Governors State University
Biola University
California State University, San Bernardino
Bryant University
Walden University
Learning Comprehensive Global Features In Person Re-Identification: Ensuring Discriminativeness Of More Local Regions,
2023
Singapore Management University
Learning Comprehensive Global Features In Person Re-Identification: Ensuring Discriminativeness Of More Local Regions, Jiali Xia, Jianqiang Huang, Shibao Zheng, Qin Zhou, Bernt Schiele, Xian-Sheng Hua, Qianru Sun
Research Collection School Of Computing and Information Systems
Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, global embedding learned by naive classification model tends to be trapped in the most discriminative local region, leading to poor evaluation performance. To address the issue, we propose a novel baseline network that learns strong global feature termed as Comprehensive Global Embedding (CGE), ensuring more local regions of global feature maps to be discriminative. In this work, two key modules are proposed including Non-parameterized Local Classifier ...
Online Hyperparameter Optimization For Class-Incremental Learning,
2023
Singapore Management University
Online Hyperparameter Optimization For Class-Incremental Learning, Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun
Research Collection School Of Computing and Information Systems
Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existing CIL models can achieve the optimal tradeoff in different data-receiving settings—where typically the training-from-half (TFH) setting needs more stability, but the training-from-scratch (TFS) needs more plasticity. To this end, we design an online learning method that can adaptively optimize the tradeoff without knowing the setting as a priori. Specifically, we first ...
Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization,
2022
Singapore Management University
Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization, Xing Yang, Chen Zhang, Baihua Zheng
Research Collection School Of Computing and Information Systems
Time-varying dynamic Bayesian network (TVDBN) is essential for describing time-evolving directed conditional dependence structures in complex multivariate systems. In this article, we construct a TVDBN model, together with a score-based method for its structure learning. The model adopts a vector autoregressive (VAR) model to describe inter-slice and intra-slice relations between variables. By allowing VAR parameters to change segment-wisely over time, the time-varying dynamics of the network structure can be described. Furthermore, considering some external information can provide additional similarity information of variables. Graph Laplacian is further imposed to regularize similar nodes to have similar network structures. The regularized maximum a ...
R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference,
2022
Singapore Management University
R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao
Research Collection School Of Computing and Information Systems
Document-level natural language inference (DocNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents. Current datasets and baselines largely follow sentence-level settings, but fail to address the issues raised by longer documents. In this paper, we establish a general solution, named Retrieval, Reading and Fusion (R2F) framework, and a new setting, by analyzing the main challenges of DocNLI: interpretability, long-range dependency, and cross-sentence inference. The basic idea of the framework is to simplify document-level task into a set of sentence-level tasks, and improve both performance and ...
Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective,
2022
Singapore Management University
Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu
Research Collection School Of Computing and Information Systems
Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient ...
Cold Calls To Enhance Class Participation And Student Engagement,
2022
Singapore Management University
Cold Calls To Enhance Class Participation And Student Engagement, M. Thulasidas, Aldy Gunawan
Research Collection School Of Computing and Information Systems
The question whether cold calls increase student engagement in the classroom has not been conclusively answered in the literature. This study describes the automated system to implement unbiased, randomized cold calling by posing a question, allowing all students to think first and then calling on a particular student to respond. Since we already have a measure of the level of student engagement as the self-reported classparticipation entries from the students, its correlation to cold calling is also further studied. The results show that there is a statistically significant increase in the class participation reported, and therefore in student engagement, in ...
Rural America Is Still Technologically Behind: Why It Matters Now More Than Ever,
2022
Minnesota State University - Mankato
Rural America Is Still Technologically Behind: Why It Matters Now More Than Ever, Paul Force-Emery Mackie
Social Work Department Publications
No abstract provided.
Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis,
2022
Purdue University
Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis, Darin Tsai, Alan Zhang, Aloysius Rebeiro
The Journal of Purdue Undergraduate Research
No abstract provided.
Data Sharing Through Open Access Data Repositories,
2022
Kennesaw State University
Data Sharing Through Open Access Data Repositories, Karin Bennedsen
All Things Open
The National Institutes of Health has expanded their data sharing requirements for obtaining funding to now include all awards for research producing scientific data to accelerate “biomedical research discovery, in part, by enabling validation of research results, providing accessibility to high-value datasets, and promoting data reuse for future research studies.” The new policy requiring a Data Management & Sharing Plan (DMSP) for all applications goes into effect January 25th, 2023. A DMSP includes where the data will be stored. This lightning talk will review Open Access Data Repositories. Don’t let the task of trying to find data storage hold you ...
Ergo: Event Relational Graph Transformer For Document-Level Event Causality Identification,
2022
Singapore Management University
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 ...
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 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.
Equivariance And Invariance Inductive Bias For Learning From Insufficient Data,
2022
Singapore Management 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 ...
Tgdm: Target Guided Dynamic Mixup For Cross-Domain Few-Shot Learning,
2022
Singapore Management University
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 ...
Interactive Contrastive Learning For Self-Supervised Entity Alignment,
2022
Singapore Management University
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 ...
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 ...
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 ...
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 ...
Adaptive Structural Similarity Preserving For Unsupervised Cross Modal Hashing,
2022
Singapore Management University
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 ...
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 ...
