Open Access. Powered by Scholars. Published by Universities.®

Databases and Information Systems Commons™

Open Access. Powered by Scholars. Published by Universities.®

Singapore Management University

Discipline
Keyword
Publication Year
Publication
Publication Type

Articles 631 - 660 of 3555

Full-Text Articles in Databases and Information Systems

Meta-Complementing The Semantics Of Short Texts In Neural Topic Models, Ce Zhang, Hady Wirawan Lauw Nov 2022

Meta-Complementing The Semantics Of Short Texts In Neural Topic Models, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Topic models infer latent topic distributions based on observed word co-occurrences in a text corpus. While typically a corpus contains documents of variable lengths, most previous topic models treat documents of different lengths uniformly, assuming that each document is sufficiently informative. However, shorter documents may have only a few word co-occurrences, resulting in inferior topic quality. Some other previous works assume that all documents are short, and leverage external auxiliary data, e.g., pretrained word embeddings and document connectivity. Orthogonal to existing works, we remedy this problem within the corpus itself by proposing a Meta-Complement Topic Model, which improves topic quality …

Go to article

Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang Nov 2022

Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper we study how to measure stereotypical bias in pre-trained vision-language models. We leverage a recently released text-only dataset, StereoSet, which covers a wide range of stereotypical bias, and extend it into a vision-language probing dataset called VLStereoSet to measure stereotypical bias in vision-language models. We analyze the differences between text and image and propose a probing task that detects bias by evaluating a model’s tendency to pick stereotypical statements as captions for anti-stereotypical images. We further define several metrics to measure both a vision-language model’s overall stereotypical bias and its intra-modal and inter-modal bias. Experiments on six …

Go to article

M-Edese: Multi-Domain, Easily Deployable, And Efficiently Searchable Encryption, Jiaming Yuan, Yingjiu Li, Jianting Ning, Robert H. Deng Nov 2022

M-Edese: Multi-Domain, Easily Deployable, And Efficiently Searchable Encryption, Jiaming Yuan, Yingjiu Li, Jianting Ning, Robert H. Deng

Research Collection School Of Computing and Information Systems

Searchable encryption is an essential component of cryptography, which allows users to search for keywords and retrieve records from an encrypted database at cloud storage while ensuring the confidentiality of users’ queries. While most existing research on searchable encryption focuses on the single domain setting, we propose the first Multi-Domain, Easily-Deployable, Efficiently-Searchable Encryption (M-EDESE) system that allows users to query keywords cross domains with high efficiency and preserved privacy without additional cooperation from the cloud storage. In the multi-domain setting, a user who belongs to a domain can query keywords from another domain under an inter-domain partnership. Any party can …

Go to article

What Motivates Software Practitioners To Contribute To Inner Source?, Zhiyuan Wan, Xin Xia, Yun Zhang, David Lo, Daibing Zhou, Qiuyuan Chen, Ahmed E. Hassan Nov 2022

What Motivates Software Practitioners To Contribute To Inner Source?, Zhiyuan Wan, Xin Xia, Yun Zhang, David Lo, Daibing Zhou, Qiuyuan Chen, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Software development organizations have adopted open source development practices to support or augment their software development processes, a phenomenon referred to as inner source. Given the rapid adoption of inner source, we wonder what motivates software practitioners to contribute to inner source projects. We followed a mixed-methods approach--a qualitative phase of interviews with 20 interviewees, followed by a quantitative phase of an exploratory survey with 124 respondents from 13 countries across four continents. Our study uncovers practitioners' motivation to contribute to inner source projects, as well as how the motivation differs from what motivates practitioners to participate in open source …

Go to article

Graph Neural Network With Self-Attention And Multi-Task Learning For Credit Default Risk Prediction, Zihao Li, Xianzhi Wang, Lina Yao, Yakun Chen, Guandong Xu, Ee-Peng Lim Nov 2022

Graph Neural Network With Self-Attention And Multi-Task Learning For Credit Default Risk Prediction, Zihao Li, Xianzhi Wang, Lina Yao, Yakun Chen, Guandong Xu, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

We propose a graph neural network with self-attention and multi-task learning (SaM-GNN) to leverage the advantages of deep learning for credit default risk prediction. Our approach incorporates two parallel tasks based on shared intermediate vectors for input vector reconstruction and credit default risk prediction, respectively. To better leverage supervised data, we use self-attention layers for feature representation of categorical and numeric data; we further link raw data into a graph and use a graph convolution module to aggregate similar information and cope with missing values during constructing intermediate vectors. Our method does not heavily rely on feature engineering work and …

Go to article

Delving Deep Into Pixelized Face Recovery And Defense, Zhixuan Zhong, Yong Du, Yang Zhou, Jiangzhong Cao, Shengfeng He Nov 2022

Delving Deep Into Pixelized Face Recovery And Defense, Zhixuan Zhong, Yong Du, Yang Zhou, Jiangzhong Cao, Shengfeng He

Research Collection School Of Computing and Information Systems

Pixelization is arguably one of the most well-adopted deterministic obfuscation techniques for privacy preservation purposes. Although the recovery of pixelized faces is underexplored, the powerful deep neural networks might combat this problem in a data-driven manner. As a consequence, an unbreakable pixelization approach is desired. To achieve this goal, in this paper, we delve into two contradictory problems of unrecoverable pixelization and its counterpart, depixelization, by leveraging the best recovery to strengthen the robustness of the unrecoverable pixelized patterns. In particular, on the offensive end of recovery, we combat the large and continuous nature of pixelized regions by proposing two …

Go to article

Photovoltaic Cells For Energy Harvesting And Indoor Positioning, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef Nov 2022

Photovoltaic Cells For Energy Harvesting And Indoor Positioning, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef

Research Collection School Of Computing and Information Systems

We propose SoLoc, a lightweight probabilistic fingerprinting-based technique for energy-free device-free indoor localization. The system harnesses photovoltaic currents harvested by the photovoltaic cells in smart environments for simultaneously powering digital devices and user positioning. The basic principle is that the location of the human interferes with the lighting received by the photovoltaic cells, thus producing a location fingerprint on the generated photocurrents. To ensure resilience to noisy measurements, SoLoc constructs probability distributions as a photovoltaic fingerprint at each location. Then, we employ a probabilistic graphical model for estimating the user location in the continuous space. Results show that SoLoc can …

Go to article

Investigating Bloom's Cognitive Skills In Foundation And Advanced Programming Courses From Students' Discussions, Joel Jer Wei Lim, Gottipati Swapna, Kyong Jin Shim Nov 2022

Investigating Bloom's Cognitive Skills In Foundation And Advanced Programming Courses From Students' Discussions, Joel Jer Wei Lim, Gottipati Swapna, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Programming courses provide students with the skills to develop complex business applications. Teaching and learning programming is challenging, and collaborative learning is proposed to help with this challenge. Online discussion forums promote networking with other learners such that they can build knowledge collaboratively. It aids students open their horizons of thought processes to acquire cognitive skills. Cognitive analysis of discussion is critical to understand students' learning process. In this paper, we propose Bloom's taxonomy based cognitive model for programming discussion forums. We present machine learning (ML) based solution to extract students' cognitive skills. Our evaluations on compupting courses show that …

Go to article

Codematcher: A Tool For Large-Scale Code Search Based On Query Semantics Matching, Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang Nov 2022

Codematcher: A Tool For Large-Scale Code Search Based On Query Semantics Matching, Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang

Research Collection School Of Computing and Information Systems

Due to the emergence of large-scale codebases, such as GitHub and Gitee, searching and reusing existing code can help developers substantially improve software development productivity. Over the years, many code search tools have been developed. Early tools leveraged the information retrieval (IR) technique to perform an efficient code search for a frequently changed large-scale codebase. However, the search accuracy was low due to the semantic mismatch between query and code. In the recent years, many tools leveraged Deep Learning (DL) technique to address this issue. But the DL-based tools are slow and the search accuracy is unstable.In this paper, we …

Go to article

Artificial Intelligence For Natural Disaster Management, Guansong Pang Nov 2022

Artificial Intelligence For Natural Disaster Management, Guansong Pang

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) can leverage massive amount of diverse types of data, such as geospatial data, social media data, and wireless network sensor data, to enhance our understanding of natural disasters, their forecasting and detection, and humanitarian assistance in natural disaster management (NDM). Due to this potential, different communities have been dedicating enormous efforts to the development and/or adoption of AI technologies for NDM. This article provides an overview of these efforts and discusses major challenges and opportunities in this topic.

Go to article

A Fine-Grained Data Set And Analysis Of Tangling In Bug Fixing Commits, Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher Ahmed Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristóf Szabados, Helge Spieker, Matej Madeja, Nathaniel G. Hoy, Christoph Treude, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo-Palacios, Roberto Verdecchia, Paramvir Singh Nov 2022

A Fine-Grained Data Set And Analysis Of Tangling In Bug Fixing Commits, Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher Ahmed Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristóf Szabados, Helge Spieker, Matej Madeja, Nathaniel G. Hoy, Christoph Treude, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo-Palacios, Roberto Verdecchia, Paramvir Singh

Research Collection School Of Computing and Information Systems

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs.Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits.Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the …

Go to article

Tgdm: Target Guided Dynamic Mixup For Cross-Domain Few-Shot Learning, Linhai Zhuo, Yuqian Fu, Jingjing Chen, Yixin Cao, Yu-Gang Jiang Oct 2022

Tgdm: Target Guided Dynamic Mixup For Cross-Domain Few-Shot Learning, Linhai Zhuo, Yuqian Fu, Jingjing Chen, Yixin Cao, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the target domain. To help knowledge transfer, this paper introduces an intermediate domain generated by mixing images in the source and the target domain. Specifically, to generate the optimal intermediate domain for different target data, we propose a novel target guided dynamic mixup (TGDM) framework that leverages the target …

Go to article

Hierarchical Hourglass Convolutional Network For Efficient Video Classification, Yi Tan, Yanbin Hao, Hao Zhang, Shuo Wang Oct 2022

Hierarchical Hourglass Convolutional Network For Efficient Video Classification, Yi Tan, Yanbin Hao, Hao Zhang, Shuo Wang

PhD Student’s Publications Collection

Videos naturally contain dynamic variation over the temporal axis, which will result in the same visual clues (e.g., semantics, objects) changing their scale, position, and perspective patterns between adjacent frames. A primary trend in video CNN is adopting spatial-2D convolution for spatial semantics and temporal-1D convolution for temporal dynamics. Though the direction achieves a favorable balance between efficiency and efficacy, it suffers from misalignment of visual clues with large displacements. Particularly, rigid temporal convolution would fail to capture correct motions when a specific target moves out of the reception field of temporal convolution between adjacent frames.To tackle large visual displacements …

Go to article

Adaptive Structural Similarity Preserving For Unsupervised Cross Modal Hashing, Liang Li, Baihua Zheng, Weiwei Sun Oct 2022

Adaptive Structural Similarity Preserving For Unsupervised Cross Modal Hashing, Liang Li, Baihua Zheng, Weiwei Sun

Research Collection School Of Computing and Information Systems

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well …

Go to article

Interactive Contrastive Learning For Self-Supervised Entity Alignment, Kaisheng Zeng, Zhenhao Dong, Lei Hou, Yixin Cao, Minghao Hu, Jifan Yu, Xin Lv, Lei Cao, Xin Wang, Haozhuang Liu, Yi Huang, Jing Wan, Juanzi Li Oct 2022

Interactive Contrastive Learning For Self-Supervised Entity Alignment, Kaisheng Zeng, Zhenhao Dong, Lei Hou, Yixin Cao, Minghao Hu, Jifan Yu, Xin Lv, Lei Cao, Xin Wang, Haozhuang Liu, Yi Huang, Jing Wan, Juanzi Li

Research Collection School Of Computing and Information Systems

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the-art (SOTA) selfsupervised EA approach draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, it advocates the minimum information requirement for self-supervised EA, while we argue that self-described KG’s side information (e.g., entity name, relation name, …

Go to article

Ergo: Event Relational Graph Transformer For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Kunquan Deng, Mukai Li, Kun Wang, Jing Shao, Yan Zhang Oct 2022

Ergo: Event Relational Graph Transformer For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Kunquan Deng, Mukai Li, Kun Wang, Jing Shao, Yan Zhang

Research Collection School Of Computing and Information Systems

Document-level Event Causality Identification (DECI) aims to identify event-event causal relations in a document. Existing works usually build an event graph for global reasoning across multiple sentences. However, the edges between events have to be carefully designed through heuristic rules or external tools. In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework1 for DECI, to ease the graph construction and improve it over the noisy edge issue. Different from conventional event graphs, we define a pair of events as a node and build a complete event relational graph without any prior knowledge or tools. This naturally …

Go to article

On Mitigating Hard Clusters For Face Clustering, Yingjie Chen, Huasong Zhong, Chong Chen, Chen Shen, Jianqiang Huang, Tao Wang, Yun Liang, Qianru Sun Oct 2022

On Mitigating Hard Clusters For Face Clustering, Yingjie Chen, Huasong Zhong, Chong Chen, Chen Shen, Jianqiang Huang, Tao Wang, Yun Liang, Qianru Sun

Research Collection School Of Computing and Information Systems

Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images. It remains challenging to identify small or sparse face image clusters that we call hard clusters, which is caused by the heterogeneity, i.e., high variations in size and sparsity, of the clusters. Consequently, the conventional way of using a uniform threshold (to identify clusters) often leads to a terrible misclassification for the samples that should belong to hard clusters. We tackle this problem by leveraging the neighborhood information of samples and inferring the cluster memberships (of samples) in a probabilistic way. We introduce …

Go to article

Ngram-Oaxe: Phrase-Based Order-Agnostic Cross Entropy For Non-Autoregressive Machine Translation, Cunxiao Du, Zhaopeng Tu, Longyue Wang, Jing Jiang Oct 2022

Ngram-Oaxe: Phrase-Based Order-Agnostic Cross Entropy For Non-Autoregressive Machine Translation, Cunxiao Du, Zhaopeng Tu, Longyue Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Recently, a new training oaxe loss has proven effective to ameliorate the effect of multimodality for non-autoregressive translation (NAT), which removes the penalty of word order errors in the standard cross-entropy loss. Starting from the intuition that reordering generally occurs between phrases, we extend oaxe by only allowing reordering between ngram phrases and still requiring a strict match of word order within the phrases. Extensive experiments on NAT benchmarks across language pairs and data scales demonstrate the effectiveness and universality of our approach. Further analyses show that ngram noaxe indeed improves the translation of ngram phrases, and produces more fluent …

Go to article

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 Oct 2022

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 …

Go to article

Explanation Guided Contrastive Learning For Sequential Recommendation, Lei Wang, Ee-Peng Lim, Zhiwei Liu, Tianxiang Zhao Oct 2022

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 …

Go to article

Artificial Intelligence, Consumers, And The Experience Economy, Hannah H. Chang, Anirban Mukherjee Oct 2022

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 …

Go to article

Using Machine Learning To Extract Insights From Consumer Data, Hannah H. Chang, Anirban Mukherjee Oct 2022

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 …

Go to article

Automatic Pull Request Title Generation, Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, Donggyun Han, David Lo, Lingxiao Jiang Oct 2022

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 …

Go to article

Social Access And Representation For Autistic Adult Livestreamers, Terrance Mok, Anthony Tang, Adam Mccrimmon, Lora Oehlberg Oct 2022

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.

Go to article

Locally Varying Distance Transform For Unsupervised Visual Anomaly Detection, Wen-Yan Lin, Zhonghang Liu, Siying Liu Oct 2022

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 …

Go to article

Multi-Functional Job Roles To Support Operations In A Multi-Faceted Jewel Enabled By Ai And Digital Transformation, Steven M. Miller Oct 2022

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.

Go to article

Field Experiments In Operations Management, Yang Gao, Meng Li, Shujing Sun Oct 2022

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 …

Go to article

Equivariance And Invariance Inductive Bias For Learning From Insufficient Data, Tan Wang, Qianru Sun, Sugiri Pranata, Karlekar Jayashree, Hanwang Zhang Oct 2022

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 …

Go to article

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 Oct 2022

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, …

Go to article

Soci: A Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Jiaming Yuan, Ximeng Liu, Yongdong Wu, Hwee Hwa Pang, Robert H. Deng Oct 2022

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 …

Go to article

First Prev Next Last