Databases and Information Systems Commons^™

Effective Graph Kernels For Evolving Functional Brain Networks, Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu

Research Collection School Of Computing and Information Systems

The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel …

Analysis And Optimization Of Contract Data Schema, Franklin Sun

Theses and Dissertations

agement, development, and growth of U.S Air Force assets demand extensive organizational communication and structuring. These interactions yield substantial amounts of contracting and administrative information. Over 4 million such contracts as a means towards obtaining valuable insights on Department of Defense resource usage. This set of contracting data is largely not optimized for backend service in an analytics environment. To this end, the following research evaluates the efficiency and performance of various data structuring methods. Evaluated designs include a baseline unstructured schema, a Data Mart schema, and a snowflake schema. Overall design success metrics include ease of use by end …

A Parameter Discovery Process For The Data Washing Machine Created For Unsupervised Data Curation, Kris E. Anderson

Theses and Dissertations

The Data Washing Machine (DWM) is a known and documented open-source Python Jupyter Notebook project that is the foundation for an Unsupervised Data Curation process. The DWM ingests reference data without a prior data cleansing activity and ultimately runs Entity Resolution (ER) on acceptable entity data to cluster duplicate references within the dataset. The DWM currently has 17 modifiable parameters that are used to help tokenize, cleanse, organize, link and cluster like references. With such a large number of parameters, some type of beginning settings as optimal as possible are needed for the DWM process for it to be useful …

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 introduce the …

Flexible Job-Shop Scheduling Via Graph Neural Network And Deep Reinforcement Learning, Wen Song, Xinyang Chen, Qiqiang Li, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching rules (PDRs) for solving complex scheduling problems. However, the existing works face challenges in dealing with flexibility, which allows an operation to be scheduled on one out of multiple machines and is often required in practice. Such one-to-many relationship brings additional complexity in both decision making and state representation. This article considers the well-known flexible job-shop scheduling problem and addresses these issues by proposing a novel DRL method to learn high-quality PDRs end to end. The operation selection and the machine assignment are combined as a composite decision. …

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 …

Generalization Bounds For Inductive Matrix Completion In Low-Noise Settings, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Yann Guermeur, Marius Kloft

Research Collection School Of Computing and Information Systems

We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of the noise and in particular approach zero in the exact recovery case; (2) even in the presence of noise, they converge to zero when the sample size approaches infinity; and (3) for a fixed dimension of the side information, they only have a logarithmic dependence on the size of the matrix. Differently …

Contrastive Learning Approach To Word-In-Context Task For Low-Resource Languages, Pei-Chi Lo, Yang-Yin Lee, Hsien-Hao Chen, Agus Trisnajaya Kwee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Word in context (WiC) task aims to determine whether a target word’s occurrences in two sentences share the same sense. In this paper, we propose a Contrastive Learning WiC (CLWiC) framework to improve the learning of sentence/word representations and classification of target word senses in the sentence pair when performing WiC on lowresource languages. In representation learning, CLWiC trains a pre-trained language model’s ability to cope with lowresource languages using both unsupervised and supervised contrastive learning. The WiC classifier learning further finetunes the language model with WiC classification loss under two classifier architecture options, SGBERT and WiSBERT, which use single-encoder …

Cross-Domain Graph Anomaly Detection Via Anomaly-Aware Contrastive Alignment, Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, Christopher Leckie

Research Collection School Of Computing and Information Systems

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, …

On Generalized Degree Fairness In Graph Neural Networks, Zemin Liu, Trung Kien Nguyen, Yuan Fang

Research Collection School Of Computing and Information Systems

Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. While several recent approaches have been proposed to eliminate the bias rooted in sensitive attributes, they ignore the other key input of GNNs, namely the neighbors of a node, which can introduce bias since GNNs hinge on neighborhood structures to generate node representations. In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes. In this paper, we first define …

Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurram, Akshat Kumar

Research Collection School Of Computing and Information Systems

The k-center clustering algorithm, introduced over 35 years ago, is known to be robust to class imbalance prevalent in many clustering problems and has various applications such as data summarization, document clustering, and facility location determination. Unfortunately, existing k-center algorithms provide highly suboptimal solutions that can limit their practical application, reproducibility, and clustering quality. In this paper, we provide a novel scalable and globally optimal solution to a popular variant of the k-center problem known as generalized L1 k-center clustering that uses L1 distance and allows the selection of arbitrary vectors as cluster centers. We show that this clustering objective …

Learning Relation Prototype From Unlabeled Texts For Long-Tail Relation Extraction, Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua

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 …

Mirror: Mining Implicit Relationships Via Structure-Enhanced Graph Convolutional Networks, Jiaying Liu, Feng Xia, Jing Ren, Bo Xu, Guansong Pang, Lianhua Chi

Research Collection School Of Computing and Information Systems

Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a …

Mitigating Popularity Bias For Users And Items With Fairness-Centric Adaptive Recommendation, Zhongzhou Liu, Yuan Fang, Min Wu

Research Collection School Of Computing and Information Systems

Recommendation systems are popular in many domains. Researchers usually focus on the effectiveness of recommendation (e.g., precision) but neglect the popularity bias that may affect the fairness of the recommendation, which is also an important consideration that could influence the benefits of users and item providers. A few studies have been proposed to deal with the popularity bias, but they often face two limitations. Firstly, most studies only consider fairness for one side - either users or items, without achieving fairness jointly for both. Secondly, existing methods are not sufficiently tailored to each individual user or item to cope with …

Future Aware Pricing And Matching For Sustainable On-Demand Ride Pooling, Xianjie Zhang, Pradeep Varakantham, Hao Jiang

Research Collection School Of Computing and Information Systems

The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to fewer vehicles) and aggregation companies like Uber (higher revenue). To achieve these benefits, two key interlinked challenges have to be solved effectively: (a) pricing – setting prices to customer requests for taxis; and (b) matching – assignment of customers (that accepted the prices) to taxis/cars. Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests. In this …

Real-Time Hierarchical Map Segmentation For Coordinating Multi-Robot Exploration, Tianze Luo, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Coordinating a team of autonomous agents to explore an environment can be done by partitioning the map of the environment into segments and allocating the segments as targets for the individual agents to visit. However, given an unknown environment, map segmentation must be conducted in a continuous and incremental manner. In this paper, we propose a novel real-time hierarchical map segmentation method for supporting multi-agent exploration of indoor environments, wherein clusters of regions of segments are formed hierarchically from randomly sampled points in the environment. Each cluster is then assigned with a cost-utility value based on the minimum cost possible …

Alignment-Enriched Tuning For Patch-Level Pre-Trained Document Image Models, Lei Wang, Jiabang He, Xing Xu, Ning Liu, Hui Liu

Research Collection School Of Computing and Information Systems

Alignment between image and text has shown promising im provements on patch-level pre-trained document image mod els. However, investigating more effective or finer-grained alignment techniques during pre-training requires a large amount of computation cost and time. Thus, a question natu rally arises: Could we fine-tune the pre-trained models adap tive to downstream tasks with alignment objectives and achieve comparable or better performance? In this paper, we pro pose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific super vised and alignment-aware contrastive objective. Specifically, weintroduce an extra …

The Application Of Graph Technology For Improving Entity Resolution Results In The Context Of Group Membership, Md Abdus Salam Siddique

Theses and Dissertations

The main objective of Entity resolution (ER) is to find duplicate records within the same data table from the same source or different data tables from various sources. A traditional pair-wise supervised entity resolution matching depends on pre-built rules for finding matched records. On the other hand, unsupervised or semisupervised also relies on pair-wise matching. In the maximum case, group membership is left behind for consideration. In this dissertation, I have discussed the design, implementation and evaluation of a graph-based entity resolution for group membership to enhance the pair-wise matching ER system. I have designed and implemented a pipeline for …

Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden

National Training Aircraft Symposium (NTAS)

An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has …

Development Of Open-Source Software 'Pystage', Victor T. Norman, Brandon Husted, Zhonglin 'Loya' Niu

Summer Research

PyStage is an open-source project that seeks to help students bridge the gap between block-based programming in Scratch and text-based programming in Python.

A New Credit Scoring Model To Reduce Potential Predatory Lending: A Design Science Approach, Anna Zakowska

CGU Theses & Dissertations

This research examines the potential impact of implementing a novel credit scoring model that integrates attributes beyond the traditional FICO model. It aims to address issues related to predatory lending and the financial exclusion affecting individuals often categorized as 'credit invisible,' 'credit unscorable,' 'unbanked,' and 'underbanked.' These individuals typically face difficulties in establishing or repairing a credit history, which poses a challenge for financial institutions in accurately evaluating their creditworthiness. This gap in the credit assessment process often opens doors to unfair lending practices. To tackle this problem, a systematically designed, built, tested, and evaluated innovative credit scoring model was …

Understanding U.S. Customers' Intention To Adopt Robo-Advisor Technology, Deborah Wall

Walden Dissertations and Doctoral Studies

Finance and information technology scholars wrote that there is a literature gap on what factors drive investors in Western financial markets to use a Robo-advisor to manage their investments. The purpose of this qualitative, single case study with embedded units is to understand the adoption intentions of retail investors in U.S. markets to use a Robo-advisor instead of a human advisor. A single case study design addressed the literature gap, and qualitative data from seven semi=structured interviews, reflective field notes, and archival data were triangulated to answer the research question. This study was grounded in a theoretical framework that includes …

Strategies To Increase Competitive Advantage In The Automotive Manufacturing Supply Chain, Amber Willis

Walden Dissertations and Doctoral Studies

Some automotive manufacturing supply chain leaders lack strategies that are needed to implement information technology (IT) systems. Business leaders are concerned with implementing IT systems to achieve and maintain a competitive advantage. Grounded in the resource-based view theory (RBV), the purpose of this qualitative single case study was to explore information system strategies used by leaders in the automotive manufacturing supply chain to achieve competitive advantage. Participants were five leaders of an automotive manufacturing supply chain organization who implemented IT systems. Data were collected through semistructured interviews and a review of organization project documents. Through thematic analysis, five themes were …

Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei

Walden Dissertations and Doctoral Studies

Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and …

Campus Safety Data Gathering, Classification, And Ranking Based On Clery-Act Reports, Walaa F. Abo Elenin

College of Graduate Studies: Theses & Dissertations

Most existing campus safety rankings are based on criminal incident history with minimal or no consideration of campus security conditions and standard safety measures. Campus safety information published by universities/colleges is usually conceptual/qualitative and not quantitative and are based-on criminal records of these campuses. Thus, no explicit and trusted ranking method for these campuses considers the level of compliance with the standard safety measures. A quantitative safety measure is important to compare different campuses easily and to learn about specific campus safety conditions.

In this thesis, we utilize Clery-Act reports of campuses to automatically analyze their safety conditions and generate …

T-Counter: Trustworthy And Efficient Cpu Resource Measurement Using Sgx In The Cloud, Chuntao Dong, Qingni Shen, Xuhua Ding, Daoqing Yu, Wu Luo, Pengfei Wu, Zhonghai Wu

Research Collection School Of Computing and Information Systems

As cloud services have become popular, and their adoption is growing, consumers are becoming more concerned about the cost of cloud services. Cloud Service Providers (CSPs) generally use a pay-per-use billing scheme in the cloud services model: consumers use resources as they needed and are billed for their resource usage. However, CSPs are untrusted and privileged; they have full control of the entire operating system (OS) and may tamper with bills to cheat consumers. So, how to provide a trusted solution that can keep track of and verify the consumers’ resource usage has been a challenging problem. In this paper, …

Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: , which describes the position, size, and layout of each view in the display space; and, which indicates the interaction between pairwise views. We build a new dataset containing 854 dashboards crawled online, …

Champions For Social Good: How Can We Discover Social Sentiment And Attitude-Driven Patterns In Prosocial Communication?, Raghava Rao Mukkamala, Robert J. Kauffman, Helle Zinner Henriksen

Research Collection School Of Computing and Information Systems

The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strategy to inform people about displaced populations and refugee emergencies. It is actively engaging public figures to increase awareness through its prosocial communications and improve social informedness and support for policy changes in its services. We studied the Twitter communications of UNHCR social media champions and investigated their role as high-profile influencers. In this study, we offer a design science research and data analytics framework and propositions based on the social informedness theory we propose in this paper to assess communication about UNHCR’s mission. Two variables—refugee-emergency and champion …

A Geospatial Analysis Of Crime Hotspots, Campus Safety Measures, And The Campus Community’S Perceived Safety At Murray State University, Rachel Stuckey

Murray State Theses and Dissertations

Murray State University (Murray State) is in far Western Kentucky. Murray State prides itself on being a safe campus for prospective students. In this study spatial analysis was used to analyze the relationship among crime hotspots, campus safety measures, and students’ perception of safety on campus the campus of Murray State. A survey was distributed to students to determine the areas of the campus where students feel safe and unsafe. Geographical Information Systems (GIS) were used to determine the location of crime hotspots over eight years (2014-2022) to measure the spatial relationship between logged crime hotspots and perceived safe and …

Causal Interventional Training For Image Recognition, Wei Qin, Hanwang Zhang, Richang Hong, Ee-Peng Lim, Qianru Sun

Research Collection School Of Computing and Information Systems

Deep learning models often fit undesired dataset bias in training. In this paper, we formulate the bias using causal inference, which helps us uncover the ever-elusive causalities among the key factors in training, and thus pursue the desired causal effect without the bias. We start from revisiting the process of building a visual recognition system, and then propose a structural causal model (SCM) for the key variables involved in dataset collection and recognition model: object, common sense, bias, context, and label prediction. Based on the SCM, one can observe that there are “good” and “bad” biases. Intuitively, in the image …