Li
Research Collection School Of Computing and Information Systems
Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory’s [25] hierarchical generative process and non-semantic factors by …
Secure Hierarchical Deterministic Wallet Supporting Stealth Address,
2022
Shanghai Jiao Tong University
Secure Hierarchical Deterministic Wallet Supporting Stealth Address, Xin Yin, Zhen Liu, Guomin Yang, Guoxing Chen, Haojin Zhu
Research Collection School Of Computing and Information Systems
Over the past decade, cryptocurrency has been undergoing a rapid development. Digital wallet, as the tool to store and manage the cryptographic keys, is the primary entrance for the public to access cryptocurrency assets. Hierarchical Deterministic Wallet (HDW), proposed in Bitcoin Improvement Proposal 32 (BIP32), has attracted much attention and been widely used in the community, due to its virtues such as easy backup/recovery, convenient cold-address management, and supporting trust-less audits and applications in hierarchical organizations. While HDW allows the wallet owner to generate and manage his keys conveniently, Stealth Address (SA) allows a payer to generate fresh address (i.e., …
Robustness And Cross-Lingual Transfer: An Exploration Of Out-Of-Distribution Scenario In Natural Language Processing,
2022
Singapore Management University
Robustness And Cross-Lingual Transfer: An Exploration Of Out-Of-Distribution Scenario In Natural Language Processing, Yu, Sicheng
Dissertations and Theses Collection (Open Access)
Most traditional machine learning or deep learning methods are based on the premise that training data and test data are independent and identical distributed, i.e., IID. However, it is just an ideal situation. In real-world applications, test set and training data often follow different distributions, which we refer to as the out of distribution, i.e., OOD, setting. As a result, models trained with traditional methods always suffer from an undesirable performance drop on the OOD test set. It's necessary to develop techniques to solve this problem for real applications. In this dissertation, we present four pieces of work in the …
Hierarchical Semantic-Aware Neural Code Representation,
2022
Singapore Management University
Hierarchical Semantic-Aware Neural Code Representation, Yuan Jiang, Xiaohong Su, Christoph Treude, Tiantian Wang
Research Collection School Of Computing and Information Systems
Code representation is a fundamental problem in many software engineering tasks. Despite the effort made by many researchers, it is still hard for existing methods to fully extract syntactic, structural and sequential features of source code, which form the hierarchical semantics of the program and are necessary to achieve a deeper code understanding. To alleviate this difficulty, we propose a new supervised approach based on the novel use of Tree-LSTM to incorporate the sequential and the global semantic features of programs explicitly into the representation model. Unlike previous techniques, our proposed model can not only learn low-level syntactic information within …
