Databases and Information Systems Commons^™

Querying Recurrent Convoys Over Trajectory Data, Munkh-Erdene Yadamjav, Zhifeng Bao, Baihua Zheng, Farhana M. Choudhury, Hanan Samet

Research Collection School Of Computing and Information Systems

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the movement pattern. In this study, we define a problem of finding recurrent pattern of co-moving objects from streaming trajectories and propose an efficient solution that enables us to discover recent co-moving object patterns repeated within a given time period. Experimental results on …

Persona Perception Scale: Development And Exploratory Validation Of An Instrument For Evaluating Individuals' Perceptions Of Personas, Joni Salminen, Joao M. Santos, Haewoon Kwak, Jisun An, Soon-Gyo Jung

Research Collection School Of Computing and Information Systems

Although used in many domains, the evaluation of personas is difficult due to the lack of validated measurement instruments. To tackle this challenge, we propose the Persona Perception Scale (PPS), a survey instrument for evaluating how individuals perceive personas. We develop the scale by reviewing relevant literature from social psychology, persona studies, and Human-Computer Interaction to find relevant constructs and items for measuring persona perceptions. Following initial pilot testing, we conduct an exploratory validation of the scale with 412 respondents and find that the constructs and items of the scale perform satisfactorily for deployment. The research has implications for both …

An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia

Research Collection School Of Computing and Information Systems

Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory …

Time-Warped Sparse Non-Negative Factorization For Functional Data Analysis, Chen Zhang, Steven C. H. Hoi, Fugee Tsung

Research Collection School Of Computing and Information Systems

This article proposes a novel time-warped sparse non-negative factorization method for functional data analysis. The proposed method on the one hand guarantees the extracted basis functions and their coefficients to be positive and interpretable, and on the other hand is able to handle weakly correlated functions with different features. Furthermore, the method incorporates time warping into factorization and hence allows the extracted basis functions of different samples to have temporal deformations. An efficient framework of estimation algorithms is proposed based on a greedy variable selection approach. Numerical studies together with case studies on real-world data demonstrate the efficacy and applicability …

Research Directions For Sharing Economy Issues, Robert J. Kauffman, Maurizio Naldi

Research Collection School Of Computing and Information Systems

The sharing economy proposes a new approach to designing and delivering products and services, that aims at avoiding waste, improving efficiency, and favoring bottom-up change. In this research commentary, we survey the current state of things and propose some directions for research. We first describe the industries, products, and services currently representing the sharing paradigm, the technology platforms enabling it, the business models driving it, and the regulatory issues. We envisage that promising areas of research should include: (1) devising more efficient algorithms; (2) considering ecological and prosocial objective functions; (3) dealing with regulatory issues; (4) expanding the span of …

Deepstyle: User Style Embedding For Authorship Attribution Of Short Texts, Zhiqiang Hu, Roy Ka-Wei Lee, Lei Wang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications. Recent works have shown that deep learning methods could achieve significant accuracy improvement for the AA task. Nevertheless, most of these proposed methods represent user posts using a single type of features (e.g., word bi-grams) and adopt a text classification approach to address the task. Furthermore, these methods offer very limited explainability of the AA results. In this paper, we address these limitations by proposing DeepStyle, a novel embedding-based framework that learns the representations …

Weakly Paired Multi-Domain Image Translation, M.Y. Zhang, Zhiwu Huang, D.P. Paudel, J. Thoma, Gool L. Van

Research Collection School Of Computing and Information Systems

In this paper, we aim at studying the new problem of weakly paired multi-domain image translation. To this end, we collect a dataset that contains weakly paired images from multiple domains. Two images are considered to be weakly paired if they are captured from nearby locations and share an overlapping field of view. These images are possibly captured by two asynchronous cameras—often resulting in images from separate domains, e.g. summer and winter. Major motivations for using weakly paired images are: (i) performance improvement towards that of paired data; (ii) cheap labels and abundant data availability. For the first time in …

Global Optimization Algorithms For Image Registration And Clustering, Cuicui Zheng

Dissertations

Global optimization is a classical problem of finding the minimum or maximum value of an objective function. It has applications in many areas, such as biological image analysis, chemistry, mechanical engineering, financial analysis, deep learning and image processing. For practical applications, it is important to understand the efficiency of global optimization algorithms. This dissertation develops and analyzes some new global optimization algorithms and applies them to practical problems, mainly for image registration and data clustering.

First, the dissertation presents a new global optimization algorithm which approximates the optimum using only function values. The basic idea is to use the points …

Changing The Focus: Worker-Centric Optimization In Human-In-The-Loop Computations, Mohammadreza Esfandiari

Dissertations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back …

An Automated Feedback System To Support Student Learning Of Conceptual Knowledge In Writing-To-Learn Activities, Ye Xiong

Dissertations

As a pedagogical strategy, Writing-to-Learn (WTL) intends to use writing to improve students’ understanding of course content. However, most of the existing feedback systems for writing are mainly focused on improving students’ writing skills rather than their conceptual development. In this dissertation, an automatic approach is proposed to generate timely, actionable, and individualized feedback based on comparing knowledge representations extracted from lecture slides and individual students’ writing assignments. The novelty of the proposed approach lies in the feedback generation: to help students assimilate new knowledge into their existing knowledge better, their current knowledge is modeled as a set of matching …

Blockchain Technology And Freight Forwarder Exploration Of Implications Focused On Practitioners In Shanghai, Johannes Van Bohemen

World Maritime University Dissertations

No abstract provided.

Machine Learning And Deep Learning Based Entity Resolution Approaches For Unstructured References, Xinming Li

Theses and Dissertations

As a fundamental task in data integration and data quality, Entity Resolution (ER) has been investigated for decades in various domains. The emerging volume of heterogeneously structured data, and even unstructured data, poses a challenge to traditional ER methods. This research is to explore machine learning and deep learning approach to address the challenge from unstructured references data. This research starts with pairwise matching, the core function of all ER tasks. Based on the similarity score vector derived from our designed similarity measurement tool, scoring matrix, machine leaning enhances the performance significantly compared to the manually threshold method. Without similarity …

Arlegislation: An R Package Of Arkansas Legislation Data And An Exploratory Use Case For Using Machine Learning To Identify Public Corruption, Nathan P. Chaney

Theses and Dissertations

This thesis describes the creation of a natural-language dataset from a corpus of legislation passed in the State of Arkansas between 2001 and 2019. The dataset also includes metadata about individual acts of legislation and the lawmakers who sponsored them. This thesis describes the creation of the dataset, including the transformation of raw textual input using various natural language processing techniques such as sentiment analysis and topic modeling. Finally, this thesis examines a use case for identifying corrupt lawmakers using machine learning tools trained on transformations of the dataset.

Multi‑View Clustering For Multi‑Omics Data Using Unifed Embedding, Mohammed Hasanuzzaman, Sayantan Mitra, Sriparna Saha

Articles

In real world applications, data sets are often comprised of multiple views, which provide consensus and complementary information to each other. Embedding learning is an effective strategy for nearest neighbour search and dimensionality reduction in large data sets. This paper attempts to learn a unified probability distribution of the points across different views and generates a unified embedding in a low-dimensional space to optimally preserve neighbourhood identity. Probability distributions generated for each point for each view are combined by conflation method to create a single unified distribution. The goal is to approximate this unified distribution as much as possible when …

Multi‑View Clustering For Multi‑Omics Data Using Unifed Embedding, Mohammed Hasanuzzaman, Sayantan Mitra, Sriparna Saha

Department of Computer Science Publications

In real world applications, data sets are often comprised of multiple views, which provide consensus and complementary information to each other. Embedding learning is an effective strategy for nearest neighbour search and dimensionality reduction in large data sets. This paper attempts to learn a unified probability distribution of the points across different views and generates a unified embedding in a low-dimensional space to optimally preserve neighbourhood identity. Probability distributions generated for each point for each view are combined by conflation method to create a single unified distribution. The goal is to approximate this unified distribution as much as possible when …

Snow-Albedo Feedback In Northern Alaska: How Vegetation Influences Snowmelt, Lucas C. Reckhaus

Theses and Dissertations

This paper investigates how the snow-albedo feedback mechanism of the arctic is changing in response to rising climate temperatures. Specifically, the interplay of vegetation and snowmelt, and how these two variables can be correlated. This has the potential to refine climate modelling of the spring transition season. Research was conducted at the ecoregion scale in northern Alaska from 2000 to 2020. Each ecoregion is defined by distinct topographic and ecological conditions, allowing for meaningful contrast between the patterns of spring albedo transition across surface conditions and vegetation types. The five most northerly ecoregions of Alaska are chosen as they encompass …

Using High-Performance Computing Profilers To Understand The Performance Of Graph Algorithms, Costain Nachuma

LSU New Orleans Theses and Dissertations

An algorithm designer working with parallel computing systems should know how the characteristics of their implemented algorithm affects various performance aspects of their parallel program. It would be beneficial to these designers if each algorithm came with a specific set of standards that identified which algorithms worked better for a specified system. Therefore, the goal of this paper is to take implementations of four graphing algorithms, extract their features such as memory consumption, scalability using profilers (Vtunes /Tau) to determine which algorithms work to their fullest potential in one of the three systems: GPU, shared memory system, or distributed memory …

Social Participation Performance Of Wheelchair Users Using Clustering And Geolocational Sensor's Data, Yukun Yin, Kar Way Tan

Research Collection School Of Computing and Information Systems

For wheelchair users, social participation and physical mobility play a significant part in determining their mental health and quality of life outcomes. However, little is known about how wheelchair users move about and engage in social interactions within their life-spaces. In this project, we investigate the social participation performance of the wheelchair users based on a combination of geolocational and lifestyle survey data collected over a period of three months. This paper adopts a multi-variate approach combining geolocational travel patterns and various factors such as independence, willingness and self-perception to provide multi-faceted analysis to their lifestyles. We provide profiles of …

Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad A. Al-Ramahi, Cherie Noteboom

Computer Information Systems Faculty Publications (Archived)

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic's MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted …

Colleague To Banner Migration: Data Conversion Guide For Institutional Research, Laura Osborn

Masters Theses & Doctoral Dissertations

When the SDBOR decided to migrate their current student information system into a shared system with HR and Finance, adjustments needed to be made to accommodate for current Banner settings and work around tables that were already populated with HRFIS data. The change in data type of the student identifier from that of a 7-digit numeric field to a 9- digit alpha-numeric field poses problems for running aggregate data calculations. Additional complications include having some information such as first-generation status that was not migrated between the systems, and cases such as college coding where tables that were designed for student …

A Gis-Based Method For Archival And Visualization Of Microstructural Data From Drill Core Samples., Elliott Holmes

Electronic Theses and Dissertations

Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and explore the available microstructural data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed procedures for: 1. A GIS-based approach for spatially referenced visualization and storage of microstructural data from drill core billet samples; and 2. Producing 3D models of …

Diffusion Of Falsehoods On Social Media, Kelvin Kizito King

Theses and Dissertations

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher …

Creativity And Engagement In Ideas Crowdsourcing: A Situation Awareness Perspective, James Gitau Wairimu

Theses and Dissertations

This dissertation investigates the influence of performance feedback in user motivation and creativity development in idea crowdsourcing engagement. Creativity occurs when users of idea crowdsourcing communities engage in direct and indirect interactions that expose them to a pool of knowledge that enhances their cognitive development leading to the contribution of novel ideas for innovation in organizations. Additionally, participant motivation to engage in ideas crowdsourcing is increased through rewards and conditions that make the ideation process more inclusive and enjoyable. An idea network design is developed by applying social network analysis principles. The idea network design consists of mechanisms for motivating …

A Systematic Density-Based Clustering Method Using Anchor Points, Yizhang Wang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a more generic clustering method that considers all the aforementioned properties of the natural clusters, we propose a novel clustering algorithm named Anchor Points based Clustering (APC). The anchor points in APC are characterized by having a relatively large distance from data points with higher densities. We take anchor points as centers to obtain intermediate clusters, which can divide the whole dataset more appropriately so as to …

A Fast Anderson-Chebyshev Acceleration For Nonlinear Optimization, Zhize Li, Jian Li

Research Collection School Of Computing and Information Systems

Anderson acceleration (or Anderson mixing) is an efficient acceleration method for fixed point iterations $x_{t+1}=G(x_t)$, e.g., gradient descent can be viewed as iteratively applying the operation $G(x) \triangleq x-\alpha\nabla f(x)$. It is known that Anderson acceleration is quite efficient in practice and can be viewed as an extension of Krylov subspace methods for nonlinear problems. In this paper, we show that Anderson acceleration with Chebyshev polynomial can achieve the optimal convergence rate $O(\sqrt{\kappa}\ln\frac{1}{\epsilon})$, which improves the previous result $O(\kappa\ln\frac{1}{\epsilon})$ provided by (Toth and Kelley, 2015) for quadratic functions. Moreover, we provide a convergence analysis for minimizing general nonlinear problems. Besides, …

Meta-Learning On Heterogeneous Information Networks For Cold-Start Recommendation, Yuanfu Lu, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Cold-start recommendation has been a challenging problem due to sparse user-item interactions for new users or items. Existing efforts have alleviated the cold-start issue to some extent, most of which approach the problem at the data level. Earlier methods often incorporate auxiliary data as user or item features, while more recent methods leverage heterogeneous information networks (HIN) to capture richer semantics via higher-order graph structures. On the other hand, recent meta-learning paradigm sheds light on addressing cold-start recommendation at the model level, given its ability to rapidly adapt to new tasks with scarce labeled data, or in the context of …

Interface Design Of Web-Based Educational Platforms For Young Students, Lurong He, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

With the outbreak of the Covid-19 pandemic, almost all the school programs in the United States are closed. Most educational programs have moved online. Both teachers and students are facing challenges adapting to this new teaching and learning mode. This paper will focus on the interface design of webbased educational platforms for students and teachers from elementary and middle schools. The age of students from elementary and middle schools typically ranges from 5 to 13 years old. Their computer literacy level is still preliminary. So traditional online teaching platforms may not be appropriate for them. The design of learning systems …

An Ensemble Of Epoch-Wise Empirical Bayes For Few-Shot Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. “Epoch-wise'' means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. ”Empirical'' means that the hyperparameters, e.g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent …

An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how …

Learning Transferrable Parameters For Long-Tailed Sequential User Behavior Modeling, Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Sequential user behavior modeling plays a crucial role in online user-oriented services, such as product purchasing, news feed consumption, and online advertising. The performance of sequential modeling heavily depends on the scale and quality of historical behaviors. However, the number of user behaviors inherently follows a long-tailed distribution, which has been seldom explored. In this work, we argue that focusing on tail users could bring more benefits and address the long tails issue by learning transferrable parameters from both optimization and feature perspectives. Specifically, we propose a gradient alignment optimizer and adopt an adversarial training scheme to facilitate knowledge transfer …